From 5930aeceec504adfcfcad4237d402ae671865734 Mon Sep 17 00:00:00 2001 From: lukelabrie Date: Mon, 7 Aug 2023 16:52:55 +0200 Subject: [PATCH 01/32] add dynamic model --- dynamic_model/main.py | 249 ++++++++++++++++++++++++++++++++++ dynamic_model/parameters.py | 234 ++++++++++++++++++++++++++++++++ dynamic_model/plot_results.py | 94 +++++++++++++ 3 files changed, 577 insertions(+) create mode 100644 dynamic_model/main.py create mode 100644 dynamic_model/parameters.py create mode 100644 dynamic_model/plot_results.py diff --git a/dynamic_model/main.py b/dynamic_model/main.py new file mode 100644 index 0000000..b6f23b1 --- /dev/null +++ b/dynamic_model/main.py @@ -0,0 +1,249 @@ +from parameters import * +import numpy as np +from scipy.integrate import ode +import matplotlib.pyplot as plt +import copy + +def dydtMSRE(t,y,delays,rho_ext): + + ''' + Returns derivative of state vector y; y' or dy/dt, of the MSRE system. + + The y vector contains the following: + + T_in_rc = Inlet temperature (°C) of radiator coolant, will be equal to + the outlet temperature of the heat exchanger (T_hc4) plus + relevant time delay + + T_out_rc = Outlet temperature (°C) of radiator coolant + + T_in_air = Inlet temperature (°C) of air in radiator + + T_out_air = Outlet temperature (°C) of air in radiator + + T_in_hf = Inlet temperature (°C) of heat exchanger fuel, will be equal + to the outlet temperature of the core (T_cf2) plus relevant + time delay + + T_hf* = Temperature (°C) of heat exchanger fuel node * + + T_ht* = Temperature (°C) of heat exchanger tube node * + + T_hc* = Temperature (°C) of heat exchanger coolant node * + + T_in_cf = Inlet temperature (°C) of core fuel, will be equal to the + outlet temperature of the heat exchanger (T_hf4) plus + relevant time dela + + S = neutro source perturbation term + + rho_fb = feedback reactivity (from fuel and graphite temperatures) + + rho_ext = external reactivity (reactivity insertion) + + rho_tot = total reactivity = rho_0 + rho_fb + rho_ext (rho_0 = + steady-state reactivity, constant) + + n = neutron density n(t) + + C* = precursor concentration of group * + + T_cg = Temperature (°C) of core graphite node + + T_cf* = Temperature (°C) of core fuel node * + + *_delay = Parameter at time t = t - delay + + Other parameters are defined in parameters.py + ''' + + # unpack state variables + T_out_rc, T_out_air, T_hf1, T_hf2, T_hf3, T_hf4, T_ht1, T_ht2, T_hc1, \ + T_hc2, T_hc3, T_hc4, n, C1, C2, C3, C4, C5, C6, T_cg, T_cf1, T_cf2 = y + + # delay terms + T_out_rc_delay, T_hc4_delay, T_cf2_delay, T_hf4_delay, C1_delay, \ + C2_delay, C3_delay, C4_delay, C5_delay, C6_delay = delays + + # reactivity + rho = (a_f/2)*((-T0_f1+T_cf1)+(-T0_f2+T_cf2)) + a_g*(-T0_g1+T_cg) + rho_ext + + # derivatives + dydt = [ + (W_rp/mn_rp)*(T_hc4_delay-T_out_rc) + (hA_rpn/mcp_rpn)*(T_out_air-T_out_rc), # T_out_rc # T_out_rc + -((W_rs/mn_rs)+(hA_rsn/mcp_rsn))*T_out_air + (hA_rsn/mcp_rsn)*T_out_rc + (W_rs/mn_rs)*Trs_in, # T_out_air + -((W_p/mn_p)+(hA_pn/mcp_pn))*T_hf1 + (hA_pn/mcp_pn)*T_ht1 + (W_p/mn_p)*T_cf2_delay, # T_hf1 + (W_p/mn_p)*(T_hf1-T_hf2) + (hA_pn/mcp_pn)*(T_ht1-T_hf1), # T_hf2 + -((W_p/mn_p)+(hA_pn/mcp_pn))*T_hf3 + (hA_pn/mcp_pn)*T_ht2 + (W_p/mn_p)*T_hf2, # T_hf3 + (W_p/mn_p)*(T_hf3-T_hf4) + (hA_pn/mcp_pn)*(T_ht2-T_hf3), # T_hf4 + (2*hA_pn/mcp_tn)*(T_hf1-T_ht1) + (2*hA_sn/mcp_tn)*(T_hc3-T_ht1), # T_ht1 + (2*hA_pn/mcp_tn)*(T_hf3-T_ht2) + (2*hA_sn/mcp_tn)*(T_hc1-T_ht2), # T_ht2 + -((W_s/mn_s)+(hA_sn/mcp_sn))*T_hc1 + (hA_sn/mcp_sn)*T_ht2 + (W_s/mn_s)*T_out_rc_delay, # T_hc1 + (W_s/mn_s)*(T_hc1-T_hc2) + (hA_sn/mcp_sn)*(T_ht2-T_hc1), # T_hc2 + -((W_s/mn_s)+(hA_sn/mcp_sn))*T_hc3 + (hA_sn/mcp_sn)*T_ht1 + (W_s/mn_s)*T_hc2, # T_hc3 + (W_s/mn_s)*(T_hc3-T_hc4) + (hA_sn/mcp_sn)*(T_ht1-T_hc3), # T_hc4 + (rho-beta_t)*n/Lam+lam[0]*C1+lam[1]*C2+lam[2]*C3+lam[3]*C4+lam[4]*C5+lam[5]*C6, # n (no source insertion) + n*beta[0]/Lam-lam[0]*C1-C1/tau_c+C1_delay*np.exp(-lam[0]*tau_l)/tau_c, # C1 + n*beta[1]/Lam-lam[1]*C2-C2/tau_c+C2_delay*np.exp(-lam[1]*tau_l)/tau_c, # C2 + n*beta[2]/Lam-lam[2]*C3-C3/tau_c+C3_delay*np.exp(-lam[2]*tau_l)/tau_c, # C3 + n*beta[3]/Lam-lam[3]*C4-C4/tau_c+C4_delay*np.exp(-lam[3]*tau_l)/tau_c, # C4 + n*beta[4]/Lam-lam[4]*C5-C5/tau_c+C5_delay*np.exp(-lam[4]*tau_l)/tau_c, # C5 + n*beta[5]/Lam-lam[5]*C6-C6/tau_c+C6_delay*np.exp(-lam[5]*tau_l)/tau_c, # C6 + (hA_fg/mcp_g1)*(T_cf1 - T_cg) + k_g*P*n/mcp_g1, # T_cg + W_f/mn_f*(T_hf4_delay-T_cf1) + (k_f1*P*n/mcp_f1) + (hA_fg*k_1*(T_cg - T_cf1)/mcp_f1), # T_cf1 + W_f/mn_f*(T_cf1 - T_cf2) + (k_f2*P*n/mcp_f2) + (hA_fg*k_2*(T_cg - T_cf1)/mcp_f2) # T_cf2 + ] + return dydt + +def get_tIdx(t,tao,timeVec): + ''' + Returns index of time t = t-tau + ''' + td = t-tao + diff_min = 999999.9999999 + idx = 0 + for t in enumerate(timeVec): + diff = abs(td-t[1]) + if (diff=t_insert): + rho_ext = insert + else: + rho_ext = 0.0 + + # delay parameters + d_terms = [] + derivs = [dydtMSRE(t0,y0,d0,rho_ext)] + + # takes one step at a time and then accounts for delay terms + i = 0 + while (t_start < t_stop): + # take one step + if (i == 0): + t_start = t0 + r.set_initial_value(y0,t0).set_f_params(d0,0.0) + r.integrate(1.0) + sol.append(sol_interim[0]) + sol.append(sol_interim[1]) + else: + t_start = sol[-1][0] + if (t_start>=t_insert): + rho_ext = insert + else: + rho_ext = 0.0 + r.set_initial_value(y_next,t_start).set_f_params(d_new,rho_ext) + r.integrate(t_start+1.0) + sol.append(sol_interim[1]) + derivs.append(dydtMSRE(t_start,y_next,d_new,rho_ext)) + + # account for delays, linear interpolation for time differences + # core fuel inlet + d_new = [0]*10 + idx_cf_in = 0 + dt_cf = 0.0 + if (t_start > tau_hx_c): + idx_cf_in, dt_cf = get_tIdx(t_start,tau_hx_c,[s[0] for s in sol]) + d_new[3] = sol[idx_cf_in][6] + dt_cf*derivs[idx_cf_in][5] + + # heat exchanger fuel inlet + idx_hf_in = 0 + dt_hf = 0.0 + if (t_start > tau_c_hx): + idx_hf_in, dt_hf = get_tIdx(t_start,tau_c_hx,[s[0] for s in sol]) + d_new[2] = sol[idx_hf_in][22] + dt_hf*derivs[idx_hf_in][21] + + # heat exchanger coolant inlet + idx_hc_in = 0 + dt_hc = 0.0 + if (t_start > tau_r_hx): + idx_hc_in, dt_hc = get_tIdx(t_start,tau_r_hx,[s[0] for s in sol]) + d_new[0] = sol[idx_hc_in][1] + dt_hc*derivs[idx_hc_in][0] + + # radiator coolant inlet + idx_rc_in = 0 + dt_rc = 0.0 + if (t_start > tau_hx_r): + idx_rc_in, dt_rc = get_tIdx(t_start,tau_hx_r,[s[0] for s in sol]) + d_new[1] = sol[idx_rc_in][12] + dt_rc*derivs[idx_rc_in][11] + + # precursors + idx_c = 0 + dt_c = 0.0 + if (t_start > tau_l): + idx_c, dt_c = get_tIdx(t_start,tau_l,[s[0] for s in sol]) + d_new[4] = sol[idx_c][14] + dt_c*derivs[idx_c][13] + d_new[5] = sol[idx_c][15] + dt_c*derivs[idx_c][14] + d_new[6] = sol[idx_c][16] + dt_c*derivs[idx_c][15] + d_new[7] = sol[idx_c][17] + dt_c*derivs[idx_c][16] + d_new[8] = sol[idx_c][18] + dt_c*derivs[idx_c][17] + d_new[9] = sol[idx_c][19] + dt_c*derivs[idx_c][18] + + d_terms.append(d_new) + + # initial condiiton for next step + y_next = sol[-1][1:] + + # empty interim solution + sol_interim = [] + + # display progress + print(f"{t_start}") + + i += 1 + + # plot single parameter + of_interest = 14 + plt.plot([s[0] for s in sol],[s[of_interest] for s in sol]) + plt.show() + + # check delay behavior + # for i in range(len(sol)-1): + # print(f"t: {sol[i][0]}, hf4: {sol[i][6]}, c1_delay: {d_terms[i][3]}") + + # write output data + output_filename = f"sim_out_{t_stop}_{P}" + results = open(output_filename,'w+') + for k in range(len(sol)): + for col in range(len(sol[0])): + results.write(f"{sol[k][col]} ") + results.write("\n") + + return None + +main() \ No newline at end of file diff --git a/dynamic_model/parameters.py b/dynamic_model/parameters.py new file mode 100644 index 0000000..365490a --- /dev/null +++ b/dynamic_model/parameters.py @@ -0,0 +1,234 @@ +import numpy as np +import pandas as pd +import math +pi = math.pi + +# Perturbations +# SOURCE INSERTION +# No source insertion +sourcedata = np.array([0, 0, 0]) +sourcetime = np.array([0, 50, 100]) +# % 1 (n/no)/s for 10 seconds +# sourcedata = np.array([0, 10, 0]) +# sourcetime = np.array([0, 10, 20]) +source = pd.Series(sourcedata, index=sourcetime) + +# REACTIVITY INSERTION +# No reactivity insertion +simtime = 10 +reactdata = np.array([0, 5E-4]) +reacttime = np.array([0, 2500]) +# Periodic 60 PCM for 50 seconds +# simtime = 500 +# periodic = np.array([[0, 0], [50, 6e-4], [100, 0], [150, -6e-4], [200, 0], [250, 6e-4], [300, 0], [350, -6e-4], [400, 0]]) +# reactdata = periodic[:, 1] +# reacttime = periodic[:, 0] +# Step up 60 pcm +# simtime = 1000 +# reactdata = np.array([0, 6e-3]) +# reacttime = np.array([0, 300]) +# # Step down -60 pcm for 10 sec +# simtime = 100 +# reactdata = np.array([0, -6e-4]) +# reacttime = np.array([0, 50]) +# # Pulse 600 pcm for 0.1 sec +# simtime = 30 +# reactdata = np.array([0, 6e-3, 0]) +# reacttime = np.array([0, 10, 10.1]) + +react = pd.Series(reactdata, index=reacttime) + +ts_max = 1e-1 # maximum timestep (s) + +# NEUTRONICS DATA +tau_l = 16.73 # ORNL-TM-0728 %16.44; % (s) +tau_c = 8.46 # ORNL-TM-0728 %8.460; % (s) +P = 8 # Thermal Power in MW ORNL-TM-1070, p.2 +n_frac0 = 5 # initial fractional neutron density n/n0 (n/cm^3/s) +Lam = 2.400E-04 # mean generation time ORNL-TM-1070 p.15 U235 +# Lam = 4.0E-04; # mean generation time ORNL-TM-1070 p.15 U233 +lam = np.array([1.240E-02, 3.05E-02, 1.11E-01, 3.01E-01, 1.140E+00, 3.014E+00]) +beta = np.array([0.000223, 0.001457, 0.001307, 0.002628, 0.000766, 0.00023]) # U235 +# beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009]) # U233 +beta_t = np.sum(beta) # total delayed neutron fraction MSRE +rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel +C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c)) + +# Feedback co-efficients +a_f = -8.71E-05 # U235 (drho/°C) fuel salt temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -5.904E-05; % ORNL-TM-0728 p. 101 % +a_g = -6.66E-05 # U235 (drho/°C) graphite temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -6.624E-05; % ORNL-TM-0728 p.101 + +# CORE HEAT TRANSFER PARAMETERS +# FUEL PARAMETERS - DONE +vdot_f = 7.5708E-02 # ORNL-TM-0728 % 7.571e-2; % vol. flow rate (m^3/s) ORNL-TM-1647 p.3, ORNL-TM-0728 p.12 +rho_f = 2.14647E+03 # (partially enriched U-235)ORNL-TM-0728 p.8 2.243E+03; % (Th-U) density of fuel salt (kg/m^3) ORNL-TM-0728 p.8 +W_f = 1.623879934566580e+02 # 1.83085e+02;%vdot_f*rho_f; % 182.78; % calcd from m_dot*cp*delT=P; vdot_f*rho_f; % fuel flow rate (kg/s) +# tau_f_c = tau_c; % ORNL-TM-0728 % 8.45; % transit time of fuel in core (s) ORNL-TM-1070 p.15, TDAMSRE p.5 +m_f = W_f * tau_c # fuel mass in core (kg) +nn_f = 2 # number of fuel nodes in core model +mn_f = m_f / nn_f # fuel mass per node (kg) +# cp_f = 4.2*9/5; % (MJ/deg-C) total fuel heat capacity TDAMSRE p.5 +scp_f = 1.9665E-3 # specific heat capacity of fuel salt (MJ/kg-C) ORNL-TM-0728 p.8 + +# Core Upflow - DONE +v_g = 1.95386 # graphite volume(m^3) ORNL-TM-0728 p. 101 +rho_g = 1.860E3 # graphite density (kg/m^3) ORNL-3812 p.77, ORNL-TM-0728 p.87 +m_g = v_g * rho_g # graphite mass (kg) +cp_g = 3.6 * 9 / 5 # TDAMSRE p.5 graphite total heat capacity (MW-s/C) ORNL-TM-1647 p.3 +scp_g = 1.773E-3 # cp_g/m_g; % graphite specific heat capacity (MW-s/kg-C) ORNL-TM-1647 p.3 +mcp_g1 = m_g * scp_g # (mass of material x heat capacity of material) of graphite per lump (MW-s/°C) +mcp_f1 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) +mcp_f2 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) +hA_fg = 0.02 * 9 / 5 # (fuel to graphite heat transfer coeff x heat transfer area) (MW/°C) ORNL-TM-1647 p.3, TDAMSRE p.5 +k_g = 0.07 # fraction of total power generated in the graphite ORNL-TM-0728 p.9 +k_1 = 0.5 # fraction of heat transferred from graphite which goes to the first fuel lump +k_2 = 0.5 # fraction of heat transferred from graphite which goes to the second fuel lump +k_f = 0.93 # fraction of heat generated in fuel - that generated in the external loop ORNL-TM-0728 p.9 +k_f1 = k_f / nn_f # fraction of total power generated in lump f1 +k_f2 = k_f / nn_f # fraction of total power generated in lump f2 + +# New node for power deposited in fuel outside the core +k_out = 1 - (k_g + k_f) # fraction of power generated in fuel in external loop ORNL-TM-0728 p.9 +m_out = W_f # (kg) Mass of node such that resident time is 1 sec (W_f needs to be defined) + +# Initial conditions - DONE +Tf_in = 6.3222E+02 # in °C ORNL-TM-1647 p.2 +T0_f2 = 6.5727E+02 # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 +T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2 # 6.405952380952389e+02; in °C +T0_g1 = T0_f1 + (k_g * P / hA_fg) # 6.589285714285924e+02; in °C +# T0_out = k_out * P / m_out / scp_f + T0_f2 # in °C (scp_f needs to be defined) + + +# Heat Exchanger - DONE +# Geometry +d_he = 16 # (in) he diameter ORNL-TM-0728 p. 164 +h_he = 72 # (in) active height % 96; %(in) he height ORNL-TM-0728 p. 164 +od_tube = 0.5 # (in) coolant tube OD ORNL-TM-0728 p. 164 +id_tube = od_tube - 2 * 0.042 # (in) coolant tube ID ORNL-TM-0728 p. 164 +n_tube = 159 # number of coolant tubes ORNL-TM-0728 p. 164 +a_tube = 254 * 144 # (in^2) total area of tubes ORNL-TM-0728 p. 164 +l_tube = a_tube / n_tube / (np.pi * od_tube) # (in) tube length +v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube # (in^3) hx shell volume occupied by tubes +v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube # (in^3) hx volume occupied by coolant +v_he = (d_he / 2) ** 2 * np.pi * h_he # (in^3) volume of heat exchanger shell +v_he_fuel = v_he - v_tube # (in^3) volume available to fuel in shell + +# Unit conversions +in_m = 1.63871e-5 # 1 cubic inch = 1.63871e-5 cubic meters + +# PRIMARY FLOW PARAMETERS - DONE +W_p = W_f # fuel flow rate (kg/s) + +m_p = v_he_fuel * in_m * rho_f # fuel mass in PHE (kg) +nn_p = 4 # number of fuel nodes in PHE +mn_p = m_p / nn_p # fuel mass per node (kg) +cp_p = scp_f # fuel heat capacity (MJ/(kg-C)) + +# SECONDARY FLOW PARAMETERS - DONE +vdot_s = 5.36265E-02 # ORNL-TM-0728 p. 164 % 5.236E-02; % coolant volume flow rate (m^3/s) ORNL-TM-1647 p.3 +rho_s = 1.922e3 # coolant salt density (kg/m^3) ORNL-TM-0728 p.8 +W_s = 1.005793369810108e+02 # vdot_s*rho_s; % calcd from mdot*cp*delT; vdot_s*rho_s; % coolant flow rate (kg/s) ORNL-TM-1647 p.3 + +m_s = v_cool * in_m * rho_s # coolant mass in PHE (kg) +nn_s = 4 # number of coolant nodes in PHE +mn_s = m_s / nn_s # coolant mass per node (kg) +scp_s = 2.39E-3 # cp_s/m_s; % specific heat capacity of coolant (MJ/(kg-C) ORNL-TM-0728 p.8 + +A_phe = 2.359E+01 # effective area for heat transfer (primary and secondary, m^2) ORNL-TM-0728 p.164 + +ha_p = 6.480E-01 # heat transfer*area coefficient from primary to tubes (MW/C) ORNL-TM-1647 p.3 +ha_s = 3.060E-01 # heat transfer*area coefficient from tubes to secondary (MW/C) ORNL-TM-1647 p.3 + +# Primary Side +mcp_pn = mn_p * cp_p # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C +hA_pn = ha_p / nn_s # 3.030; % (primary to tube heat transfer coeff x heat transfer area) in MW/°C + +# Tubes - DONE +nn_t = 2 # number of nodes of tubes in the model +rho_tube = 8.7745E+03 # (kg/m^3) density of INOR-8 ORNL-TM-0728 p.20 +m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t # mass of tubes (kg) +scp_t = 5.778E-04 # specific heat capacity of tubes (MJ/(kg-C)) ORNL-TM-0728 p.20 +mcp_tn = m_tn * scp_t # mass*(heat capacity) of tubes per lump in MW-s/°C + +# Secondary Side - DONE +mcp_sn = mn_s * scp_s # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C +hA_sn = ha_s / nn_s # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Initial conditions - DONE +# Primary nodes +Tp_in = T0_f2 # in °C ORNL-TM-1647 p.2 +T0_p4 = Tf_in # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 +T0_p1 = Tp_in + (T0_p4 - Tp_in) / 4 # in °C +T0_p2 = Tp_in + 2 * (T0_p4 - Tp_in) / 4 # in °C +T0_p3 = Tp_in + 3 * (T0_p4 - Tp_in) / 4 # in °C + +# Secondary nodes +Ts_in = 5.4611E+02 # in °C ORNL-TM-1647 p.2 +T0_s4 = 5.7939E+02 # in °C ORNL-TM-1647 p.2 +T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s # in °C +T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s # in °C +T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s # in °C +# Tube nodes +T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn) # in °C +T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn) # in °C + +# Radiator Parameters - DONE + +# Initial conditions - DONE +# Primary nodes +Trp_in = T0_s4 # 5.933E+02; % in °C ORNL-TM-1647 p.2 +T0_rp = Ts_in # in °C ORNL-TM-1647 p.2 + +# Secondary nodes - DONE +Trs_in = 37.78 # (C) air inlet temperature ORNL-TM-1647 p.2 +T0_rs = 148.9 # (C) air exit temperature ORNL-TM-1647 p.2 + +# Radiator Geometry +od_rad = 0.01905 # (m) outer diameter of tubes in the radiator ORNL-TM-0728 p.296 +tube_wall_thick = 0.0018288 # (m) thickness of tubes in the radiator ORNL-TM-0728 p.296 +id_rad = od_rad - 2 * tube_wall_thick +n_rtubes = 120 # number of tubes in the radiator (rows times tubes per row) ORNL-TM-0728 p.296 +l_rtube = 9.144 # (m) length of tubes in the radiator ORNL-TM-0728 p.296 +v_rp = pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes # volume available to salt in the radiator +# v_rtube = pi * (od_rad / 2) ** 2 * l_rtube * n_rtubes - v_rp # volume of metal in radiator tubes *TUBES NOT MODELED + +n_tpr = 12 # number of tubes per row in the radiator matrix +n_row = 10 # number rows in the radiator matrix +tube_space = 0.0381 # (m) spacing between tubes and rows of matrix +v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube # volume of air inside radiator + +# PRIMARY FLOW PARAMETERS - DONE +W_rp = W_s # coolant salt flow rate (kg/s) +m_rp = v_rp * rho_s # coolant salt mass in rad (kg) +nn_rp = 1 # number of coolant salt nodes in the radiator +mn_rp = m_rp / nn_rp # coolant mass per node (kg) +cp_rp = scp_s # coolant specific heat capacity (MJ/(kg-C)) + +# SECONDARY FLOW PARAMETERS - DONE +vdot_rs = 94.389 # ORNL-TM-0728 p. 296; 78.82; % air volume flow rate (m^3/s) ORNL-TM-1647 p.2 +rho_rs = 1.1237 # air density (kg/m^3) REFPROP (310K and 0.1MPa) +W_rs = vdot_rs * rho_rs # air flow rate (kg/s) + +m_rs = v_rs * rho_rs # coolant air mass in rad (kg) +nn_rs = 1 # number of coolant nodes in rad +mn_rs = m_rs / nn_rs # coolant mass per node (kg) +scp_rs = 1.0085E-3 # (MJ/kg-C) specific heat capacity of air at (air_out+air_in)/2 REFPROP + +A_rad = 6.503E1 # (m^2) surface area of the radiator ORNL-TM-0728 p.14 +h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2) # cald as: P/A_rad/((T0_rp+Trp_in)/2-(T0_rs+Trs_in)/2) 3.168E-4; % (MW/m^2-C) polimi thesis + +# Primary Side +mcp_rpn = mn_rp * cp_rp # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C +hA_rpn = h_roverall * A_rad / nn_rs # 3.030; % (primary to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Secondary Side - DONE +mcp_rsn = mn_rs * scp_rs # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C +hA_rsn = h_roverall * A_rad / nn_rs # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Pure time delays between components - DONE +tau_hx_c = 8.67 # (sec) delay from hx to core TDAMSRE p.6 +tau_c_hx = 3.77 # (sec) subtracted 1 sec for external loop power generation node resident time; delay from core to fuel hx TDAMSRE p.6 +tau_hx_r = 4.71 # (sec) fertile hx to core TDAMSRE p.6 +tau_r_hx = 8.24 # (sec) core to fertile hx TDAMSRE p.6 + +first_val = (rho_0 - beta_t) * n_frac0 / Lam + lam[0] * C0[0] + lam[1] * C0[1] + lam[2] * C0[2] + lam[3] * C0[3] + lam[4] * C0[4] + lam[5] * C0[5] \ No newline at end of file diff --git a/dynamic_model/plot_results.py b/dynamic_model/plot_results.py new file mode 100644 index 0000000..b2133dc --- /dev/null +++ b/dynamic_model/plot_results.py @@ -0,0 +1,94 @@ +import matplotlib.pyplot as plt +from parameters import * + +# 1: radiatior coolant inlet temp +# 2: radiator coolant outlet temp +# 3: radiator air outlet temp +# 4: heat exchanger fuel inlet temp +# 5: heat exchanger fuel node 1 temp +# 6: heat exchanger fuel node 2 temp +# 7: heat exchanger fuel node 3 temp +# 8: heat exchanger fuel node 4 temp +# 9: heat exchanger tube node 1 temp +# 10: heat exchanger tube node 2 temp +# 11: heat exchanger coolant inlet temp +# 12: heat exchanger coolant node 1 +# 13: heat exchanger coolant node 2 +# 14: heat exchanger coolant node 3 +# 15: heat exchanger coolant node 4 +# 16: core fuel inlet temp +# 17: k +# 18: C1 +# 19: C2 +# 20: C3 +# 21: C4 +# 22: C5 +# 23: C6 +# 24: core graphite temp +# 25: core fuel node 1 temp +# 26: core fuel node 2 temp + +filename1 = "sim_out_1000.0_1.txt" +filename5 = "sim_out_1000.0_5.txt" +filename8 = "sim_out_1000.0_8.txt" + +sol1 = [] +k_file = open(filename1, 'r') +for k in k_file.readlines(): + sol1.append(k.split()) +k_file.close() + +sol5 = [] +k_file = open(filename5, 'r') +for k in k_file.readlines(): + sol5.append(k.split()) +k_file.close() + +sol8 = [] +k_file = open(filename8, 'r') +for k in k_file.readlines(): + sol8.append(k.split()) +k_file.close() + +sol1 = [[float(j) for j in s] for s in sol1] +sol5 = [[float(j) for j in s] for s in sol5] +sol8 = [[float(j) for j in s] for s in sol8] + +k = 10000 +test_pow1 = [(1*s[13]-1) for s in sol1] +test_pow5 = [(5*s[13]-5) for s in sol5] +test_pow8 = [(8*s[13]-8) for s in sol8] +tidx1 = [t[0] for t in enumerate(sol1) if (t[1][0] >= 500.00 and t[1][0] <= 800.00)] +tidx5 = [t[0] for t in enumerate(sol5) if (t[1][0] >= 500.00 and t[1][0] <= 800.00)] +tidx8 = [t[0] for t in enumerate(sol8) if (t[1][0] >= 500.00 and t[1][0] <= 800.00)] +t1 = [s[0] for s in sol1[tidx1[0]:tidx1[-1]]] +t5 = [s[0] for s in sol5[tidx5[0]:tidx5[-1]]] +t8 = [s[0] for s in sol8[tidx8[0]:tidx8[-1]]] + +# Create a figure and a 3x1 grid of subplots +fig, axs = plt.subplots(3, 1, figsize=(6, 12)) + +# Plot data on the first subplot +axs[0].plot(t1, test_pow1[tidx1[0]:tidx1[-1]]) +axs[0].set_title('1 MW') +#axs[0].set_xlabel('x') +axs[0].set_ylabel('dP') +axs[0].set_xticklabels([]) +axs[0].set_xticks([]) + +# Plot data on the second subplot +axs[1].plot(t5, test_pow5[tidx5[0]:tidx5[-1]]) +axs[1].set_title('5 MW') +#axs[1].set_xlabel('x') +axs[1].set_ylabel('dP') +axs[1].set_xticklabels([]) +axs[1].set_xticks([]) + +# Plot data on the third subplot +axs[2].plot(t8, test_pow8[tidx8[0]:tidx8[-1]]) +axs[2].set_title('8 MW') +axs[2].set_xlabel('x') +axs[2].set_ylabel('dP') + +plt.tight_layout() +plt.show() \ No newline at end of file From ca3cb023a45c8570498a2804d5a2df4a2f2dee29 Mon Sep 17 00:00:00 2001 From: Luke Labrie-Cleary Date: Mon, 7 Aug 2023 17:04:41 +0200 Subject: [PATCH 02/32] Create README.md --- dynamic_model/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 dynamic_model/README.md diff --git a/dynamic_model/README.md b/dynamic_model/README.md new file mode 100644 index 0000000..c9908fd --- /dev/null +++ b/dynamic_model/README.md @@ -0,0 +1 @@ +Non-linear dynamic model of the MSRE based on [Singh et al.](https://www.sciencedirect.com/science/article/pii/S030645491730381X). The model uses non-linear neutron kinetic equations, coupled to 1D linear heat transfer equations in order to model reactor dynamics. The neutron kinetics are expressed in terms of fractional power. As described in Singh et al., "The premise is that reactor power is proportional to neutron density, with all other parameters held fixed." The different components of the reactor, namely the core, heat exchanger and radiator are nodalized such that the dynamics of each is governed by its own system of equations, coupled to the other systems by delayed inlet/outlet terms. From d97fce65c1dd1e6293ab07bdc3e3289241544ca1 Mon Sep 17 00:00:00 2001 From: Luke Labrie-Cleary Date: Mon, 7 Aug 2023 17:36:12 +0200 Subject: [PATCH 03/32] Update README.md --- dynamic_model/README.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/dynamic_model/README.md b/dynamic_model/README.md index c9908fd..8d25864 100644 --- a/dynamic_model/README.md +++ b/dynamic_model/README.md @@ -1 +1,13 @@ +# Model Non-linear dynamic model of the MSRE based on [Singh et al.](https://www.sciencedirect.com/science/article/pii/S030645491730381X). The model uses non-linear neutron kinetic equations, coupled to 1D linear heat transfer equations in order to model reactor dynamics. The neutron kinetics are expressed in terms of fractional power. As described in Singh et al., "The premise is that reactor power is proportional to neutron density, with all other parameters held fixed." The different components of the reactor, namely the core, heat exchanger and radiator are nodalized such that the dynamics of each is governed by its own system of equations, coupled to the other systems by delayed inlet/outlet terms. + +# Method +The model herein uses SciPy's ODE library. Sample implementation is shown below: + +```python +backend = 'dopri5' +r = ode(dydtMSRE).set_integrator(backend,max_step=0.10) +r.integrate(100.00) +``` + +However, since SciPy's ODE library does not support delay differential equations, the delay terms are stored and handled manually. Since the 'dopri5' method uses adaptive time-stepping, linear interpolation is used for approximating the value of the delay terms near the closest timestep to the delay. From 4bf2f3d52b2e0a3384ba145fb843a89d231917eb Mon Sep 17 00:00:00 2001 From: Luke Labrie-Cleary Date: Mon, 7 Aug 2023 17:39:09 +0200 Subject: [PATCH 04/32] Update README.md --- dynamic_model/README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/dynamic_model/README.md b/dynamic_model/README.md index 8d25864..b5fb413 100644 --- a/dynamic_model/README.md +++ b/dynamic_model/README.md @@ -1,6 +1,13 @@ # Model Non-linear dynamic model of the MSRE based on [Singh et al.](https://www.sciencedirect.com/science/article/pii/S030645491730381X). The model uses non-linear neutron kinetic equations, coupled to 1D linear heat transfer equations in order to model reactor dynamics. The neutron kinetics are expressed in terms of fractional power. As described in Singh et al., "The premise is that reactor power is proportional to neutron density, with all other parameters held fixed." The different components of the reactor, namely the core, heat exchanger and radiator are nodalized such that the dynamics of each is governed by its own system of equations, coupled to the other systems by delayed inlet/outlet terms. +Neutron kinetics are governed by + +```math +\frac{{dn(t)}}{{dt}} = \frac{{(q(t) - b)}}{{K}} n(t) + \sum_{{i=1}}^6 k_i C_i(t) + S(t) + +``` + # Method The model herein uses SciPy's ODE library. Sample implementation is shown below: From 1a4eff508ed3e8462db45de0b93bdc58b4b5110e Mon Sep 17 00:00:00 2001 From: Luke Labrie-Cleary Date: Tue, 8 Aug 2023 12:08:31 +0200 Subject: [PATCH 05/32] Update README.md --- dynamic_model/README.md | 40 +++++++++++++++++++++++++++++++++++----- 1 file changed, 35 insertions(+), 5 deletions(-) diff --git a/dynamic_model/README.md b/dynamic_model/README.md index b5fb413..63f1570 100644 --- a/dynamic_model/README.md +++ b/dynamic_model/README.md @@ -1,14 +1,44 @@ # Model -Non-linear dynamic model of the MSRE based on [Singh et al.](https://www.sciencedirect.com/science/article/pii/S030645491730381X). The model uses non-linear neutron kinetic equations, coupled to 1D linear heat transfer equations in order to model reactor dynamics. The neutron kinetics are expressed in terms of fractional power. As described in Singh et al., "The premise is that reactor power is proportional to neutron density, with all other parameters held fixed." The different components of the reactor, namely the core, heat exchanger and radiator are nodalized such that the dynamics of each is governed by its own system of equations, coupled to the other systems by delayed inlet/outlet terms. +Non-linear dynamic model of the MSRE based on [Singh et al.](https://www.sciencedirect.com/science/article/pii/S030645491730381X). The model uses a "nodalized" approach, in which the reactor system is composed of coupled subsystems, each of which contain multiple nodes to represent the various dynamics contributing to neutron density (reactor power) and heat transfer. -Neutron kinetics are governed by +## Kinetics +The neutron kinetics are expressed in terms of fractional power. As described in Singh et al., "The premise is that reactor power is proportional to neutron density, with all other parameters held fixed." In the model herien, kinetics are described by the following set of equations ```math -\frac{{dn(t)}}{{dt}} = \frac{{(q(t) - b)}}{{K}} n(t) + \sum_{{i=1}}^6 k_i C_i(t) + S(t) - +\begin{equation} +\frac{{dn(t)}}{{dt}} = \frac{{(\rho(t) - \beta)}}{\Lambda} n(t) + \sum_{{i=1}}^6 k_i C_i(t) + S(t) +\end{equation} ``` -# Method +where $S(t)$ is an external source term, and $C_i(t)$ is the concentration of the $i^{th}$ precursor group (six total for this model) with + +```math +\frac{dC_i(t)}{dt} = \frac{\beta_i}{\Lambda}n(t)-\lambda_i C_i(t) - \frac{C_i(t)}{\tau_C} + \frac{C_i(t - \tau_L) e^{-\lambda_i \tau_L}}{\tau_C} +``` + +where $\rho(t)$ is the total reactivity such that + +```math +\rho(t)=\rho_0+\rho_{fb}(t)+\rho_{ext} +``` + +where $\rho_0$ is the reactivity necessary for steady state operation, found by solving the above two equations in the steady state, which yields + +```math +\rho_0 = \beta - \sum_{i=1}^6 \frac{\beta_i}{1+\frac{1}{\lambda_i \tau_C}(1-e^{-\lambda_i \tau_L})} +``` + +and $\rho_{ext}$ is external reactivity, e.g. from a reactivity insertion, and $\rho_{fb}$ is the feedback reactivity due to temperature differences in the core nodes, expressed as + +```math +\rho_{fb}(t) = \alpha_f \sum_{i=1}^{n} I_{fi} (T_{f,0} - T_f(t)) + \alpha_g \sum_{i=1}^{n} I_{gi} (T_{g,0} - T_g(t)), \quad \text{where} \sum_{\text{regions}} I_{fi} = \sum_{\text{regions}} I_{gi} = 1.0 +``` + +where $\alpha_f$ and $\alpha_g$ are the fuel and graphite temperature-reactivity coefficients respectively, and $I$ represents the weighted nuclear importance factor of each region. + +The kinetics are coupled to the heat exchanger node via the feedback reactivity, $\rho_{fb}(t)$, which depends on the temperature of the fuel and graphite nodes, which ultimately depend on the fuel inlet temperature. Note, the differential equation govenring neutron density, $\frac{dn(t)}{dt}$, is nonlinear due to the fact that $n(t)$ is multiplied with $\rho(t)$. + +## Method The model herein uses SciPy's ODE library. 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z7jJQd8`P8q4g%#r0B*+s9rE@Fc)l=jsOT7I8#QR6b6FRER#s}X&3hkE2 z3EUNJeaEC>IKkaS>L_skO&92ZSkV4Q&Quneu_Z21-2}9G9TmU|r zWDls<4y=8D0Qb?h0{s@WZNk#anKS2cz=j)?@4f5D(!LICxB|-%{RCixR>CAh0CfB+ zXo{1U6|v*O370?W1zc>ut|2{?`d97XtYsr?qC7bOEagCeXwQ@Su|} z;Pz8_;J9qjm1DMv4Q^V{bZk4tYbof)bYR2%(e3;H%>1lgs(|K)WR>Et-Yt1`-=sac z96a*z)*R?XA2rZ89&kGGG4MnPSKzvBV665WI^odsO%xWT0**5(belHhG}j5d<+clH z1Qm2E*smn`@SfH?MNAh4GKI;hZ5#Rzo?Pz+**(xSKqh&KcS^`VKsV}&?0W?Zgt fLjzsu73)9wc%Ph!8jY7{GXR07tDnm{r-UW|sy*l| literal 0 HcmV?d00001 From 37644202c71129f1de4921c9656f40f7e7b4bd93 Mon Sep 17 00:00:00 2001 From: Luke Labrie-Cleary Date: Tue, 8 Aug 2023 15:31:44 +0200 Subject: [PATCH 07/32] Update README.md --- dynamic_model/README.md | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/dynamic_model/README.md b/dynamic_model/README.md index 63f1570..d14b21f 100644 --- a/dynamic_model/README.md +++ b/dynamic_model/README.md @@ -1,5 +1,7 @@ # Model -Non-linear dynamic model of the MSRE based on [Singh et al.](https://www.sciencedirect.com/science/article/pii/S030645491730381X). The model uses a "nodalized" approach, in which the reactor system is composed of coupled subsystems, each of which contain multiple nodes to represent the various dynamics contributing to neutron density (reactor power) and heat transfer. +Non-linear dynamic model of the MSRE based on [Singh et al.](https://www.sciencedirect.com/science/article/pii/S030645491730381X). The model uses a "nodalized" approach, in which the reactor system is composed of coupled subsystems, each of which contain multiple nodes to represent the various dynamics contributing to neutron density (reactor power) and heat transfer. This model is a replication of the one-region nodal model implemented in Singh et al. A schematic of the nodes is shown below + +![](figures/msre_one_region_diagram.png) ## Kinetics The neutron kinetics are expressed in terms of fractional power. As described in Singh et al., "The premise is that reactor power is proportional to neutron density, with all other parameters held fixed." In the model herien, kinetics are described by the following set of equations @@ -38,6 +40,18 @@ where $\alpha_f$ and $\alpha_g$ are the fuel and graphite temperature-reactivity The kinetics are coupled to the heat exchanger node via the feedback reactivity, $\rho_{fb}(t)$, which depends on the temperature of the fuel and graphite nodes, which ultimately depend on the fuel inlet temperature. Note, the differential equation govenring neutron density, $\frac{dn(t)}{dt}$, is nonlinear due to the fact that $n(t)$ is multiplied with $\rho(t)$. +## Heat Transfer + +The equations governing heat transfer contain three terms; a source term and a sink term, occuring from either mass transfer (1D flow) or conduction via wetted interface, as well as a fractional power generation term, which is zero at all nodes outside of the core. The equations governing the two core fuel nodes are, for example + +```math +\frac{dT_{f1}}{dt} = \frac{W_f}{m_{f1}}(T_{f_{in}}-T_{f1}) + \frac{K_1 P_0 (\frac{n}{n_0})}{m_{f1} C_{pf}} + \left( \frac{K_{g1}}{K_{g1}+K_{g2}} \right) \frac{hA_{fg}}{m_{f1}C_{pf}}(T_g - T_{f1}) +``` +```math +\frac{dT_{f2}}{dt} = \frac{W_f}{m_{f2}}(T_{f1}-T_{f2}) + \frac{K_2 P_0 (\frac{n}{n_0})}{m_{f2} C_{pf}} + \left( \frac{K_{g2}}{K_{g1}+K_{g2}} \right) \frac{hA_{fg}}{m_{f2}C_{pf}}(T_g - T_{f1}) +``` +"Here, $W_f$ is the mass flow rate of fuel salt, $m_{f1}$ and $m_{f2}$ represent the mass of fuel nodes 1 and 2 respectively, $C_{pf}$ represents the fuel salt specific heat capacity, $K_1$ and $K_2$ are the fraction of total power generated in fuel nodes 1 and 2, $K_{g1}$ and $K_{g2}$ represent the fraction of power generated in the graphite transferred to each fuel node, $hA_{fg}$ is the product of area and heat transfer coefficient for the fuel-graphite interface, $P_0$ is the nominal power which multiplied with fractional neutron density n/no gives the instantaneous power, and the $T$s represent the temperatures of the various nodes. Note that the direction of heat transfer depends on the instantaneous temperature of the various nodes." (Singh et. al). + ## Method The model herein uses SciPy's ODE library. Sample implementation is shown below: From bb6d3d41a1fab9c9363d1955fc899dfc884be6da Mon Sep 17 00:00:00 2001 From: Luke Labrie-Cleary Date: Wed, 9 Aug 2023 10:08:14 +0200 Subject: [PATCH 08/32] Update README.md --- dynamic_model/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dynamic_model/README.md b/dynamic_model/README.md index d14b21f..f7613a1 100644 --- a/dynamic_model/README.md +++ b/dynamic_model/README.md @@ -52,7 +52,7 @@ The equations governing heat transfer contain three terms; a source term and a s ``` "Here, $W_f$ is the mass flow rate of fuel salt, $m_{f1}$ and $m_{f2}$ represent the mass of fuel nodes 1 and 2 respectively, $C_{pf}$ represents the fuel salt specific heat capacity, $K_1$ and $K_2$ are the fraction of total power generated in fuel nodes 1 and 2, $K_{g1}$ and $K_{g2}$ represent the fraction of power generated in the graphite transferred to each fuel node, $hA_{fg}$ is the product of area and heat transfer coefficient for the fuel-graphite interface, $P_0$ is the nominal power which multiplied with fractional neutron density n/no gives the instantaneous power, and the $T$s represent the temperatures of the various nodes. Note that the direction of heat transfer depends on the instantaneous temperature of the various nodes." (Singh et. al). -## Method +# Method The model herein uses SciPy's ODE library. Sample implementation is shown below: ```python From 9cb08fb10076df2f77c7a1da6b5de4648ebc5ad1 Mon Sep 17 00:00:00 2001 From: Luke Labrie-Cleary Date: Thu, 21 Sep 2023 15:13:25 +0200 Subject: [PATCH 09/32] Update README.md --- dynamic_model/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dynamic_model/README.md b/dynamic_model/README.md index f7613a1..3225198 100644 --- a/dynamic_model/README.md +++ b/dynamic_model/README.md @@ -8,7 +8,7 @@ The neutron kinetics are expressed in terms of fractional power. As described in ```math \begin{equation} -\frac{{dn(t)}}{{dt}} = \frac{{(\rho(t) - \beta)}}{\Lambda} n(t) + \sum_{{i=1}}^6 k_i C_i(t) + S(t) +\frac{{dn(t)}}{{dt}} = \frac{{(\rho(t) - \beta)}}{\Lambda} n(t) + \sum_{{i=1}}^6 \lambda_i C_i(t) + S(t) \end{equation} ``` @@ -33,7 +33,7 @@ where $\rho_0$ is the reactivity necessary for steady state operation, found by and $\rho_{ext}$ is external reactivity, e.g. from a reactivity insertion, and $\rho_{fb}$ is the feedback reactivity due to temperature differences in the core nodes, expressed as ```math -\rho_{fb}(t) = \alpha_f \sum_{i=1}^{n} I_{fi} (T_{f,0} - T_f(t)) + \alpha_g \sum_{i=1}^{n} I_{gi} (T_{g,0} - T_g(t)), \quad \text{where} \sum_{\text{regions}} I_{fi} = \sum_{\text{regions}} I_{gi} = 1.0 +\rho_{fb}(t) = \alpha_f \sum_{i=1}^{n} I_{fi} (T_{f,0} - T_{f_i}(t)) + \alpha_g \sum_{i=1}^{n} I_{gi} (T_{g,0} - T_{g_i}(t)), \quad \text{where} \sum_{\text{regions}} I_{fi} = \sum_{\text{regions}} I_{gi} = 1.0 ``` where $\alpha_f$ and $\alpha_g$ are the fuel and graphite temperature-reactivity coefficients respectively, and $I$ represents the weighted nuclear importance factor of each region. From ae944e2e86d6bc6de395901f60da873b7661daeb Mon Sep 17 00:00:00 2001 From: Luke Labrie-Cleary Date: Sun, 24 Sep 2023 11:56:05 +0200 Subject: [PATCH 10/32] Update README.md --- dynamic_model/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dynamic_model/README.md b/dynamic_model/README.md index 3225198..c291864 100644 --- a/dynamic_model/README.md +++ b/dynamic_model/README.md @@ -33,7 +33,7 @@ where $\rho_0$ is the reactivity necessary for steady state operation, found by and $\rho_{ext}$ is external reactivity, e.g. from a reactivity insertion, and $\rho_{fb}$ is the feedback reactivity due to temperature differences in the core nodes, expressed as ```math -\rho_{fb}(t) = \alpha_f \sum_{i=1}^{n} I_{fi} (T_{f,0} - T_{f_i}(t)) + \alpha_g \sum_{i=1}^{n} I_{gi} (T_{g,0} - T_{g_i}(t)), \quad \text{where} \sum_{\text{regions}} I_{fi} = \sum_{\text{regions}} I_{gi} = 1.0 +\rho_{fb}(t) = \alpha_f \sum_{i=1}^{n} I_{fi} (T_{f_i,0} - T_{f_i}(t)) + \alpha_g \sum_{i=1}^{n} I_{gi} (T_{g,0} - T_{g_i}(t)), \quad \text{where} \sum_{\text{regions}} I_{fi} = \sum_{\text{regions}} I_{gi} = 1.0 ``` where $\alpha_f$ and $\alpha_g$ are the fuel and graphite temperature-reactivity coefficients respectively, and $I$ represents the weighted nuclear importance factor of each region. From 309c19325260787acb138661b0221e99d5edfe81 Mon Sep 17 00:00:00 2001 From: church89 Date: Tue, 24 Oct 2023 14:30:09 +0200 Subject: [PATCH 11/32] inor and graphite sample materials were swapped --- h5m/msre_reactor_1e-2_swap.h5m | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 h5m/msre_reactor_1e-2_swap.h5m diff --git a/h5m/msre_reactor_1e-2_swap.h5m b/h5m/msre_reactor_1e-2_swap.h5m new file mode 100644 index 0000000..b2c598b --- /dev/null +++ b/h5m/msre_reactor_1e-2_swap.h5m @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:329389c2a06e7634e9d87b6e7aa0bc25ad55720a26329def5ad78694b3eb4b68 +size 240522156 From 1a3a43f4f191167c56c00d4266f405818307ae04 Mon Sep 17 00:00:00 2001 From: church89 Date: Tue, 24 Oct 2023 14:43:32 +0200 Subject: [PATCH 12/32] inor and graphite sample materials were swapped --- .gitattributes | 1 + h5m/msre_reactor_1e-2.h5m | 3 --- 2 files changed, 1 insertion(+), 3 deletions(-) delete mode 100644 h5m/msre_reactor_1e-2.h5m diff --git a/.gitattributes b/.gitattributes index b634d85..9b951d7 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1 +1,2 @@ *.pdf filter=lfs diff=lfs merge=lfs -text +h5m/*.h5 filter=lfs diff=lfs merge=lfs -text diff --git a/h5m/msre_reactor_1e-2.h5m b/h5m/msre_reactor_1e-2.h5m deleted file mode 100644 index 7a36fbf..0000000 --- a/h5m/msre_reactor_1e-2.h5m +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:61cb64dbd83498e17df1f1566a323e8a9c00cd38528bd0adcc8c5b87b924cb24 -size 240521996 From 61cbf5cd6bb7526e5a3d7453c43b099c30ff05c6 Mon Sep 17 00:00:00 2001 From: lukelabrie Date: Fri, 10 Nov 2023 16:55:30 +0100 Subject: [PATCH 13/32] include jitcdde implementation of dynamic model --- dynamic_model/parameters.py | 2 +- .../{ => scipyODE_implementation}/main.py | 16 +- .../scipyODE_implementation/parameters.py | 234 +++++ .../plot_results.py | 0 dynamic_model/simulation_study.ipynb | 913 ++++++++++++++++++ 5 files changed, 1158 insertions(+), 7 deletions(-) rename dynamic_model/{ => scipyODE_implementation}/main.py (97%) create mode 100644 dynamic_model/scipyODE_implementation/parameters.py rename dynamic_model/{ => scipyODE_implementation}/plot_results.py (100%) create mode 100644 dynamic_model/simulation_study.ipynb diff --git a/dynamic_model/parameters.py b/dynamic_model/parameters.py index 365490a..d92d5c5 100644 --- a/dynamic_model/parameters.py +++ b/dynamic_model/parameters.py @@ -44,7 +44,7 @@ ts_max = 1e-1 # maximum timestep (s) tau_l = 16.73 # ORNL-TM-0728 %16.44; % (s) tau_c = 8.46 # ORNL-TM-0728 %8.460; % (s) P = 8 # Thermal Power in MW ORNL-TM-1070, p.2 -n_frac0 = 5 # initial fractional neutron density n/n0 (n/cm^3/s) +n_frac0 = 1.0 # initial fractional neutron density n/n0 (n/cm^3/s) Lam = 2.400E-04 # mean generation time ORNL-TM-1070 p.15 U235 # Lam = 4.0E-04; # mean generation time ORNL-TM-1070 p.15 U233 lam = np.array([1.240E-02, 3.05E-02, 1.11E-01, 3.01E-01, 1.140E+00, 3.014E+00]) diff --git a/dynamic_model/main.py b/dynamic_model/scipyODE_implementation/main.py similarity index 97% rename from dynamic_model/main.py rename to dynamic_model/scipyODE_implementation/main.py index b6f23b1..1f6af37 100644 --- a/dynamic_model/main.py +++ b/dynamic_model/scipyODE_implementation/main.py @@ -91,7 +91,8 @@ def dydtMSRE(t,y,delays,rho_ext): n*beta[5]/Lam-lam[5]*C6-C6/tau_c+C6_delay*np.exp(-lam[5]*tau_l)/tau_c, # C6 (hA_fg/mcp_g1)*(T_cf1 - T_cg) + k_g*P*n/mcp_g1, # T_cg W_f/mn_f*(T_hf4_delay-T_cf1) + (k_f1*P*n/mcp_f1) + (hA_fg*k_1*(T_cg - T_cf1)/mcp_f1), # T_cf1 - W_f/mn_f*(T_cf1 - T_cf2) + (k_f2*P*n/mcp_f2) + (hA_fg*k_2*(T_cg - T_cf1)/mcp_f2) # T_cf2 + W_f/mn_f*(T_cf1 - T_cf2) + (k_f2*P*n/mcp_f2) + (hA_fg*k_2*(T_cg - T_cf1)/mcp_f2), # T_cf2 + ] return dydt @@ -134,7 +135,7 @@ def main(): # timing parameters t0 = 0.0 t_start = t0 - t_stop = 5000 + t_stop = 500.00 # solution sol = [] @@ -223,14 +224,17 @@ def main(): sol_interim = [] # display progress - print(f"{t_start}") + #print(f"{t_start}") i += 1 # plot single parameter - of_interest = 14 - plt.plot([s[0] for s in sol],[s[of_interest] for s in sol]) - plt.show() + #of_interest = 13 + #ti = [s[0] for s in sol] + #oi = [s[of_interest] for s in sol] + #print(type(ti[0])) + #plt.plot(ti,oi) + #plt.show() # check delay behavior # for i in range(len(sol)-1): diff --git a/dynamic_model/scipyODE_implementation/parameters.py b/dynamic_model/scipyODE_implementation/parameters.py new file mode 100644 index 0000000..d92d5c5 --- /dev/null +++ b/dynamic_model/scipyODE_implementation/parameters.py @@ -0,0 +1,234 @@ +import numpy as np +import pandas as pd +import math +pi = math.pi + +# Perturbations +# SOURCE INSERTION +# No source insertion +sourcedata = np.array([0, 0, 0]) +sourcetime = np.array([0, 50, 100]) +# % 1 (n/no)/s for 10 seconds +# sourcedata = np.array([0, 10, 0]) +# sourcetime = np.array([0, 10, 20]) +source = pd.Series(sourcedata, index=sourcetime) + +# REACTIVITY INSERTION +# No reactivity insertion +simtime = 10 +reactdata = np.array([0, 5E-4]) +reacttime = np.array([0, 2500]) +# Periodic 60 PCM for 50 seconds +# simtime = 500 +# periodic = np.array([[0, 0], [50, 6e-4], [100, 0], [150, -6e-4], [200, 0], [250, 6e-4], [300, 0], [350, -6e-4], [400, 0]]) +# reactdata = periodic[:, 1] +# reacttime = periodic[:, 0] +# Step up 60 pcm +# simtime = 1000 +# reactdata = np.array([0, 6e-3]) +# reacttime = np.array([0, 300]) +# # Step down -60 pcm for 10 sec +# simtime = 100 +# reactdata = np.array([0, -6e-4]) +# reacttime = np.array([0, 50]) +# # Pulse 600 pcm for 0.1 sec +# simtime = 30 +# reactdata = np.array([0, 6e-3, 0]) +# reacttime = np.array([0, 10, 10.1]) + +react = pd.Series(reactdata, index=reacttime) + +ts_max = 1e-1 # maximum timestep (s) + +# NEUTRONICS DATA +tau_l = 16.73 # ORNL-TM-0728 %16.44; % (s) +tau_c = 8.46 # ORNL-TM-0728 %8.460; % (s) +P = 8 # Thermal Power in MW ORNL-TM-1070, p.2 +n_frac0 = 1.0 # initial fractional neutron density n/n0 (n/cm^3/s) +Lam = 2.400E-04 # mean generation time ORNL-TM-1070 p.15 U235 +# Lam = 4.0E-04; # mean generation time ORNL-TM-1070 p.15 U233 +lam = np.array([1.240E-02, 3.05E-02, 1.11E-01, 3.01E-01, 1.140E+00, 3.014E+00]) +beta = np.array([0.000223, 0.001457, 0.001307, 0.002628, 0.000766, 0.00023]) # U235 +# beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009]) # U233 +beta_t = np.sum(beta) # total delayed neutron fraction MSRE +rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel +C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c)) + +# Feedback co-efficients +a_f = -8.71E-05 # U235 (drho/°C) fuel salt temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -5.904E-05; % ORNL-TM-0728 p. 101 % +a_g = -6.66E-05 # U235 (drho/°C) graphite temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -6.624E-05; % ORNL-TM-0728 p.101 + +# CORE HEAT TRANSFER PARAMETERS +# FUEL PARAMETERS - DONE +vdot_f = 7.5708E-02 # ORNL-TM-0728 % 7.571e-2; % vol. flow rate (m^3/s) ORNL-TM-1647 p.3, ORNL-TM-0728 p.12 +rho_f = 2.14647E+03 # (partially enriched U-235)ORNL-TM-0728 p.8 2.243E+03; % (Th-U) density of fuel salt (kg/m^3) ORNL-TM-0728 p.8 +W_f = 1.623879934566580e+02 # 1.83085e+02;%vdot_f*rho_f; % 182.78; % calcd from m_dot*cp*delT=P; vdot_f*rho_f; % fuel flow rate (kg/s) +# tau_f_c = tau_c; % ORNL-TM-0728 % 8.45; % transit time of fuel in core (s) ORNL-TM-1070 p.15, TDAMSRE p.5 +m_f = W_f * tau_c # fuel mass in core (kg) +nn_f = 2 # number of fuel nodes in core model +mn_f = m_f / nn_f # fuel mass per node (kg) +# cp_f = 4.2*9/5; % (MJ/deg-C) total fuel heat capacity TDAMSRE p.5 +scp_f = 1.9665E-3 # specific heat capacity of fuel salt (MJ/kg-C) ORNL-TM-0728 p.8 + +# Core Upflow - DONE +v_g = 1.95386 # graphite volume(m^3) ORNL-TM-0728 p. 101 +rho_g = 1.860E3 # graphite density (kg/m^3) ORNL-3812 p.77, ORNL-TM-0728 p.87 +m_g = v_g * rho_g # graphite mass (kg) +cp_g = 3.6 * 9 / 5 # TDAMSRE p.5 graphite total heat capacity (MW-s/C) ORNL-TM-1647 p.3 +scp_g = 1.773E-3 # cp_g/m_g; % graphite specific heat capacity (MW-s/kg-C) ORNL-TM-1647 p.3 +mcp_g1 = m_g * scp_g # (mass of material x heat capacity of material) of graphite per lump (MW-s/°C) +mcp_f1 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) +mcp_f2 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) +hA_fg = 0.02 * 9 / 5 # (fuel to graphite heat transfer coeff x heat transfer area) (MW/°C) ORNL-TM-1647 p.3, TDAMSRE p.5 +k_g = 0.07 # fraction of total power generated in the graphite ORNL-TM-0728 p.9 +k_1 = 0.5 # fraction of heat transferred from graphite which goes to the first fuel lump +k_2 = 0.5 # fraction of heat transferred from graphite which goes to the second fuel lump +k_f = 0.93 # fraction of heat generated in fuel - that generated in the external loop ORNL-TM-0728 p.9 +k_f1 = k_f / nn_f # fraction of total power generated in lump f1 +k_f2 = k_f / nn_f # fraction of total power generated in lump f2 + +# New node for power deposited in fuel outside the core +k_out = 1 - (k_g + k_f) # fraction of power generated in fuel in external loop ORNL-TM-0728 p.9 +m_out = W_f # (kg) Mass of node such that resident time is 1 sec (W_f needs to be defined) + +# Initial conditions - DONE +Tf_in = 6.3222E+02 # in °C ORNL-TM-1647 p.2 +T0_f2 = 6.5727E+02 # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 +T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2 # 6.405952380952389e+02; in °C +T0_g1 = T0_f1 + (k_g * P / hA_fg) # 6.589285714285924e+02; in °C +# T0_out = k_out * P / m_out / scp_f + T0_f2 # in °C (scp_f needs to be defined) + + +# Heat Exchanger - DONE +# Geometry +d_he = 16 # (in) he diameter ORNL-TM-0728 p. 164 +h_he = 72 # (in) active height % 96; %(in) he height ORNL-TM-0728 p. 164 +od_tube = 0.5 # (in) coolant tube OD ORNL-TM-0728 p. 164 +id_tube = od_tube - 2 * 0.042 # (in) coolant tube ID ORNL-TM-0728 p. 164 +n_tube = 159 # number of coolant tubes ORNL-TM-0728 p. 164 +a_tube = 254 * 144 # (in^2) total area of tubes ORNL-TM-0728 p. 164 +l_tube = a_tube / n_tube / (np.pi * od_tube) # (in) tube length +v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube # (in^3) hx shell volume occupied by tubes +v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube # (in^3) hx volume occupied by coolant +v_he = (d_he / 2) ** 2 * np.pi * h_he # (in^3) volume of heat exchanger shell +v_he_fuel = v_he - v_tube # (in^3) volume available to fuel in shell + +# Unit conversions +in_m = 1.63871e-5 # 1 cubic inch = 1.63871e-5 cubic meters + +# PRIMARY FLOW PARAMETERS - DONE +W_p = W_f # fuel flow rate (kg/s) + +m_p = v_he_fuel * in_m * rho_f # fuel mass in PHE (kg) +nn_p = 4 # number of fuel nodes in PHE +mn_p = m_p / nn_p # fuel mass per node (kg) +cp_p = scp_f # fuel heat capacity (MJ/(kg-C)) + +# SECONDARY FLOW PARAMETERS - DONE +vdot_s = 5.36265E-02 # ORNL-TM-0728 p. 164 % 5.236E-02; % coolant volume flow rate (m^3/s) ORNL-TM-1647 p.3 +rho_s = 1.922e3 # coolant salt density (kg/m^3) ORNL-TM-0728 p.8 +W_s = 1.005793369810108e+02 # vdot_s*rho_s; % calcd from mdot*cp*delT; vdot_s*rho_s; % coolant flow rate (kg/s) ORNL-TM-1647 p.3 + +m_s = v_cool * in_m * rho_s # coolant mass in PHE (kg) +nn_s = 4 # number of coolant nodes in PHE +mn_s = m_s / nn_s # coolant mass per node (kg) +scp_s = 2.39E-3 # cp_s/m_s; % specific heat capacity of coolant (MJ/(kg-C) ORNL-TM-0728 p.8 + +A_phe = 2.359E+01 # effective area for heat transfer (primary and secondary, m^2) ORNL-TM-0728 p.164 + +ha_p = 6.480E-01 # heat transfer*area coefficient from primary to tubes (MW/C) ORNL-TM-1647 p.3 +ha_s = 3.060E-01 # heat transfer*area coefficient from tubes to secondary (MW/C) ORNL-TM-1647 p.3 + +# Primary Side +mcp_pn = mn_p * cp_p # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C +hA_pn = ha_p / nn_s # 3.030; % (primary to tube heat transfer coeff x heat transfer area) in MW/°C + +# Tubes - DONE +nn_t = 2 # number of nodes of tubes in the model +rho_tube = 8.7745E+03 # (kg/m^3) density of INOR-8 ORNL-TM-0728 p.20 +m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t # mass of tubes (kg) +scp_t = 5.778E-04 # specific heat capacity of tubes (MJ/(kg-C)) ORNL-TM-0728 p.20 +mcp_tn = m_tn * scp_t # mass*(heat capacity) of tubes per lump in MW-s/°C + +# Secondary Side - DONE +mcp_sn = mn_s * scp_s # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C +hA_sn = ha_s / nn_s # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Initial conditions - DONE +# Primary nodes +Tp_in = T0_f2 # in °C ORNL-TM-1647 p.2 +T0_p4 = Tf_in # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 +T0_p1 = Tp_in + (T0_p4 - Tp_in) / 4 # in °C +T0_p2 = Tp_in + 2 * (T0_p4 - Tp_in) / 4 # in °C +T0_p3 = Tp_in + 3 * (T0_p4 - Tp_in) / 4 # in °C + +# Secondary nodes +Ts_in = 5.4611E+02 # in °C ORNL-TM-1647 p.2 +T0_s4 = 5.7939E+02 # in °C ORNL-TM-1647 p.2 +T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s # in °C +T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s # in °C +T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s # in °C +# Tube nodes +T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn) # in °C +T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn) # in °C + +# Radiator Parameters - DONE + +# Initial conditions - DONE +# Primary nodes +Trp_in = T0_s4 # 5.933E+02; % in °C ORNL-TM-1647 p.2 +T0_rp = Ts_in # in °C ORNL-TM-1647 p.2 + +# Secondary nodes - DONE +Trs_in = 37.78 # (C) air inlet temperature ORNL-TM-1647 p.2 +T0_rs = 148.9 # (C) air exit temperature ORNL-TM-1647 p.2 + +# Radiator Geometry +od_rad = 0.01905 # (m) outer diameter of tubes in the radiator ORNL-TM-0728 p.296 +tube_wall_thick = 0.0018288 # (m) thickness of tubes in the radiator ORNL-TM-0728 p.296 +id_rad = od_rad - 2 * tube_wall_thick +n_rtubes = 120 # number of tubes in the radiator (rows times tubes per row) ORNL-TM-0728 p.296 +l_rtube = 9.144 # (m) length of tubes in the radiator ORNL-TM-0728 p.296 +v_rp = pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes # volume available to salt in the radiator +# v_rtube = pi * (od_rad / 2) ** 2 * l_rtube * n_rtubes - v_rp # volume of metal in radiator tubes *TUBES NOT MODELED + +n_tpr = 12 # number of tubes per row in the radiator matrix +n_row = 10 # number rows in the radiator matrix +tube_space = 0.0381 # (m) spacing between tubes and rows of matrix +v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube # volume of air inside radiator + +# PRIMARY FLOW PARAMETERS - DONE +W_rp = W_s # coolant salt flow rate (kg/s) +m_rp = v_rp * rho_s # coolant salt mass in rad (kg) +nn_rp = 1 # number of coolant salt nodes in the radiator +mn_rp = m_rp / nn_rp # coolant mass per node (kg) +cp_rp = scp_s # coolant specific heat capacity (MJ/(kg-C)) + +# SECONDARY FLOW PARAMETERS - DONE +vdot_rs = 94.389 # ORNL-TM-0728 p. 296; 78.82; % air volume flow rate (m^3/s) ORNL-TM-1647 p.2 +rho_rs = 1.1237 # air density (kg/m^3) REFPROP (310K and 0.1MPa) +W_rs = vdot_rs * rho_rs # air flow rate (kg/s) + +m_rs = v_rs * rho_rs # coolant air mass in rad (kg) +nn_rs = 1 # number of coolant nodes in rad +mn_rs = m_rs / nn_rs # coolant mass per node (kg) +scp_rs = 1.0085E-3 # (MJ/kg-C) specific heat capacity of air at (air_out+air_in)/2 REFPROP + +A_rad = 6.503E1 # (m^2) surface area of the radiator ORNL-TM-0728 p.14 +h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2) # cald as: P/A_rad/((T0_rp+Trp_in)/2-(T0_rs+Trs_in)/2) 3.168E-4; % (MW/m^2-C) polimi thesis + +# Primary Side +mcp_rpn = mn_rp * cp_rp # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C +hA_rpn = h_roverall * A_rad / nn_rs # 3.030; % (primary to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Secondary Side - DONE +mcp_rsn = mn_rs * scp_rs # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C +hA_rsn = h_roverall * A_rad / nn_rs # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Pure time delays between components - DONE +tau_hx_c = 8.67 # (sec) delay from hx to core TDAMSRE p.6 +tau_c_hx = 3.77 # (sec) subtracted 1 sec for external loop power generation node resident time; delay from core to fuel hx TDAMSRE p.6 +tau_hx_r = 4.71 # (sec) fertile hx to core TDAMSRE p.6 +tau_r_hx = 8.24 # (sec) core to fertile hx TDAMSRE p.6 + +first_val = (rho_0 - beta_t) * n_frac0 / Lam + lam[0] * C0[0] + lam[1] * C0[1] + lam[2] * C0[2] + lam[3] * C0[3] + lam[4] * C0[4] + lam[5] * C0[5] \ No newline at end of file diff --git a/dynamic_model/plot_results.py b/dynamic_model/scipyODE_implementation/plot_results.py similarity index 100% rename from dynamic_model/plot_results.py rename to dynamic_model/scipyODE_implementation/plot_results.py diff --git a/dynamic_model/simulation_study.ipynb b/dynamic_model/simulation_study.ipynb new file mode 100644 index 0000000..193eaf3 --- /dev/null +++ b/dynamic_model/simulation_study.ipynb @@ -0,0 +1,913 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from jitcdde import jitcdde, y, t\n", + "from parameters import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define System" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# delays\n", + "taus = [tau_c, tau_c_hx, tau_hx_c, tau_hx_r, tau_l, tau_r_hx]\n", + "\n", + "# dT/dt for radiator nodes\n", + "T_out_rc = (W_rp/mn_rp)*(y(11,t-tau_hx_r)-y(0)) + (hA_rpn/mcp_rpn)*(y(1)-y(0)) # T_out_rc: y(0)\n", + "T_out_air = -((W_rs/mn_rs)+(hA_rsn/mcp_rsn))*y(1) + (hA_rsn/mcp_rsn)*y(0) + (W_rs/mn_rs)*Trs_in # T_out_air: y(1)\n", + "\n", + "# dT/dt for heat exchanger nodes\n", + "T_hf1 = -((W_p/mn_p)+(hA_pn/mcp_pn))*y(2) + (hA_pn/mcp_pn)*y(6) + (W_p/mn_p)*y(21,t-tau_c_hx) # T_hf1: y(2)\n", + "T_hf2 = (W_p/mn_p)*(y(2)-y(3)) + (hA_pn/mcp_pn)*(y(6)-y(2)) # T_hf2: y(3)\n", + "T_hf3 = -((W_p/mn_p)+(hA_pn/mcp_pn))*y(4) + (hA_pn/mcp_pn)*y(7) + (W_p/mn_p)*y(3) # T_hf3: y(4)\n", + "T_hf4 = (W_p/mn_p)*(y(4)-y(5)) + (hA_pn/mcp_pn)*(y(7)-y(4)) # T_hf4: y(5)\n", + "T_ht1 = (2*hA_pn/mcp_tn)*(y(2)-y(6)) + (2*hA_sn/mcp_tn)*(y(10)-y(6)) # T_ht1: y(6)\n", + "T_ht2 = (2*hA_pn/mcp_tn)*(y(4)-y(7)) + (2*hA_sn/mcp_tn)*(y(8)-y(7)) # T_ht2: y(7)\n", + "T_hc1 = -((W_s/mn_s)+(hA_sn/mcp_sn))*y(8) + (hA_sn/mcp_sn)*y(7) + (W_s/mn_s)*y(0,t-tau_r_hx) # T_hc1: y(8)\n", + "T_hc2 = (W_s/mn_s)*(y(8)-y(9)) + (hA_sn/mcp_sn)*(y(7)-y(8)) # T_hc2: y(9)\n", + "T_hc3 = -((W_s/mn_s)+(hA_sn/mcp_sn))*y(10) + (hA_sn/mcp_sn)*y(6) + (W_s/mn_s)*y(9) # T_hc3: y(10)\n", + "T_hc4 = (W_s/mn_s)*(y(10)-y(11)) + (hA_sn/mcp_sn)*(y(6)-y(10)) # T_hc4: y(11)\n", + "\n", + "# dn/dt\n", + "n = (y(22)-beta_t)*y(12)/Lam+lam[0]*y(13)+lam[1]*y(14)+lam[2]*y(15)+lam[3]*y(16)+lam[4]*y(17)+lam[5]*y(18) # n (no source insertion): y(12)\n", + "\n", + "# dC_i/dt (precursor concentrations)\n", + "C1 = y(12)*beta[0]/Lam-lam[0]*y(13)-y(13)/tau_c+y(13,t-tau_l)*np.exp(-lam[0]*tau_l)/tau_c # C1: y(13)\n", + "C2 = y(12)*beta[1]/Lam-lam[1]*y(14)-y(14)/tau_c+y(14,t-tau_l)*np.exp(-lam[1]*tau_l)/tau_c # C2: y(14)\n", + "C3 = y(12)*beta[2]/Lam-lam[2]*y(15)-y(15)/tau_c+y(15,t-tau_l)*np.exp(-lam[2]*tau_l)/tau_c # C3: y(15)\n", + "C4 = y(12)*beta[3]/Lam-lam[3]*y(16)-y(16)/tau_c+y(16,t-tau_l)*np.exp(-lam[3]*tau_l)/tau_c # C4: y(16)\n", + "C5 = y(12)*beta[4]/Lam-lam[4]*y(17)-y(17)/tau_c+y(17,t-tau_l)*np.exp(-lam[4]*tau_l)/tau_c # C5: y(17)\n", + "C6 = y(12)*beta[5]/Lam-lam[5]*y(18)-y(18)/tau_c+y(18,t-tau_l)*np.exp(-lam[5]*tau_l)/tau_c # C6: y(18)\n", + "\n", + "# dT/dt core nodes\n", + "T_cg = (hA_fg/mcp_g1)*(y(20)-y(19)) + k_g*P*y(12)/mcp_g1 # T_cg: y(19)\n", + "T_cf1 = W_f/mn_f*(y(5,t-tau_hx_c)-y(20)) + (k_f1*P*y(12)/mcp_f1) + (hA_fg*k_1*(y(19)-y(20))/mcp_f1) # T_cf1: y(20)\n", + "T_cf2 = W_f/mn_f*(y(20)-y(21)) + (k_f2*P*y(12)/mcp_f2) + (hA_fg*k_2*(y(19)-y(20))/mcp_f2) # T_cf2: y(21)\n", + "\n", + "# rho y(22)\n", + "rho = (a_f/2)*(T_cf1 + T_cf2) + (a_g)*(T_cg)\n", + "\n", + "# initial reactivity \n", + "rho_initial = 0.000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve with JiTCDDE" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating, compiling, and loading C code.\n", + "Using default integration parameters.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/envs/thesis_env/lib/python3.9/site-packages/jitcdde/_jitcdde.py:795: UserWarning: You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\n", + " warn(\"You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\")\n", + "/mnt/envs/thesis_env/lib/python3.9/site-packages/jitcdde/_jitcdde.py:795: UserWarning: You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\n", + " warn(\"You did not explicitly handle initial discontinuities. Proceed only if you know what you are doing. This is only fine if you somehow chose your initial past such that the derivative of the last anchor complies with the DDE. In this case, you can set the attribute `initial_discontinuities_handled` to `True` to suppress this warning. See https://jitcdde.rtfd.io/#discontinuities for details.\")\n", + "/mnt/envs/thesis_env/lib/python3.9/site-packages/jitcdde/_jitcdde.py:792: UserWarning: The target time is smaller than the current time. No integration step will happen. The returned state will be extrapolated from the interpolating Hermite polynomial for the last integration step. You may see this because you try to integrate backwards in time, in which case you did something wrong. You may see this just because your sampling step is small, in which case there is no need to worry.\n", + " warn(\"The target time is smaller than the current time. No integration step will happen. The returned state will be extrapolated from the interpolating Hermite polynomial for the last integration step. You may see this because you try to integrate backwards in time, in which case you did something wrong. You may see this just because your sampling step is small, in which case there is no need to worry.\")\n" + ] + } + ], + "source": [ + "# instantiate jitcdde object\n", + "DDE = jitcdde([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n", + " T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])\n", + "\n", + "# set initial conditions\n", + "DDE.constant_past([T0_rp, T0_rs, T0_p1,T0_p2, T0_p3, T0_p4, T0_t1, T0_t2, T0_s1, T0_s2, \n", + " T0_s3, T0_s4, n_frac0, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5], \n", + " T0_g1, T0_f1, T0_f2,rho_initial])\n", + "\n", + "# jitcdde solver parameters \n", + "t0 = 0.0\n", + "tf = 1000.00\n", + "T = np.arange(t0,tf,0.01)\n", + "\n", + "sol_jit = []\n", + "for t_x in T:\n", + " sol_jit.append(DDE.integrate(t_x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vary temeprature feedback coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating, compiling, and loading C code.\n", + "Using default integration parameters.\n", + "Generating, compiling, and loading C code.\n", + "Using default integration parameters.\n", + "Generating, compiling, and loading C code.\n", + "Using default integration parameters.\n", + "Generating, compiling, and loading C code.\n", + "Using default integration parameters.\n" + ] + } + ], + "source": [ + "# solution vectors\n", + "sols = []\n", + "sols = [sol_jit]\n", + "\n", + "# feedbacks\n", + "factors_f = [1/2, 1/4, 1/8, 0]\n", + "factors_g = [1/2, 1/4, 1/8, 0]\n", + "\n", + "for i in range(len(factors_f)):\n", + "\n", + " # reinstantiate jitcdde objects\n", + " import sys\n", + " sys.modules.pop('jitcdde')\n", + " from jitcdde import jitcdde, y, t\n", + "\n", + " # rho y(22)\n", + " a_f_i = factors_f[i]\n", + " a_g_i = factors_g[i]\n", + " rho = a_f_i*(a_f/2)*(T_cf1 + T_cf2) + a_g_i*(a_g)*(T_cg)\n", + "\n", + " # instantiate jitcdde object\n", + " DDE = jitcdde([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n", + " T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])\n", + "\n", + " # set initial conditions\n", + " DDE.constant_past([T0_rp, T0_rs, T0_p1,T0_p2, T0_p3, T0_p4, T0_t1, T0_t2, T0_s1, T0_s2, \n", + " T0_s3, T0_s4, n_frac0, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5], \n", + " T0_g1, T0_f1, T0_f2,rho_initial])\n", + " \n", + " sol_i = []\n", + " for t_x in T:\n", + " sol_i.append(DDE.integrate(t_x))\n", + " sols.append(sol_i)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '$\\\\rho$')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,axs = plt.subplots(2,3,figsize=(18,12))\n", + "\n", + "# fuel temps\n", + "#axs[0,0].set_xlim([0,20])\n", + "axs[0,0].plot(T,[s[20] for s in sols[0]],label=\"core 1\") \n", + "axs[0,0].plot(T,[s[21] for s in sols[0]],label=\"core 2\") \n", + "axs[0,0].plot(T,[s[2] for s in sols[0]],label=\"hx 1\") \n", + "axs[0,0].plot(T,[s[3] for s in sols[0]],label=\"hx 2\")\n", + "axs[0,0].plot(T,[s[4] for s in sols[0]],label=\"hx 3\")\n", + "axs[0,0].plot(T,[s[5] for s in sols[0]],label=\"hx 4\") \n", + "axs[0,0].legend()\n", + "axs[0,0].set_title(\"Fuel Node Temperatures (C)\")\n", + "axs[0,0].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "# coolant temps\n", + "#axs[0,1].set_xlim([0,20])\n", + "axs[0,1].plot(T,[s[8] for s in sols[0]],label=\"hx 1\") \n", + "axs[0,1].plot(T,[s[9] for s in sols[0]],label=\"hx 2\") \n", + "axs[0,1].plot(T,[s[10] for s in sols[0]],label=\"hx 3\") \n", + "axs[0,1].plot(T,[s[11] for s in sols[0]],label=\"hx 4\")\n", + "axs[0,1].plot(T,[s[0] for s in sols[0]],label=\"r 1\")\n", + "axs[0,1].legend()\n", + "axs[0,1].set_title(\"Coolant Node Temperatures (C)\")\n", + "axs[0,1].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "# tube node temps\n", + "#axs[0,2].set_xlim([0,20])\n", + "axs[0,2].plot(T,[s[6] for s in sols[0]],label=\"hx 1\") \n", + "axs[0,2].plot(T,[s[7] for s in sols[0]],label=\"hx 2\") \n", + "axs[0,2].legend()\n", + "axs[0,2].set_title(\"Tube Node Temperatures (C)\")\n", + "axs[0,2].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "# precursor concentrations\n", + "#axs[1,2].set_xlim([0,20])\n", + "axs[1,2].plot(T,[s[13] for s in sols[0]],label=\"C1\") \n", + "axs[1,2].plot(T,[s[14] for s in sols[0]],label=\"C2\") \n", + "axs[1,2].plot(T,[s[15] for s in sols[0]],label=\"C3\") \n", + "axs[1,2].plot(T,[s[16] for s in sols[0]],label=\"C4\")\n", + "axs[1,2].plot(T,[s[17] for s in sols[0]],label=\"C5\")\n", + "axs[1,2].plot(T,[s[18] for s in sols[0]],label=\"C6\")\n", + "axs[1,2].legend()\n", + "axs[1,2].set_xlabel(\"t (s)\")\n", + "axs[1,2].set_yscale(\"log\")\n", + "axs[1,2].set_ylabel(r\"concentration (1/cm$^3$)\")\n", + "axs[1,2].legend(loc=\"right\")\n", + "axs[1,2].set_title(\"Precursor Concentrations\")\n", + "\n", + "# multiplication factor temp\n", + "#axs[1,0].set_xlim([0,20])\n", + "axs[1,0].plot(T,[s[12] for s in sols[0]],label=\"n\") \n", + "axs[1,0].set_xlabel(\"t (s)\")\n", + "axs[1,0].set_title(r\"$n$\")\n", + "axs[1,0].set_ylabel(r\"$\\frac{n}{n_0}$\")\n", + "\n", + "# reactivity\n", + "#axs[1,1].set_xlim([0,20])\n", + "axs[1,1].plot(T,[s[22] for s in sols[0]],label=\"n\") \n", + "axs[1,1].set_xlabel(\"t (s)\")\n", + "axs[1,1].set_title(r\"$\\rho$\")\n", + "\n", + "#plt.figure(figsize=(8,6))\n", + "#plt.plot(T,[s[12] for s in sols[0]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[1]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[2]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[3]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[4]],label=r\"$(\\alpha_f,\\alpha_g) = $ (0.0,0.0) pcm\")\n", + "#plt.xlabel(\"t (s)\")\n", + "#plt.ylabel(r\"$n/n_0$\",rotation=0)\n", + "#plt.legend()\n", + "#plt.ylim([0,20])\n", + "#plt.xlim([0,500])\n", + "#plt.title(r\"Reactor Response vs $\\alpha$\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Feedback" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '$\\\\rho$')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,axs = plt.subplots(2,3,figsize=(18,12))\n", + "\n", + "t_stop = 125\n", + "\n", + "# fuel temps\n", + "#axs[0,0].set_xlim([0, 500])\n", + "#axs[0,0].set_ylim([600, 1000])\n", + "axs[0,0].set_xlim([0,t_stop])\n", + "axs[0,0].plot(T,[s[20] for s in sols[0]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[0,0].plot(T,[s[20] for s in sols[1]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n", + "axs[0,0].plot(T,[s[20] for s in sols[2]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n", + "axs[0,0].plot(T,[s[20] for s in sols[3]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n", + "axs[0,0].plot(T,[s[20] for s in sols[4]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\")\n", + "axs[0,0].legend()\n", + "axs[0,0].set_title(\"Fuel Node Temperatures (C)\")\n", + "axs[0,0].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "# coolant temps\n", + "#axs[0,1].set_xlim([0, 1000])\n", + "#axs[0,1].set_ylim([500, 1000])\n", + "axs[0,1].set_xlim([0,t_stop])\n", + "axs[0,1].plot(T,[s[8] for s in sols[0]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[0,1].plot(T,[s[8] for s in sols[1]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n", + "axs[0,1].plot(T,[s[8] for s in sols[2]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n", + "axs[0,1].plot(T,[s[8] for s in sols[3]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n", + "axs[0,1].plot(T,[s[8] for s in sols[4]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\")\n", + "axs[0,1].legend()\n", + "axs[0,1].set_title(\"Coolant Node Temperatures (C)\")\n", + "axs[0,1].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "# tube node temps\n", + "#axs[0,2].set_xlim([0, 500])\n", + "#axs[0,2].set_ylim([500, 1000])\n", + "axs[0,2].set_xlim([0,t_stop])\n", + "axs[0,2].plot(T,[s[8] for s in sols[0]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[0,2].plot(T,[s[8] for s in sols[1]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n", + "axs[0,2].plot(T,[s[8] for s in sols[2]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n", + "axs[0,2].plot(T,[s[8] for s in sols[3]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n", + "axs[0,2].plot(T,[s[8] for s in sols[4]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\")\n", + "\n", + "# tube node temps\n", + "axs[0,2].plot(T,[s[9] for s in sols[0]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[0,2].plot(T,[s[9] for s in sols[1]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n", + "axs[0,2].plot(T,[s[9] for s in sols[2]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n", + "axs[0,2].plot(T,[s[9] for s in sols[3]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n", + "axs[0,2].plot(T,[s[9] for s in sols[4]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\")\n", + "\n", + "axs[0,2].legend()\n", + "axs[0,2].set_title(\"Tube Node Temperatures (C)\")\n", + "axs[0,2].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "# precursor concentrations\n", + "#axs[1,2].set_xlim([0, 500])\n", + "#axs[1,2].set_ylim([1e1,1e3])\n", + "axs[1,2].set_xlim([0,t_stop])\n", + "axs[1,2].plot(T,[s[13] for s in sols[0]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[1,2].plot(T,[s[13] for s in sols[1]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n", + "axs[1,2].plot(T,[s[13] for s in sols[2]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n", + "axs[1,2].plot(T,[s[13] for s in sols[3]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n", + "axs[1,2].plot(T,[s[13] for s in sols[4]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\")\n", + "axs[1,2].legend()\n", + "axs[1,2].set_xlabel(\"t (s)\")\n", + "axs[1,2].set_yscale(\"log\")\n", + "axs[1,2].legend(loc=\"right\")\n", + "axs[1,2].set_title(\"Precursor Concentrations\")\n", + "axs[1,2].set_ylabel(r\"concentration (1/cm$^3$)\")\n", + "\n", + "# multiplication factor \n", + "#axs[1,0].set_ylim([0,20])\n", + "#axs[1,0].set_xlim([0,500])\n", + "axs[1,0].set_xlim([0,t_stop])\n", + "axs[1,0].plot(T,[s[12] for s in sols[0]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[1,0].plot(T,[s[12] for s in sols[1]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[1,0].plot(T,[s[12] for s in sols[2]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[1,0].plot(T,[s[12] for s in sols[3]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[1,0].plot(T,[s[12] for s in sols[4]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[1,0].set_xlabel(\"t (s)\")\n", + "axs[1,0].set_title(r\"$n$\")\n", + "axs[1,0].set_ylabel(r\"$\\frac{n}{n_0}$\")\n", + "\n", + "# reactivity\n", + "#axs[1,0].set_ylim([0,0])\n", + "#axs[1,0].set_xlim([0,500])\n", + "axs[1,1].set_xlim([0,t_stop])\n", + "axs[1,1].plot(T,[s[22] for s in sols[0]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[1,1].plot(T,[s[22] for s in sols[1]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[1,1].plot(T,[s[22] for s in sols[2]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[1,1].plot(T,[s[22] for s in sols[3]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[1,1].plot(T,[s[22] for s in sols[4]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "axs[1,1].set_xlabel(\"t (s)\")\n", + "axs[1,1].set_title(r\"$\\rho$\")\n", + "\n", + "#plt.figure(figsize=(8,6))\n", + "#plt.plot(T,[s[12] for s in sols[0]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[1]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[2]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[3]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[4]],label=r\"$(\\alpha_f,\\alpha_g) = $ (0.0,0.0) pcm\")\n", + "#plt.xlabel(\"t (s)\")\n", + "#plt.ylabel(r\"$n/n_0$\",rotation=0)\n", + "#plt.legend()\n", + "#plt.ylim([0,20])\n", + "#plt.xlim([0,500])\n", + "#plt.title(r\"Reactor Response vs $\\alpha$\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating, compiling, and loading C code.\n", + "Using default integration parameters.\n", + "Generating, compiling, and loading C code.\n", + "Using default integration parameters.\n", + "Generating, compiling, and loading C code.\n", + "Using default integration parameters.\n", + "Generating, compiling, and loading C code.\n", + "Using default integration parameters.\n", + "Generating, compiling, and loading C code.\n", + "Using default integration parameters.\n" + ] + } + ], + "source": [ + "# redefine system and get new solution set \n", + "\n", + "# delays\n", + "taus = [tau_c, tau_c_hx, tau_hx_c, tau_hx_r, tau_l, tau_r_hx]\n", + "\n", + "# adjust delay term \n", + "#tau_hx_c *= 0.5\n", + "tau_c_hx *= 0.5\n", + "#tau_r_hx *= 0.5\n", + "\n", + "# dT/dt for radiator nodes\n", + "T_out_rc = (W_rp/mn_rp)*(y(11,t-tau_hx_r)-y(0)) + (hA_rpn/mcp_rpn)*(y(1)-y(0)) # T_out_rc: y(0)\n", + "T_out_air = -((W_rs/mn_rs)+(hA_rsn/mcp_rsn))*y(1) + (hA_rsn/mcp_rsn)*y(0) + (W_rs/mn_rs)*Trs_in # T_out_air: y(1)\n", + "\n", + "# dT/dt for heat exchanger nodes\n", + "T_hf1 = -((W_p/mn_p)+(hA_pn/mcp_pn))*y(2) + (hA_pn/mcp_pn)*y(6) + (W_p/mn_p)*y(21,t-tau_c_hx) # T_hf1: y(2)\n", + "T_hf2 = (W_p/mn_p)*(y(2)-y(3)) + (hA_pn/mcp_pn)*(y(6)-y(2)) # T_hf2: y(3)\n", + "T_hf3 = -((W_p/mn_p)+(hA_pn/mcp_pn))*y(4) + (hA_pn/mcp_pn)*y(7) + (W_p/mn_p)*y(3) # T_hf3: y(4)\n", + "T_hf4 = (W_p/mn_p)*(y(4)-y(5)) + (hA_pn/mcp_pn)*(y(7)-y(4)) # T_hf4: y(5)\n", + "T_ht1 = (2*hA_pn/mcp_tn)*(y(2)-y(6)) + (2*hA_sn/mcp_tn)*(y(10)-y(6)) # T_ht1: y(6)\n", + "T_ht2 = (2*hA_pn/mcp_tn)*(y(4)-y(7)) + (2*hA_sn/mcp_tn)*(y(8)-y(7)) # T_ht2: y(7)\n", + "T_hc1 = -((W_s/mn_s)+(hA_sn/mcp_sn))*y(8) + (hA_sn/mcp_sn)*y(7) + (W_s/mn_s)*y(0,t-tau_r_hx) # T_hc1: y(8)\n", + "T_hc2 = (W_s/mn_s)*(y(8)-y(9)) + (hA_sn/mcp_sn)*(y(7)-y(8)) # T_hc2: y(9)\n", + "T_hc3 = -((W_s/mn_s)+(hA_sn/mcp_sn))*y(10) + (hA_sn/mcp_sn)*y(6) + (W_s/mn_s)*y(9) # T_hc3: y(10)\n", + "T_hc4 = (W_s/mn_s)*(y(10)-y(11)) + (hA_sn/mcp_sn)*(y(6)-y(10)) # T_hc4: y(11)\n", + "\n", + "# dn/dt\n", + "n = (y(22)-beta_t)*y(12)/Lam+lam[0]*y(13)+lam[1]*y(14)+lam[2]*y(15)+lam[3]*y(16)+lam[4]*y(17)+lam[5]*y(18) # n (no source insertion): y(12)\n", + "\n", + "# dC_i/dt (precursor concentrations)\n", + "C1 = y(12)*beta[0]/Lam-lam[0]*y(13)-y(13)/tau_c+y(13,t-tau_l)*np.exp(-lam[0]*tau_l)/tau_c # C1: y(13)\n", + "C2 = y(12)*beta[1]/Lam-lam[1]*y(14)-y(14)/tau_c+y(14,t-tau_l)*np.exp(-lam[1]*tau_l)/tau_c # C2: y(14)\n", + "C3 = y(12)*beta[2]/Lam-lam[2]*y(15)-y(15)/tau_c+y(15,t-tau_l)*np.exp(-lam[2]*tau_l)/tau_c # C3: y(15)\n", + "C4 = y(12)*beta[3]/Lam-lam[3]*y(16)-y(16)/tau_c+y(16,t-tau_l)*np.exp(-lam[3]*tau_l)/tau_c # C4: y(16)\n", + "C5 = y(12)*beta[4]/Lam-lam[4]*y(17)-y(17)/tau_c+y(17,t-tau_l)*np.exp(-lam[4]*tau_l)/tau_c # C5: y(17)\n", + "C6 = y(12)*beta[5]/Lam-lam[5]*y(18)-y(18)/tau_c+y(18,t-tau_l)*np.exp(-lam[5]*tau_l)/tau_c # C6: y(18)\n", + "\n", + "# dT/dt core nodes\n", + "T_cg = (hA_fg/mcp_g1)*(y(20)-y(19)) + k_g*P*y(12)/mcp_g1 # T_cg: y(19)\n", + "T_cf1 = W_f/mn_f*(y(5,t-tau_hx_c)-y(20)) + (k_f1*P*y(12)/mcp_f1) + (hA_fg*k_1*(y(19)-y(20))/mcp_f1) # T_cf1: y(20)\n", + "T_cf2 = W_f/mn_f*(y(20)-y(21)) + (k_f2*P*y(12)/mcp_f2) + (hA_fg*k_2*(y(19)-y(20))/mcp_f2) # T_cf2: y(21)\n", + "\n", + "# rho y(22)\n", + "rho = (a_f/2)*(T_cf1 + T_cf2) + (a_g)*(T_cg)\n", + "\n", + "# initial reactivity \n", + "rho_initial = 0.000\n", + "\n", + "# instantiate jitcdde object\n", + "DDE = jitcdde([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n", + " T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])\n", + "\n", + "# set initial conditions\n", + "DDE.constant_past([T0_rp, T0_rs, T0_p1,T0_p2, T0_p3, T0_p4, T0_t1, T0_t2, T0_s1, T0_s2, \n", + " T0_s3, T0_s4, n_frac0, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5], \n", + " T0_g1, T0_f1, T0_f2,rho_initial])\n", + "\n", + "# jitcdde solver parameters \n", + "t0 = 0.0\n", + "tf = 1000.00\n", + "T = np.arange(t0,tf,0.01)\n", + "\n", + "sol_jit = []\n", + "for t_x in T:\n", + " sol_jit.append(DDE.integrate(t_x))\n", + "\n", + "sols.append(sol_jit)\n", + "\n", + "# feedbacks\n", + "factors_f = [1/2, 1/4, 1/8, 0]\n", + "factors_g = [1/2, 1/4, 1/8, 0]\n", + "\n", + "for i in range(len(factors_f)):\n", + "\n", + " # reinstantiate jitcdde objects\n", + " import sys\n", + " sys.modules.pop('jitcdde')\n", + " from jitcdde import jitcdde, y, t\n", + "\n", + " # rho y(22)\n", + " a_f_i = factors_f[i]\n", + " a_g_i = factors_g[i]\n", + " rho = a_f_i*(a_f/2)*(T_cf1 + T_cf2) + a_g_i*(a_g)*(T_cg)\n", + "\n", + " # instantiate jitcdde object\n", + " DDE = jitcdde([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n", + " T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])\n", + "\n", + " # set initial conditions\n", + " DDE.constant_past([T0_rp, T0_rs, T0_p1,T0_p2, T0_p3, T0_p4, T0_t1, T0_t2, T0_s1, T0_s2, \n", + " T0_s3, T0_s4, n_frac0, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5], \n", + " T0_g1, T0_f1, T0_f2,rho_initial])\n", + " \n", + " sol_i = []\n", + " for t_x in T:\n", + " sol_i.append(DDE.integrate(t_x))\n", + " sols.append(sol_i)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + ] + } + ], + "source": [ + "fig,axs = plt.subplots(2,3,figsize=(18,12))\n", + "\n", + "t_stop = 30\n", + "vert = 10.555\n", + "\n", + "# fuel temps\n", + "#axs[0,0].set_xlim([0, 500])\n", + "#axs[0,0].set_ylim([600, 1000])\n", + "axs[0,0].set_xlim([0,t_stop])\n", + "axs[0,0].plot(T,[s[20] for s in sols[0]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n", + "axs[0,0].plot(T,[s[20] for s in sols[1]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\",color='g')\n", + "axs[0,0].plot(T,[s[20] for s in sols[2]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\",color='r')\n", + "axs[0,0].plot(T,[s[20] for s in sols[3]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\",color='y')\n", + "axs[0,0].plot(T,[s[20] for s in sols[4]],label=r\"core 1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\",color='m')\n", + "axs[0,0].legend()\n", + "axs[0,0].set_title(\"Fuel Node Temperatures (C)\")\n", + "axs[0,0].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "# coolant temps\n", + "#axs[0,1].set_xlim([0, 1000])\n", + "#axs[0,1].set_ylim([500, 1000])\n", + "axs[0,1].set_xlim([0,t_stop])\n", + "axs[0,1].plot(T,[s[8] for s in sols[0]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n", + "axs[0,1].plot(T,[s[8] for s in sols[1]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\",color='g')\n", + "axs[0,1].plot(T,[s[8] for s in sols[2]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\",color='r')\n", + "axs[0,1].plot(T,[s[8] for s in sols[3]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\",color='y')\n", + "axs[0,1].plot(T,[s[8] for s in sols[4]],label=r\"hx 1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\",color='m')\n", + "axs[0,1].legend()\n", + "axs[0,1].set_title(\"Coolant Node Temperatures (C)\")\n", + "axs[0,1].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "# tube node temps\n", + "#axs[0,2].set_xlim([0, 500])\n", + "#axs[0,2].set_ylim([500, 1000])\n", + "axs[0,2].set_xlim([0,t_stop])\n", + "axs[0,2].plot(T,[s[8] for s in sols[0]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n", + "axs[0,2].plot(T,[s[8] for s in sols[1]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\",color='g')\n", + "axs[0,2].plot(T,[s[8] for s in sols[2]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\",color='r')\n", + "axs[0,2].plot(T,[s[8] for s in sols[3]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\",color='y')\n", + "axs[0,2].plot(T,[s[8] for s in sols[4]],label=r\"t1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\",color='m')\n", + "\n", + "# tube node temps\n", + "axs[0,2].plot(T,[s[9] for s in sols[0]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n", + "axs[0,2].plot(T,[s[9] for s in sols[1]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\",color='g')\n", + "axs[0,2].plot(T,[s[9] for s in sols[2]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\",color='r')\n", + "axs[0,2].plot(T,[s[9] for s in sols[3]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\",color='y')\n", + "axs[0,2].plot(T,[s[9] for s in sols[4]],label=r\"t2: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\",color='m')\n", + "\n", + "axs[0,2].legend()\n", + "axs[0,2].set_title(\"Tube Node Temperatures (C)\")\n", + "axs[0,2].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "# precursor concentrations\n", + "#axs[1,2].set_xlim([0, 500])\n", + "#axs[1,2].set_ylim([1e1,1e3])\n", + "axs[1,2].set_xlim([0,t_stop])\n", + "axs[1,2].plot(T,[s[13] for s in sols[0]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n", + "axs[1,2].plot(T,[s[13] for s in sols[1]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\",color='g')\n", + "axs[1,2].plot(T,[s[13] for s in sols[2]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\",color='r')\n", + "axs[1,2].plot(T,[s[13] for s in sols[3]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\",color='y')\n", + "axs[1,2].plot(T,[s[13] for s in sols[4]],label=r\"C1: $(\\alpha_f,\\alpha_g) = $ (-0.00,-0.00) pcm\",color='m')\n", + "axs[1,2].legend()\n", + "axs[1,2].set_xlabel(\"t (s)\")\n", + "axs[1,2].set_yscale(\"log\")\n", + "axs[1,2].legend(loc=\"right\")\n", + "axs[1,2].set_title(\"Precursor Concentrations\")\n", + "axs[1,2].set_ylabel(r\"concentration (1/cm$^3$)\")\n", + "\n", + "# multiplication factor \n", + "#axs[1,0].set_ylim([0,20])\n", + "#axs[1,0].set_xlim([0,500])\n", + "axs[1,0].set_xlim([0,t_stop])\n", + "axs[1,0].plot(T,[s[12] for s in sols[0]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n", + "axs[1,0].plot(T,[s[12] for s in sols[1]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='g')\n", + "axs[1,0].plot(T,[s[12] for s in sols[2]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='r')\n", + "axs[1,0].plot(T,[s[12] for s in sols[3]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='y')\n", + "axs[1,0].plot(T,[s[12] for s in sols[4]],label=r\"n: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='m')\n", + "axs[1,0].set_xlabel(\"t (s)\")\n", + "axs[1,0].set_title(r\"$n$\")\n", + "axs[1,0].set_ylabel(r\"$\\frac{n}{n_0}$\")\n", + "axs[1,0].legend()\n", + "\n", + "# reactivity\n", + "#axs[1,0].set_ylim([0,0])\n", + "#axs[1,0].set_xlim([0,500])\n", + "axs[1,1].set_xlim([0,t_stop])\n", + "axs[1,1].plot(T,[s[22] for s in sols[0]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='b')\n", + "axs[1,1].plot(T,[s[22] for s in sols[1]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='g')\n", + "axs[1,1].plot(T,[s[22] for s in sols[2]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='r')\n", + "axs[1,1].plot(T,[s[22] for s in sols[3]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='y')\n", + "axs[1,1].plot(T,[s[22] for s in sols[4]],label=r\"$\\rho$: $(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\",color='m')\n", + "axs[1,1].set_xlabel(\"t (s)\")\n", + "axs[1,1].set_title(r\"$\\rho$\")\n", + "axs[1,1].legend()\n", + "\n", + "#plt.figure(figsize=(8,6))\n", + "#plt.plot(T,[s[12] for s in sols[0]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[1]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[2]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[3]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[4]],label=r\"$(\\alpha_f,\\alpha_g) = $ (0.0,0.0) pcm\")\n", + "#plt.xlabel(\"t (s)\")\n", + "#plt.ylabel(r\"$n/n_0$\",rotation=0)\n", + "#plt.legend()\n", + "#plt.ylim([0,20])\n", + "#plt.xlim([0,500])\n", + "#plt.title(r\"Reactor Response vs $\\alpha$\")\n", + "\n", + "\n", + "#######################################################################################################\n", + "\n", + "# adjusted delay term \n", + "\n", + "# fuel temps\n", + "#axs[0,0].set_xlim([0, 500])\n", + "#axs[0,0].set_ylim([600, 1000])\n", + "axs[0,0].set_xlim([0,t_stop])\n", + "axs[0,0].plot(T,[s[20] for s in sols[5]],linestyle='-.',color='b')\n", + "axs[0,0].plot(T,[s[20] for s in sols[6]],linestyle='-.',color='g')\n", + "axs[0,0].plot(T,[s[20] for s in sols[7]],linestyle='-.',color='r')\n", + "axs[0,0].plot(T,[s[20] for s in sols[8]],linestyle='-.',color='y')\n", + "axs[0,0].plot(T,[s[20] for s in sols[9]],linestyle='-.',color='m')\n", + "axs[0,0].legend()\n", + "axs[0,0].set_title(\"Fuel Node Temperatures (C)\")\n", + "axs[0,0].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "axs[0,0].axvline(x=vert, color='black', linestyle=':')\n", + "# coolant temps\n", + "#axs[0,1].set_xlim([0, 1000])\n", + "#axs[0,1].set_ylim([500, 1000])\n", + "axs[0,1].set_xlim([0,t_stop])\n", + "axs[0,1].plot(T,[s[8] for s in sols[5]],linestyle='-.',color='b')\n", + "axs[0,1].plot(T,[s[8] for s in sols[6]],linestyle='-.',color='g')\n", + "axs[0,1].plot(T,[s[8] for s in sols[7]],linestyle='-.',color='r')\n", + "axs[0,1].plot(T,[s[8] for s in sols[8]],linestyle='-.',color='y')\n", + "axs[0,1].plot(T,[s[8] for s in sols[9]],linestyle='-.',color='m')\n", + "axs[0,1].legend()\n", + "axs[0,1].set_title(\"Coolant Node Temperatures (C)\")\n", + "axs[0,1].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "# tube node temps\n", + "#axs[0,2].set_xlim([0, 500])\n", + "#axs[0,2].set_ylim([500, 1000])\n", + "axs[0,2].set_xlim([0,t_stop])\n", + "axs[0,2].plot(T,[s[8] for s in sols[5]],linestyle='-.',color='b')\n", + "axs[0,2].plot(T,[s[8] for s in sols[6]],linestyle='-.',color='g')\n", + "axs[0,2].plot(T,[s[8] for s in sols[7]],linestyle='-.',color='r')\n", + "axs[0,2].plot(T,[s[8] for s in sols[8]],linestyle='-.',color='y')\n", + "axs[0,2].plot(T,[s[8] for s in sols[9]],linestyle='-.',color='m')\n", + "\n", + "# tube node temps\n", + "axs[0,2].plot(T,[s[9] for s in sols[5]],linestyle='-.',color='b')\n", + "axs[0,2].plot(T,[s[9] for s in sols[6]],linestyle='-.',color='g')\n", + "axs[0,2].plot(T,[s[9] for s in sols[7]],linestyle='-.',color='r')\n", + "axs[0,2].plot(T,[s[9] for s in sols[8]],linestyle='-.',color='y')\n", + "axs[0,2].plot(T,[s[9] for s in sols[9]],linestyle='-.',color='m')\n", + "\n", + "axs[0,2].legend()\n", + "axs[0,2].set_title(\"Tube Node Temperatures (C)\")\n", + "axs[0,2].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "# precursor concentrations\n", + "#axs[1,2].set_xlim([0, 500])\n", + "#axs[1,2].set_ylim([1e1,1e3])\n", + "axs[1,2].set_xlim([0,t_stop])\n", + "axs[1,2].plot(T,[s[13] for s in sols[5]],linestyle='-.',color='b')\n", + "axs[1,2].plot(T,[s[13] for s in sols[6]],linestyle='-.',color='g')\n", + "axs[1,2].plot(T,[s[13] for s in sols[7]],linestyle='-.',color='r')\n", + "axs[1,2].plot(T,[s[13] for s in sols[8]],linestyle='-.',color='y')\n", + "axs[1,2].plot(T,[s[13] for s in sols[9]],linestyle='-.',color='m')\n", + "axs[1,2].legend()\n", + "axs[1,2].set_xlabel(\"t (s)\")\n", + "axs[1,2].set_yscale(\"log\")\n", + "axs[1,2].legend(loc=\"right\")\n", + "axs[1,2].set_title(\"Precursor Concentrations\")\n", + "axs[1,2].set_ylabel(r\"concentration (1/cm$^3$)\")\n", + "\n", + "# multiplication factor \n", + "#axs[1,0].set_ylim([0,20])\n", + "#axs[1,0].set_xlim([0,500])\n", + "axs[1,0].set_xlim([0,t_stop])\n", + "axs[1,0].plot(T,[s[12] for s in sols[5]],linestyle='-.',color='b')\n", + "axs[1,0].plot(T,[s[12] for s in sols[6]],linestyle='-.',color='g')\n", + "axs[1,0].plot(T,[s[12] for s in sols[7]],linestyle='-.',color='r')\n", + "axs[1,0].plot(T,[s[12] for s in sols[8]],linestyle='-.',color='y')\n", + "axs[1,0].plot(T,[s[12] for s in sols[9]],linestyle='-.',color='m')\n", + "axs[1,0].set_xlabel(\"t (s)\")\n", + "axs[1,0].set_title(r\"$n$\")\n", + "axs[1,0].set_ylabel(r\"$\\frac{n}{n_0}$\")\n", + "axs[1,0].axvline(x=vert, color='black', linestyle=':')\n", + "\n", + "# reactivity\n", + "#axs[1,0].set_ylim([0,0])\n", + "#axs[1,0].set_xlim([0,500])\n", + "axs[1,1].set_xlim([0,t_stop])\n", + "axs[1,1].plot(T,[s[22] for s in sols[5]],linestyle='-.',color='b')\n", + "axs[1,1].plot(T,[s[22] for s in sols[6]],linestyle='-.',color='g')\n", + "axs[1,1].plot(T,[s[22] for s in sols[7]],linestyle='-.',color='r')\n", + "axs[1,1].plot(T,[s[22] for s in sols[8]],linestyle='-.',color='y')\n", + "axs[1,1].plot(T,[s[22] for s in sols[9]],linestyle='-.',color='m')\n", + "axs[1,1].set_xlabel(\"t (s)\")\n", + "axs[1,1].set_title(r\"$\\rho$\")\n", + "axs[1,1].axvline(x=vert, color='black', linestyle=':')\n", + "\n", + "#plt.figure(figsize=(8,6))\n", + "#plt.plot(T,[s[12] for s in sols[0]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-8.71,-6.66) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[1]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-4.36,-3.33) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[2]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-2.18,-1.67) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[3]],label=r\"$(\\alpha_f,\\alpha_g) = $ (-1.09,-0.83) pcm\")\n", + "#plt.plot(T,[s[12] for s in sols[4]],label=r\"$(\\alpha_f,\\alpha_g) = $ (0.0,0.0) pcm\")\n", + "#plt.xlabel(\"t (s)\")\n", + "#plt.ylabel(r\"$n/n_0$\",rotation=0)\n", + "#plt.legend()\n", + "#plt.ylim([0,20])\n", + "#plt.xlim([0,500])\n", + "#plt.title(r\"Reactor Response vs $\\alpha$\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(sols)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "thesis_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b3c34717878559297d91fdce41f67323cf3156ec Mon Sep 17 00:00:00 2001 From: lukelabrie Date: Fri, 10 Nov 2023 17:05:42 +0100 Subject: [PATCH 14/32] update dynamic model readme --- dynamic_model/README.md | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/dynamic_model/README.md b/dynamic_model/README.md index c291864..787f9f3 100644 --- a/dynamic_model/README.md +++ b/dynamic_model/README.md @@ -53,12 +53,26 @@ The equations governing heat transfer contain three terms; a source term and a s "Here, $W_f$ is the mass flow rate of fuel salt, $m_{f1}$ and $m_{f2}$ represent the mass of fuel nodes 1 and 2 respectively, $C_{pf}$ represents the fuel salt specific heat capacity, $K_1$ and $K_2$ are the fraction of total power generated in fuel nodes 1 and 2, $K_{g1}$ and $K_{g2}$ represent the fraction of power generated in the graphite transferred to each fuel node, $hA_{fg}$ is the product of area and heat transfer coefficient for the fuel-graphite interface, $P_0$ is the nominal power which multiplied with fractional neutron density n/no gives the instantaneous power, and the $T$s represent the temperatures of the various nodes. Note that the direction of heat transfer depends on the instantaneous temperature of the various nodes." (Singh et. al). # Method -The model herein uses SciPy's ODE library. Sample implementation is shown below: +The model herein uses [JiTCDDE](https://jitcdde.readthedocs.io/en/stable/), a numerical solver for delayed differential equations. Sample implementation is shown below: ```python -backend = 'dopri5' -r = ode(dydtMSRE).set_integrator(backend,max_step=0.10) -r.integrate(100.00) +# instantiate jitcdde object +DDE = jitcdde([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1, + T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho]) + +# set initial conditions +DDE.constant_past([T0_rp, T0_rs, T0_p1,T0_p2, T0_p3, T0_p4, T0_t1, T0_t2, T0_s1, T0_s2, + T0_s3, T0_s4, n_frac0, C0[0], C0[1], C0[2], C0[3], C0[4], C0[5], + T0_g1, T0_f1, T0_f2,rho_initial]) + +# jitcdde solver parameters +t0 = 0.0 +tf = 1000.00 +T = np.arange(t0,tf,0.01) + +sol_jit = [] +for t_x in T: + sol_jit.append(DDE.integrate(t_x)) ``` -However, since SciPy's ODE library does not support delay differential equations, the delay terms are stored and handled manually. Since the 'dopri5' method uses adaptive time-stepping, linear interpolation is used for approximating the value of the delay terms near the closest timestep to the delay. +The [scipyODE_implementation](./scipyODE_implementation/) contains a manual implementation using the `dopri5` method of SciPy's ODE library. However, since SciPy's ODE library does not support delay differential equations, the delay terms are stored and handled manually. Since the 'dopri5' method uses adaptive time-stepping, linear interpolation is used for approximating the value of the delay terms near the closest timestep to the delay. From 62cb7f91e3ca0df82dfabf00d9475313776ac0b5 Mon Sep 17 00:00:00 2001 From: lukelabrie Date: Fri, 10 Nov 2023 17:06:29 +0100 Subject: [PATCH 15/32] update dynamic model readme --- dynamic_model/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dynamic_model/README.md b/dynamic_model/README.md index 787f9f3..38dd08c 100644 --- a/dynamic_model/README.md +++ b/dynamic_model/README.md @@ -53,7 +53,7 @@ The equations governing heat transfer contain three terms; a source term and a s "Here, $W_f$ is the mass flow rate of fuel salt, $m_{f1}$ and $m_{f2}$ represent the mass of fuel nodes 1 and 2 respectively, $C_{pf}$ represents the fuel salt specific heat capacity, $K_1$ and $K_2$ are the fraction of total power generated in fuel nodes 1 and 2, $K_{g1}$ and $K_{g2}$ represent the fraction of power generated in the graphite transferred to each fuel node, $hA_{fg}$ is the product of area and heat transfer coefficient for the fuel-graphite interface, $P_0$ is the nominal power which multiplied with fractional neutron density n/no gives the instantaneous power, and the $T$s represent the temperatures of the various nodes. Note that the direction of heat transfer depends on the instantaneous temperature of the various nodes." (Singh et. al). # Method -The model herein uses [JiTCDDE](https://jitcdde.readthedocs.io/en/stable/), a numerical solver for delayed differential equations. Sample implementation is shown below: +The model herein uses [JiTCDDE](https://jitcdde.readthedocs.io/en/stable/), a numerical solver for delay-differential equations. Sample implementation is shown below: ```python # instantiate jitcdde object From 1e06779e0975fbf30111fffcd968e6f6bd0f2efe Mon Sep 17 00:00:00 2001 From: church89 Date: Thu, 11 Jan 2024 14:13:19 +0100 Subject: [PATCH 16/32] add h5m file --- .gitattributes | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitattributes b/.gitattributes index 9b951d7..77c1700 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,2 +1,3 @@ *.pdf filter=lfs diff=lfs merge=lfs -text h5m/*.h5 filter=lfs diff=lfs merge=lfs -text +*.h5m filter=lfs diff=lfs merge=lfs -text From 5b01326119084d03a66ba694c954498d07b14aba Mon Sep 17 00:00:00 2001 From: Aslak Stubsgaard Date: Sat, 2 Mar 2024 22:42:23 +0100 Subject: [PATCH 17/32] Create hx.md --- heatexchanger/hx.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 heatexchanger/hx.md diff --git a/heatexchanger/hx.md b/heatexchanger/hx.md new file mode 100644 index 0000000..30239f1 --- /dev/null +++ b/heatexchanger/hx.md @@ -0,0 +1,23 @@ + +### msre primary heat exchanger +![](heatexchanger/docs/phexcadmodel.png) + +[onshape primary heat exchanger cad model](https://cad.onshape.com/documents/03be2f510296a2e264886390/w/8cfbca3b7b9682dd4e53a998/e/54728fd981a1b4f5594c73d6), open to copy and use freely. chapter 4.1 in the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf) mentioned above covers the cad construction details extensively with references to original msre reports. + +![](heatexchanger/docs/phexflowpaths.png) + +[simscale primary heat exchanger simulation model](https://www.simscale.com/projects/MalcolmAkner/phex_-_final_version/). simulation results for primary heat exchanger can be viewed in chapter 6.2.2.1 of the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf), with comparisons to msre data in chapter 7.1.1. + +![](heatexchanger/docs/phexreal.png) + +primary heat exchanger produced and installed in the msre. + + +### msre radiator +![](heatexchanger/docs/radiatorcadmodel.png) + +[onshape radiator cad model](https://cad.onshape.com/documents/bf944323ed6a82e05924078c/w/2a25d73c5a3a66824d2d5fbd/e/a83d5535602a053216fedff4) open to copy and use freely. chapter 4.2 of the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf) covers the cad construction details with origianl references to msre reports. + +![](heatexchanger/docs/radiatorreal.png) + +radiator produced and installed in the msre. 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zVNDK_9Q#t#>f)IL@p|m+jlzJUhOZd$Pf(E$crI+uc>?Ji_@Q-s0?Ah23cpsEoH>ni z=||>6`>C?wrNcXgIj66yX3pwG?ddQe3t)!w z9UTRt=@CYScmd~zyi^I){sO3uvx|HWb9;zktL39(f#Zij(#`6sC(8-a=DY0LY2;&i z2T%$3jC~joid$5M^6054H_0(?&Lk?Jq?bKT^DJHa%hGvP&x z7b;ak4Z$xAK$ElStM^y(boeKHPb01YPB!h}jBgJ*K`xd5Rm$G!30p}py#vCM>JHVSs{7L@yaS~AirE#6-lU`E*tpvX! zIV4}3)6jtztX+Qi$ATcK9D-6+{*!g~rvLl%e-Ze<2>kyNfeDMrQ?h0=new_Efv0Dm OKv_ZKanU2Qm;Vo99+h+e From 1f0555bb719377d36061812837756b0ac4dce4f2 Mon Sep 17 00:00:00 2001 From: Aslak Stubsgaard Date: Sat, 2 Mar 2024 22:48:45 +0100 Subject: [PATCH 19/32] Update README.md --- README.md | 67 +++++++++++++------------------------------------------ 1 file changed, 15 insertions(+), 52 deletions(-) diff --git a/README.md b/README.md index f862f7e..8c6bbb9 100644 --- a/README.md +++ b/README.md @@ -1,73 +1,36 @@ # msre [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) -detailed cad model of the [msre](https://en.wikipedia.org/wiki/Molten-Salt_Reactor_Experiment) (molten salt reactor experiment), operated by oak ridge national laboratory 1965-69. +detailed cad model and openmc benchmarks of the [msre](https://en.wikipedia.org/wiki/Molten-Salt_Reactor_Experiment) (molten salt reactor experiment), operated by oak ridge national laboratory 1965-69. -## msre core -![](core/docs/msre.png) +[onshape cad model v17](https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/) +cad model includes crude drawings of the insulation, thermal shield, and the reactor pit, but not detailes such as components or piping. +all part names' begining correspond to it's material, e.g. graphite, salt, or inor-8 (hasteloy n). +all core parts are thermally expanded to the operating temperature (currently to the temperature of the zero power experiments during commisioning). note that graphite thermal expansion coefficients are only given for room temperature and thus likely underpredicted and the control rod assembly temperature is unknow and like much higher than currently assigned. [core/msrecore.pdf](core/docs/msrecore.pdf) lists reference of the msre core design, documented in the old msre reports and located [here](https://github.com/openmsr/msr-archive/blob/master/README.md). -## msre step files +individual parts or assemblies can be exported directly from onshape. step files of entire msre assembly and one control rod assembly can be found in [](step_files/). -[step files](step_files/) of entire msre assembly and control rod. +this work and the cad models are under the GNU General Public License v3.0 -## h5m -[h5m](h5m/) surface mesh of the previous step files for OpenMC simulation. +![](core/docs/msre.png) -**Note:** the h5m files are generated with `Coreform Cubit` with a surface tolerance of 1e-2 cm. +![](core/docs/msre-pit.png) -## docker msre -[msre docker](msre_docker/) conatiner which includes not only support for OpenMC with DAGMC/MOAB, embree, and double_down libraries, but also a Jupyter notebook server. - -## openmc notebooks -[openmc notebooks](openmc_notebooks/) of the msre in form of jupyter notebooks. - -Examples include: +# openmc benchmark +openmc benchmark include: - msre cad with settable control rods - msre isothermal temperature coefficient calculation - msre depletion analysis with fission products removal -## prerequisites -### openmc -[openmc](https://docs.openmc.org/en/stable/) automated source installation scripts for linux can be found [here](https://github.com/openmsr/openmc_install_scripts) -### CAD_to_openMC -[CAD_to_openMC](https://github.com/openmsr/CAD_to_openMC) is an open-source package to convert CAD geometry (in the form of '.step' files) into an openmc-readable h5m file +prerequisites +- [openmc](https://docs.openmc.org/en/stable/) automated source installation scripts for linux can be found [here](https://github.com/openmsr/openmc_install_scripts) +- [CAD_to_openMC](https://github.com/openmsr/CAD_to_openMC) is an open-source package to convert CAD geometry (in the form of '.step' files) into an openmc-readable h5m file -## msre heat exchangers - +# msre heat exchangers thermohydralics open-access [master's thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf) produced by Malcolm Akner about simulations of the heat exchangers of the msre, titled: Validating results from the Molten Salt Reactor Experiment by use of turbulent CFD simulations - A study of a modified U-tube shell-and-tube primary heat exchanger and radiator with molten salts - -### msre primary heat exchanger -![](heatexchanger/docs/phexcadmodel.png) - -[onshape primary heat exchanger cad model](https://cad.onshape.com/documents/03be2f510296a2e264886390/w/8cfbca3b7b9682dd4e53a998/e/54728fd981a1b4f5594c73d6), open to copy and use freely. chapter 4.1 in the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf) mentioned above covers the cad construction details extensively with references to original msre reports. - -![](heatexchanger/docs/phexflowpaths.png) - -[simscale primary heat exchanger simulation model](https://www.simscale.com/projects/MalcolmAkner/phex_-_final_version/). simulation results for primary heat exchanger can be viewed in chapter 6.2.2.1 of the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf), with comparisons to msre data in chapter 7.1.1. - -![](heatexchanger/docs/phexreal.png) - -primary heat exchanger produced and installed in the msre. - - -### msre radiator -![](heatexchanger/docs/radiatorcadmodel.png) - -[onshape radiator cad model](https://cad.onshape.com/documents/bf944323ed6a82e05924078c/w/2a25d73c5a3a66824d2d5fbd/e/a83d5535602a053216fedff4) open to copy and use freely. chapter 4.2 of the [thesis](https://ltu.diva-portal.org/smash/get/diva2:1546993/FULLTEXT01.pdf) covers the cad construction details with origianl references to msre reports. - -![](heatexchanger/docs/radiatorreal.png) - -radiator produced and installed in the msre. - ---- - -please contact [me](https://github.com/aslakstubsgaard) if you want to contribute. -note that this work and the cad models are under the GNU General Public License v3.0 - ---- From 3b027582dffe44c16ae855b5d8695e574fec8361 Mon Sep 17 00:00:00 2001 From: Aslak Stubsgaard Date: Sat, 2 Mar 2024 22:51:19 +0100 Subject: [PATCH 20/32] Delete core/docs/msre.png --- core/docs/msre.png | Bin 226466 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 core/docs/msre.png diff --git a/core/docs/msre.png b/core/docs/msre.png deleted file mode 100644 index a097eb45ed198a46e6dba59f88d54d817eace408..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 226466 zcma&OXH=70)HRw01c(rNXd!e21R^LcKxk5wrUg^bU6ieD8Ped+(1sM$Q<=Q=aT+@3rQdYp%HyV`*-*k41n50)gx^F*Z03 zfxzM5uMbE@@K2B*>@*<|9K^&x*P7%wmC2N0JyNlA*6OYuQCC;v78!QymMITHQ&u)M z+D=K}(4{;p*O-{?Rx?zL|6TXNx03gaRgeWa(JDNTp5rQ4dJXDsnaJ;~X@6)ZEh~MV zskyhmdZl-*rLATE>{^z%U-0bL<@Lk(Tan0U@_#mSn 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implementation of the dynamic model --- .../parameters.py | 0 .../simulation_study.ipynb | 0 .../parameters_U233.cpython-311.pyc | Bin 0 -> 4697 bytes .../data/ORNL_msre_1MW_U233_insertion.csv | 130 ++++++++ .../data/ORNL_msre_1MW_U233_magnitude.csv | 57 ++++ ...NL_msre_1MW_U233_magnitude_theoretical.csv | 83 +++++ .../data/ORNL_msre_1MW_U233_phase.csv | 47 +++ .../ORNL_msre_1MW_U233_phase_theoretical.csv | 77 +++++ .../data/ORNL_msre_5MW_U233_insertion.csv | 135 ++++++++ .../data/ORNL_msre_5MW_U233_magnitude.csv | 52 +++ ...NL_msre_5MW_U233_magnitude_theoretical.csv | 49 +++ .../data/ORNL_msre_5MW_U233_phase.csv | 54 ++++ .../ORNL_msre_5MW_U233_phase_theoretical.csv | 57 ++++ .../data/ORNL_msre_8MW_U233_insertion.csv | 163 ++++++++++ .../data/ORNL_msre_8MW_U233_magnitude.csv | 60 ++++ ...NL_msre_8MW_U233_magnitude_theoretical.csv | 49 +++ .../data/ORNL_msre_8MW_U233_phase.csv | 58 ++++ .../ORNL_msre_8MW_U233_phase_theoretical.csv | 76 +++++ .../data/frequency_response_results_1_MW.csv | 101 ++++++ .../data/frequency_response_results_5_MW.csv | 100 ++++++ .../data/frequency_response_results_8_MW.csv | 100 ++++++ .../simulink_msre_1MW_U233_insertion.xlsx | Bin 0 -> 1184402 bytes .../simulink_msre_5MW_U233_insertion.xlsx | Bin 0 -> 1049279 bytes .../simulink_msre_8MW_U233_insertion.xlsx | Bin 0 -> 1321849 bytes .../msrDynamics_implementation/frq_script.py | 168 ++++++++++ .../model_frq_response.ipynb | 297 ++++++++++++++++++ .../model_step.ipynb | 283 +++++++++++++++++ .../parameters_U233.py | 151 +++++++++ .../parameters_U235.py | 201 ++++++++++++ 29 files changed, 2548 insertions(+) rename dynamic_model/{ => jitcdde_implementation}/parameters.py (100%) rename dynamic_model/{ => jitcdde_implementation}/simulation_study.ipynb (100%) create mode 100644 dynamic_model/msrDynamics_implementation/__pycache__/parameters_U233.cpython-311.pyc create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_insertion.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_magnitude.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_magnitude_theoretical.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_phase.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_phase_theoretical.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_insertion.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude_theoretical.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase_theoretical.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_insertion.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_magnitude.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_magnitude_theoretical.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_phase.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_phase_theoretical.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/frequency_response_results_1_MW.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/frequency_response_results_5_MW.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/frequency_response_results_8_MW.csv create mode 100644 dynamic_model/msrDynamics_implementation/data/simulink_msre_1MW_U233_insertion.xlsx create mode 100644 dynamic_model/msrDynamics_implementation/data/simulink_msre_5MW_U233_insertion.xlsx create mode 100644 dynamic_model/msrDynamics_implementation/data/simulink_msre_8MW_U233_insertion.xlsx create mode 100644 dynamic_model/msrDynamics_implementation/frq_script.py create mode 100644 dynamic_model/msrDynamics_implementation/model_frq_response.ipynb create mode 100644 dynamic_model/msrDynamics_implementation/model_step.ipynb create mode 100644 dynamic_model/msrDynamics_implementation/parameters_U233.py create mode 100644 dynamic_model/msrDynamics_implementation/parameters_U235.py diff --git a/dynamic_model/parameters.py b/dynamic_model/jitcdde_implementation/parameters.py similarity index 100% rename from dynamic_model/parameters.py rename to dynamic_model/jitcdde_implementation/parameters.py diff --git a/dynamic_model/simulation_study.ipynb b/dynamic_model/jitcdde_implementation/simulation_study.ipynb similarity index 100% rename from dynamic_model/simulation_study.ipynb rename to dynamic_model/jitcdde_implementation/simulation_study.ipynb diff --git 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          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,axs = plt.subplots(2,1,figsize=(6,12))\n", + "\n", + "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n", + "\n", + "# function to update the style of each axis\n", + "def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n", + " ax.set_title(title,fontsize=20)\n", + " ax.set_xlabel(x_label,fontsize=14)\n", + " ax.set_ylabel(y_label,fontsize=14)\n", + " ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n", + " ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n", + " ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n", + "\n", + "update_axis_style(axs[0],\"Magnitude\")\n", + "axs[0].plot(df_jit['Frequency'],df_jit['Gain'],label=\"msrDynamics\")\n", + "axs[0].plot(df_ORNL_mag_t['f'],df_ORNL_mag_t['mag'],label=\"ORNL: theoretical\",color=colors[1],linestyle=\"--\")\n", + "axs[0].scatter(df_ORNL_mag['f'],df_ORNL_mag['mag'],marker=\"2\",color=colors[2],label=\"ORNL: measured\")\n", + "axs[0].set_yscale(\"log\")\n", + "axs[0].set_xscale(\"log\")\n", + "axs[0].set_xlim([9e-3,1e0])\n", + "axs[0].set_ylim([1e2,3.5e3])\n", + "axs[0].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n", + "axs[0].legend()\n", + "\n", + "update_axis_style(axs[1],\"Phase\",x_label='Frequency (rad/s)',y_label=\"Phase(deg)\")\n", + "axs[1].plot(df_jit['Frequency'],df_jit['Phase Shift'],label=\"msrDynamics\")\n", + "axs[1].plot(df_ORNL_phase_t['f'],df_ORNL_phase_t['phase'],label=\"ORNL: theoretical\",color=colors[1],linestyle=\"--\")\n", + "axs[1].scatter(df_ORNL_phase['f'],df_ORNL_phase['phase'],marker=\"2\",color=colors[2],label=\"ORNL-TM-2997\")\n", + "axs[1].set_xscale(\"log\")\n", + "axs[1].set_xlim([9e-3,1e0])\n", + "axs[1].legend()\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_adj = pd.read_csv('frequency_response_results_8_MW_double_tau.csv')\n", + "\n", + "\n", + "fig,axs = plt.subplots(2,1,figsize=(10,10))\n", + "\n", + "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n", + "\n", + "# function to update the style of each axis\n", + "def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n", + " ax.set_title(title)\n", + " ax.set_xlabel(x_label)\n", + " ax.set_ylabel(y_label)\n", + " ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n", + " ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n", + " ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n", + "\n", + "update_axis_style(axs[0],r\"Magnitude, $\\tau = \\tau_l + \\tau_c$\")\n", + "axs[0].plot(df_jit['Frequency'],df_jit['Gain'],label=r\"$\\tau = \\tau_l + \\tau_c$\") \n", + "axs[0].set_yscale(\"log\")\n", + "axs[0].set_xscale(\"log\")\n", + "axs[0].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n", + "axs[0].set_xlim([1e-2,2e0])\n", + "p = tau_c+tau_l\n", + "f_dip = (2*math.pi)/(p)\n", + "f_dip2 = (2*math.pi)/((p)/2)\n", + "f_peak = (2*math.pi)/(2*(p))\n", + "f_peak2 = (2*math.pi)/((2/3)*(p))\n", + "axs[0].axvline(x=f_peak,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau)$\",color=colors[1])\n", + "axs[0].axvline(x=f_dip,linestyle=\"--\",label=r\"$f = 2\\pi / \\tau$\",color=colors[2])\n", + "axs[0].axvline(x=f_peak2,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau/3)$\",color=colors[4])\n", + "axs[0].axvline(x=f_dip2,linestyle=\"--\",label=r\"$f = 2\\pi / (\\tau/2)$\",color=colors[3])\n", + "axs[0].legend()\n", + "\n", + "update_axis_style(axs[1],r\"Magnitude, $\\tau = 2\\tau_l + 2\\tau_c$\")\n", + "axs[1].plot(df_adj['Frequency'],df_adj['Gain'],label=r\"$\\tau = 2(\\tau_l + \\tau_c)$\") \n", + "axs[1].set_yscale(\"log\")\n", + "axs[1].set_xscale(\"log\")\n", + "axs[1].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n", + "axs[1].set_xlim([1e-2,2e0])\n", + "p2 = 2*(tau_c+tau_l)\n", + "f_dip = (2*math.pi)/(p2)\n", + "f_dip2 = (2*math.pi)/((p2)/2)\n", + "f_peak = (2*math.pi)/(2*(p2))\n", + "f_peak2 = (2*math.pi)/((2/3)*(p2))\n", + "axs[1].axvline(x=f_peak,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau)$\",color=colors[1])\n", + "axs[1].axvline(x=f_dip,linestyle=\"--\",label=r\"$f = 2\\pi / \\tau$\",color=colors[2])\n", + "axs[1].axvline(x=f_peak2,linestyle=\"--\",label=r\"$f = 2\\pi / (2\\tau/3)$\",color=colors[4])\n", + "axs[1].axvline(x=f_dip2,linestyle=\"--\",label=r\"$f = 2\\pi / (\\tau/2)$\",color=colors[3])\n", + "axs[1].legend()\n", + "\n", + "plt.tight_layout()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_adj = pd.read_csv('frequency_response_results_8_MW_double_tau.csv')\n", + "\n", + "\n", + "fig,axs = plt.subplots(2,1,figsize=(10,10))\n", + "\n", + "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n", + "\n", + "# function to update the style of each axis\n", + "def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n", + " ax.set_title(title)\n", + " ax.set_xlabel(x_label)\n", + " ax.set_ylabel(y_label)\n", + " ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n", + " ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n", + " ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n", + "\n", + "# Filling between ranges where inc_dec is True\n", + "inc_dec = np.ones(len(df_jit))\n", + "for f in enumerate(df_jit['Frequency']):\n", + " delay_input = np.cos(f[1]*((-p))) # t = 2pi-tau\n", + " if (delay_input > 0):\n", + " inc_dec[f[0]] = 0\n", + "\n", + "# Find start and end points of True and False segments in inc_dec and fill between them\n", + "start_true = None\n", + "start_false = None\n", + "for i in range(len(df_jit['Frequency'])):\n", + " if inc_dec[i]:\n", + " if start_false is not None:\n", + " # Fill the False segment before starting the True segment\n", + " axs[0].fill_between(df_jit['Frequency'][start_false:i], 0, df_jit['Gain'][start_false:i], alpha=0.23, color='red',interpolate=True)\n", + " start_false = None\n", + " if start_true is None:\n", + " start_true = i # Mark the start of a True segment\n", + " else:\n", + " if start_true is not None:\n", + " # Fill the True segment before starting the False segment\n", + " axs[0].fill_between(df_jit['Frequency'][start_true:i], 0, df_jit['Gain'][start_true:i], alpha=0.23, color='green',interpolate=True)\n", + " start_true = None\n", + " if start_false is None:\n", + " start_false = i # Mark the start of a False segment\n", + "\n", + "# Check if there is a segment that goes until the end\n", + "if start_true is not None:\n", + " axs[0].fill_between(df_jit['Frequency'][start_true:], 0, df_jit['Gain'][start_true:], alpha=0.3, color='green',interpolate=True)\n", + "if start_false is not None:\n", + " axs[0].fill_between(df_jit['Frequency'][start_false:], 0, df_jit['Gain'][start_false:], alpha=0.3, color='red',interpolate=True)\n", + "\n", + "update_axis_style(axs[0],r\"Magnitude, $\\tau = \\tau_l + \\tau_c$\")\n", + "axs[0].plot(df_jit['Frequency'],df_jit['Gain'],label=r\"$\\tau = \\tau_l + \\tau_c$\") \n", + "axs[0].set_yscale(\"log\")\n", + "axs[0].set_xscale(\"log\")\n", + "axs[0].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n", + "axs[0].set_xlim([1e-2,2e0])\n", + "\n", + "red_patch = mpatches.Patch(color='red', alpha=0.3, label=r\"$\\frac{d\\rho_{in}}{dt}(t-2 \\pi /f) \\cdot \\frac{d\\rho_{in}}{dt}(t) > 0$\")\n", + "green_patch = mpatches.Patch(color='green', alpha=0.3, label=r\"$\\frac{d\\rho_{in}}{dt}(t-2 \\pi /f) \\cdot \\frac{d\\rho_{in}}{dt}(t) < 0$\")\n", + "\n", + "axs[0].legend(handles=[axs[0].get_lines()[0], red_patch, green_patch])\n", + "\n", + "######################################################################################\n", + "# Double tau\n", + "######################################################################################\n", + "\n", + "update_axis_style(axs[1],r\"Magnitude, $\\tau = 2\\tau_l + 2\\tau_c$\")\n", + "axs[1].plot(df_adj['Frequency'],df_adj['Gain'],label=r\"$\\tau = 2(\\tau_l + \\tau_c)$\") \n", + "axs[1].set_yscale(\"log\")\n", + "axs[1].set_xscale(\"log\")\n", + "axs[1].set_ylabel(r\"$\\frac{\\delta n}{\\delta \\rho n_0}$\")\n", + "axs[1].set_xlim([1e-2,2e0])\n", + "axs[1].legend(handles=[axs[1].get_lines()[0], red_patch, green_patch])\n", + "\n", + "# Filling between ranges where inc_dec is True\n", + "inc_dec = np.ones(len(df_adj))\n", + "for f in enumerate(df_adj['Frequency']):\n", + " delay_input = np.cos(f[1]*(-p2)) # t = 2pi-tau\n", + " if (delay_input > 0):\n", + " inc_dec[f[0]] = 0\n", + "\n", + "# Find start and end points of True and False segments in inc_dec and fill between them\n", + "start_true = None\n", + "start_false = None\n", + "for i in range(len(df_adj['Frequency'])):\n", + " if inc_dec[i]:\n", + " if start_false is not None:\n", + " # Fill the False segment before starting the True segment\n", + " axs[1].fill_between(df_adj['Frequency'][start_false:i], 0, df_adj['Gain'][start_false:i], alpha=0.23, color='red',interpolate=True)\n", + " start_false = None\n", + " if start_true is None:\n", + " start_true = i # Mark the start of a True segment\n", + " else:\n", + " if start_true is not None:\n", + " # Fill the True segment before starting the False segment\n", + " axs[1].fill_between(df_adj['Frequency'][start_true:i], 0, df_adj['Gain'][start_true:i], alpha=0.23, color='green',interpolate=True)\n", + " start_true = None\n", + " if start_false is None:\n", + " start_false = i # Mark the start of a False segment\n", + "\n", + "# Check if there is a segment that goes until the end\n", + "if start_true is not None:\n", + " axs[1].fill_between(df_adj['Frequency'][start_true:], 0, df_adj['Gain'][start_true:], alpha=0.3, color='green',interpolate=True)\n", + "if start_false is not None:\n", + " axs[1].fill_between(df_adj['Frequency'][start_false:], 0, df_adj['Gain'][start_false:], alpha=0.3, color='red',interpolate=True)\n", + "\n", + "\n", + "\n", + "plt.tight_layout()\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "thesis_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/dynamic_model/msrDynamics_implementation/model_step.ipynb b/dynamic_model/msrDynamics_implementation/model_step.ipynb new file mode 100644 index 0000000..81c67f5 --- /dev/null +++ b/dynamic_model/msrDynamics_implementation/model_step.ipynb @@ -0,0 +1,283 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from parameters_U233 import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from jitcdde import t\n", + "from msrDynamics import Node, System\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# unpack ORNL data\n", + "df_ORNL = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_insertion.csv\",names=['t','dP'])\n", + "df_ORNL = df_ORNL.sort_values(df_ORNL.columns[0])\n", + "df_simulink = pd.read_excel(f\"./data/simulink_msre_{P}MW_U233_insertion.xlsx\")\n", + "i_trans = [i for i in range(len(df_simulink)) if df_simulink['time'][i] >= 2500]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "T = df_simulink['time']\n", + "MSRE = System()\n", + "\n", + "# radiator\n", + "T_out_rc = Node(m = mn_rp, scp = mcp_rpn/mn_rp, W = W_rp, y0 = T0_rp)\n", + "T_out_air = Node(m = mn_rs, scp = mcp_rsn/mn_rs, W = W_rs, y0 = T0_rs)\n", + "\n", + "# heat exchanger\n", + "T_hf1 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p1)\n", + "T_hf2 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p2)\n", + "T_hf3 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p3)\n", + "T_hf4 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p4)\n", + "T_ht1 = Node(m = m_tn, scp = scp_t, y0 = T0_t1)\n", + "T_ht2 = Node(m = m_tn, scp = scp_t, y0 = T0_t2)\n", + "T_hc1 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s1)\n", + "T_hc2 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s2)\n", + "T_hc3 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s3)\n", + "T_hc4 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s4)\n", + "\n", + "# core \n", + "n = Node(y0 = n_frac0)\n", + "C1 = Node(y0 = C0[0])\n", + "C2 = Node(y0 = C0[1])\n", + "C3 = Node(y0 = C0[2])\n", + "C4 = Node(y0 = C0[3])\n", + "C5 = Node(y0 = C0[4])\n", + "C6 = Node(y0 = C0[5])\n", + "rho = Node(y0 = 0.0)\n", + "\n", + "# add reactivity input\n", + "t_ins = 2500\n", + "def rho_insert(t):\n", + " if (t t_ins) and (T[i] < t_ins + duration)]\n", + "ref_P = P*n.y_out[i_insert[0]-100]\n", + "dP = [(k*P)-ref_P for k in n.y_out]\n", + "\n", + "delta = t_ins - df_ORNL['t'][0] - t_ins\n", + "for i in range(len(df_ORNL)):\n", + " df_ORNL['t'][i] += delta\n", + "\n", + "ref_P_simulink = df_simulink['Mux(4)'][i_trans[0]-100]*P\n", + "i_window = [i for i in i_trans if df_simulink['time'][i]-t_ins <= duration]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n", + "\n", + "# function to update the style of each axis\n", + "def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n", + " ax.set_title(title,fontsize=20)\n", + " ax.set_xlabel(x_label,fontsize=14)\n", + " ax.set_ylabel(y_label,fontsize=14)\n", + " ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n", + " ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n", + " ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n", + "\n", + "fig, ax = plt.subplots()\n", + "update_axis_style(ax,f\"Step Reactivity Insertion Response: {int(inserted*(10**5))}pcm, {int(P)}MW, U233 Fuel\")\n", + "ax.plot(T[i_insert[0]:i_insert[-1]+1]-t_ins,dP[i_insert[0]:i_insert[-1]+1],label=\"msrDynamics\",color=colors[0])\n", + "ax.plot(df_simulink['time'][i_window]-t_ins,df_simulink['Mux(4)'][i_window]*P-ref_P_simulink,color=colors[1],linestyle=\"--\",label=\"Simulink, Singh et al.\")\n", + "ax.plot(df_ORNL.iloc[:,0],df_ORNL.iloc[:,1],label=\"ORNL-TM-2997\",color=colors[2],linestyle=\"--\")\n", + "ax.set_ylabel(r\"$\\Delta P$\")\n", + "ax.set_xlabel(r\"$t$(s)\")\n", + "ax.legend()\n", + "\n", + "plt.tight_layout()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "thesis_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/dynamic_model/msrDynamics_implementation/parameters_U233.py b/dynamic_model/msrDynamics_implementation/parameters_U233.py new file mode 100644 index 0000000..423b850 --- /dev/null +++ b/dynamic_model/msrDynamics_implementation/parameters_U233.py @@ -0,0 +1,151 @@ +import numpy as np + +# domain +t0 = 0.0 +tf = 5000.00 +T = np.arange(t0,tf,0.01) + +# REACTIVITY INSERTION +inserted = 1.39e-4 # 1MW +# inserted = 1.96e-4 # 5MW +# inserted = 2.48e-4 # 8MW + +# NEUTRONICS DATA +tau_l = 16.73 +tau_c = 8.46 +# P = 0.1 +# P = 5 +P = 8 +n_frac0 = 1 # initial fractional neutron density n/n0 +Lam = 4.0E-04 +lam = np.array([1.260E-02, 3.370E-02, 1.390E-01, 3.250E-01, 1.130E+00, 2.500E+00]) +beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009]) +beta_t = np.sum(beta) # total delayed neutron fraction MSRE +rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel +C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c)) + +# Feedback coefficients +a_f = -11.034E-5 +a_g = -05.814E-5 + +# CORE HEAT TRANSFER PARAMETERS +vdot_f = 7.5708E-02 +rho_f = 2.14647E+03 +W_f = 1.623879934566580e+02 +m_f = W_f * tau_c +nn_f = 2 +mn_f = m_f / nn_f +scp_f = 1.9665E-3 + +# Core Upflow +v_g = 1.95386 +rho_g = 1.860E3 +m_g = v_g * rho_g +scp_g = 1.773E-3 +mcp_g1 = m_g * scp_g +mcp_f1 = mn_f * scp_f +mcp_f2 = mn_f * scp_f +hA_fg = 0.02 * 9 / 5 +k_g = 0.07 +k_1 = 0.5 +k_2 = 0.5 +k_f = 0.93 +k_f1 = k_f / nn_f +k_f2 = k_f / nn_f + +# Heat Exchanger +d_he = 16 +h_he = 72 +od_tube = 0.5 +id_tube = od_tube - 2 * 0.042 +n_tube = 159 +a_tube = 254 * 144 +l_tube = a_tube / n_tube / (np.pi * od_tube) +v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube +v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube +v_he = (d_he / 2) ** 2 * np.pi * h_he +v_he_fuel = v_he - v_tube +in_m = 1.63871e-5 +W_p = W_f +m_p = v_he_fuel * in_m * rho_f +nn_p = 4 +mn_p = m_p / nn_p +cp_p = scp_f +vdot_s = 5.36265E-02 +rho_s = 1.922e3 +W_s = 1.005793369810108e+02 +m_s = v_cool * in_m * rho_s +nn_s = 4 +mn_s = m_s / nn_s +scp_s = 2.39E-3 +A_phe = 2.359E+01 +ha_p = 6.480E-01 +ha_s = 3.060E-01 +mcp_pn = mn_p * cp_p +hA_pn = ha_p / nn_s +nn_t = 2 +rho_tube = 8.7745E+03 +m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t +scp_t = 5.778E-04 +mcp_tn = m_tn * scp_t +mcp_sn = mn_s * scp_s +hA_sn = ha_s / nn_s + +# Initial conditions +Tf_in = 6.3222E+02 +T0_f2 = 6.5727E+02 +T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2 +T0_g1 = T0_f1 + (k_g * P / hA_fg) +Tp_in = T0_f2 +T0_p4 = Tf_in +T0_p1 = Tp_in - (Tp_in - T0_p4) / 4 +T0_p2 = Tp_in - 2 * (Tp_in - T0_p4) / 4 +T0_p3 = Tp_in - 3 * (Tp_in - T0_p4) / 4 +Ts_in = 5.4611E+02 +T0_s4 = 5.7939E+02 +T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s +T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s +T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s +T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn) +T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn) + +# Radiator Parameters +Trp_in = T0_s4 +T0_rp = Ts_in +Trs_in = 37.78 +T0_rs = 148.9 +od_rad = 0.01905 +tube_wall_thick = 0.0018288 +id_rad = od_rad - 2 * tube_wall_thick +n_rtubes = 120 +l_rtube = 9.144 +v_rp = np.pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes +n_tpr = 12 +n_row = 10 +tube_space = 0.0381 +v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube +W_rp = W_s +m_rp = v_rp * rho_s +nn_rp = 1 +mn_rp = m_rp / nn_rp +cp_rp = scp_s +vdot_rs = 94.389 +rho_rs = 1.1237 +W_rs = vdot_rs * rho_rs +m_rs = v_rs * rho_rs +nn_rs = 1 +mn_rs = m_rs / nn_rs +scp_rs = 1.0085E-3 +A_rad = 6.503E1 +h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2) +mcp_rpn = mn_rp * cp_rp +hA_rpn = h_roverall * A_rad / nn_rs +mcp_rsn = mn_rs * scp_rs +hA_rsn = h_roverall * A_rad / nn_rs + +# Pure time delays between components +tau_hx_c = 8.67 #+2.145 +tau_c_hx = 3.77 #+2.145 +tau_hx_r = 4.71 +tau_r_hx = 8.24 + diff --git a/dynamic_model/msrDynamics_implementation/parameters_U235.py b/dynamic_model/msrDynamics_implementation/parameters_U235.py new file mode 100644 index 0000000..918de9b --- /dev/null +++ b/dynamic_model/msrDynamics_implementation/parameters_U235.py @@ -0,0 +1,201 @@ +import numpy as np +import math +pi = math.pi + +# domain +t0 = 0.0 +tf = 1000.00 +T = np.arange(t0,tf,0.01) + +# NEUTRONICS DATA +tau_l = 16.73 # ORNL-TM-0728 %16.44; % (s) +tau_c = 8.46 # ORNL-TM-0728 %8.460; % (s) +P = 8.0 # Thermal Power in MW ORNL-TM-1070, p.2 +n_frac0 = 1.0 # initial fractional neutron density n/n0 (n/cm^3/s) +Lam = 2.400E-04 # mean generation time ORNL-TM-1070 p.15 U235 +# Lam = 4.0E-04; # mean generation time ORNL-TM-1070 p.15 U233 +lam = np.array([1.240E-02, 3.05E-02, 1.11E-01, 3.01E-01, 1.140E+00, 3.014E+00]) +beta = np.array([0.000223, 0.001457, 0.001307, 0.002628, 0.000766, 0.00023]) # U235 +# beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009]) # U233 +beta_t = np.sum(beta) # total delayed neutron fraction MSRE +rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel +C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c)) + +# Feedback co-efficients +a_f = -8.71E-05 # U235 (drho/°C) fuel salt temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -5.904E-05; % ORNL-TM-0728 p. 101 % +a_g = -6.66E-05 # U235 (drho/°C) graphite temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -6.624E-05; % ORNL-TM-0728 p.101 + +# CORE HEAT TRANSFER PARAMETERS +# FUEL PARAMETERS - DONE +vdot_f = 7.5708E-02 # ORNL-TM-0728 % 7.571e-2; % vol. flow rate (m^3/s) ORNL-TM-1647 p.3, ORNL-TM-0728 p.12 +rho_f = 2.14647E+03 # (partially enriched U-235)ORNL-TM-0728 p.8 2.243E+03; % (Th-U) density of fuel salt (kg/m^3) ORNL-TM-0728 p.8 +W_f = 1.623879934566580e+02 # 1.83085e+02;%vdot_f*rho_f; % 182.78; % calcd from m_dot*cp*delT=P; vdot_f*rho_f; % fuel flow rate (kg/s) +# tau_f_c = tau_c; % ORNL-TM-0728 % 8.45; % transit time of fuel in core (s) ORNL-TM-1070 p.15, TDAMSRE p.5 +m_f = W_f * tau_c # fuel mass in core (kg) +nn_f = 2 # number of fuel nodes in core model +mn_f = m_f / nn_f # fuel mass per node (kg) +# cp_f = 4.2*9/5; % (MJ/deg-C) total fuel heat capacity TDAMSRE p.5 +scp_f = 1.9665E-3 # specific heat capacity of fuel salt (MJ/kg-C) ORNL-TM-0728 p.8 + +# Core Upflow - DONE +v_g = 1.95386 # graphite volume(m^3) ORNL-TM-0728 p. 101 +rho_g = 1.860E3 # graphite density (kg/m^3) ORNL-3812 p.77, ORNL-TM-0728 p.87 +m_g = v_g * rho_g # graphite mass (kg) +cp_g = 3.6 * 9 / 5 # TDAMSRE p.5 graphite total heat capacity (MW-s/C) ORNL-TM-1647 p.3 +scp_g = 1.773E-3 # cp_g/m_g; % graphite specific heat capacity (MW-s/kg-C) ORNL-TM-1647 p.3 +mcp_g1 = m_g * scp_g # (mass of material x heat capacity of material) of graphite per lump (MW-s/°C) +mcp_f1 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) +mcp_f2 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) +hA_fg = 0.02 * 9 / 5 # (fuel to graphite heat transfer coeff x heat transfer area) (MW/°C) ORNL-TM-1647 p.3, TDAMSRE p.5 +k_g = 0.07 # fraction of total power generated in the graphite ORNL-TM-0728 p.9 +k_1 = 0.5 # fraction of heat transferred from graphite which goes to the first fuel lump +k_2 = 0.5 # fraction of heat transferred from graphite which goes to the second fuel lump +k_f = 0.93 # fraction of heat generated in fuel - that generated in the external loop ORNL-TM-0728 p.9 +k_f1 = k_f / nn_f # fraction of total power generated in lump f1 +k_f2 = k_f / nn_f # fraction of total power generated in lump f2 + +# New node for power deposited in fuel outside the core +k_out = 1 - (k_g + k_f) # fraction of power generated in fuel in external loop ORNL-TM-0728 p.9 +m_out = W_f # (kg) Mass of node such that resident time is 1 sec (W_f needs to be defined) + +# Initial conditions - DONE +Tf_in = 6.3222E+02 # in °C ORNL-TM-1647 p.2 +T0_f2 = 6.5727E+02 # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 +T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2 # 6.405952380952389e+02; in °C +T0_g1 = T0_f1 + (k_g * P / hA_fg) # 6.589285714285924e+02; in °C +# T0_out = k_out * P / m_out / scp_f + T0_f2 # in °C (scp_f needs to be defined) + + +# Heat Exchanger - DONE +# Geometry +d_he = 16 # (in) he diameter ORNL-TM-0728 p. 164 +h_he = 72 # (in) active height % 96; %(in) he height ORNL-TM-0728 p. 164 +od_tube = 0.5 # (in) coolant tube OD ORNL-TM-0728 p. 164 +id_tube = od_tube - 2 * 0.042 # (in) coolant tube ID ORNL-TM-0728 p. 164 +n_tube = 159 # number of coolant tubes ORNL-TM-0728 p. 164 +a_tube = 254 * 144 # (in^2) total area of tubes ORNL-TM-0728 p. 164 +l_tube = a_tube / n_tube / (np.pi * od_tube) # (in) tube length +v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube # (in^3) hx shell volume occupied by tubes +v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube # (in^3) hx volume occupied by coolant +v_he = (d_he / 2) ** 2 * np.pi * h_he # (in^3) volume of heat exchanger shell +v_he_fuel = v_he - v_tube # (in^3) volume available to fuel in shell + +# Unit conversions +in_m = 1.63871e-5 # 1 cubic inch = 1.63871e-5 cubic meters + +# PRIMARY FLOW PARAMETERS - DONE +W_p = W_f # fuel flow rate (kg/s) + +m_p = v_he_fuel * in_m * rho_f # fuel mass in PHE (kg) +nn_p = 4 # number of fuel nodes in PHE +mn_p = m_p / nn_p # fuel mass per node (kg) +cp_p = scp_f # fuel heat capacity (MJ/(kg-C)) + +# SECONDARY FLOW PARAMETERS - DONE +vdot_s = 5.36265E-02 # ORNL-TM-0728 p. 164 % 5.236E-02; % coolant volume flow rate (m^3/s) ORNL-TM-1647 p.3 +rho_s = 1.922e3 # coolant salt density (kg/m^3) ORNL-TM-0728 p.8 +W_s = 1.005793369810108e+02 # vdot_s*rho_s; % calcd from mdot*cp*delT; vdot_s*rho_s; % coolant flow rate (kg/s) ORNL-TM-1647 p.3 + +m_s = v_cool * in_m * rho_s # coolant mass in PHE (kg) +nn_s = 4 # number of coolant nodes in PHE +mn_s = m_s / nn_s # coolant mass per node (kg) +scp_s = 2.39E-3 # cp_s/m_s; % specific heat capacity of coolant (MJ/(kg-C) ORNL-TM-0728 p.8 + +A_phe = 2.359E+01 # effective area for heat transfer (primary and secondary, m^2) ORNL-TM-0728 p.164 + +ha_p = 6.480E-01 # heat transfer*area coefficient from primary to tubes (MW/C) ORNL-TM-1647 p.3 +ha_s = 3.060E-01 # heat transfer*area coefficient from tubes to secondary (MW/C) ORNL-TM-1647 p.3 + +# Primary Side +mcp_pn = mn_p * cp_p # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C +hA_pn = ha_p / nn_s # 3.030; % (primary to tube heat transfer coeff x heat transfer area) in MW/°C + +# Tubes - DONE +nn_t = 2 # number of nodes of tubes in the model +rho_tube = 8.7745E+03 # (kg/m^3) density of INOR-8 ORNL-TM-0728 p.20 +m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t # mass of tubes (kg) +scp_t = 5.778E-04 # specific heat capacity of tubes (MJ/(kg-C)) ORNL-TM-0728 p.20 +mcp_tn = m_tn * scp_t # mass*(heat capacity) of tubes per lump in MW-s/°C + +# Secondary Side - DONE +mcp_sn = mn_s * scp_s # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C +hA_sn = ha_s / nn_s # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Initial conditions - DONE +# Primary nodes +Tp_in = T0_f2 # in °C ORNL-TM-1647 p.2 +T0_p4 = Tf_in # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 +T0_p1 = Tp_in + (T0_p4 - Tp_in) / 4 # in °C +T0_p2 = Tp_in + 2 * (T0_p4 - Tp_in) / 4 # in °C +T0_p3 = Tp_in + 3 * (T0_p4 - Tp_in) / 4 # in °C + +# Secondary nodes +Ts_in = 5.4611E+02 # in °C ORNL-TM-1647 p.2 +T0_s4 = 5.7939E+02 # in °C ORNL-TM-1647 p.2 +T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s # in °C +T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s # in °C +T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s # in °C +# Tube nodes +T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn) # in °C +T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn) # in °C + +# Radiator Parameters - DONE + +# Initial conditions - DONE +# Primary nodes +Trp_in = T0_s4 # 5.933E+02; % in °C ORNL-TM-1647 p.2 +T0_rp = Ts_in # in °C ORNL-TM-1647 p.2 + +# Secondary nodes - DONE +Trs_in = 37.78 # (C) air inlet temperature ORNL-TM-1647 p.2 +T0_rs = 148.9 # (C) air exit temperature ORNL-TM-1647 p.2 + +# Radiator Geometry +od_rad = 0.01905 # (m) outer diameter of tubes in the radiator ORNL-TM-0728 p.296 +tube_wall_thick = 0.0018288 # (m) thickness of tubes in the radiator ORNL-TM-0728 p.296 +id_rad = od_rad - 2 * tube_wall_thick +n_rtubes = 120 # number of tubes in the radiator (rows times tubes per row) ORNL-TM-0728 p.296 +l_rtube = 9.144 # (m) length of tubes in the radiator ORNL-TM-0728 p.296 +v_rp = pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes # volume available to salt in the radiator +# v_rtube = pi * (od_rad / 2) ** 2 * l_rtube * n_rtubes - v_rp # volume of metal in radiator tubes *TUBES NOT MODELED + +n_tpr = 12 # number of tubes per row in the radiator matrix +n_row = 10 # number rows in the radiator matrix +tube_space = 0.0381 # (m) spacing between tubes and rows of matrix +v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube # volume of air inside radiator + +# PRIMARY FLOW PARAMETERS - DONE +W_rp = W_s # coolant salt flow rate (kg/s) +m_rp = v_rp * rho_s # coolant salt mass in rad (kg) +nn_rp = 1 # number of coolant salt nodes in the radiator +mn_rp = m_rp / nn_rp # coolant mass per node (kg) +cp_rp = scp_s # coolant specific heat capacity (MJ/(kg-C)) + +# SECONDARY FLOW PARAMETERS - DONE +vdot_rs = 94.389 # ORNL-TM-0728 p. 296; 78.82; % air volume flow rate (m^3/s) ORNL-TM-1647 p.2 +rho_rs = 1.1237 # air density (kg/m^3) REFPROP (310K and 0.1MPa) +W_rs = vdot_rs * rho_rs # air flow rate (kg/s) + +m_rs = v_rs * rho_rs # coolant air mass in rad (kg) +nn_rs = 1 # number of coolant nodes in rad +mn_rs = m_rs / nn_rs # coolant mass per node (kg) +scp_rs = 1.0085E-3 # (MJ/kg-C) specific heat capacity of air at (air_out+air_in)/2 REFPROP + +A_rad = 6.503E1 # (m^2) surface area of the radiator ORNL-TM-0728 p.14 +h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2) # cald as: P/A_rad/((T0_rp+Trp_in)/2-(T0_rs+Trs_in)/2) 3.168E-4; % (MW/m^2-C) polimi thesis + +# Primary Side +mcp_rpn = mn_rp * cp_rp # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C +hA_rpn = h_roverall * A_rad / nn_rs # 3.030; % (primary to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Secondary Side - DONE +mcp_rsn = mn_rs * scp_rs # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C +hA_rsn = h_roverall * A_rad / nn_rs # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Pure time delays between components - DONE +tau_hx_c = 8.67 # (sec) delay from hx to core TDAMSRE p.6 +tau_c_hx = 3.77 # (sec) subtracted 1 sec for external loop power generation node resident time; delay from core to fuel hx TDAMSRE p.6 +tau_hx_r = 4.71 # (sec) fertile hx to core TDAMSRE p.6 +tau_r_hx = 8.24 # (sec) core to fertile hx TDAMSRE p.6 + +first_val = (rho_0 - beta_t) * n_frac0 / Lam + lam[0] * C0[0] + lam[1] * C0[1] + lam[2] * C0[2] + lam[3] * C0[3] + lam[4] * C0[4] + lam[5] * C0[5] \ No newline at end of file From 23e07063248b3fce26eadf79e14b957acf144a52 Mon Sep 17 00:00:00 2001 From: church89 Date: Fri, 19 Apr 2024 14:03:19 +0200 Subject: [PATCH 23/32] Add new h5m files --- MSRE_235_233_Power_History_R24E.xlsx | Bin 31395 -> 0 bytes h5m/msre_full.h5m | 3 + msre_NEW.py | 318 - msre_control_rod.h5m | 3 + msre_depletion_post_processing.py | 194 - msre_power_history.py | 339 - msre_power_history_restart.py | 365 - openmc_notebooks/ENDF-B-VIII.0_chain_msr.xml | 16420 ---------------- openmc_notebooks/README.md | 18 +- openmc_notebooks/msre_cad.ipynb | 1336 -- .../msre_depletion_fp-removal-rates.ipynb | 1904 -- 11 files changed, 10 insertions(+), 20890 deletions(-) delete mode 100644 MSRE_235_233_Power_History_R24E.xlsx create mode 100644 h5m/msre_full.h5m delete mode 100644 msre_NEW.py create mode 100644 msre_control_rod.h5m delete mode 100644 msre_depletion_post_processing.py delete 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Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.025e-14 1.025e-14 9.56001e-13 2.23e-12 8.27001e-11 5.53e-10 1.99e-08 1.09e-07 7.26001e-07 4.15e-06 1.445e-05 1.445e-05 2.907e-05 2.907e-05 9.32301e-05 9.32301e-05 7.79801e-05 7.79801e-05 0.0001506 2.1205e-05 2.1205e-05 2.017e-05 8.25001e-07 8.25001e-07 2.08e-07 1.5e-12 1.44e-13 0.0 0.0 0.0 2.89377e-12 1.03e-13 0.0 0.0 0.0 2.03e-14 2.143001e-12 3.72e-10 1.48e-08 9.33001e-07 1.011e-05 0.000911741 0.00426691 0.0176672 0.00732244 0.00732238 0.0117246 0.00273216 0.00120457 0.00013291 1.344e-05 3.29e-07 1.26999e-08 1.0481533145e-09 8.9324e-13 1.06527e-14 0.0 0.0 0.0 5.58e-13 1.4e-10 7.20001e-09 7.20001e-09 5e-07 5e-07 5.2e-06 1.273e-05 0.00028619 0.00253694 0.0178747 0.0380204 0.0622889 0.0553085 0.0422027 0.00870013 0.000791151 1.45974e-05 2.36e-07 3.92999e-09 3.21e-11 2.40035e-13 0.0 9.26448e-19 0.0 0.0 1.68e-14 4.16e-12 1.42e-09 4.74e-09 1.08e-07 3.24e-07 2.378e-05 0.00016189 6.93801e-05 0.00208248 0.00176 0.00312913 0.0207869 0.025318 0.0317716 0.0109423 0.00663706 0.00111 0.00021298 8.75001e-06 4.47996e-07 5.15e-09 6.8730941891e-10 5.31e-13 8.1503e-15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.22e-14 5.06e-12 1.5e-10 5.01e-10 2.89e-08 1.26e-07 4.21e-07 7.82001e-06 1.245e-05 4.168e-05 0.00025498 0.00022786 0.00022786 0.000732511 0.000367375 0.000367375 0.00043273 0.00014598 4.08e-06 7.98001e-07 1.61e-07 2.34e-08 2.81e-09 7.02998e-07 3.94e-08 7.0700091023e-10 7.44001e-12 0.0 0.0 1.04e-12 5.22e-11 1.28e-10 2.35e-08 9.08001e-07 2.415e-05 0.00030305 0.00287468 0.00641331 0.00663851 0.00140071 0.00027059 6.12801e-05 7.62001e-06 3.8e-07 2.53e-08 1.04e-09 3.16e-11 2.68999e-13 0.0 0.0 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As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.25003e-15 5.25003e-15 4.15002e-13 9.68005e-13 2.63001e-11 1.76001e-10 5.91003e-09 2.25001e-08 1.51001e-07 1.54001e-06 4.66002e-06 4.66002e-06 9.71005e-06 9.71005e-06 3.73402e-05 3.73402e-05 2.11301e-05 2.11301e-05 3.19902e-05 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0.0 0.0 0.0 0.0 0.0 0.0 4.96003e-13 4.05002e-12 1.72001e-11 6.39003e-10 2.99002e-09 1.27001e-08 3.03002e-07 1.76001e-06 9.20005e-06 2.70801e-05 8.06904e-05 0.000109441 0.000168241 5.38503e-05 1.21001e-05 1.78001e-06 1.79001880005e-07 1.2600096928e-09 0.000346132 0.000121731 5.98203e-05 4.41002e-05 5.66803e-05 1.06989e-05 1.98937e-06 1.07825e-07 7.02996e-09 1.0084157607e-10 1.61001e-12 0.0 0.0 5.05003e-13 3.91502e-11 3.91502e-11 5.58003e-09 3.04002e-07 5.40003e-06 5.58103e-05 0.000423462 0.00199003 0.00263701 0.0020443 0.00135635 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 500000.0 14000000.0 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.61978e-14 7.05984e-13 1.25997e-10 8.9798e-10 6.58985e-09 2.05995e-07 1.55996e-06 1.55996e-06 8.6998e-06 8.6998e-06 3.73192e-05 3.73192e-05 7.94082e-05 7.94082e-05 0.000291653 0.000156126 0.000156126 0.000229135 3.72392e-05 3.72392e-05 2.12495e-05 3.34992e-06 4.5599e-07 3.73992e-11 4.4199e-12 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Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.18e-14 1.18e-14 8.95e-13 8.95e-13 1.51e-11 1.42e-06 1.96e-10 2.25e-09 0.00010312 7.27e-08 8.36e-07 8.7e-06 3.111e-05 3.111e-05 0.00014498 0.00014498 0.00060054 0.00060054 0.00114651 0.00114651 0.00408832 0.00263485 0.00263485 0.00561925 0.00223666 0.00223666 0.00305186 0.00161752 0.00070336 0.0002986 7.964000782049999e-05 1.240249999778e-05 4.309805877e-07 2.530385e-07 0.0 0.0 0.0 8.27e-14 7.7e-12 5.02e-10 2.18e-08 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- - - - 500000.0 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 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Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 2.19031e-14 2.40534e-12 2.40534e-12 2.47034e-10 2.47034e-10 2.94041e-09 6.85096e-09 1.12016e-08 7.46104e-08 6.26087e-07 5.21073e-07 3.49049e-06 1.4282e-05 2.51235e-05 2.51235e-05 3.31746e-05 3.31746e-05 3.82753e-05 3.82753e-05 1.74224e-05 1.74224e-05 1.31318e-05 1.66523e-06 1.66523e-06 8.93125e-07 1.01014e-07 1.01014e-07 2.39033e-12 2.1503e-13 5.33074e-14 0.0 0.0 0.0 1.50377e-10 1.21017e-11 0.0 6.23087e-13 3.96055e-11 1.98028e-09 4.63064e-08 1.02014e-06 1.07015e-05 9.22129e-05 0.000385014 0.00147418 0.00285551 0.00422233 0.00185125 0.00185125 0.00266197 0.000976966 0.000383003 6.81695e-05 9.82137e-06 8.00112e-07 4.8499e-08 3.56835e-10 2.46071e-11 6.4304e-13 0.0 1.30018e-13 2.60036e-11 1.90027e-09 1.37019e-07 1.50521e-06 1.50521e-06 3.24595e-05 3.24595e-05 0.000178485 0.000436991 0.00349225 0.0106743 0.0318485 0.0425816 0.0447042 0.024914 0.0110974 0.00196259 0.000293471 1.56834e-05 1.04015e-06 3.45023e-08 1.00014e-09 1.08708e-11 1.75024e-13 3.21045e-14 6.57092e-11 2.20031e-10 1.12016e-08 9.22129e-08 3.09043e-07 1.61022e-06 4.82067e-06 6.96197e-05 0.000309263 0.000132538 0.00211022 0.00175784 0.00312628 0.013727 0.01585101 0.0166492 0.0105524 0.00525937 0.0010215 0.000191327 1.65923e-05 1.25997e-06 3.68051e-08 4.74155e-10 1.89026e-11 7.02958e-13 0.0 0.0 0.0 0.0 6.24087e-14 5.31074e-12 2.08029e-11 6.97097e-11 2.22031e-09 7.10099e-09 2.38033e-08 3.95055e-07 7.69107e-07 2.57036e-06 1.61723e-05 1.01514e-05 3.39847e-05 7.01198e-05 2.92141e-05 2.92141e-05 4.89568e-05 1.4152e-05 1.4152e-05 2.1183e-05 7.9011e-07 3.06043e-07 2.35033e-07 4.62064e-08 7.171e-09 1.35019e-09 2.09014e-06 2.21031e-07 6.190865375e-09 1.21017e-10 0.0 1.74024e-10 9.69135e-09 9.54133e-08 2.34033e-07 7.9111e-06 8.33416e-05 0.000673654 0.00340505 0.0100725 0.0150525 0.0179206 0.00777837 0.00428852 0.00119587 0.000291851 3.53549e-05 4.01056e-06 2.64037e-07 1.24017e-08 3.56024e-10 7.42104e-12 7.83109e-14 3.38447e-05 3.5755e-05 2.32432e-05 1.34919e-05 4.18058e-06 1.09015e-06 1.21017e-07 1.2504180350000001e-08 6.041311066e-10 2.75038e-11 3.7205160750000005e-10 1.4802064869999997e-11 0.0 4.12058e-13 3.94055e-09 1.82025e-07 4.68065e-06 4.11507e-05 4.11507e-05 0.000337082 0.000337082 0.00189421 0.00189421 0.0146614 0.00696935 0.00760881 0.0357144 0.0301554 0.0199896 0.006287 0.00151473 0.000179045 1.25017e-05 4.71066e-07 1.8826e-08 1.64023e-10 2.4823e-12 1.65023e-14 4.59064e-07 3.20045e-06 3.97055e-06 9.27129e-06 5.07771e-05 2.65537e-05 7.96711e-05 0.000188306 0.000167293 0.00014055 5.87582e-05 2.42334e-05 4.36061e-06 6.4209e-07 4.45062e-08 2.420336874e-09 6.82095e-11 1.49021e-12 0.0 8.86124e-14 3.5805e-12 9.57134e-11 1.51021e-09 1.83026e-08 1.02014e-07 5.48076e-07 1.60022e-06 4.17058e-06 5.55077e-06 8.29116e-07 7.22101e-06 5.12071e-06 2.94041e-06 1.07015e-06 3.39047e-07 5.7508e-08 7.8815015e-09 0.0 1.84026e-13 4.44062e-12 9.26129e-11 1.14016e-09 9.2813e-09 4.07057e-08 6.08085e-09 1.71024e-07 3.82053e-07 7.58106e-07 7.83109e-07 6.78095e-07 0.0 1.4602e-11 9.22129e-09 3.96722e-08 3.96722e-08 3.96722e-08 1.18016e-06 3.15544e-06 3.15544e-06 2.78739e-05 6.10385e-05 0.000122787 0.000125037 0.000113226 6.11785e-05 2.45734e-05 6.11085e-06 1.39019e-06 1.86026e-07 2.01028e-08 1.090691076e-09 4.84068e-11 1.36119e-05 4.54063e-06 1.36019e-06 2.26032e-07 2.920884066e-08 2.12044002e-09 9.201284915999999e-11 1.53021e-13 9.39131e-12 3.27046e-10 1.06015e-08 1.76025e-07 2.20031e-06 7.146e-06 7.146e-06 8.00812e-05 0.00011054 0.00011054 0.000616496 0.000829546 0.000981117 0.000586542 0.000309173 8.39517e-05 1.72724e-05 1.87026e-06 1.63023e-07 7.241485066e-09 3.11043e-10 6.21087e-12 0.0 0.0 9.01126e-13 4.34061e-11 1.02014e-09 2.00028e-08 2.03028e-07 1.52021e-06 6.83095e-06 2.21931e-05 4.17758e-05 6.74994e-05 5.30074e-05 4.04756e-05 1.87326e-05 8.05112e-06 1.95027e-06 3.6105e-07 3.81053e-08 3.22160016e-09 1.620474034e-10 0.0 0.0 4.05057e-14 3.33046e-12 2.10029e-10 3.16044e-09 3.16044e-09 1.55022e-07 1.66023e-06 3.89054e-07 1.95327e-05 1.54922e-05 0.000103664 0.000523703 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Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.59976e-14 9.59976e-14 1.08997e-11 2.54994e-11 2.79993e-10 1.87995e-09 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Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.05e-13 6.05e-13 4.65e-11 1.26e-10 1.52e-09 1.11e-08 2.69e-07 4.47e-07 3.28e-06 1.69e-05 3.797e-05 3.797e-05 9.407e-05 9.407e-05 0.00017399 0.00017399 0.0001456 0.0001456 0.00019026 2.6755e-05 2.6755e-05 1.796e-05 1.125e-06 1.125e-06 2.23e-07 2.19e-12 2.03e-13 1.44e-14 0.0 0.0 3.835211202000001e-09 2.7e-11 0.0 0.0 1.31e-14 1.59e-12 9.45e-11 7.54e-09 1.55e-07 2.83e-06 2.014e-05 0.00012502 0.00054717 0.00187076 0.00132574 0.00132573 0.00335932 0.00174954 0.00079111 0.00015532 2.58e-05 1.83e-06 9.64942e-08 8.298320421e-09 2.25857e-11 4.92294e-13 0.0 0.0 6.53e-14 1.41e-11 1.94e-09 6.3e-08 6.3e-08 2.68e-06 2.68e-06 1.985e-05 5.65e-05 0.00072176 0.0037584 0.0151408 0.0296191 0.0446873 0.0381554 0.0245497 0.0075714 0.00170573 0.000189444 1.619e-05 7.06981e-07 2.22e-08 1.17913e-09 3.07e-12 2.71279e-14 0.0 7.19e-14 2.7e-13 2.72e-11 3.77e-10 1.42e-09 1.83e-08 6.5e-08 2.94e-06 3.018e-05 1.116e-05 0.00045363 0.00062571 0.00132962 0.00733469 0.012556939999999999 0.0186772 0.0129486 0.00727152 0.0019233 0.00039835 3.256e-05 2.52985e-06 7.12e-08 7.762317945e-09 2.05e-11 4.75628e-13 0.0 0.0 0.0 0.0 0.0 0.0 3.34e-13 1.26e-12 1.86e-10 1.64e-09 6.15e-09 1.63e-07 4.36e-07 1.64e-06 2.236e-05 2.18e-05 8.202e-05 0.00033637 0.00024971 0.00024971 0.00053145 0.00020682 0.00020682 0.00020607 5.26e-05 2.11e-06 5.3e-07 1.73e-07 5.27e-08 8.66e-09 6.25616e-06 6.67e-07 2.6199996171000002e-08 5.56e-10 0.0 7.07e-13 8.74e-11 1.06e-09 3e-09 2.41e-07 4.85e-06 6.65e-05 0.00053455 0.00281657 0.00688898 0.0128484 0.0115307 0.00886797 0.00375124 0.00123714 0.00021289 2.288e-05 1.3e-06 4.86e-08 1.01997e-09 2.4499978619000003e-11 2.35e-13 1.91e-09 7.13e-09 1.01e-08 1.32e-08 1.28e-08 1.16e-08 3.38e-09 6.14108e-10 5.51e-11 5.569995783e-12 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7.35848e-05 4.8999e-06 1.43996e-07 2.7100029411000003e-09 7.00107e-12 0.00125798 0.00025163 1.878e-05 8.17951e-07 1.44e-13 2.3005e-15 3.94806e-17 2.85077e-17 1.03929e-12 0.0 6.15e-11 3.98e-09 3.16e-08 1.54e-07 2.13e-06 2.13e-06 3.943e-05 3.943e-05 0.00061851 0.00175682 0.00175682 0.00927359 0.0228823 0.00241305 0.0243985 0.0131964 0.0131964 0.00612082 0.00612074 0.00552512 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5e-14 1.07e-12 4.42e-11 1.62e-10 7.91e-10 2.46e-08 6.57e-08 3.21e-07 3.1e-06 9.91e-06 2.844e-05 6.623e-05 7.873e-05 4.84e-05 1.998e-05 4.27e-06 9.52e-07 8.44e-08 4.2700471000000005e-09 1.13e-10 0.0277419 0.0126749 0.0037838 0.00041 1.25e-06 6.94e-08 1.62e-09 8.60003e-10 5.3199e-08 8.652739916e-10 2e-12 0.0 9.59e-14 1.24e-11 6.5e-10 6.5e-10 6.65e-08 2.41e-06 3.554e-05 0.00040125 0.00245128 0.0106111 0.0218705 0.0356606 0.033605 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 4.99e-14 4.09e-13 1.86e-12 6.84e-12 7.86e-11 1.17e-09 1.17e-09 2.14e-08 2.14e-08 1.04e-07 4.73e-07 4.88e-07 5.61e-06 4.952e-05 1.656e-05 0.00019043 0.00070322 0.00110278 0.00110278 0.0021357 0.0021357 0.00313357 0.00313357 0.00361197 0.00361197 0.00628958 0.0021184 0.0021184 0.00254829 0.000484725 0.000484725 0.00031851 7.595e-05 1.172e-05 2.38e-06 5.99999987e-07 8.41169e-08 1.84321e-09 1.55e-10 0.0 1.77e-13 9.21999e-12 3.03e-10 6.78e-09 1.12e-07 1.33e-06 1.171e-05 7.337e-05 0.00034067 0.0011037 0.00278651 0.00232441 0.00232464 0.00532319 0.00394766 0.00142161 0.0003797 6.596e-05 9.64999e-06 8.6036e-07 8.78739e-09 1.27474e-09 6.65557e-11 0.0 2.32e-11 1.24e-09 4.95e-08 1.12e-06 8.345e-06 8.345e-06 9.03549e-05 9.03549e-05 0.00014654 0.00066759 0.00449417 0.0110418 0.0167879 0.0236162 0.0243673 0.0136911 0.00599992 0.00202937 0.00047618 7.77605e-05 8.90999e-06 6.76766e-07 3.76e-08 1.1224e-09 3.71e-11 2.61646e-12 0.0 5.09e-12 3.12e-11 1.35e-09 5.57e-09 3.42e-08 1.23e-07 6.98e-07 1.311e-05 0.0001128 2.476e-05 0.000949429 0.000948249 0.00336197 0.00924793 0.014738890000000001 0.014684 0.0131936 0.00685337 0.0024254 0.00063954 0.00011218 9.05326e-06 1.05e-06 1.34916e-08 2.360136e-09 1.05769e-10 0.0 0.0 0.0 1.89e-13 8.28e-12 2.79e-10 9.99999e-10 6.14e-09 1.39e-07 1.89e-07 1.16e-06 1.076e-05 1.064e-05 6.536e-05 0.00031809 0.00013847 0.00085059 0.00237267 0.00212165 0.00212165 0.00584142 0.00398467 0.00398467 0.00593828 0.0042193 0.00227016 0.00080653 0.00035946 0.00014577 4.064e-05 5.34815e-06 4.7e-07 2.74e-08 7.69e-10 1.74e-12 3.82e-09 1.31e-07 3.44e-07 1.57e-06 1.866e-05 0.00013722 0.00072113 0.00204532 0.00437947 0.00719603 0.00846476 0.00695207 0.00396451 0.00163355 0.00051124 0.00013549 1.759e-05 1.86e-06 1.45e-07 7.27749e-09 2.51e-10 5.67e-12 3.67e-06 8.17e-06 7.19e-06 6.26e-06 4.13e-06 1.96e-06 7.47e-07 1.61253e-07 2.72177e-08 3.42000213e-09 9.37999192e-10 1.81000359e-10 7.15e-14 9.78999e-11 1.17e-07 2.29e-06 3.353e-05 0.000140945 0.000140945 0.00080891 0.00080891 0.00411544 0.00411544 0.0129206 0.0160893 0.015786 0.0344926 0.0230007 0.0146548 0.00690046 0.00170878 0.00032947 4.79e-05 4.63e-06 2.69225e-07 1.500244e-08 2.33243e-10 1.03e-11 0.0 3.12e-08 3.36e-07 2.5e-07 1.14e-06 5.82e-06 2.66e-06 1.506e-05 3.932e-05 7.243e-05 8.159e-05 7.182e-05 4.859e-05 2.409e-05 8.27e-06 2.02e-06 3.0399996699999996e-07 2.840775e-08 1.79e-09 0.0 3.35e-13 8.46e-12 1.38e-10 1.62e-09 1.68e-08 1.09e-07 4.17e-07 1.1e-06 1.89e-06 2.05e-06 1.54e-07 1.36e-06 1.22e-06 6.06e-07 2.31e-07 5.53e-08 9.73999e-09 2.2918e-09 6.38e-14 1.79e-12 3.2e-11 3.3e-10 2.04e-09 6.69e-09 2.73e-08 2.06e-09 6.92e-08 1.22e-07 1.35e-07 1.15e-07 1.28e-07 4.43e-14 4.89e-11 1.07e-08 4.62666e-08 4.62666e-08 4.62666e-08 9.17999e-07 2.175e-06 2.175e-06 1.505e-05 3.66e-05 6.036e-05 6.918e-05 5.821e-05 3.878e-05 1.662e-05 4.51e-06 9.07999e-07 1.34e-07 1.49e-08 7.94803e-10 6.27261e-11 1.9e-06 1.97e-06 7.91e-07 3.02e-07 8.759394189999999e-08 1.78164e-08 2.8700018569999998e-09 3.67e-14 2.28e-12 5.29e-11 1.27e-09 2.03e-08 2.29e-07 1.03e-06 1.03e-06 1.141e-05 2.3245e-05 2.3245e-05 0.00014496 0.00033505 0.00057408 0.00072612 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1.77e-06 9.46999e-07 7.66e-06 3.414e-05 8.352e-05 0.00015657 0.00022807 0.00024789 0.00019489 0.00011143 4.188e-05 1.016e-05 1.73e-06 1.730641e-07 1.11e-08 0.0141057 0.00765227 0.00317734 0.00130801 0.00049998 0.00012133 1.50913e-05 1.91574e-06 1.58959e-07 6.23639e-09 3.84e-10 1.11e-12 8.68999e-11 3.67e-09 4.76e-08 4.76e-08 1.68e-06 2.198e-05 0.00016897 0.00083582 0.00325208 0.00855122 0.0168227 0.0202016 0.0212573 - - - - - - - - - - - - - 500000.0 14000000.0 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.09e-14 2.09e-14 2.66e-12 7.19001e-12 8.68001e-11 6.37e-10 3.69e-08 9.42001e-08 6.91001e-07 6.57e-06 2.307e-05 2.307e-05 5.019e-05 5.019e-05 0.00011689 0.00011689 0.00013409 0.00013409 0.00025915 5.6325e-05 5.6325e-05 4.592e-05 4.625e-06 4.625e-06 1.5e-06 1.56e-11 1.24e-12 8.56001e-14 0.0 0.0 7.95562e-17 1.67e-10 0.0 0.0 0.0 3.94e-13 4.66e-11 3.6e-09 1.18e-07 2.95e-06 2.381e-05 0.0001658 0.00052284 0.00177766 0.00155722 0.00155721 0.00440053 0.00264888 0.00116949 0.00031313 4.037e-05 3.24e-06 2.51984e-07 1.677442504e-08 6.13922e-11 1.78317e-12 0.0 0.0 0.0 2.52e-12 3.63e-10 1.37e-08 1.37e-08 6.05e-07 6.05e-07 5e-06 1.422e-05 0.00026384 0.00161534 0.00810438 0.022019 0.0390244 0.0434907 0.0345319 0.0122888 0.00331626 0.000365841 3.544e-05 2.13866e-06 7.79001e-08 2.56e-09 1.47e-11 1.41782e-13 0.0 4.71e-14 1.77e-13 2.28e-11 3.77e-10 1.42e-09 1.32e-08 4.69e-08 2e-06 2.423e-05 8.96001e-06 0.0004085 0.00061775 0.00131271 0.00664215 0.01517838 0.016141 0.0123102 0.00932726 0.00278164 0.000731101 7.65401e-05 6.80958e-06 2.7e-07 2.33576589e-08 1.45e-10 2.53002e-12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.56e-14 9.70001e-12 1.41e-10 5.31e-10 2.8e-08 1.2e-07 4.5e-07 5.53e-06 6.5e-06 2.446e-05 0.00013778 0.00014526 0.00014526 0.00047749 0.000228755 0.000228755 0.00038113 0.0001588 6.42e-06 1.49e-06 4.68e-07 7.97001e-08 1.49e-08 2.4e-09 2.21e-06 1.1500016596e-07 2.6200030297e-09 0.0 5.62e-14 1.01e-11 1.93e-10 5.48e-10 4.27e-08 1.18e-06 2.24e-05 0.00025601 0.00190276 0.00594181 0.0138259 0.0129168 0.0107641 0.00576415 0.00214233 0.00030455 5.119e-05 4.07e-06 2.23e-07 8.10978e-09 1.4499993484999998e-10 1.55e-12 1.87e-08 4.65e-08 7.03001e-08 8.43001e-08 7.45001e-08 4.66e-08 6.67e-09 1.030257e-09 1.580149e-10 1.8799974230000003e-11 4.800007153600001e-13 3.5699982825e-14 0.0 0.0 5.92e-12 6.21e-10 6.17e-08 8.60001e-07 8.60001e-07 2.1795e-05 2.1795e-05 0.000205435 0.000205435 0.00242999 0.00388921 0.00388951 0.0198989 0.0264877 0.0340917 0.0168314 0.00836754 0.00170939 0.00024528 1.522e-05 2.29654e-07 1.4000792000999999e-08 1.3426138583e-09 2.6e-12 7.04789e-14 5.13e-11 9.92001e-10 2.96e-09 8.00001e-09 9.25001e-08 1.27e-07 4.5e-07 2.65e-06 2.98e-06 3.92e-06 5.85e-06 6.82e-06 2.83e-06 9.09001e-07 8.41001e-08 7.240002204e-09 2.65e-10 9.61001e-12 0.0 0.0 0.0 0.0 1.51e-13 4.17e-12 4.21e-11 2.89e-10 8.95001e-10 4.39e-09 5.7e-09 7.77001e-10 6.58e-09 2.86e-09 1.94e-09 6.52e-10 1.43e-10 2.15e-11 1.2e-12 0.0 0.0 0.0 0.0 1.22e-14 1.29e-13 4.6e-13 6.27e-14 3.06e-12 8.16001e-12 1.33e-11 1.41e-11 5.54e-12 0.0 0.0 8.13001e-12 9.80001e-11 9.80001e-11 9.80001e-11 6.12e-09 2.985e-08 2.985e-08 5.57e-07 1.56e-06 4.44e-06 6.92001e-06 6.33e-06 4.75e-06 2.38e-06 5.34e-07 9.74001e-08 1.64e-08 1.66e-09 5.879500002283e-11 1.53e-12 1.32e-08 9.53001e-09 6.65e-09 2.11e-09 7.9112e-10 1.22e-10 7.000001682e-12 0.0 0.0 2.19e-14 1.82e-12 1.05e-10 4.17e-09 1.865e-08 1.865e-08 3.64e-07 2.025e-06 2.025e-06 3.317e-05 0.00011113 0.00030802 0.00028473 0.00025212 0.00010204 4.299e-05 8.23001e-06 1.16e-06 6.760614999999999e-08 2.689998015e-09 5.82e-11 0.0 0.0 0.0 0.0 4.27e-13 1.83e-11 4.34e-10 5.04e-09 3.51e-08 2.15e-07 4.48e-07 1.21e-06 1.25e-06 9.42001e-07 3.46e-07 2.06e-07 4.37e-08 5.99e-09 3.69e-10 3.42349e-11 1.46e-12 0.0 0.0 0.0 0.0 2.77e-14 1.39e-12 1.39e-12 1.04e-10 2.06e-09 4.21e-10 1.12e-07 2.52e-07 1.85e-06 2.684e-05 0.00014301 1.95e-05 0.00054134 0.000507575 0.000507575 0.00149407 0.00145222 0.00115483 0.00042538 0.00010785 1.151e-05 1.16e-06 3.5000063554e-08 1.04e-09 1.57397e-11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.22e-14 3.5e-13 4.78e-14 1.35e-12 3.66e-12 2.9e-11 1.93e-11 5.23e-11 1.17e-10 1.71e-10 1.96e-10 4.33e-11 4.33e-11 4.35e-11 5.05e-12 0.0 4.4e-13 0.0 0.0 0.0 1.53e-14 1.91e-12 4.61e-06 1.7e-06 0.00012797 0.00046765 0.00046765 0.00341074 0.00341074 0.00748773 0.00664269 0.0278252 0.0147447 0.014722 0.0201981 0.00785056 0.0025685 0.00041864 6.45781e-05 3.14e-06 1.3177023026e-07 7.432251394e-09 1.38477e-11 0.0 0.0 0.0 0.0 0.0 1.04e-14 0.0 3.58e-13 1.27e-12 1.4e-10 2.87e-11 1.6e-09 1.6e-09 1.6e-09 9.93001e-08 2.03e-08 1.1e-06 1.555e-07 1.555e-07 1.013e-05 2.07e-06 1.63867e-05 1.63867e-05 1.63867e-05 0.00015094 3.091e-05 0.000114713 0.000114713 0.000114713 0.00044636 9.14201e-05 0.00025648 0.00025648 0.00024356 0.00024356 0.00018483 0.00018483 0.00015195 0.00015195 7.91701e-05 7.91701e-05 7.03801e-05 7.03801e-05 0.0001343 0.0001343 0.0001343 4.7085e-05 4.7085e-05 6.36e-06 4.149999206e-07 6.410169e-09 4.18458e-11 2.27e-13 0.0 0.0 4.025e-14 4.025e-14 1.23e-11 9.77001e-10 1.33e-10 8.42001e-08 2.31e-06 4.73e-07 5.506e-05 0.0003793 7.76901e-05 0.00407572 0.00868016 0.024174 0.0383483 0.0418305 0.0253967 0.010742 0.00235236 0.00040524 3.72067e-05 2.62e-06 9.18975e-08 2.29e-09 1.36074e-11 0.0 3.71e-13 3.58e-09 1.36e-07 3.64e-06 4.659e-05 0.0005011 0.00213156 0.00918512 0.0162954 0.0213933 0.00662247 0.00662247 0.00572697 0.00134143 0.00032999 3.134e-05 1.79744e-06 7.09001e-08 2.26986e-09 2.87e-11 2.23269e-13 0.0 0.0 0.0 0.0 0.0 0.0 1.93e-10 5.98e-11 1.04e-11 1.3400036953e-12 0.000692831 0.00259307 0.00759961 0.0122157 0.0109531 0.010907 0.00382878 0.002011 0.00111736 0.00034615 6.599e-05 3.38788e-06 2.76872e-07 5.21955e-09 1.7400052182e-10 3.852e-13 0.0 0.0 0.0 1.16e-14 1.16e-14 3.87e-12 3.33e-10 2.15e-08 5.8e-07 1.117e-05 0.00010291 0.00461727 0.00461727 0.0158022 0.006509 0.006509 0.0101103 0.00106194 0.00106194 0.00101832 9.30801e-05 1.87567e-05 2.02e-06 6.16123e-06 2e-07 2.126842841e-09 1.277918564e-10 1.43699e-13 0.0 0.0 2.68e-13 2.66e-11 1.53e-09 3.14e-10 4.22e-08 2.48e-08 1.74e-06 3.57e-07 3.015e-05 0.00026037 5.333e-05 0.00111814 0.00031538 0.00043012 0.00494625 0.0 0.0 6.97001e-14 1.11e-11 1.12e-09 4.49e-08 1.26e-06 1.23e-05 9.25101e-05 0.00039134 0.00114768 0.00126969 0.0017134 0.00104868 0.00050654 0.00016612 2.865e-05 3.11e-06 3.04e-07 1.01e-08 4.2802290000000003e-10 6.670003626e-12 7.06001e-09 3.58e-08 1.8e-07 4.38e-07 1.35e-06 1.86e-06 1.02e-06 3.79e-07 2.45e-07 7.33001e-08 1.1800674e-08 8.630007585000001e-10 2.87e-11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.12e-10 1.36e-09 1.16e-07 4.32e-07 9.62001e-07 2.114e-05 8.89201e-05 0.00027339 0.00045188 0.00041867 0.00027114 0.00013905 3.561e-05 6.09e-06 5.56e-07 3.98e-08 1.38e-09 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.37001e-13 6.87e-11 3.12e-09 1.7e-08 4.59e-08 1.12e-06 8.33001e-06 3.719e-05 3.85934e-05 3.85934e-05 3.85934e-05 0.0002452 0.0001225 0.0001225 0.00026806 9.51601e-05 3.693e-05 8.15001e-06 1.06e-06 1.09e-07 7.01001e-09 1.8707030002289e-10 3.76e-12 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0.00021535 7.56601e-05 0.00067406 0.000791941 0.00027825 0.00219418 0.00253687 0.000891331 0.00290379 0.00290379 0.00924179 0.00324501 0.00664956 0.00664956 0.0038365 0.0038365 0.00236698 0.00044058 3.424e-05 1.51996e-06 5.8300751001e-08 8.49001e-10 3.356e-05 1.97995e-06 7.86985e-08 6.69884442e-09 0.0 0.0 0.0 1.45e-12 7.25001e-11 7.25001e-11 1.59e-08 3.06e-07 6.26e-08 1.003e-05 0.00013882 0.0012941 0.00629805 0.0206696 0.0336112 0.0487207 0.040511 0.0281933 0.00993213 0.00264204 0.000347032 0.0 0.0 0.0 2.01e-13 1.99e-14 6.54e-12 9.21001e-11 9.11001e-12 8.66001e-10 5.65e-09 2.28e-08 3.82e-08 4.96e-08 6.18e-08 4.27e-08 1.22e-08 2.62e-09 5.33e-10 8.73001e-11 5.4377e-12 3.61e-13 8.41001e-07 1.271e-05 4.507e-05 4.507e-05 0.00055693 0.00011294 0.00047066 0.00036959 0.00065344 0.000730551 0.00066685 0.00038281 0.00010152 2.607e-05 3.66478e-06 2.56e-07 6.16684e-09 2.73012e-10 1.56371e-12 5.0196e-14 0.0 0.0 0.0 1.195e-11 1.195e-11 1.22e-09 4.04e-08 8.27001e-09 0.0 0.0 0.0 0.0 1.48e-14 1.06e-12 1.06e-12 2.33e-10 2.18e-09 8.19001e-09 2.53e-12 9.98001e-12 3.75e-11 9.25001e-10 4.22e-09 1.2e-08 2.42e-07 1.35e-06 3.85e-06 0.0018687 0.00230506 0.00656057 0.0250351 0.0117382 0.033409 0.0394022 0.0206098 0.00960128 0.00170247 0.00026906 2.115e-05 1.3299969196e-06 9.197753215000001e-08 8.34001e-10 0.0 0.0 0.0 0.0 0.0 3.53e-14 1.48e-13 1.33e-13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.3e-09 4.82e-08 1.37e-07 6.46e-06 3.395e-05 9.66201e-05 0.00032904 0.000889641 0.00174944 0.00501423 0.0239376 0.0353893 0.049908 0.0361502 0.0228153 0.00839015 0.00177872 0.000209898 1.61026e-05 5.02987e-07 1.1000022644e-08 1.43449e-11 0.00221499 0.00052467 5.336e-05 4.7033e-06 7.73001e-13 9.54674e-15 1.83512e-16 1.11418e-16 0.0 0.0 1.67e-11 1.24e-09 1.26e-08 6.17e-08 9.15001e-07 9.15001e-07 1.8985e-05 1.8985e-05 0.00030873 0.000959456 0.000959456 0.00618272 0.0182633 0.00240969 0.0243645 0.0163875 0.0163875 0.00951209 0.00951187 0.00953442 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.02e-14 2.9e-12 1.3e-10 4.38e-10 2.14e-09 6.04e-08 1.02e-07 4.96e-07 2.3e-06 6.08e-06 3.117e-05 7.54801e-05 0.00012003 9.33501e-05 3.745e-05 8.43001e-06 1.37e-06 1.35e-07 9.470152999999999e-09 2.89e-10 0.0292036 0.0150075 0.0059369 0.00128139 3.34e-06 2.88e-07 7.53001e-09 3.36118e-09 6.87e-12 8.775341586e-09 9.340009045e-11 0.0 2.14e-14 3.44e-12 1.93e-10 1.93e-10 2.17e-08 7.83001e-07 1.237e-05 0.00016645 0.0011884 0.00664028 0.0172901 0.0343777 0.0355789 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 1.04e-14 6.95e-14 3.16e-13 1.07e-12 1.23e-11 2.185e-10 2.185e-10 4.32e-09 4.32e-09 2.82e-08 1.29e-07 1.34e-07 1.54e-06 1.647e-05 7.01e-06 8.063e-05 0.00040435 0.00064889 0.00064889 0.00139176 0.00139176 0.0024756 0.0024756 0.00328853 0.00328853 0.00644788 0.00236518 0.00236518 0.00310855 0.000598525 0.000598525 0.00039939 9.274e-05 1.553e-05 2.15e-06 3.24999939e-07 7.58127e-08 2.1086e-09 1.34e-10 0.0 1.03e-13 6.46e-12 2.06e-10 4.975e-09 9.01e-08 1.14e-06 1.023e-05 6.863e-05 0.0003176 0.00087279 0.00248484 0.00232164 0.00232181 0.00476856 0.00282936 0.00157062 0.00033379 7.305e-05 8.39e-06 6.89688e-07 9.21904e-09 8.85399e-10 5.11804e-11 0.0 9.13e-12 7.23e-10 2.93e-08 7.54e-07 5.995e-06 5.995e-06 6.45e-05 6.45e-05 0.00012103 0.00055135 0.00294265 0.00853103 0.0132317 0.0189431 0.0245739 0.0151658 0.00738296 0.00276518 0.00070059 0.000103112 1.316e-05 9.06677e-07 5.4e-08 8.49986e-10 5.69e-11 2.67655e-12 0.0 3.61e-12 2.22e-11 1.09e-09 4.51e-09 2.77e-08 8.54e-08 4.84e-07 1.085e-05 0.00010555 2.317e-05 0.00083689 0.00067361 0.00238825 0.0100602 0.01245754 0.0165865 0.0127451 0.00674383 0.00227561 0.00052185 8.399e-05 8.74604e-06 7.73e-07 1.1848e-08 1.6700711e-09 7.85836e-11 0.0 0.0 0.0 1.86e-14 9.06e-13 4.05e-11 1.48e-10 9.09e-10 2.58e-08 4.61e-08 2.83e-07 3.69e-06 3.86e-06 2.371e-05 0.00013814 7.684e-05 0.00047202 0.00162658 0.00171043 0.00171043 0.00544388 0.0038093 0.0038093 0.00632752 0.00464468 0.00253703 0.00102367 0.00034801 0.00010391 4.081e-05 5.92789e-06 4.79e-07 2.21e-08 8.81e-10 6.5e-13 1.86e-09 5.37e-08 1.71e-07 7.78e-07 8.63e-06 7.258e-05 0.00050607 0.00161468 0.00396159 0.0074182 0.00951154 0.00732322 0.00505032 0.00188711 0.0006636 0.00015558 2.576e-05 3.11e-06 1.76e-07 9.48663e-09 3.85e-10 9.34e-12 2.51e-06 5.62e-06 5.55e-06 4.07e-06 2.61e-06 1.13e-06 4.49e-07 9.251199999999999e-08 1.4507060000000001e-08 1.49999818e-09 3.70999718e-10 6.629997510000001e-11 5.63e-14 4.64e-11 6.5e-08 1.86e-06 2.619e-05 0.000123965 0.000123965 0.000721695 0.000721695 0.00239355 0.00239355 0.0126712 0.0116746 0.0116757 0.0298219 0.0207534 0.0131569 0.00766995 0.00209 0.0004388 6.91e-05 7.2e-06 3.97488e-07 2.170322e-08 3.22513e-10 1.4e-11 0.0 2.29e-08 2.58e-07 2.25e-07 1.02e-06 4.4e-06 2.04e-06 1.156e-05 2.881e-05 5.805e-05 6.295e-05 5.444e-05 3.369e-05 1.633e-05 5.2e-06 1.24e-06 1.82999738e-07 1.410287e-08 9e-10 0.0 7.73e-14 2.59e-12 4.74e-11 6.26e-10 5.77e-09 4.12e-08 1.86e-07 5.7e-07 1.16e-06 1.53e-06 1.15e-07 1.22e-06 1.18e-06 6.03e-07 2.67e-07 6.48e-08 1.24e-08 2.6457999999999997e-09 0.0 2.41e-13 5.15e-12 7.11e-11 6.56e-10 2.88e-09 1.36e-08 1.02e-09 3.89e-08 8.37e-08 9.75e-08 9e-08 9.41e-08 0.0 1.33e-11 5.45e-09 2.13333e-08 2.13333e-08 2.13333e-08 6.03e-07 1.185e-06 1.185e-06 9.98e-06 2.887e-05 5.761e-05 7.047e-05 6.137e-05 3.953e-05 1.957e-05 6.43e-06 1.48e-06 2.27e-07 2.4e-08 1.3615300000000002e-09 1.100498e-10 1.16e-06 1.18e-06 5.2e-07 1.66e-07 4.55768117e-08 8.205580000000001e-09 1.339999627e-09 2.23e-14 1.53e-12 4.38e-11 8.87e-10 1.52e-08 1.71e-07 8.75e-07 8.75e-07 9.35e-06 1.9495e-05 1.9495e-05 0.00012072 0.00028518 0.00047609 0.00061841 0.00054515 0.00027576 0.00013085 3.919e-05 5.86e-06 4.79183e-07 3.66000476e-08 1.080052e-09 0.0 0.0 7.48e-13 2.26e-11 5.58e-10 5.55e-09 5.99e-08 4.26e-07 2.1e-06 6.03e-06 1.231e-05 1.817e-05 2.076e-05 1.575e-05 8.48e-06 3.15e-06 8.2e-07 1.15e-07 2.25e-08 2.13416e-09 1.76138e-10 0.0 0.0 0.0 3.71e-13 1.58e-11 2.2e-10 2.2e-10 1.1e-08 1.26e-07 1.55e-08 1.39e-06 8.09e-07 9.31e-06 5.373e-05 0.00018748 1.411e-05 0.00058066 0.00057841 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Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 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1.3699e-06 0.000194946 0.00127244 0.0033705 0.0109237 0.0257356 0.0358394 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.39998e-14 6.47994e-14 2.87998e-12 2.10998e-11 1.50999e-09 7.79993e-09 5.71995e-08 8.57993e-07 3.71997e-06 3.71997e-06 1.30099e-05 1.30099e-05 5.08996e-05 5.08996e-05 7.23294e-05 7.23294e-05 0.000185938 4.68746e-05 4.68746e-05 4.80096e-05 5.98495e-06 5.98495e-06 2.79998e-06 3.64997e-07 2.34998e-11 1.84998e-12 3.89997e-13 1.51999e-14 1.27787e-15 1.4500155998e-09 0.0 0.0 0.0 0.0 4.03996e-13 5.47995e-11 5.47995e-09 1.89998e-07 2.84998e-06 2.85098e-05 0.000164029 0.000682804 0.000738834 0.000738834 0.00306922 0.00275592 0.00207053 0.000588585 0.000167579 2.01198e-05 2.09991e-06 7.446756532000001e-08 4.044541255e-09 6.1315e-12 0.0 0.0 0.0 0.0 1.88998e-12 1.22499e-10 1.22499e-10 3.16497e-08 3.16497e-08 2.20998e-07 6.29995e-07 2.12998e-05 0.000200798 0.0023551 0.0155707 0.0266072 0.0361057 0.0301256 0.023198 0.0115105 0.00240278 0.000386187 2.5629e-05 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6.440101999e-08 2.4599769882e-09 0.0489287 0.0341132 0.0222015 0.00625857 0.00116425 5.48995e-06 2.73998e-07 1.78e-08 1.84998e-10 1.4247714229e-08 4.3399582953e-10 0.0 0.0 0.0 8.89992e-13 8.89992e-13 2.00998e-10 1.66999e-08 4.92996e-07 1.29099e-05 0.000147749 0.0012463 0.00560655 0.020377 0.0336336 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 2.58971e-14 1.17987e-13 3.91956e-13 4.50949e-12 6.89922e-11 6.89922e-11 1.64981e-09 1.64981e-09 9.07897e-09 4.13953e-08 5.10942e-08 5.87933e-07 6.1393e-06 3.32962e-06 3.82957e-05 0.000195908 0.000228664 0.000228664 0.000897678 0.000897678 0.00176985 0.00176985 0.00270491 0.00270491 0.00617606 0.00270529 0.00270529 0.00350687 0.000908967 0.000908967 0.000673774 0.000188769 3.61159e-05 6.85922e-06 9.30894384e-07 2.280314935e-07 8.63484e-09 9.409325955e-10 0.0 1.35985e-14 7.69913e-13 3.83956e-11 1.05688e-09 2.19975e-08 3.20964e-07 3.67958e-06 2.78068e-05 0.000165241 0.00062044 0.0016751 0.00167502 0.00167554 0.00562858 0.00460058 0.00259346 0.000780921 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Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.77019e-14 1.02005e-13 2.51013e-12 1.84009e-11 1.14006e-09 5.33027e-09 3.9102e-08 6.78034e-07 3.52018e-06 3.52018e-06 1.30307e-05 1.30307e-05 4.43722e-05 4.43722e-05 7.37937e-05 7.37937e-05 0.00019752 6.99935e-05 6.99935e-05 7.54338e-05 1.36957e-05 1.36957e-05 9.17046e-06 1.30006e-06 6.34032e-11 5.59028e-12 4.29021e-13 1.69008e-14 1.22335e-15 0.0 0.0 0.0 0.0 1.32007e-14 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7.39005e-18 0.0 1.10005e-13 1.42007e-11 2.11011e-10 1.03005e-09 2.58013e-08 2.58013e-08 9.00045e-07 9.00045e-07 2.51513e-05 0.000137717 0.000137717 0.00134182 0.00701425 0.00128321 0.0129743 0.0129975 0.0129975 0.0151334 0.0151316 0.0246284 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.79024e-14 2.34012e-12 1.11006e-11 5.43027e-11 1.47007e-09 3.57018e-09 1.74009e-08 3.44017e-07 2.97015e-06 2.15011e-05 5.89329e-05 0.000139387 0.000137227 6.52633e-05 2.36712e-05 5.44027e-06 6.62033e-07 5.370435008e-08 2.0401010577e-09 0.0425255 0.0356499 0.0274154 0.0095015 0.00253552 8.1104e-06 2.65013e-07 1.73041e-08 1.48007e-10 4.9733e-14 1.8800962688e-09 0.0 0.0 1.27006e-14 1.17006e-12 1.17006e-12 2.37012e-10 1.58008e-08 4.44022e-07 9.86049e-06 0.000126456 0.000969878 0.00450454 0.0169497 0.0298164 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 1.34e-14 6.93e-14 7.97e-13 1.465e-11 1.465e-11 3.905e-10 3.905e-10 3.33e-09 1.52e-08 1.94e-08 2.23e-07 2.8e-06 1.74e-06 1.996e-05 0.00011728 0.00022974 0.00022974 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Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.9001e-14 6.53007e-13 8.29009e-11 5.43006e-10 3.98005e-09 1.14001e-07 1.10001e-06 1.10001e-06 5.42006e-06 5.42006e-06 2.28003e-05 2.28003e-05 5.03406e-05 5.03406e-05 0.000182742 8.5161e-05 8.5161e-05 0.000124381 2.62303e-05 2.62303e-05 1.93602e-05 3.37004e-06 2.38003e-10 1.77002e-11 1.43002e-12 9.48011e-14 3.87447e-15 0.0 0.0 0.0 0.0 0.0 8.2901e-14 1.34002e-11 7.10008e-10 3.81004e-08 1.10001e-06 1.41702e-05 8.6851e-05 0.000483505 0.000620807 0.000620807 0.00269701 0.00320674 0.00238959 0.000963311 0.000284013 5.48406e-05 5.90483e-06 1.1498184931999999e-07 1.4845763385e-08 3.05042e-11 0.0 0.0 0.0 0.0 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Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 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As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 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Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.23e-09 7.81e-09 5.23e-08 1.02e-06 2.34e-06 2.34e-06 1.167e-05 1.167e-05 3.383e-05 3.383e-05 4.769e-05 4.769e-05 0.00010366 2.533e-05 2.533e-05 2.392e-05 2.565e-06 2.565e-06 8.32e-07 3.3e-11 3.79e-12 6.21e-13 8.45e-14 0.0 6.94485e-16 7.77e-10 0.0 0.0 0.0 0.0 5.49e-13 3.31e-11 2.37e-09 7.42e-08 1.6e-06 1.817e-05 6.705e-05 0.00055369 0.00053081 0.00053081 0.00208586 0.00145284 0.00131612 0.00033129 9.225e-05 1.011e-05 7.88975e-07 3.592873099e-08 2.4270864017e-09 5.0364e-12 0.0 0.0 0.0 3.86e-14 9.9e-12 5.45e-10 5.45e-10 3.78e-08 3.78e-08 6.08e-07 1.49e-06 4.012e-05 0.00037236 0.00252227 0.00826079 0.0194607 0.028409 0.0317843 0.0148075 0.00587759 0.00127832 0.00016263 1.50615e-05 5.05e-07 2.11934e-08 2.3800001533e-10 1.04824e-12 0.0 0.0 0.0 1.99e-13 6.01e-12 2.01e-11 2.82e-10 8.46e-10 1.01e-07 1.52e-06 6.53e-07 4.315e-05 0.00010039 0.00017848 0.00384693 0.00483809 0.0120211 0.0125855 0.0107576 0.0045702 0.00135711 0.0002113 2.45094e-05 1.32e-06 5.349128384e-08 9.51e-10 6.13219e-12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.8e-14 1.87e-12 6.27e-12 8.39e-10 2.85e-09 9.55e-09 2.99e-07 5.66e-07 1.89e-06 1.528e-05 2.115e-05 2.115e-05 8.218e-05 4.8535e-05 4.8535e-05 8.679e-05 3.782e-05 3.13e-06 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1.04e-08 3.78e-07 2.54e-08 2.54e-08 2.54e-08 4.19e-07 1.23e-06 1.045e-06 1.045e-06 3.716e-05 9.573e-05 0.000163 0.00148869 0.00254247 0.00432905 0.0201325 0.0149893 0.0108544 0.0254247 0.00427147 0.00427147 0.00233012 0.00026989 2.082e-05 9.240609e-07 3.180000103e-08 0.0 0.0 0.0 0.0 0.0 3.94e-13 4.38e-12 2.93e-11 3.24e-09 8.34e-08 1.25e-08 1.48e-06 3.92e-06 2.622e-05 0.00022944 0.00084955 0.00019928 0.00258639 0.00547079 0.00543984 0.00383144 0.00175213 0.00037854 6.39141e-05 4.31757e-06 2.38995e-07 2.0581333707e-08 1.1e-10 0.0 0.0 0.0 0.0 0.0 0.0 4.64e-14 4.7e-12 2.38e-10 8.54e-09 1.48e-07 1.14e-06 6.53e-06 2.58e-05 9.038e-05 7.462e-05 7.522e-05 6.32e-06 2.03e-06 6.08e-07 1.55e-07 1.31e-08 9.38689e-10 3.7910300024232e-11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.745e-13 1.745e-13 3.66e-11 3.66e-10 1.23e-09 4.4e-08 1.91e-07 4.67e-07 6.31e-06 1.856e-05 7.58e-06 8.069e-05 8.668e-05 3.541e-05 0.00045057 0.00096355 0.00039356 0.0022366 0.0022366 0.00555127 0.00226296 0.00513936 0.00513936 0.00413845 0.00413845 0.00424046 0.00097619 0.00011594 6.75986e-06 2.6000743e-07 4.0300049235e-09 0.0001153 1.12037e-05 4.8699e-07 3.9551733467e-08 2.01e-10 1.31439e-12 0.0 0.0 6.25e-13 6.25e-13 1.7e-10 9.78e-09 2.29e-09 5.22e-07 1.124e-05 0.00015986 0.00102712 0.00551357 0.0142523 0.0303959 0.0350436 0.0288665 0.0127525 0.0048157 0.00100391 0.0 0.0 0.0 0.0 0.0 3.35e-13 8.2e-12 9.11e-13 3.01e-11 2.11e-10 2.04e-09 9.82e-09 2.44e-08 3.2e-08 3.99e-08 1.38e-08 6.22e-09 8.64e-10 1.94e-10 7.9114e-12 1.03e-12 1.46e-07 4.6e-06 2.307e-05 2.307e-05 0.00051649 0.00172761 0.00489219 0.012531 0.016749 0.0141551 0.00939825 0.00375866 0.00073526 8.941e-05 1.11706e-05 8.05e-07 7.3768e-08 8.950371e-10 3.606913541e-11 5.50001e-14 0.0 0.0 0.0 3.33e-13 3.33e-13 6.56e-11 4.25e-09 9.97e-10 0.0 0.0 0.0 0.0 0.0 9.75e-15 9.75e-15 3.05e-12 4.22e-11 1.41e-10 4.53e-14 1.47e-13 4.91e-13 2.07e-11 2.33e-10 5.71e-10 2.19e-08 1.12e-07 2.75e-07 0.00038905 0.00082964 0.00203639 0.0151594 0.00990784 0.0242572 0.0450616 0.030997 0.0155913 0.00341944 0.00068281 6.722e-05 4.790000939e-06 2.0311128014e-07 3.9700036591000005e-09 0.0 0.0 0.0 0.0 0.0 0.0 1.09e-14 2.22e-14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.68e-11 2.36e-09 5.77e-09 7.1e-07 5.99e-06 1.468e-05 9.385e-05 0.00021899 0.00224053 0.00548022 0.0107779 0.0221441 0.038841 0.0370456 0.0279421 0.0113323 0.00316033 0.000522616 6.10857e-05 2.53995e-06 8.140001533e-08 1.3950593979e-08 0.0075229 0.0030077 0.00035425 4.71005e-05 1.86e-06 4.17535e-08 4.309195648e-09 0.0 0.0 0.0 2.5e-13 3.1e-11 6.01e-10 2.56e-09 6.3e-08 6.3e-08 1.84e-06 1.84e-06 5.443e-05 0.00029801 0.00029801 0.00306045 0.0132872 0.00243738 0.0219364 0.01934 0.01934 0.0163782 0.0163775 0.0261666 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.64e-13 4.87e-12 2.08e-11 6.39e-10 1.39e-09 5.94e-09 5.83e-08 3.3e-07 1.66e-06 2.63e-06 8.84e-06 8.76e-06 9.36e-06 3.62e-06 6.64e-07 1.6e-07 1.1100386e-08 4.3200010345999997e-10 0.035816 0.0319338 0.0147489 0.00501726 0.00085146 7.42783e-05 7.66569e-06 6.90203e-13 1.44e-13 2.42273e-15 0.0 0.0 0.0 2.56e-14 2.37e-12 2.37e-12 4.25e-10 2.97e-08 9.32e-07 2.154e-05 0.00023803 0.00178588 0.00675353 0.0203619 0.0351406 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 2.58e-14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.72e-11 7.13e-10 6.14e-08 1.36e-07 9.939999e-07 1.002e-05 2.004e-05 2.004e-05 5.988e-05 5.988e-05 0.00010522 0.00010522 0.00016444 0.00016444 0.00027022 6.8625e-05 6.8625e-05 4.382e-05 5.18e-06 5.18e-06 1.71e-06 5.01e-11 7.98e-12 7.44e-13 6.74e-14 0.0 4.11994e-16 1.8800282e-09 0.0 0.0 0.0 1.34e-13 7.99e-12 3.43e-10 2.2e-08 3.53e-07 5.03e-06 4.762e-05 0.00025953 0.00110562 0.00110365 0.00110377 0.00259777 0.00227658 0.00114453 0.00036179 8.144e-05 9.81e-06 6.87932e-07 3.534220384e-08 2.30343e-10 9.45463e-12 0.0 0.0 0.0 2.11e-12 3.33e-10 1.08e-08 1.08e-08 3.95e-07 3.95e-07 4.01e-06 1.141e-05 0.00016767 0.00106778 0.00480765 0.0131799 0.0244643 0.0289477 0.0225008 0.0108748 0.00336063 0.000659454 7.369e-05 5.94481e-06 2.49e-07 2.01472e-08 1.18e-10 2.23045e-12 0.0 0.0 1.92e-14 3.26e-12 5.91e-11 2.22e-10 2.93e-09 1.04e-08 5.49e-07 7.12e-06 2.63e-06 9.937e-05 0.00022606 0.00048038 0.00269963 0.0063890100000000005 0.0112024 0.0111744 0.0070157 0.00399455 0.00114979 0.00020471 2.34245e-05 1.43e-06 6.775794692000001e-08 1.2800138e-09 1.33097e-11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.2e-14 1.21e-11 2.18e-10 8.2e-10 3.31e-08 8.43e-08 3.17e-07 3.82e-06 4.27e-06 1.606e-05 9.381e-05 0.00010944 0.00010944 0.00032106 0.00014865 0.00014865 0.00020872 0.0001046 5.82e-06 2.98e-06 8.99e-07 3.11e-07 1.48e-07 1.23e-08 6.2e-06 3.789999844e-07 1.3600046469e-08 0.0 7.22e-14 9.1e-12 1.63e-10 4.64e-10 3e-08 8.19e-07 1.309e-05 0.00013494 0.00086098 0.00321134 0.00747019 0.011026 0.0106795 0.00715056 0.002846 0.0008175 0.00012456 1.691e-05 9.13e-07 4.79979e-08 1.6400038389e-09 1.65e-11 1.1e-09 2.25e-09 8.21e-09 1.53e-10 1.9e-08 1.15e-08 4.46e-09 1.1504050001496e-09 1.430162e-10 1.2799974539999999e-11 5.449998916e-14 0.0 0.0 0.0 4.61e-12 6.21e-10 5.93e-08 1.25e-06 1.25e-06 2.441e-05 2.441e-05 0.00029268 0.00029268 0.00288653 0.00404682 0.00466452 0.0205819 0.0313498 0.0326904 0.0198198 0.0101892 0.00285017 0.00047262 4.78e-05 5.76038e-07 9.770664e-08 5.193347122e-09 3e-11 9.02914e-13 2.32e-12 3.44e-11 2.31e-10 6.25e-10 1.04e-10 1.86e-08 6.59e-08 3.41e-07 9.31e-07 1.81e-06 1.82e-06 1.41e-06 1.7e-06 5.22e-07 1.43e-07 2.560004909e-08 2.530124e-09 1.33e-10 0.0 0.0 0.0 1.51e-14 4.72e-13 7.28e-12 5.85e-11 3.83e-10 1.81e-09 5.7e-09 8.3e-09 1.13e-09 1.8e-08 5.56e-09 2.23e-09 7.99e-10 1.96e-09 3.2e-10 4.7566e-10 0.0 0.0 0.0 0.0 2.21e-14 3.74e-13 8.57e-13 1.17e-13 3.08e-12 8.11e-12 1.37e-10 1.47e-10 1.4e-09 0.0 0.0 2.44e-11 2.33333e-10 2.33333e-10 2.33333e-10 1.69e-08 8.35e-08 8.35e-08 1.57e-06 8.42e-06 1.363e-05 1.837e-05 2.173e-05 1.361e-05 4.83e-06 1.52e-06 3.71e-07 6.37e-08 5.88e-09 6.229550003513e-10 1.03e-11 5.56e-10 4.47e-10 6.21e-10 4.21e-12 1.8309759999999998e-10 3.75e-11 4.8199982339999996e-12 0.0 0.0 0.0 0.0 1.33e-11 4.23e-10 4.26e-09 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Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 4.84136e-14 3.62102e-13 1.65046e-12 1.27036e-12 1.47041e-11 1.34538e-10 1.34538e-10 3.24091e-09 3.24091e-09 2.05058e-08 9.34263e-08 1.16033e-07 1.33037e-06 1.21934e-05 6.36179e-06 7.31406e-05 0.000365943 0.000607021 0.000607021 0.00147853 0.00147853 0.00265109 0.00265109 0.00352728 0.00352728 0.00712868 0.00270138 0.00270138 0.00306556 0.000636074 0.000636074 0.000366653 7.75418e-05 1.04729e-05 1.29036e-06 2.290642381e-07 5.5820161249999996e-08 1.09371e-09 1.26035e-10 0.0 2.32065e-14 1.48042e-12 6.73189e-11 2.0375729999999997e-09 3.96111e-08 5.94167e-07 5.86165e-06 3.84708e-05 0.000182351 0.000560327 0.00127874 0.00105226 0.0010524 0.00227732 0.00163451 0.000953108 0.000305006 7.89022e-05 1.31537e-05 1.60013e-06 1.3625390899999998e-07 1.00643e-09 1.01568e-10 0.0 7.86221e-14 6.89194e-12 4.34122e-10 1.87053e-08 2.03057e-07 2.03057e-07 3.10587e-06 3.10587e-06 1.24735e-05 5.6836e-05 0.000449106 0.00205294 0.00682369 0.0155201 0.0232134 0.0211958 0.0147959 0.00710988 0.00225659 0.000498269 5.44053e-05 4.15316e-06 2.08058e-07 1.87358e-08 1.4104e-10 3.6151e-12 0.0 3.2109e-13 1.97055e-12 1.20034e-10 6.12172e-10 3.76106e-09 1.48042e-08 8.41236e-08 1.66047e-06 1.54343e-05 3.39095e-06 0.000138959 0.000136838 0.000485156 0.00222395 0.00442009 0.0070839 0.00769934 0.0064665 0.00342141 0.00101877 0.000291782 4.22202e-05 3.52099e-06 2.3990337879999996e-07 5.341664046000001e-09 8.80787e-11 0.0 0.0 0.0 0.0 3.2009e-13 1.54043e-11 7.4721e-11 4.59129e-10 1.15032e-08 2.73077e-08 1.68047e-07 2.30065e-06 2.69076e-06 1.65246e-05 0.000114852 6.87993e-05 0.000422649 0.00148154 0.00169474 0.00169474 0.00573566 0.00372062 0.00372062 0.00718192 0.00506819 0.00264432 0.000953928 0.000306306 0.000113532 4.69432e-05 8.27123e-06 6.95195e-07 3.651022714e-08 1.19033e-09 0.0 5.42152e-12 2.70076e-10 1.76049e-09 8.00225e-09 2.47069e-07 4.10115e-06 4.04014e-05 0.000240468 0.00103732 0.00309965 0.00603717 0.00872524 0.00714357 0.00418151 0.00170923 0.000460639 8.24632e-05 1.0603e-05 9.07255e-07 4.61069e-08 1.650468213e-09 3.26092e-11 2.88081e-05 3.99212e-05 3.71905e-05 2.57572e-05 1.19133e-05 4.12116e-06 9.30261e-07 1.531440283573e-07 1.690880112e-08 1.200337056e-09 2.5907325280000002e-09 2.010565823e-10 0.0 8.12228e-13 2.91082e-09 8.5324e-08 1.81051e-06 1.36638e-05 1.36638e-05 0.000104809 0.000104809 0.000576062 0.000576062 0.00472398 0.00592432 0.00592535 0.0211202 0.0283178 0.0266822 0.0171266 0.00651167 0.00184917 0.000341056 4.01313e-05 9.609849e-07 1.100461042e-07 8.01156221e-09 4.42124e-11 2.47745e-12 1.20034e-07 1.0503e-06 9.77275e-07 4.45125e-06 2.1186e-05 8.25232e-06 4.67631e-05 0.000109441 0.000147942 0.000153343 0.000113292 5.65459e-05 2.05458e-05 5.48154e-06 8.87249e-07 1.020291497e-07 7.262762203e-09 3.30093e-10 0.0 0.0 1.0703e-13 5.22147e-12 1.80051e-10 3.24091e-09 3.42096e-08 2.87081e-07 1.49042e-06 5.73161e-06 1.34438e-05 1.01028e-06 2.71576e-05 3.39796e-05 3.27592e-05 2.22963e-05 1.09831e-05 3.79107e-06 9.98606941775822e-07 0.0 1.81051e-14 7.21203e-13 2.09059e-11 3.84108e-10 5.05142e-09 3.9011e-08 2.93082e-09 2.54071e-07 1.03029e-06 2.90082e-06 5.62158e-06 8.28233e-06 0.0 7.09199e-14 2.33066e-10 2.07392e-09 2.07392e-09 2.07392e-09 1.03029e-07 5.55156e-07 5.55156e-07 7.17202e-06 3.30193e-05 8.64243e-05 0.000196125 0.000310017 0.000296363 0.000192134 9.83877e-05 3.49798e-05 9.53268e-06 1.70048e-06 2.2338208918789998e-07 1.8714972720000002e-08 1.4204e-05 8.12228e-06 3.18089e-06 8.76246e-07 1.56194042e-07 2.041274198e-08 1.690473503e-09 1.69048e-14 1.20034e-12 4.36123e-11 1.22034e-09 2.17061e-08 2.81079e-07 1.16533e-06 1.16533e-06 1.4154e-05 2.96033e-05 2.96033e-05 0.000165667 0.000359081 0.00056803 0.000602319 0.000479445 0.000251171 9.21759e-05 2.34366e-05 3.53099e-06 3.4719732789999996e-07 2.6207375709999998e-08 1.0403236094e-09 0.0 0.0 0.0 4.6313e-13 2.27064e-11 7.23203e-10 1.35038e-08 1.7805e-07 1.30037e-06 7.80219e-06 3.23491e-05 7.65015e-05 0.000116863 0.00014269 0.000119214 7.8142e-05 3.46998e-05 1.16333e-05 2.56072e-06 4.21258531059e-07 4.6258612808109996e-08 0.0 0.0 0.0 1.75049e-13 8.69244e-12 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0.028254 0.0297746 0.0222227 0.0124745 0.00483644 0.0012692 0.0001859238 1.97475e-05 1.30019e-06 5.311492249e-08 1.89823e-10 0.00555327 0.00135842 0.000189153 1.77515e-05 1.03029e-06 6.92016e-08 5.60742e-10 9.96643e-11 8.4508e-12 0.0 2.1206e-11 1.14032e-09 4.85136e-09 3.9211e-08 5.55156e-07 5.55156e-07 7.57213e-06 7.57213e-06 0.000193864 0.000689744 0.000689744 0.00572601 0.0162714 0.00167039 0.0261695 0.0161008 0.0161008 0.0129214 0.012921 0.0136318 0.0 0.0 7.22203e-14 2.06058e-12 1.55044e-13 4.59129e-11 6.29177e-10 6.24175e-09 1.73049e-10 4.2712e-09 5.72161e-08 6.4018e-08 5.18146e-07 3.83108e-06 2.05058e-06 1.66147e-05 6.00969e-05 0.000145661 0.000251361 0.000297844 0.000270686 0.000182491 7.88122e-05 2.4737e-05 4.94139e-06 6.54184e-07 5.85264028e-08 2.960831461e-09 0.0292526 0.0187775 0.00748789 0.00203626 0.000350649 3.58809e-05 4.24063e-06 7.53715e-07 6.39095e-08 7.6648e-10 5.52155e-11 0.0 3.26092e-14 3.66103e-12 1.37039e-10 1.37039e-10 1.09031e-08 4.06114e-07 8.46238e-06 0.000100308 0.000807307 0.00367572 0.010916 0.0219352 0.0282636 - - - - - - - - - - - - 500000.0 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.87e-10 3.13e-09 2.29e-08 1.04e-06 6.2e-06 6.2e-06 3.083e-05 3.083e-05 9.864e-05 9.864e-05 0.00017574 0.00017574 0.00037722 0.00013128 0.00013128 0.00011357 1.4605e-05 1.4605e-05 4.83e-06 4.4e-07 8.68e-12 9.46e-13 7.18e-14 0.0 4.95732e-16 0.0 0.0 0.0 0.0 0.0 5.76e-13 1e-10 4.27e-09 1.68e-07 3.09e-06 3.069e-05 0.0001812 0.00071897 0.00075911 0.00075911 0.00239397 0.00243084 0.00112505 0.00039761 6.864e-05 7.58e-06 4.54991e-07 9.382219819e-09 9.689098991e-10 9.70998e-13 0.0 0.0 0.0 0.0 4.6e-13 3.94e-11 3.94e-11 3.805e-09 3.805e-09 9.52e-08 2.71e-07 9.32e-06 0.00013312 0.0011105 0.00468407 0.016797 0.0242343 0.0273059 0.0165933 0.0071335 0.00182721 0.00026673 3.14908e-05 1.16e-06 3.69445e-08 9.5100011536e-10 3.39396e-12 0.0 0.0 0.0 1.27e-13 3.39e-12 1.28e-11 2.73e-10 9.67e-10 6.34e-08 1.21e-06 4.48e-07 2.897e-05 9.988e-05 0.00021224 0.00144865 0.0048855700000000005 0.00892584 0.0107015 0.00866721 0.00405893 0.00114909 0.00019656 2.08964e-05 1.35e-06 3.2140826924e-08 9.88e-10 3.80096e-12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.19e-13 1.2e-12 3.59e-10 4.38e-09 1.65e-08 4.5e-07 1.24e-06 4.65e-06 3.843e-05 6.79e-05 6.79e-05 0.00029669 0.00020267 0.00020267 0.0003485 0.00020169 7.12e-05 3.06e-06 1.08e-06 2.9e-07 8.18e-08 1.48e-08 1.88e-09 5.2499960855e-07 1.6900008675e-08 0.0 0.0 0.0 2.17e-13 6.17e-13 1.12e-10 7.32e-09 4.04e-07 8.14e-06 0.00010711 0.0007206 0.00299296 0.00729902 0.00976122 0.0115642 0.00473448 0.00244877 0.00061023 9.063e-05 7.88e-06 4.62864e-07 1.46999945276e-08 2.8200047545e-10 7.83e-10 3.54e-09 1.07e-08 1.62e-08 1.18e-08 6.61e-09 3.04e-09 4.44048700027498e-10 4.95e-11 6.390004640000001e-12 1.17999758793e-14 0.0 0.0 0.0 0.0 2.34e-12 3.9e-10 2.15e-08 2.15e-08 8.05e-07 8.05e-07 1.848e-05 1.848e-05 0.00043095 0.00136716 0.00136716 0.010164 0.024161 0.0311363 0.0354731 0.0168313 0.00629775 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Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 6.4e-14 1.73e-13 2.8e-12 2.06e-11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.74e-14 2.54e-07 7.61e-07 5.58e-06 4.853e-05 0.00010266 0.00010266 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Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As75_m1 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd123_m1 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co65 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu65 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu70_m2 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe65 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge81_m1 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In111_m1 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb91_m1 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni65 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Rh99_m1 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb129_m1 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y87_m1 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr89_m1 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 3.44996e-14 8.0599e-14 1.25998e-12 8.4399e-12 2.07997e-10 2.07997e-10 0.0 0.0 0.0 0.0 0.0 3.26996e-14 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As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd123_m1 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co65 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu65 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu70_m2 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe65 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge81_m1 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In111_m1 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb91_m1 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni65 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Rh99_m1 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb129_m1 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y87_m1 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr89_m1 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 6.12957e-14 1.65988e-13 2.47983e-12 1.81988e-11 3.77474e-10 3.77474e-10 0.0 0.0 0.0 0.0 0.0 3.82973e-14 7.54948e-13 2.15985e-12 1.57989e-11 3.399752e-06 1.611178e-05 1.611178e-05 4.708484e-05 4.708484e-05 0.0001484157 0.0001484157 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1.17492e-10 1.389908e-08 5.439664e-07 1.147093e-05 0.0001467252 0.0009416287 0.005260177 0.0146495 0.03112226 0.04212997 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As75_m1 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd123_m1 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co65 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu65 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu70_m2 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe65 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge81_m1 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In111_m1 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb91_m1 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni65 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Rh99_m1 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb129_m1 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y87_m1 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr89_m1 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 6.12916e-14 1.65977e-13 2.47967e-12 1.81975e-11 3.77448e-10 3.77448e-10 0.0 0.0 0.0 0.0 0.0 3.82947e-14 7.54897e-13 2.1597e-12 1.57978e-11 3.406247e-06 1.628019e-05 1.628019e-05 4.883636e-05 4.883636e-05 0.0001731762 0.0001731762 0.0002028033 0.0002028033 0.000324297 5.088199e-05 5.088196e-05 3.713808e-05 3.192775e-06 3.192775e-06 7.35325e-07 9.40871e-12 9.28874e-13 6.15916e-14 0.0 0.0 7.3744e-17 2.26969e-10 0.0 0.0 1.4498e-14 9.13875e-13 5.59924e-11 1.869842e-09 0.0 4.842296e-08 9.487944e-07 7.795322e-06 8.505804e-05 0.0002748502 0.0008820342 0.0006789114 0.0006789114 0.001531861 0.0009467602 0.0004264799 0.0001047508 2.271981e-05 1.929674e-06 1.279725e-07 1.209864778e-08 5.22758e-11 2.35487e-12 0.0 0.0 5.33928e-14 1.49979e-11 2.09971e-09 6.349174e-08 6.349174e-08 2.365104e-06 2.365104e-06 1.432679e-05 4.082567e-05 0.0004937723 0.002686453 0.01174068 0.02099503 0.03095683 0.02745292 0.01880166 0.006408629 0.00214438 0.000339298 4.020852e-05 2.40925e-06 1.10984e-07 6.485476e-09 4.10944e-11 4.8573e-13 0.0 3.86947e-14 1.4498e-13 1.26983e-11 2.56965e-10 9.67869e-10 7.449016e-09 2.639676e-08 8.341813e-07 8.255957e-06 3.043283e-06 0.0001020483 0.0001676009 0.0003678233 0.002240207 0.004022651 0.006876617 0.005456449 0.003778979 0.001153311 0.0003231886 4.776053e-05 4.907508e-06 2.309647e-07 2.947949959e-08 2.26969e-10 5.79786e-12 0.0 0.0 0.0 1.40981e-14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.479146e-09 5.129484e-08 1.930001e-07 3.449225e-06 7.238827e-06 2.78527e-05 0.0001782183 0.0001641953 0.0001641953 0.0005320446 0.0002389901 0.0002389901 0.0003400472 8.99065e-05 0.0 4.264171e-06 1.250058e-06 3.440203e-07 9.308699e-08 1.569782e-08 1.879747e-09 1.989397e-06 1.1598221271e-07 2.989594e-09 0.0 6.79907e-13 6.68908e-11 8.45885e-10 2.409674e-09 5.539249e-08 2.46022e-06 3.683472e-05 0.0002934587 0.001782098 0.006047432 0.01146037 0.01333709 0.01277571 0.007014497 0.00276586 0.0006150703 0.0001099173 9.589198e-06 5.789539e-07 2.479552e-08 7.85892694e-10 1.22983e-11 0.0 7.610217e-09 2.213427e-08 2.7655e-08 4.363755e-08 3.11376e-08 1.592185e-08 4.884434e-09 5.96114979e-10 7.14905e-11 3.9594648319999995e-12 8.918879683e-14 0.0 0.0 0.0 2.13971e-11 2.599642e-09 2.16974e-07 3.315255e-06 3.315255e-06 6.282938e-05 6.282938e-05 0.0006314397 0.0006314397 0.01010148 0.006787517 0.006199492 0.02496778 0.02699139 0.02454658 0.01133458 0.004234008 0.001077225 0.0001733899 1.326903e-05 3.269128e-07 2.31982798e-08 1.14531e-10 5.57924e-12 3.08269e-13 0.0 2.05593e-11 4.88209e-10 1.410632e-09 3.911811e-09 6.996436e-08 7.238315e-08 3.030971e-07 0.0 1.700493e-06 3.700029e-06 3.129391e-06 2.743717e-06 1.377592e-06 6.380881e-07 2.062376e-07 3.069836e-08 4.9393612710000004e-09 2.66963e-10 1.08985e-11 0.0 0.0 0.0 3.56952e-13 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As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd123_m1 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co65 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu65 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu70_m2 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe65 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge81_m1 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In111_m1 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb91_m1 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni65 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Rh99_m1 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb129_m1 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y87_m1 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr89_m1 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 1.58372e-14 5.52298e-12 6.35494e-11 4.00942e-10 1.824287e-09 3.508244e-09 4.029447e-08 1.498489e-07 1.498489e-07 6.264234e-07 6.264234e-07 1.383035e-06 6.300321e-06 3.317208e-06 3.809164e-05 0.000166379 4.137204e-05 0.0004618671 0.001239084 0.001400519 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0.003852285 0.0009249503 0.0001831545 2.130975e-05 1.764302e-06 2.788074e-08 2.084199e-09 3.38534e-10 0.0 9.25175e-13 2.997044e-09 0.0 1.303033e-07 2.134945e-07 1.733592e-06 1.03221e-05 1.03221e-05 7.492444e-05 7.492444e-05 0.0008849431 0.001497472 0.001497472 0.007310491 0.01795518 0.001447014 0.0229033 0.01318106 0.01314681 0.01033865 0.009167422 0.01238349 4.94162e-14 6.27475e-13 5.3626e-12 2.8868e-11 2.17511e-12 2.11497e-10 1.082543e-09 2.666182e-09 3.01709e-11 4.33018e-10 6.394891e-09 5.913912e-09 4.78131e-08 3.547323e-07 1.944512e-07 1.573314e-06 6.584751e-06 1.752204e-05 3.202902e-05 5.059566e-05 5.540757e-05 4.15231e-05 2.837401e-05 1.723197e-05 6.779927e-06 1.904548e-06 2.708438228443e-07 2.7364154899999997e-08 0.02401954 0.0145997 0.007339035 0.001968911 0.0003544811 3.86982e-05 3.12607e-06 4.683738e-07 1.693984e-08 3.009885e-09 9.77297e-11 3.76886e-13 4.32015e-11 1.653887e-09 0.0 2.190141e-08 2.190141e-08 8.048076e-07 1.209128e-05 0.000102 0.0005952179 0.002394204 0.005753979 0.01413874 0.02290324 0.03139249 - - - - - - - - - - - - - 0.0253 500000.0 14000000.0 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 8.07e-14 5.4e-13 2.55e-11 2.55e-11 0.0 0.0 0.0 0.0 0.0 0.0 1.57e-13 4.72e-13 3.16e-12 5.13e-07 3.29e-06 3.29e-06 1.413e-05 1.413e-05 4.854e-05 4.854e-05 9.163e-05 9.163e-05 0.00023551 4.928e-05 4.928e-05 3.984e-05 4.235e-06 4.235e-06 1.36e-06 1.64e-11 1.42e-12 1.59e-13 2.15e-14 0.0 2.41167e-16 8.090123e-10 0.0 0.0 0.0 3.78e-14 2.3989999999999998e-12 1.91e-10 5.46e-09 1.92e-07 3.1e-06 1.26e-05 7.444e-05 0.00046268 0.00031477 0.00031477 0.00124372 0.00104862 0.00056053 0.00018059 3.852e-05 5.04e-06 3.33979e-07 1.597069593e-08 8.55857e-11 3.63088e-12 0.0 0.0 0.0 4.54e-13 1.07e-10 5.3e-09 5.3e-09 2.825e-07 2.825e-07 3.44e-06 8.43e-06 0.00015348 0.00105862 0.00529778 0.0144411 0.0262526 0.0304572 0.0264623 0.0130281 0.0050774 0.000989617 0.00015217 1.2352e-05 7.57e-07 3.23249e-08 5.159998926e-10 3.5729e-12 0.0 0.0 0.0 1.42e-12 1.34e-11 4.49e-11 8.27e-10 2.48e-09 1.85e-07 1.46e-06 6.27e-07 3.533e-05 8.135e-05 0.00014463 0.0009835199 0.0024791 0.0046163 0.0054179 0.00373004 0.00172851 0.00058695 9.982e-05 1.20226e-05 6.43e-07 4.3802304680000004e-08 5.95e-10 8.06868e-12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.1e-10 3.71e-09 1.24e-08 3.44e-07 8.93e-07 2.99e-06 3.133e-05 5.767e-05 5.767e-05 0.00018887 0.000105845 0.000105845 0.00018369 9.051e-05 4.22e-06 9.97e-07 4.65e-07 1.88e-07 4.46e-08 7.2e-09 5.34e-06 3.779998927e-07 1.3599987092999999e-08 0.0 2.13e-14 3.6e-12 8.61e-11 2.11e-10 1.61e-08 4.51e-07 7.89e-06 8.124e-05 0.00060862 0.00228479 0.0069001 0.0106286 0.0125326 0.00900059 0.00502201 0.00167115 0.00037452 4.34e-05 4.13e-06 2.43993e-07 6.989997557e-09 1.3099977499e-10 8.15e-10 1.65e-09 2.95e-09 4.74e-09 4.63e-09 2.03e-09 9.28e-10 2.18060000029e-10 2.18e-11 2.7300048029999997e-12 2.5100029855e-14 0.0 0.0 0.0 9.08e-13 1.58e-10 1.86e-08 4.495e-07 4.495e-07 1.2815e-05 1.2815e-05 0.00014986 0.00014986 0.00208135 0.00341604 0.00341636 0.0178912 0.0264298 0.031019 0.0180297 0.00930267 0.0024484 0.0004964 5.3e-05 1.26444e-05 1.510159e-07 8.023073721e-09 6.54e-11 9.31523e-13 1.84e-12 2.91e-11 1.14e-10 2.66e-10 4.28e-09 7.32e-09 2.2e-08 9.33e-08 3.12e-07 6.13e-07 5.52e-07 6.66e-07 3.02e-07 1.73e-07 4.5e-08 2.899995688e-09 2.5901049999999997e-10 2.27e-11 0.0 0.0 0.0 0.0 3.82e-13 1.27e-11 1.74e-10 5.9e-10 5.59e-09 1.81e-08 3.12e-08 4.66e-09 8.96e-08 7.91e-08 5.61e-08 2.53e-08 6.41e-09 9.16e-10 1.3626e-10 0.0 0.0 0.0 0.0 2.37e-14 5.6e-13 4.1e-12 6.12e-13 2.97e-11 1.1e-10 2.13e-10 2.19e-10 2.25e-10 0.0 0.0 1.54e-12 2.74667e-11 2.74667e-11 2.74667e-11 2.63e-09 2.095e-08 2.095e-08 6.54e-07 3.59e-06 1.823e-05 4.964e-05 9.673e-05 8.475e-05 5.061e-05 5.47e-06 2.73e-06 3.56e-07 4.09e-08 4.6618999512060006e-09 2.3315799955304e-10 5.34e-10 3.46e-10 2.64e-10 1.24e-10 4.72e-11 5.63e-12 9.769996242e-13 0.0 0.0 0.0 6.96e-14 4.42e-12 1.21e-10 1.53e-09 1.53e-09 4.46e-08 1.42e-07 1.42e-07 2.39e-06 7.77e-06 3.365e-05 7.17e-05 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0.0022343 0.000332214 3.25145e-05 1.74995e-06 6.509997424999999e-08 1.8249225882e-08 0.00653283 0.00183419 0.00031427 3.77156e-05 2.5e-06 1.16762e-07 8.047173371e-09 1.35455e-11 3.75754e-13 0.0 4.05e-13 4.84e-11 6.48e-10 2.76e-09 7.4e-08 7.4e-08 1.99e-06 1.99e-06 5.808e-05 0.00029926 0.00029926 0.00279651 0.0106585 0.00190895 0.0171805 0.0147336 0.0147336 0.0130649 0.0130643 0.0166144 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.14e-14 3.43e-12 1.89e-11 8.08e-11 2.35e-09 3.35e-09 1.43e-08 1.8e-07 7.77e-07 2.13e-06 5.8e-06 8.61e-06 1.214e-05 1.131e-05 1.93e-06 6.52e-07 1.58e-07 1.0800399e-08 5.879999442e-10 0.0477838 0.0310861 0.0185158 0.00565445 0.00136851 0.000179773 1.6912e-05 4.70043e-07 1.77996e-08 0.0 0.0 0.0 0.0 8.05e-14 7.5e-12 7.5e-12 1.2e-09 7.36e-08 2.24e-06 4.181e-05 0.00042656 0.00270686 0.00984902 0.0275145 0.0382719 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 8.37e-14 6.14e-13 2.235e-11 2.235e-11 0.0 0.0 0.0 0.0 0.0 1.04e-14 3.01e-13 7.59e-13 5.57e-12 6.21e-07 3.2e-06 3.2e-06 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As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 1.79962e-13 2.05957e-12 2.05957e-11 9.37804e-11 2.32951e-10 2.67944e-09 2.49948e-08 2.49948e-08 1.30473e-07 1.30473e-07 1.63966e-07 4.97896e-06 6.10872e-07 7.01853e-06 4.48906e-05 2.08257e-05 0.00023953 0.000668251 0.000850992 0.000850992 0.00153852 0.00153852 0.00191242 0.00191333 0.0027115 0.0027115 0.0054611 0.00225206 0.00225206 0.00294828 0.000722959 0.000722959 0.000535588 0.000150859 2.97438e-05 4.91897e-06 6.228704510000001e-07 1.770084905e-07 6.04531e-09 6.338957942e-10 0.0 5.7488e-14 3.56925e-12 1.4297e-10 3.974171e-09 7.98833e-08 9.85794e-07 8.02832e-06 4.71702e-05 0.000207507 0.000648025 0.00143162 0.00113422 0.00113451 0.00240294 0.0017258 0.0010523 0.000349737 8.71318e-05 1.40771e-05 1.69877e-06 2.50679773e-07 3.34355e-09 3.00195e-10 0.0 7.43845e-12 3.52926e-09 1.58967e-08 1.01979e-06 2.57446e-06 2.57446e-06 3.51427e-05 3.51427e-05 6.26869e-05 0.00028558 0.0016701 0.00502261 0.009856 0.0159208 0.0217622 0.0159205 0.00889341 0.0040759 0.00101343 0.000181822 2.25253e-05 1.90861e-06 9.49802e-08 1.46992e-09 7.73838e-11 0.0 0.0 3.24932e-12 1.99958e-11 7.73838e-10 2.74943e-09 1.68965e-08 5.35888e-08 3.03937e-07 4.41908e-06 3.20633e-05 7.03853e-06 0.000231692 0.000198968 0.000705423 0.00285095 0.00516653 0.00691246 0.00661637 0.00450032 0.00202969 0.000680148 0.000154168 2.05351e-05 2.32951e-06 3.65816378e-07 8.288991849e-09 4.19238e-10 0.0 0.0 2.03957e-13 8.56821e-12 8.03832e-11 4.58904e-10 1.29973e-09 7.96834e-09 1.24974e-07 2.3995e-07 1.46969e-06 9.83795e-06 7.70839e-06 4.73801e-05 0.000210646 7.29848e-05 0.000448326 0.00148051 0.00147278 0.00147278 0.00486291 0.00325134 0.00325134 0.00658013 0.00521962 0.00321254 0.00141242 0.000541717 0.000141131 6.79058e-05 1.16715e-05 1.37971e-06 9.548002869999999e-08 3.51927e-09 2.17955e-13 6.66861e-10 1.98958e-08 6.10872e-08 2.77942e-07 4.07915e-06 3.49027e-05 0.000243089 0.000981115 0.00299159 0.00734381 0.00998557 0.0100586 0.00721812 0.00389826 0.00123883 0.000267754 4.37709e-05 5.44886e-06 3.8092e-07 1.86864e-08 8.6282e-10 2.71943e-11 3.39929e-07 7.08852e-07 9.578e-07 1.06978e-06 8.65819e-07 5.53884e-07 2.56946e-07 7.480437500000001e-08 1.6607597699999998e-08 2.8993909799999997e-09 7.98833545e-11 1.449700488e-11 5.95876e-14 3.3593e-11 5.11893e-08 1.75963e-05 1.57867e-05 0.000323922 0.000323922 0.000390858 0.000390858 0.00252983 0.00252983 0.00824167 0.014168 0.00889879 0.0239364 0.0214349 0.015649 0.00967686 0.00316061 0.000740795 0.000138631 1.37371e-05 9.39113e-07 4.569956811e-08 9.88573e-10 2.67944e-11 0.0 3.14934e-09 3.14934e-08 3.57925e-08 1.62966e-07 1.01979e-06 5.70881e-07 3.22933e-06 1.13576e-05 2.53547e-05 3.66623e-05 4.20612e-05 3.78721e-05 1.97459e-05 6.87856e-06 1.72964e-06 2.94938685e-07 3.19038978e-08 2.32951e-09 9.0781e-14 3.07936e-12 6.26869e-11 9.75796e-10 1.03978e-08 4.89898e-08 4.63903e-07 1.84961e-06 5.52885e-06 1.08777e-05 1.59367e-05 1.19975e-06 1.9136e-05 1.57367e-05 9.82795e-06 4.55905e-06 1.60966e-06 4.71901e-07 9.919217382000001e-08 1.67965e-13 3.87919e-12 6.59862e-11 7.1985e-10 6.09873e-09 3.53926e-08 1.34972e-07 1.01979e-08 4.36909e-07 9.1081e-07 1.47969e-06 1.98958e-06 1.9296e-06 1.99958e-14 7.97833e-11 4.65903e-08 1.91293e-07 1.91293e-07 1.91293e-07 4.99896e-06 1.26924e-05 1.26924e-05 8.52522e-05 0.000222474 0.000367683 0.000449606 0.00038181 0.00028899 0.00014232 5.40887e-05 1.57467e-05 3.03937e-06 4.64903e-07 4.92766818293e-08 3.69193943e-09 1.70964e-07 1.65965e-07 1.01979e-07 5.11893e-08 1.82367915e-08 5.03359903e-09 9.89793402e-10 0.0 2.36951e-13 8.40824e-12 2.33951e-10 4.56905e-09 6.88856e-08 3.71922e-07 3.71922e-07 5.08894e-06 1.34572e-05 1.34572e-05 0.000106858 0.000258486 0.000428741 0.000530919 0.000484349 0.000304236 0.000134012 3.95517e-05 7.1885e-06 8.46457867e-07 8.438238359999999e-08 4.2295749050000006e-09 0.0 0.0 1.12976e-11 3.63924e-10 8.18829e-09 1.03978e-07 8.82816e-07 5.09894e-06 1.88661e-05 5.19592e-05 9.61399e-05 0.000155867 0.000163016 0.000131832 8.26727e-05 3.50227e-05 1.20175e-05 2.90939e-06 5.08894e-07 6.57873187873e-08 6.32695827e-09 0.0 0.0 0.0 1.17975e-13 5.54884e-12 9.99791e-11 9.99791e-11 5.11893e-09 7.55842e-08 9.34805e-09 9.917929e-07 6.92855e-07 7.96834e-06 4.60504e-05 0.000153208 1.15276e-05 0.000434359 0.00042909 0.00042909 0.00119714 0.00119904 0.000842344 0.00038101 0.000114016 2.8814e-05 3.79921e-06 3.75921609e-07 2.31952e-08 8.79882e-10 0.0 0.0 3.19933e-12 2.4095e-13 8.30827e-10 6.2487e-11 1.51968e-09 6.93855e-09 5.53884e-08 4.16913e-09 4.87898e-08 2.22953e-07 9.69798e-07 4.3291e-07 1.96959e-06 4.2991e-06 5.7388e-06 5.54884e-06 2.08956e-06 2.08956e-06 2.62945e-06 1.20975e-06 0.0 1.17975e-11 9.26807e-09 1.76963e-07 1.69965e-06 8.1483e-06 6.40066e-05 0.00202735 8.02432e-05 0.00260308 0.0044152 0.0044152 0.00948488 0.00948488 0.0108822 0.0170218 0.0287564 0.0119564 0.0119484 0.0110188 0.00476061 0.00131993 0.000236531 3.18668e-05 3.48927e-06 3.90283044e-07 3.63857e-09 2.63965e-10 0.0 0.0 1.08977e-12 3.75922e-12 3.03937e-11 1.00979e-09 1.24974e-10 2.25953e-09 1.27973e-08 1.73964e-07 2.14955e-08 5.4122e-07 5.4122e-07 5.4122e-07 7.57842e-06 9.36804e-07 4.25411e-05 3.75422e-06 3.75422e-06 0.000183302 2.26553e-05 0.000235144 0.000235144 0.000235144 0.00174766 0.000216005 0.00203971 0.00101982 0.00101982 0.0059924 0.000740635 0.00432709 0.00432709 0.00402268 0.00402268 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3.86919e-06 4.97658e-07 4.60126e-09 4.31043e-10 0.0 1.65965e-13 6.37867e-10 2.27952e-08 5.10893e-08 4.13914e-07 3.38429e-06 3.38429e-06 3.65724e-05 3.65724e-05 0.000505694 0.000954776 0.000954776 0.00681201 0.013932 0.00132398 0.0207426 0.0117938 0.0117938 0.0100239 0.0100235 0.0117041 0.0 1.9196e-13 3.30931e-12 3.3493e-11 2.51947e-12 3.24932e-10 2.28952e-09 1.12976e-08 1.14976e-11 2.69944e-10 3.98917e-09 5.12893e-09 4.14913e-08 3.87919e-07 2.78942e-07 2.24953e-06 1.21375e-05 3.72922e-05 9.0951e-05 0.000172394 0.000196459 0.000152898 8.76517e-05 3.55326e-05 9.48802e-06 1.73964e-06 2.04062978e-07 1.429700136e-08 0.026559 0.0184968 0.00901025 0.00285583 0.000653354 9.63519e-05 8.88354e-06 8.32204e-07 3.24932e-08 2.33428e-09 5.59883e-10 3.77921e-14 3.63924e-12 1.88961e-10 3.57425e-09 3.57425e-09 2.02958e-07 3.47927e-06 3.42828e-05 0.000292019 0.00139134 0.00502073 0.011741 0.0216102 0.0273972 - - - - - - - - - - - - 0.0253 500000.0 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 2.41e-14 1.605e-12 1.605e-12 9.24999e-11 9.24999e-11 0.0 0.0 0.0 0.0 4.82e-14 2.67e-13 1.78e-12 3.83e-11 3.38e-07 3.38e-07 3.89e-06 3.89e-06 1.898e-05 1.898e-05 4.911e-05 4.911e-05 0.00013769 5.9145e-05 5.9145e-05 5.536e-05 8.12499e-06 8.12499e-06 2.95e-06 2.53e-07 1.74e-11 1.77e-12 1.33e-13 0.0 4.33031e-16 0.0 0.0 0.0 0.0 3.59e-14 1.884e-12 1.19e-10 5.5e-09 9.60999e-08 3.23e-06 1.534e-05 7.63499e-05 0.00029482 0.00040703 0.00040703 0.00100243 0.00095219 0.00043513 0.00014834 2.866e-05 3.47e-06 2.92989e-07 5.773883484e-08 1.5513346724e-09 4.05784e-12 0.0 0.0 0.0 1.39e-14 9.78999e-12 6.5e-10 6.5e-10 4.87e-08 4.87e-08 6.35e-07 1.56e-06 3.306e-05 0.00030499 0.00175181 0.00652486 0.0144429 0.0267222 0.0289148 0.0184433 0.0090474 0.00281572 0.00050184 5.28779e-05 3.68e-06 7.32914e-08 4.5999985240000005e-09 1.6599e-11 0.0 0.0 0.0 3.47e-12 1.76e-11 5.89e-11 9.30999e-10 2.79e-09 1.31e-07 2.26e-06 9.67999e-07 3.251e-05 8.98299e-05 0.0001597 0.000943459 0.00253498 0.00436826 0.00488706 0.00398528 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0.0310012 0.0146286 0.00546027 0.001050542 0.000133129 8.54481e-06 4.0699960198000003e-07 3.4498513892e-08 0.0107817 0.00340599 0.000806549 0.000104136 8.36999e-06 2.34607e-07 2.466986942e-08 1.73019e-11 1.55944e-12 0.0 2.05e-13 2.47e-11 3.12e-10 1.33e-09 3.485e-08 3.485e-08 9.09999e-07 9.09999e-07 2.788e-05 0.00015403 0.00015403 0.00192102 0.00664172 0.00165663 0.0149682 0.0136506 0.0136506 0.0141133 0.0141123 0.022553 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.84e-13 1.23e-11 5e-11 2.13e-10 5.64e-09 1.49e-08 6.34e-08 8.78999e-08 3.41e-07 6.5e-07 1.66e-06 3.35e-06 2.72e-06 4.4e-06 1.01e-06 2.35e-07 3.99e-08 4.080103e-09 1.6799996347000002e-10 0.0428817 0.0375769 0.0252494 0.0103224 0.00273888 0.000386925 4.06853e-05 7.33357e-07 6.60984e-08 3.9184924456e-09 0.0 0.0 0.0 2.18e-14 1.675e-12 1.675e-12 3.24e-10 1.78e-08 6.97999e-07 1.416e-05 0.00016016 0.00116203 0.00561015 0.0156726 0.0334954 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 2.65e-14 1.94e-13 1.25e-11 1.25e-11 9.7e-10 9.7e-10 0.0 0.0 0.0 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Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.885e-13 3.885e-13 3.745e-11 3.745e-11 0.0 0.0 0.0 0.0 0.0 4.02e-14 2.69e-13 5.87e-12 4.88e-11 4.88e-11 2.61e-06 2.61e-06 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1.03e-07 2.7e-06 3.407e-05 0.00033626 0.00187737 0.00669772 0.01292384 0.0191831 0.00939493 0.00349628 0.00194903 0.00088386 0.000425489 0.00011063 9.82293e-06 8.96452e-07 4.3100014e-08 8.620003207e-10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.22e-14 3.2e-14 5.88e-12 1.88e-10 2.12e-10 2.29e-08 1.95333e-07 1.95333e-07 1.95333e-07 1.61e-07 5.56e-07 4.735e-07 4.735e-07 1.293e-05 3.043e-05 5.181e-05 0.00057753 0.00093618 0.001594 0.0110946 0.0126555 0.0091643 0.0280043 0.00776327 0.00776327 0.00526546 0.00083149 9.667e-05 5.68128e-06 2.7999975169999995e-07 0.0 0.0 0.0 0.0 0.0 2.97e-14 1.1e-12 7.38e-12 5.85e-10 9.63e-09 1.44e-09 3.26e-07 6.47e-07 4.33e-06 3.877e-05 0.00022518 5.282e-05 0.00111029 0.00242927 0.0033226 0.00307512 0.00189337 0.00052046 0.00013116 1.82069e-05 1.61996e-06 8.040955146000001e-08 2.6399983604e-09 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.94e-13 3.36e-11 1.65e-09 4.8e-08 7.01e-07 6.84e-06 3.668e-05 0.00015759 0.00034554 0.00056097 0.00050506 0.00035982 0.00012673 2.604e-05 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8.696509998571e-07 7.073692149e-09 5.482993997e-10 0.0 0.0 0.0 1.535e-14 1.535e-14 4.67e-12 3.39e-10 7.95e-11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.88e-14 9.3e-13 3.11e-12 5.08e-10 2.47e-14 8.28e-14 4.06e-12 3.56e-11 8.72e-11 3.6e-09 2.68e-08 6.56e-08 2.04e-06 0.00030621 0.00074968 0.00754129 0.00748159 0.0183166 0.0510263 0.0472921 0.0288303 0.0097613 0.00226132 0.00032057 3.4349996725840006e-05 7.497548579000001e-07 7.84000301e-08 0.0 0.0 0.0 0.0 0.0 1.58e-14 2.12e-13 1.56e-12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.47e-10 6.05e-10 8.47e-08 1.15e-06 2.82e-06 3.103e-05 7.24e-05 0.00030681 0.00075116 0.00653346 0.0193558 0.03793 0.0437311 0.0388294 0.0203898 0.00819289 0.001915798 0.000313708 2.56615e-05 1.350004957e-06 6.279111432000001e-08 0.0146709 0.00600634 0.00147766 0.000279989 2.623e-05 7.14022e-07 8.232431421000001e-08 2.936113716e-08 1.26363e-11 0.0 0.0 7.07e-13 1.5e-11 6.41e-11 2.51e-09 2.51e-09 1.135e-07 1.135e-07 4.88e-06 3.8785e-05 3.8785e-05 0.00063168 0.00341008 0.0009713 0.00874117 0.0107018 0.0107018 0.0153651 0.0153628 0.028997 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.34e-13 6.85e-13 2.92e-12 1.13e-10 1.82e-10 7.76e-10 1.5e-08 1.02e-07 4.85e-07 1.36e-06 2.59e-06 9.63e-06 1.044e-05 2.92e-06 1.46e-06 3.43e-07 4.530404e-08 4.740004509e-09 0.0416211 0.0403781 0.0318967 0.0153388 0.005215 0.000991576 0.000134598 4.07725e-05 4.87987e-07 2.2071572898e-08 0.0 0.0 0.0 0.0 7.05e-14 7.05e-14 1.98e-11 1.75e-09 8.48e-08 2.74e-06 4.269e-05 0.00044856 0.00260277 0.0120741 0.0266693 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 2.49e-14 1.62e-12 1.62e-12 1.01e-10 1.01e-10 0.0 0.0 0.0 0.0 3.15e-14 1.11e-13 8.17e-13 1.42e-11 4.74e-07 4.74e-07 5.86e-06 5.86e-06 3.761e-05 3.761e-05 0.00010272 0.00010272 0.00044442 0.000227485 0.000227485 0.00031244 5.897e-05 5.897e-05 3.281e-05 4.66e-06 1.63e-10 1.29e-11 9.81e-13 5.77e-14 3.25617e-15 0.0 0.0 0.0 0.0 0.0 9.660000000000001e-13 4.12e-11 1.53e-09 5.86e-08 8.41e-07 1.158e-05 5.832e-05 0.00026289 0.0002912 0.0002912 0.00104633 0.00141785 0.00100805 0.00048125 0.0001584 2.657e-05 3.26527e-06 1.0692677173000001e-07 1.773353436e-08 6.38077e-11 0.0 0.0 0.0 1.49e-14 5.35e-12 3.525e-10 3.525e-10 2.51e-08 2.51e-08 4.57e-07 1.3e-06 2.714e-05 0.00024156 0.0014284 0.00511954 0.0138656 0.0235218 0.0290388 0.019758 0.0107532 0.00313995 0.0006678 7.61719e-05 6.9e-06 1.66544e-07 1.1200008368999999e-08 5.62417e-11 0.0 0.0 0.0 1.07e-13 3.02e-12 1.14e-11 1.41e-10 5.01e-10 2.58e-08 3.49e-07 1.29e-07 6.34e-06 2.091e-05 4.442e-05 0.00033452 0.00124484 0.00266817 0.00431984 0.00521909 0.00320167 0.00156166 0.0003894 8.12722e-05 8.36e-06 2.985907838e-07 2.1300755999999998e-08 1.58568e-10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.25e-10 4.7e-10 3.81e-08 1.79e-07 6.72e-07 9.54e-06 3.202e-05 3.202e-05 0.00023727 0.00022281 0.00022281 0.00057682 0.00045192 0.00025188 1.598e-05 5.37e-06 1.48e-06 4.06e-07 8.3e-08 8.05e-09 4.369997789e-06 2.7399999046999996e-07 0.0 0.0 1.55e-13 3.97e-12 1.13e-11 7.29e-10 2.96e-08 7.76e-07 1.134e-05 0.00012742 0.00070384 0.00306813 0.00675451 0.0111979 0.0104393 0.00793419 0.00336043 0.00113805 0.00021461 2.484e-05 1.96824e-06 1.1600022954e-07 3.6499968489999997e-09 1.31e-08 3.41e-08 4.07e-08 3.37e-08 3.2e-08 2.07e-08 8.24e-09 2.2114300014503e-09 5.28122e-10 4.8799996779999994e-11 2.8899953739999998e-12 2.989998487e-13 0.0 0.0 2.84e-14 7.34e-12 1.36e-09 5.2e-08 5.2e-08 1.95e-06 1.95e-06 3.874e-05 3.874e-05 0.00070771 0.00149657 0.00149988 0.0105147 0.0177703 0.0274398 0.0248603 0.0175269 0.00742866 0.0020704 0.00033519 5.12131e-05 2.1005819999999998e-06 6.814524332e-08 2.0000475e-09 7.15706e-12 9.67e-12 1.95e-10 4.33e-10 1.17e-09 9.15e-09 1.28e-08 4.55e-08 2.43e-07 6.86e-07 1.32e-06 2.57e-06 1.96e-06 1.23e-06 8.31e-07 2.36e-07 5.760000931e-08 5.570598000000001e-09 4.109996081e-10 0.0 0.0 0.0 0.0 2.84e-14 1.2e-12 3.14e-11 5.05e-10 3.86e-09 1.24e-08 5.53e-08 7.54e-09 1.79e-07 2.45e-07 2.6e-07 2.42e-07 1.44e-07 6.79e-08 2.52056899980816e-08 0.0 0.0 0.0 0.0 0.0 1.98e-13 2.11e-12 2.88e-13 2.28e-11 1.69e-10 7.84e-10 2.68e-09 6.29e-09 0.0 0.0 1.28e-13 2.9e-12 2.9e-12 2.9e-12 3.92e-10 4.685e-09 4.685e-09 1.67e-07 1.5e-06 9.29e-06 3.134e-05 7.222e-05 0.00010204 0.00011325 7.135e-05 2.745e-05 3.75e-06 1.19e-06 1.591000043604465e-07 1.39289000419e-08 1.33e-08 1.2e-08 6.29e-09 1.68e-09 6.28375e-10 1.2402740000721e-10 1.8299967246e-11 0.0 0.0 0.0 4.71e-14 2.73e-12 9.21e-11 1.035e-09 1.035e-09 2.8e-08 1.905e-07 1.905e-07 1.86e-06 7.9e-06 3.705e-05 7.401e-05 0.00014355 0.00011194 7.52e-05 2.425e-05 6.36e-06 1.0504869997568e-06 8.380003299e-08 3.74015e-09 0.0 0.0 0.0 0.0 0.0 5.61e-13 2.93e-11 9.43e-10 1.62e-08 1.89e-07 1.15e-06 5.14e-06 1.396e-05 2.56e-05 2.232e-05 8.72e-06 6.14e-06 2.46e-06 5.19e-07 8.27705050004131e-08 1.183719998935e-08 0.0 0.0 0.0 0.0 0.0 1.31e-14 1.31e-14 2.11e-12 6.31e-11 1.29e-11 3.55e-09 5.27e-09 3.87e-08 5.98e-07 5.93e-06 8.08e-07 3.86e-05 8.4205e-05 8.4205e-05 0.0003675 0.00056923 0.00056234 0.00036005 0.00020312 4.507e-05 7.74e-06 6.899996509e-07 3.880001471e-08 4.731713333e-09 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.38e-14 4.6e-13 6.27e-14 1.27e-12 3.43e-12 8.17e-11 1.71e-10 4.61e-10 2.83e-09 7.65e-09 2.17e-08 1.51e-08 1.51e-08 4.11e-08 3.23e-08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.97e-13 1.84e-13 7.31e-06 6.929e-05 6.929e-05 0.00086801 0.00086801 0.00434071 0.00395411 0.0235261 0.0174141 0.0173345 0.0305965 0.0212822 0.00995443 0.00273063 0.000583654 6.766e-05 1.3372530809e-06 1.829900116e-07 1.715249392e-08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.06e-14 0.0 1.15333e-12 1.15333e-12 1.15333e-12 3.03e-10 6.2e-11 1.12e-08 1.58e-09 1.58e-09 3.57e-07 7.31e-08 1.72667e-06 1.72667e-06 1.72667e-06 3.14e-05 6.43e-06 4.56267e-05 4.56267e-05 4.56267e-05 0.00030412 6.229e-05 0.00027994 0.00027994 0.00043252 0.00043252 0.000387885 0.000387885 0.00041546 0.00041546 0.00036921 0.00036921 0.00034962 0.00034962 9.883e-05 9.883e-05 9.883e-05 0.00043358 0.00043358 0.00017175 1.819002113108e-05 6.480655e-07 3.5556374940000006e-08 1.16e-10 0.0 0.0 0.0 0.0 3.1e-14 3.12e-12 4.26e-13 2.36e-10 7e-09 1.43e-09 3.12e-07 3.62e-06 7.42e-07 5.78e-05 0.00032512 0.00176659 0.00489912 0.00941262 0.0111807 0.00902349 0.00501366 0.00182057 0.000333761 4.965e-05 3.68984e-06 1.8999992769e-07 6.740079636e-08 0.0 0.0 1.55e-10 7.17e-09 2.39e-07 3.59e-06 4.581e-05 0.00033071 0.00299554 0.00575369 0.0113824 0.00747222 0.00747222 0.0135788 0.00730831 0.00280219 0.0006985 0.000114933 1.167e-05 8.07944e-07 2.540221e-08 1.760832565e-09 0.0 0.0 0.0 0.0 0.0 0.0 6.06e-10 2.18e-10 3.5417999956308005e-11 3.520005014e-12 9.367e-05 0.00065512 0.00372259 0.0119178 0.0298492 0.0430323 0.0391106 0.0248298 0.0117919 0.00258703 8.36e-06 3.93e-07 1.08e-08 3.5229e-09 3.54e-12 8.884441785e-09 0.0 0.0 0.0 0.0 0.0 0.0 1.33e-12 1.4e-10 7.68e-09 3.12e-07 6.81e-06 0.00283554 0.00283554 0.015503 0.0140914 0.0140914 0.0384579 0.0144231 0.0144231 0.0203858 0.00637369 0.00166265 0.00023685 1.89426e-05 3.89e-12 1.54178e-14 3.59498e-16 1.38207e-16 0.0 0.0 0.0 5.46e-14 5.84e-12 1.2e-12 4.12e-10 2.42e-10 2.92e-08 5.98e-09 9.6e-07 1.46e-05 2.99e-06 0.00014795 4.173e-05 0.00011474 0.0013194 0.0 0.0 0.0 4.76e-14 5.57e-12 3.36e-10 1.67e-08 3.75e-07 6.26e-06 4.977e-05 0.00031301 0.00100822 0.00240876 0.00325287 0.00298802 0.00192547 0.00093694 0.00026162 5.248e-05 5.65e-06 5.01221e-07 2.390002704e-08 1.88e-09 1.14e-08 3.83e-08 7.12e-08 1.96e-07 2.93e-07 3.67e-07 2.46e-07 2e-07 4.9e-08 1.180333e-08 2.330000125e-09 2.4800004246e-10 0.0 0.0 0.0 0.0 7.88e-13 5.49e-11 8.62e-10 1.92e-09 1.04e-07 5.24e-07 1.17e-06 2.18e-09 9.21e-09 2.05e-08 3.05e-07 1.52e-06 5.07e-06 0.000109 0.00036619 0.00060018 0.00058225 0.00035312 0.00013028 2.397e-05 2.98e-06 2.03e-07 8.380412999999999e-09 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.18e-13 2.75e-11 3.83e-10 1.04e-09 4.43e-08 7.78e-07 9.02e-06 1.90067e-05 1.90067e-05 1.90067e-05 0.00024859 0.00028293 0.00028293 0.00108201 0.00118035 0.00095631 0.00045251 0.00015325 3.393e-05 5.5e-06 5.068330003725e-07 2.5211700002775e-08 0.0 0.0 6.54e-13 2.52e-11 2.52e-11 3.09e-09 8.37e-09 8.46e-08 1.86e-06 2.362e-05 0.00018056 0.000399795 0.000399795 0.0023936 0.00424923 0.00538683 0.00428835 0.00251471 0.00076501 0.00020692 3.256e-05 3.59e-06 2.1507820005129002e-07 8.540004436e-09 9.26e-07 7.050490757e-08 2.88046e-10 0.0 0.0 3.9e-13 4.21e-11 2.18e-09 4.35e-08 4.35e-08 1.84e-06 2.947e-05 0.00028021 0.00023215 0.00113246 0.00514292 0.0104498 0.0157382 0.0141228 0.00857153 0.00346122 0.00092537 0.0001384 4.6512e-05 0.0 0.0 4.18e-14 4.18e-14 1.09e-12 7.98e-12 1.39e-10 4.93e-10 2.61e-08 3.56e-09 2.64e-07 2.64e-07 9.17e-06 4.551e-05 4.551e-05 0.00050596 8.076e-05 8.076e-05 0.00045963 0.00074714 0.00092008 0.0006429 0.00059954 0.00049718 0.00029149 7.942e-05 1.586e-05 1.45e-06 8.029998937e-08 2.330153e-09 4.6100008840000005e-11 0.0 1.93e-12 1.35e-10 7.44e-09 2.22e-07 5.28e-06 6.441e-05 0.00044451 0.00210767 0.00750537 0.01222808 0.0154819 0.0104118 0.00560327 0.0022357 0.00068097 0.0003759 9.961e-05 1.49167e-05 1.27903e-06 5.789998809e-08 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3.09e-09 4.91e-14 1.85e-13 6.2e-12 4.62e-11 1.32e-10 4.51e-09 3.59e-08 1.02e-07 2.99e-06 0.00038882 0.00181903 0.00863663 0.0079044 0.0224969 0.050008 0.0409209 0.0232753 0.00617525 0.00170194 0.0002422 2.547001918485e-05 8.457763448999999e-07 6.489996635e-08 0.0 0.0 0.0 0.0 4.88e-14 6.31e-13 7.52e-12 4.58e-11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.39e-10 1.82e-09 1.9e-07 2.03e-06 5.77e-06 4.575e-05 0.0001237 0.00038889 0.00110979 0.00772219 0.0270097 0.0362733 0.0399661 0.0326423 0.0170615 0.00660042 0.001494762 0.000241067 1.91728e-05 1.0599986492e-06 7.421318911e-08 0.0139866 0.00570012 0.00151591 0.000287354 2.651e-05 9.08152e-07 9.479087615e-08 1.03535e-10 1.69911e-11 0.0 0.0 1.93e-12 3.68e-11 1.79e-10 5.95e-09 5.95e-09 2.195e-07 2.195e-07 8.23e-06 5.5775e-05 5.5775e-05 0.00079784 0.00500248 0.00098493 0.00995827 0.0108128 0.0108128 0.0133977 0.0133958 0.0254008 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.05e-14 0.0 2.52e-13 7.01e-12 1.81e-11 8.84e-11 2.23e-09 3.82e-09 1.86e-08 1.91e-07 8.19e-07 4.57e-06 7.67e-06 1.439e-05 2.361e-05 2.119e-05 1.362e-05 4.41e-06 9.02e-07 1.090139e-07 8.470003975e-09 0.0405797 0.0379631 0.0307488 0.0145779 0.00458044 0.00101519 0.000124875 2.40925e-06 4.65983e-07 2.744977958e-08 0.0 0.0 0.0 0.0 2.54e-13 2.54e-13 5.67e-11 4.17e-09 1.39e-07 4.28e-06 5.499e-05 0.00056684 0.00318832 0.0110812 0.0250902 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 5.31e-14 1.24e-13 1.06e-12 7.07e-12 1.23e-10 1.23e-10 2.565e-09 2.565e-09 0.0 0.0 0.0 0.0 5.24e-14 3.59e-13 2.4e-12 8.9e-11 1.27e-09 1.27e-09 3.229e-05 3.229e-05 0.00025005 0.00025005 0.00089718 0.00089718 0.0031394 0.00169309 0.00169309 0.00211723 0.000396925 0.000396925 0.00015184 2.26e-08 1.31e-09 5.11e-11 1.3e-12 2.04e-14 5.33541e-16 1.390106e-08 0.0 0.0 0.0 1.03e-14 6.92e-13 1.08e-11 5.12e-10 9.99e-08 1.65e-06 1.388e-05 7.56e-05 0.00028216 0.00033025 0.00033025 0.00126879 0.00111727 0.00245868 0.00104429 0.00038292 8.281e-05 1.20155e-05 5.40029939e-07 1.2293873590000001e-07 8.6637e-10 0.0 0.0 7.8e-13 7.33e-11 3.86e-09 4.365e-08 4.365e-08 1.03e-06 1.03e-06 7.19e-06 1.761e-05 0.00018459 0.00099054 0.0037291 0.0101647 0.0174776 0.0204801 0.0173 0.00958408 0.00383219 0.00114232 0.00023804 3.25388e-05 2.63e-06 2.01385e-07 5.26e-09 1.27352e-10 0.0 0.0 1.92e-14 2.72e-12 3.38e-11 1.13e-10 1.19e-09 3.58e-09 1.08e-07 1.02e-06 4.37e-07 1.545e-05 2.71e-05 4.818e-05 0.00095987 0.0023313500000000003 0.00464839 0.00657741 0.00651948 0.00431056 0.00220017 0.0006046 0.000109145 1.461e-05 9.258669739999999e-07 8.341030000000001e-08 1.521e-09 0.0 0.0 0.0 0.0 3.03e-13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.18e-07 1.53e-06 5.14e-06 8.811e-05 0.00026261 0.00026261 0.00183776 0.00174933 0.00174933 0.00414924 0.00256839 0.00043869 0.00012734 2.543e-05 3.37e-06 3.27e-07 1.36e-08 1.471e-05 1.240001056e-06 8.9900005e-08 0.0 1.68e-12 8.79e-11 9.41e-10 2.3e-09 8.29e-08 1.51e-06 1.692e-05 0.00012769 0.00066067 0.00214674 0.00493015 0.0083662 0.0100274 0.00852796 0.00449342 0.00146571 0.00044638 7.548e-05 8.42e-06 7.59822e-07 4.299996412e-08 1.609997903e-09 1e-09 2.42e-09 3.8e-09 5.34e-09 5.29e-09 3.14e-09 1.74e-09 5.43149999931e-10 1.96163e-10 1.900002297e-11 8.2700071e-13 1.519996778e-13 0.0 0.0 2.5e-10 1.26e-08 3.98e-07 3.65e-06 3.65e-06 2.906e-05 2.906e-05 0.00025766 0.00025766 0.00235787 0.00350053 0.0063074 0.0153555 0.024415 0.0280027 0.0207618 0.0102626 0.00361553 0.00081252 0.0001279 3.43824e-05 1.1604809999999998e-06 9.11701589e-08 1.7000887999999999e-09 4.72807e-11 2.02e-12 2.69e-11 6.43e-11 1.5e-10 1.54e-09 1.85e-09 5.55e-09 2.23e-08 6.43e-08 1.03e-07 2.11e-07 1.15e-07 1.81e-07 1.31e-06 7.5e-07 2.17000283094e-07 4.3519399999999996e-08 5.30000483e-09 0.0 0.0 6.3e-14 2.09e-12 4.42e-11 6.57e-10 5.97e-09 3.63e-08 1.24e-07 3.62e-07 8.43e-07 1.26e-07 1.47e-06 1.24e-06 8.46e-07 5.61e-07 2.34e-07 6.54e-08 2.5557110000239e-08 0.0 0.0 3.25e-14 6.25e-13 1.03e-11 9.74e-11 4.3e-10 6.42e-11 1.87e-09 6.82e-09 1.44e-08 2.06e-08 3.78e-08 0.0 7.8e-14 1.85e-10 2.03333e-09 2.03333e-09 2.03333e-09 9.61e-08 4.375e-07 4.375e-07 6.41e-06 2.958e-05 8.216e-05 0.00016635 0.00022507 0.0002247 0.00015565 7.131e-05 2.063e-05 4.95e-06 1.18e-06 1.5177400015399998e-07 1.052119997612e-08 8.5e-10 9.19e-10 6.52e-10 3.95e-10 1.6849299999999998e-10 4.10471000054e-11 9.15000236e-12 0.0 0.0 0.0 5.23e-14 1.46e-12 2.59e-11 2.095e-10 2.095e-10 3.45e-09 1.825e-08 1.825e-08 9.66e-08 7.68e-07 2.753e-05 8.363e-05 0.00013435 0.00015 0.00011089 5.081e-05 1.859e-05 2.864099996579e-06 9.46000628e-07 5.7710700000000003e-08 0.0 0.0 0.0 5.26e-13 2.28e-11 5.51e-10 1.06e-08 1.28e-07 8.85e-07 4.39e-06 1.419e-05 3.298e-05 5.079e-05 5.196e-05 3.269e-05 1.754e-05 9.38e-06 2.79e-06 4.65e-07 6.571049499999999e-08 8.230699970800001e-09 0.0 0.0 0.0 0.0 0.0 1.21e-14 1.21e-14 1e-12 1.65e-11 3.88e-12 5.35e-10 4.37e-10 2.93e-09 6.34e-08 4.47e-06 6.67e-07 3.522e-05 6.297e-05 6.297e-05 0.00030605 0.00050905 0.00053644 0.00046322 0.0001765 0.00015563 2.578e-05 3.590002654e-06 2.8099996549999997e-07 5.45253983e-08 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Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 2.95001e-13 6.87002e-13 1.12e-11 7.52002e-11 2.32001e-09 2.32001e-09 4.65501e-08 4.65501e-08 3.93001e-07 9.18002e-07 1.71e-10 1.14e-09 9.13002e-09 5.01001e-09 3.35001e-08 2.18e-07 2.19e-07 2.19e-07 1.074e-05 1.074e-05 4.49601e-05 4.49601e-05 0.00010584 0.00010584 0.00015543 3.52251e-05 3.52251e-05 1.953e-05 1.66e-06 1.66e-06 3.18001e-07 2.54001e-12 2.51001e-13 2.59001e-14 0.0 0.0 6.21555602e-09 7.22002e-11 0.0 0.0 0.0 1.34e-13 8.04002e-12 3.82001e-10 9.32002e-09 4.34001e-07 4.29001e-06 2.244e-05 8.73202e-05 0.000258391 0.0001951 0.0001951 0.000775942 0.000593452 0.000239031 6.72501e-05 1.498e-05 1.38e-06 9.2995e-08 7.533844577e-09 3.05581e-11 9.89614e-13 0.0 0.0 6.23001e-14 1.52e-11 2.21e-09 5.50001e-08 5.50001e-08 2.50501e-06 2.50501e-06 1.56e-05 3.82001e-05 0.000499031 0.00290084 0.00907294 0.0192363 0.0270119 0.0226364 0.0158575 0.00762913 0.00204056 0.000346394 4.44401e-05 3.24695e-06 1.58e-07 1.02358e-08 9.11002e-11 1.25814e-12 0.0 2.44001e-14 8.16002e-14 1.04e-11 1.18e-10 3.95001e-10 4.46001e-09 1.34e-08 4.38001e-07 3.76001e-06 1.61e-06 7.66302e-05 0.00014524 0.000258201 0.00114921 0.0024249099999999997 0.0042915 0.00357594 0.00236664 0.00103602 0.000278791 3.46601e-05 3.39982e-06 1.69e-07 1.4992634640999998e-08 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4.26501e-11 4.26501e-11 7.03002e-09 2.39001e-07 5.61001e-08 0.0 0.0 0.0 0.0 0.0 6.95002e-13 6.95002e-13 1.56e-10 2.87001e-09 9.62002e-09 2.86001e-12 1.42e-11 4.74001e-11 1.39e-09 1.09e-08 2.66001e-08 6.16001e-07 8.13002e-05 0.00019904 0.00194574 0.00288233 0.00668009 0.024655 0.0100338 0.0245656 0.0345746 0.0140512 0.00552162 0.000885342 0.00011952 9.21002e-06 3.95000955e-07 2.791932229e-08 1.89e-10 0.0 0.0 1.85e-14 2.73001e-13 2.48001e-12 9.65002e-12 1.73e-11 3.19001e-11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.90001e-11 5.90001e-11 9.06002e-09 1.52e-07 3.72001e-07 1.469e-05 6.89902e-05 0.00016891 0.000715782 0.00167015 0.010989 0.00737823 0.0269823 0.0377877 0.0425652 0.0298692 0.0126906 0.00304109 0.000671711 7.08934e-05 5.46979e-06 2.82988e-07 7.62001779e-09 1.81776e-11 0.00294899 0.000509371 5.93001e-05 4.48976e-06 2.13e-07 2.07833e-08 4.88748e-11 3.36916e-12 0.0 3.75001e-12 2.85001e-10 2.92001e-09 1.24e-08 2.92001e-07 2.92001e-07 6.06001e-06 6.06001e-06 0.00014342 0.000570966 0.000570966 0.00536939 0.0156038 0.00253367 0.022803 0.0158357 0.0158357 0.0116107 0.0116106 0.0113781 0.0 0.0 0.0 0.0 0.0 0.0 2.29001e-14 1.31e-13 0.0 2.74001e-13 7.39002e-12 2.43001e-11 1.04e-10 2.28001e-09 2.13e-09 9.09002e-09 3.68001e-08 2.28001e-07 5.55001e-07 1.17e-06 1.27e-06 2.97001e-06 1.51e-06 4.03001e-07 7.66002e-08 1.03e-08 6.010187e-10 4.55001e-11 0.0312811 0.0160022 0.0057871 0.0013742 0.00020976 2.20493e-05 1.20995e-06 7.69543e-08 5.94001e-10 5.19401e-12 2.12e-14 0.0 0.0 5.41001e-13 3.67001e-11 3.67001e-11 5.11001e-09 2.29001e-07 6.19001e-06 9.67402e-05 0.000869162 0.00421497 0.0148526 0.0306776 0.0417742 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 3.27e-13 8.85001e-13 1.2e-11 8.81001e-11 2.285e-09 2.285e-09 5.25001e-08 5.25001e-08 4.99001e-07 1.35e-06 3.65e-10 2.68e-09 3.66e-08 2.88e-08 2.11e-07 9.59001e-07 2.98e-06 2.98e-06 7.98401e-05 7.98401e-05 0.00020135 0.00020135 0.00028792 0.00028792 0.0004154 8.55451e-05 8.55451e-05 3.789e-05 2.125e-06 2.125e-06 1.11e-11 9.32001e-13 9.21001e-14 0.0 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9.24001e-10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.44e-08 2.29e-07 6.52001e-07 2.354e-05 4.564e-05 0.00012989 0.000872811 0.00235983 0.0019198 0.0119993 0.0316065 0.0352648 0.0377735 0.0239575 0.00751313 0.001983 0.00042712 3.46368e-05 2.32991e-06 1.04995e-07 2.709999291e-09 1.43306e-11 0.00248458 0.000502511 6.47301e-05 5.45959e-06 2.79e-07 2.03491e-08 5.08142e-11 5.71664e-12 0.0 1.98e-11 1.83e-09 1.48e-08 7.25001e-08 1.185e-06 1.185e-06 1.999e-05 1.999e-05 0.00040335 0.00105888 0.00105888 0.00877549 0.0176931 0.00264106 0.0267042 0.0127292 0.0127292 0.00914062 0.00914053 0.00779358 0.0 0.0 0.0 0.0 0.0 6.93001e-14 9.36001e-13 5.64001e-12 1.12e-13 3.58e-12 1.14e-10 2.27e-10 1.11e-09 1.34e-08 1.1e-08 5.39001e-08 5.49001e-07 2.06e-06 4.14e-06 5.81001e-06 7.85001e-06 6.46001e-06 3.18e-06 9.13001e-07 1.66e-07 1.58e-08 1.1100338000000001e-09 4.58001e-11 0.0281326 0.0156871 0.00580379 0.00149591 0.00023258 2.12597e-05 1.25995e-06 1.20269e-07 1.08e-09 1.06244e-11 6.28001e-14 0.0 3.39e-14 5.83001e-12 2.73e-10 2.73e-10 3.17e-08 1.1e-06 1.938e-05 0.00025111 0.00150461 0.00699237 0.01662 0.0307182 0.0334133 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 2.34e-14 4.97e-12 5.72e-11 2.99e-10 1.36e-09 2.54e-09 2.92e-08 2.22e-07 2.22e-07 2.125e-06 2.125e-06 5.28e-06 2.404e-05 6.26e-06 7.2e-05 0.00019421 3.528e-05 0.00040575 0.00066938 0.00056623 0.00056623 0.00085161 0.00085161 0.00223436 0.00223436 0.00386981 0.00386981 0.0067351 0.00145536 0.00145536 0.00051948 5.122e-05 5.122e-05 4.396e-05 1.858e-05 6.42e-06 2.66e-06 4.82000008e-07 2.960697e-08 3.80225e-09 1.87e-10 0.0 1.13e-12 4.54e-11 1.14e-09 2.085e-08 2.25e-07 2e-06 1.365e-05 6.214e-05 0.00021139 0.00067275 0.00130178 0.00096146 0.00096146 0.00193091 0.00127323 0.00070349 0.00021378 4.534e-05 6.37e-06 7.05514e-07 1.341834272e-07 1.70826e-09 1.15721e-10 2.92e-14 1.22e-10 3.89e-08 1.49e-07 8.03e-06 1.644e-05 1.644e-05 0.00017943 0.00017943 0.00021108 0.00096157 0.00384416 0.00894272 0.0137986 0.0172113 0.0162759 0.0106552 0.0052743 0.00173372 0.00041635 6.63143e-05 7.16e-06 5.20641e-07 2.8e-08 7.0259e-10 1.99e-11 0.0 7.92e-14 1.95e-11 1.2e-10 3.46e-09 8.38e-09 5.15e-08 1.47e-07 8.31e-07 9.37e-06 5.638e-05 1.238e-05 0.00033292 0.00024691 0.00087542 0.00316232 0.00499158 0.00568366 0.00446219 0.00288658 0.00114582 0.000324 5.619e-05 6.93522e-06 5.86e-07 1.57394e-08 1.4701040000000002e-09 1.05389e-10 0.0 1.14e-13 5.13e-12 1.69e-10 3.71e-09 5.82e-08 3.69e-08 2.27e-07 1.04e-06 5.08e-07 3.12e-06 8.47e-06 3.01e-06 1.848e-05 4.825e-05 4.875e-05 0.00029946 0.00203314 0.00292948 0.00292948 0.00850994 0.00274735 0.00274735 0.00272513 0.00177704 0.00117146 0.0006207 0.0004053 0.00011596 1.349e-05 5.5162e-06 4.95e-07 2.84e-08 5.88e-10 9.27e-12 1.31e-08 2.44e-07 5.59e-07 2.54e-06 2.461e-05 0.0001456 0.00068124 0.00223155 0.00542666 0.00911156 0.0109524 0.00944814 0.0056821 0.00231717 0.00075604 0.00013654 2.068e-05 2.05e-06 1.44e-07 5.76e-09 2.01e-10 3.97e-12 1.88e-06 3.49e-06 3.66e-06 3.02e-06 1.84e-06 7.38e-07 2.67e-07 6.108600000000001e-08 1.0306209999999999e-08 1.02000259e-09 2.22999605e-10 3.88999949e-11 7.14e-13 5.48e-10 3.67e-07 6.853e-05 6.567e-05 0.00113277 0.00113277 0.0012432 0.0012432 0.00624317 0.00624317 0.0135989 0.0106297 0.0106305 0.0226261 0.0168024 0.00991774 0.00432246 0.00125717 0.00027052 3.7e-05 3.7e-06 4.15407e-07 1.010151e-08 3.58872e-10 6.15e-12 0.0 3.85e-08 3.21e-07 2.67e-07 1.22e-06 5.34e-06 2.15e-06 1.217e-05 2.666e-05 4.439e-05 4.842e-05 4.026e-05 2.032e-05 8.6e-06 2.82e-06 5.64e-07 8.96000177e-08 1.1903670000000001e-08 9.21e-10 3.02e-12 8.37e-11 1.6e-09 1.94e-08 1.83e-07 8.48e-07 4.37e-06 1.067e-05 2.434e-05 4.042e-05 3.837e-05 2.89e-06 3.815e-05 2.185e-05 1.204e-05 4.45e-06 1.15e-06 2.43e-07 3.7891105e-08 1.19e-11 1.55e-10 1.91e-09 1.61e-08 8.01e-08 3.5e-07 8.51e-07 6.41e-08 2.24e-06 3.77e-06 4.24e-06 3.98e-06 2.8e-06 8.88e-13 1.3e-09 3.82e-07 1.32e-06 1.32e-06 1.32e-06 2.446e-05 5.1445e-05 5.1445e-05 0.00024455 0.00052024 0.0006563 0.00079897 0.00072729 0.00038873 0.00015533 3.971e-05 9.07e-06 1.59e-06 1.68e-07 1.55164e-08 8.513839999999999e-10 7.08e-07 6.25e-07 3.07e-07 1.16e-07 3.18626132e-08 5.52456e-09 8.700002680000001e-10 1.71e-13 7.53e-12 1.79e-10 3.23e-09 4.2e-08 3.81e-07 1.435e-06 1.435e-06 1.385e-05 2.5135e-05 2.5135e-05 0.000111 0.00020902 0.00031324 0.00030853 0.00023997 0.00016816 7.067e-05 2.119e-05 4.17e-06 4.7635199999999995e-07 4.3799970000000005e-08 2.140239e-09 0.0 1.14e-13 2.18e-10 5.78e-09 8.81e-08 8.85e-07 5.24e-06 2.461e-05 8.11e-05 0.00018299 0.00035553 0.00040181 0.00033317 0.00017841 8.85e-05 3.365e-05 7.85e-06 1.63e-06 2.05e-07 2.3942999999999998e-08 1.73131e-09 0.0 0.0 7.32e-14 3.09e-12 9.32e-11 9.94e-10 9.94e-10 3.41e-08 3.28e-07 4.05e-08 2.94e-06 1.12e-06 1.291e-05 5.599e-05 0.00016296 1.227e-05 0.0003647 0.00029463 0.00029463 0.00088443 0.00081049 0.00055202 0.00025188 7.093e-05 1.634e-05 2.03e-06 1.73000165e-07 9.6e-09 4.86303e-10 3.11e-14 5.41e-13 1.55e-10 1.17e-11 2.03e-08 1.53e-09 2.18e-08 9.95e-08 5.9e-07 4.44e-08 4.17e-07 1.9e-06 5.11e-06 1.8e-06 8.22e-06 1.199e-05 1.37e-05 1.071e-05 2.9e-06 2.9e-06 2.62e-06 8.85e-07 5.88e-11 1.57e-08 3.63e-07 2.28e-06 6.22e-06 3.653e-05 0.0002295 0.00653505 0.00021636 0.00456718 0.00716821 0.00716821 0.0104583 0.0104583 0.0110169 0.0172318 0.015726 0.00848873 0.00848613 0.00586123 0.00192422 0.0004201 6.711e-05 7.89456e-06 6.34e-07 1.28808e-08 1.14343e-09 6.24754e-11 0.0 3.41e-13 9.61e-11 6.65e-11 5.38e-10 2.95e-09 3.65e-10 1.81e-09 1.03e-08 4.15e-08 5.13e-09 5.30667e-08 5.30667e-08 5.30667e-08 2.97e-06 3.67e-07 6.034e-05 5.325e-06 5.325e-06 0.00064611 7.986e-05 0.00121137 0.00121137 0.00121137 0.00727701 0.00089941 0.00484006 0.00242003 0.00242003 0.00831844 0.00102812 0.00467601 0.00467601 0.00370636 0.00370636 0.00371575 0.00371575 0.00164187 0.00164187 0.000351445 0.000351445 0.00033151 0.00033151 4.728e-05 4.728e-05 4.728e-05 1.0055e-05 1.0055e-05 1.09e-06 8.42e-08 3.180246e-09 1.20542e-10 7.11e-12 1.57e-14 9.42e-13 1.91e-11 1.91e-11 1.49e-09 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2.25242e-09 3.74515e-10 1.47e-12 3.44e-09 9.32e-08 1.78e-07 1.44e-06 9.465e-06 9.465e-06 7.831e-05 7.831e-05 0.00083093 0.00149848 0.00149848 0.00758562 0.0147559 0.00110037 0.0172391 0.00831271 0.00831271 0.00564893 0.00564882 0.00563045 6.41e-13 8.95e-12 1.15e-10 9.51e-10 7.16e-11 5.67e-09 2.53e-08 8.2e-08 2.44e-10 4.63e-09 4.77e-08 4.14e-08 3.35e-07 2.17e-06 9.55e-07 7.72e-06 3.01e-05 6.802e-05 0.00011771 0.00012518 0.0001137 8.155e-05 3.713e-05 1.336e-05 4.16e-06 7.63e-07 9.614739999999999e-08 7.84e-09 0.0173712 0.00960364 0.00387565 0.00111342 0.00021714 3.26975e-05 3.26775e-06 5.43335e-07 1.4e-08 7.7836e-10 1.91e-11 4.14e-13 2.9e-11 1.29e-09 1.92e-08 1.92e-08 7.89e-07 1.075e-05 9.433e-05 0.00058965 0.00251759 0.00710962 0.0141812 0.0211558 0.0229292 - - - - - - - - - - - - - - - - - - - - - - 0.0253 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 6.77e-14 1.58e-13 2.83e-12 1.9e-11 6.85e-10 6.85e-10 3.025e-08 3.025e-08 3.58e-07 8.35e-07 1.67e-10 1.12e-09 1.17e-08 8.07e-09 5.4e-08 2.3e-07 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Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 2.18e-13 5.9e-13 6.19e-12 4.54e-11 1.2e-09 1.2e-09 3.76e-08 3.76e-08 4.04e-07 1.09e-06 2.1e-06 1.538e-05 2.29e-08 2.33e-08 1.71e-07 9.27e-07 1.21e-06 1.21e-06 4.16e-06 4.16e-06 7.368e-05 7.368e-05 0.00018568 0.00018568 0.00053218 0.00020407 0.00020407 0.0001762 2.195e-05 2.195e-05 5.15e-06 5.12e-11 3.36e-12 2.88e-13 1.66e-14 0.0 1.04173e-16 3.720149e-09 0.0 0.0 0.0 4.15e-13 1.946e-11 7.27e-10 1.37e-08 2.14e-07 1.77e-06 2.262e-05 0.00010989 0.00031119 0.00029803 0.00029803 0.00090263 0.00076324 0.00056416 0.00020449 4.982e-05 7.89e-06 7.66844e-07 6.37881578e-08 5.20856e-10 3.48144e-11 0.0 0.0 1.34e-13 2.67e-11 2.76e-09 7.95e-08 7.95e-08 2.53e-06 2.53e-06 2.01e-05 5.722e-05 0.00058807 0.00263217 0.00788526 0.0164373 0.0277553 0.0308704 0.0189637 0.00806346 0.00233798 0.000436841 5.586e-05 4.62937e-06 2.52e-07 2.59387e-08 1.92e-10 5.03781e-12 0.0 2.23e-14 8.39e-14 6.58e-12 1.28e-10 4.81e-10 4.51e-09 1.6e-08 3.65e-07 3.2e-06 1.18e-06 4.125e-05 6.961e-05 0.00014792 0.00098255 0.0022915699999999997 0.00392246 0.00443872 0.00345174 0.00169956 0.00056759 0.00011336 1.53749e-05 1.43e-06 1.24221234e-07 2.9900836999999996e-09 6.22852e-11 0.0 0.0 0.0 6.13e-14 6.2e-12 4.05e-10 3.5e-09 1.32e-08 0.0 0.0 2.15e-14 6.3e-13 1.42e-12 5.34e-12 7.43e-11 7.15e-07 2.69e-06 2.905e-05 7.145e-05 7.145e-05 0.00035938 0.000253065 0.000253065 0.00042766 0.00018068 7.53e-06 1.86e-06 6.06e-07 1.34e-07 2.24e-08 2.55e-09 6.21e-06 7.010003317e-07 3.359996939e-08 0.0 5.02e-12 3.62e-10 3.48e-09 9.92e-09 3.04e-07 4.82e-06 5.857e-05 0.00046127 0.00189604 0.00543478 0.0105506 0.0129666 0.0108875 0.00662868 0.00267464 0.00071141 0.00013762 1.59e-05 1.33e-06 7.28901e-08 2.779998063e-09 7.6e-11 2.94e-09 4.97e-09 8.41e-09 9.48e-09 1.07e-08 4.13e-09 1.42e-09 4.03235e-10 8.16176e-11 1.129999545e-11 1.67999847e-13 1.7100025489999998e-14 0.0 0.0 4.27e-11 4.35e-09 2.47e-07 3.695e-06 3.695e-06 6.1995e-05 6.1995e-05 0.000590415 0.000590415 0.005819 0.00776373 0.00776501 0.026538 0.0321642 0.0272432 0.018928 0.00838124 0.00199454 0.00030885 3.142e-05 8.58641e-07 9.18103e-08 6.29651e-10 4.73e-11 3.52798e-12 1.7e-11 1.72e-10 4.3e-10 1.16e-09 1e-08 1.39e-08 4.94e-08 1.41e-07 2.9e-07 5.23e-07 6.73e-07 6.55e-07 3.34e-07 1.43e-07 3.23e-08 1.109995483e-08 1.390141e-09 1.07e-10 0.0 0.0 1.28e-13 4.28e-12 9.11e-11 1.31e-09 1.21e-08 5.68e-08 1.56e-07 3.53e-07 4.87e-07 6.65e-08 7.13e-07 4.37e-07 3.25e-07 1.35e-07 5.18e-08 7.09e-09 1.33641509995351e-09 0.0 0.0 4.8e-14 8.17e-13 9.35e-12 8.45e-11 3.05e-10 4.16e-11 1.63e-09 3.89e-09 8.55e-09 6.55e-09 6.83e-09 0.0 3.14e-13 7.44e-10 6.21333e-09 6.21333e-09 6.21333e-09 2.72e-07 1.11e-06 1.11e-06 1.349e-05 5.128e-05 0.0001265 0.00021446 0.00024472 0.00020169 0.00011374 3.476e-05 7.03e-06 1.13e-06 1.27e-07 1.152160003747e-08 4.82328999742e-10 1.25e-09 8.93e-10 6.34e-10 2.84e-10 1.2310399999999998e-10 1.79e-11 2.269998706e-12 0.0 0.0 1.78e-14 7.92e-13 3.38e-11 4.81e-10 3.01e-09 3.01e-09 6.27e-08 2.165e-07 2.165e-07 2.36e-06 6.84e-06 1.726e-05 2.442e-05 5.523e-05 4.809e-05 2.525e-05 8.28e-06 1.95e-06 2.290801e-07 2.230002997e-08 9.610374e-10 0.0 0.0 2.17e-14 1.62e-12 6.98e-11 1.58e-09 2.71e-08 2.78e-07 1.77e-06 7.73e-06 2.208e-05 4.41e-05 5.87e-05 4.177e-05 1.931e-05 7.37e-06 2.03e-06 4.69e-07 5.12e-08 6.6032e-09 4.7362799957e-10 0.0 0.0 0.0 0.0 1.61e-14 3.16e-13 3.16e-13 2.15e-11 4.97e-10 1.02e-10 1.05e-08 1.74e-08 1.28e-07 1.03e-06 5.47e-06 7.46e-07 2.057e-05 5.4155e-05 5.4155e-05 0.00022266 0.00027683 0.00022708 0.00014117 4.638e-05 1.285e-05 1.65e-06 1.350003592e-07 7.2300032350000005e-09 1.55807e-10 0.0 0.0 0.0 0.0 3.05e-12 4.16e-13 1.22e-11 3.29e-11 2.9e-10 3.96e-11 5.44e-10 1.47e-09 8.35e-09 7.47e-09 2.02e-08 4.24e-08 7.62e-08 7.3e-08 3.25e-08 3.25e-08 2.08e-08 9.26e-09 0.0 3.31e-12 0.0 0.0 0.0 0.0 6.2e-13 5.97e-06 2.21e-06 0.0001465 0.00072292 0.00072292 0.00373698 0.00373698 0.0114812 0.0101831 0.0380389 0.0205422 0.0205171 0.0268174 0.0106847 0.00296779 0.00059219 7.46009e-05 7.3e-06 4.1394608799999997e-07 3.498440665e-08 1.26474e-10 0.0 0.0 1.08e-12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.97e-09 1.02e-09 1.47e-07 2.075e-08 2.075e-08 2.95e-06 6.05e-07 1.08067e-05 1.08067e-05 1.08067e-05 0.00012927 2.648e-05 0.000143407 0.000143407 0.000143407 0.00049334 0.00010105 0.000380455 0.000380455 0.00029211 0.00029211 0.00031344 0.00031344 0.00023904 0.00023904 0.00015278 0.00015278 7.431e-05 7.431e-05 0.000250223 0.000250223 0.000250223 0.000125945 0.000125945 3.862e-05 2.499997844e-06 8.580624e-08 7.08528e-10 1.44e-11 0.0 0.0 2.95e-14 2.95e-14 5.56e-12 2.35e-10 3.21e-11 8.79e-09 1.9e-07 3.9e-08 3.36e-06 3.406e-05 6.98e-06 0.00025655 0.00114247 0.00323603 0.00638962 0.00802249 0.0069926 0.00387976 0.00147954 0.0003951 6.31858e-05 7.08e-06 4.57938e-07 2.160003747e-08 2.90165e-10 0.0 1.38e-11 7.58e-08 1.75e-06 2.209e-05 0.00017762 0.00098781 0.00429867 0.0121807 0.01867 0.0202381 0.00764987 0.00764987 0.00755943 0.00257726 0.0006046 9.285e-05 8.83406e-06 6.13e-07 2.4795e-08 7.510283999999999e-10 9.91395e-12 0.0 0.0 0.0 0.0 0.0 0.0 5.16e-11 1.43e-11 3.96e-12 6.57000461e-13 0.00084937 0.00378227 0.0112612 0.0227925 0.0333179 0.0343435 0.0248325 0.0123756 0.0042339 0.00070774 6.871e-05 2.76962e-06 2.97e-14 3.89345e-16 0.0 0.0 0.0 0.0 0.0 6.45e-14 6.45e-14 1.52e-11 1.05e-09 4.26e-08 9.939999e-07 1.464e-05 0.00013605 0.00816979 0.00816979 0.0294497 0.0178891 0.0178891 0.0302401 0.00950859 0.00950859 0.00812986 0.00243158 0.000472542 5.985e-05 3.31933e-06 1.02e-07 3.76703e-10 0.0 0.0 0.0 0.0 6.21e-13 6.28e-11 3.02e-09 6.18e-10 8.89e-08 5.22e-08 2.66e-06 5.45e-07 4.463e-05 0.00029998 6.144e-05 0.00150786 0.00042532 0.00054752 0.00629592 0.0 3.81e-14 4.02e-12 2.84e-10 1.02e-08 2.57e-07 4.18e-06 3.836e-05 0.00021547 0.00087142 0.00215973 0.00367976 0.00447388 0.00340995 0.00180501 0.00069928 0.00018812 3.56e-05 4.27e-06 3.34e-07 1.790477e-08 6.140005002e-10 7.36e-10 2.93e-09 1.13e-08 2.98e-08 8.04e-08 7.67e-08 6.72e-08 5e-08 2.64e-08 9.99e-09 1.9307930000000002e-09 3.050004781e-10 2.5300040640000002e-11 0.0 0.0 1.49e-13 1.09e-11 5.75e-10 1.79e-08 1.22e-07 2.72e-07 6.25e-06 1.923e-05 4.28e-05 0.00038695 0.00041826 0.00093097 0.00040695 0.00061829 0.00069343 0.0005223 0.0006696 0.00058756 0.00048187 0.00021207 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Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 2.87e-12 2.1e-11 2.74e-10 7.41e-10 3.54e-09 2.59e-08 2.785e-07 2.785e-07 3.625e-06 3.625e-06 1.79e-05 4.841e-05 3.849e-05 0.00028227 6.713e-05 1.769e-05 0.00012974 0.00020352 0.00012783 0.00012783 0.00018423 0.00018423 0.00027208 0.00027208 0.00025513 0.00025513 0.00033667 3.9995e-05 3.9995e-05 8.11e-06 8.8e-12 8.8e-12 1.66e-12 1.19e-13 0.0 0.0 0.0 0.0 2.7709e-10 3.12e-11 0.0 0.0 5.86e-13 3.64e-11 8.830000000000001e-10 1.76e-08 1.64e-07 2.53e-06 1.966e-05 9.21e-05 0.00023918 0.00049472 0.00028598 0.00028598 0.00051194 0.00030077 0.00011592 2.591e-05 4.08e-06 3.38e-07 1.97943e-08 2.81434e-10 2.13146e-11 0.0 1.44e-12 2.11e-10 1.46e-08 6.91e-07 8.495e-06 8.495e-06 0.00014075 0.00014075 0.00044154 0.0012567 0.00688953 0.0154085 0.0258966 0.0282516 0.0290933 0.0165309 0.00563552 0.00123309 0.00020093 1.83642e-05 1.28e-06 5.08901e-08 1.46e-09 3.38634e-11 3.1e-13 0.0 5.05e-12 1.9e-11 1.29e-09 8.05e-09 3.03e-08 1.38e-07 4.91e-07 7.45e-06 3.604e-05 1.333e-05 0.00025602 0.00027598 0.00058646 0.00211576 0.00314137 0.00347994 0.00215898 0.00099729 0.00028502 5.481e-05 6.7e-06 5.09852e-07 2.8e-08 5.38679e-10 2.03e-11 0.0 2.76e-14 2.14e-12 1.19e-10 4.4e-09 1.36e-07 4.3e-07 1.62e-06 7.51e-10 1.79e-09 6.75e-09 5.11e-08 4.17e-08 1.57e-07 8.63e-07 7.14e-07 2.69e-06 0.00018033 0.00023224 0.00023224 0.00047367 9.977e-05 9.977e-05 4.09e-06 1.19e-06 2.9e-07 6.92e-08 1.63e-08 2.49e-09 3.72e-10 1.42972e-06 1.91e-07 1.23e-08 3.03e-10 4.79e-13 5.34e-09 1.96e-07 8.1e-07 2.3e-06 3.535e-05 0.00023558 0.00154144 0.00533955 0.0113916 0.016495 0.0169191 0.0107864 0.00528279 0.00163842 0.00036571 4.776e-05 4.87e-06 2.89e-07 1.32e-08 4.03e-10 8.64e-12 1.06e-13 5.16e-09 1.06e-08 6.61e-09 3.57e-09 2.21e-09 6.48e-10 1.89e-10 4.74122e-11 5.37e-12 4.94e-13 2.549999682e-14 0.0 0.0 3.06e-12 1.98e-08 8.68e-07 1.953e-05 0.000134605 0.000134605 0.00106444 0.00106444 0.00539595 0.00539595 0.0235034 0.0169061 0.0169066 0.0327502 0.0223549 0.0109505 0.00439512 0.00102287 0.00012453 9.97e-06 5.62e-07 4.58063e-08 4.310126e-10 9.86287e-12 5.69e-14 2.29e-10 2.52e-09 2.33e-09 6.3e-09 2.46e-08 1.81e-08 6.42e-08 1.43e-07 2.47e-07 4.05e-07 2.98e-07 1.85e-07 4.99e-08 2.4e-08 5.23e-09 7.219999689999999e-10 4.77e-11 2.58e-12 3.96e-13 1.3e-11 2.97e-10 4.28e-09 4.03e-08 2.67e-07 1.01e-06 2.11e-06 2.43e-06 2.78e-06 2.08e-06 2.84e-07 1.27e-06 4.35e-07 9.74e-08 2.85e-08 7.28e-09 8.15e-10 8.765899999999999e-11 5.34e-13 8.12e-12 6.25e-11 4.86e-10 2.55e-09 8.06e-09 1.42e-08 1.94e-09 1.94e-08 3.13e-08 4.21e-08 2.54e-08 1.6e-08 2.6e-13 7.67e-10 3.19e-07 1.07333e-06 1.07333e-06 1.07333e-06 2.053e-05 4.1655e-05 4.1655e-05 0.00022512 0.00040703 0.00054079 0.00048036 0.00029873 0.00013733 4.083e-05 6.02e-06 5.68e-07 4.56e-08 3.04e-09 1.130414e-10 2.54e-12 1e-09 8.35e-10 2.18e-10 4.52e-11 1.14e-11 1.2e-12 1.38e-13 0.0 5.33e-14 1.24e-12 2.2e-11 4.34e-10 4.42e-09 2.065e-08 2.065e-08 3.73e-07 7.25e-07 7.25e-07 4.82e-06 7.6e-06 2.149e-05 3.026e-05 2.696e-05 1.253e-05 4.68e-06 8.89e-07 1.18e-07 9.81134e-09 4.64e-10 1.23e-11 0.0 2.68e-14 9.41e-11 2.77e-09 4.69e-08 5.27e-07 3.48e-06 1.629e-05 5.147e-05 0.00011181 0.0001566 0.00016967 0.00011002 3.983e-05 8.56e-06 1.79e-06 2.87e-07 2.85e-08 1.64e-09 6.16347e-11 2.74e-12 0.0 0.0 0.0 1.68e-14 9.09e-13 1.19e-11 1.19e-11 5.89e-10 1.08e-08 2.22e-09 1.28e-07 1.16e-07 8.49e-07 3.72e-06 2.016e-05 2.75e-06 7.78e-05 7.6695e-05 7.6695e-05 0.00017937 0.00015647 7.92e-05 2.719e-05 6.44e-06 8.54e-07 6.95e-08 3.61e-09 1.06e-10 5.73072e-12 0.0 0.0 1.28e-11 1.75e-12 2.23e-09 3.04e-10 3.65e-09 9.88e-09 3.61e-08 4.92e-09 3.09e-08 8.35e-08 2.43e-07 7.95e-08 2.15e-07 2.45e-07 1.19e-07 8.44e-08 2.4e-08 2.4e-08 1.33e-08 3.45e-09 1.9e-12 0.0 0.0 0.0 7.33e-14 2.3e-12 1.782e-05 0.00015417 5.702e-05 0.00188672 0.00445413 0.00445413 0.0114763 0.0114763 0.018751 0.0166286 0.0336358 0.0107899 0.0107883 0.00658572 0.00135574 0.00021474 2.273e-05 1.7195e-06 9.18e-08 1.1975e-09 4.49307e-11 0.0 0.0 1.84e-13 9.59e-10 0.0 0.0 0.0 0.0 0.0 0.0 5.66e-14 1.16e-14 3.09333e-13 3.09333e-13 3.09333e-13 1.02e-11 2.09e-12 4.36e-06 6.15e-07 6.15e-07 5.572e-05 1.141e-05 9.108e-05 9.108e-05 9.108e-05 0.00039444 8.079e-05 0.000174653 0.000174653 0.000174653 0.00036354 7.446e-05 0.000146975 0.000146975 0.00010877 0.00010877 8.1115e-05 8.1115e-05 4.346e-05 4.346e-05 2.137e-05 2.137e-05 0.00011258 0.00011258 2.55967e-05 2.55967e-05 2.55967e-05 7.465e-06 7.465e-06 1.13e-06 4.40999639e-08 7.840191e-10 1.73811e-11 5e-14 0.0 2.56e-13 9.7e-12 9.7e-12 9.01e-10 2.36e-08 3.22e-09 4.87e-07 5.33e-06 1.09e-06 5.777e-05 0.00029412 6.024e-05 0.00138049 0.0035749 0.00576568 0.00646575 0.00501901 0.00249945 0.00086218 0.0001795 2.904e-05 2.77946e-06 1.74e-07 6.69e-09 1.99e-10 3.30567e-11 7.52e-13 1e-08 1.027e-05 0.00011753 0.00070843 0.00287141 0.00776558 0.018692 0.0266767 0.0217997 0.0130291 0.00264684 0.00264684 0.00138578 0.00025476 3.035e-05 2.39e-06 1.16966e-07 4.13e-09 9.15e-11 1.45e-12 0.0 0.0 0.0 9.36e-14 9.36e-14 8.95e-13 8.95e-13 1.62e-11 5.54e-12 5.23e-13 4.3099974499999996e-14 0.00750669 0.0180109 0.0304292 0.0354266 0.0312074 0.0189555 0.00823339 0.00236828 0.00044093 4.226e-05 1.95e-06 3.85925e-08 0.0 0.0 0.0 0.0 0.0 1.49e-14 1.84e-12 7.55e-11 7.55e-11 8.14e-09 2.57e-07 5.27e-06 5.771e-05 0.00043299 0.00212405 0.0154278 0.0154278 0.0329038 0.0115462 0.0115462 0.0120514 0.00215551 0.00215551 0.00114598 0.00019084 2.21836e-05 1.47e-06 5.06853e-08 6.92e-10 7.4795e-12 0.0 5.93e-14 6.85e-12 5.14e-10 2.08e-08 4.96e-07 1.02e-07 6.78e-06 3.98e-06 0.00010308 2.111e-05 0.00087502 0.00309652 0.00063423 0.00824062 0.0023244 0.00174506 0.020068 2.28e-12 1.13e-10 5.93e-09 1.6e-07 2.66e-06 2.985e-05 0.00020319 0.00085213 0.00252901 0.00490819 0.00667577 0.00553344 0.00360959 0.00144878 0.00044528 9.119e-05 1.343e-05 1.22e-06 8.18e-08 3.12e-09 9.58e-11 1.66e-12 3.57e-09 1.59e-08 2.41e-08 2.91e-08 4.47e-08 3.17e-08 2.43e-08 1.55e-08 4.77e-09 1.12e-09 1.180183e-10 2.07e-11 1.72e-12 5.96e-14 4.84e-12 2.53e-10 9.31e-09 2.01e-07 3.24e-06 1.028e-05 2.288e-05 0.0002359 0.00032028 0.00071289 0.00320603 0.00152579 0.00339611 0.00520976 0.00294021 0.00151607 0.00083289 0.00062371 0.00044955 0.00019572 2.908e-05 1.55e-06 3.13e-08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.85e-11 8.79e-10 3.09e-08 5.72e-07 7.1e-06 1.482e-05 4.006e-05 0.00029842 0.0009915 0.00210994 0.00104413 0.00104413 0.00104413 0.00309578 0.00107455 0.00107455 0.00108038 0.00037102 8.949e-05 1.472e-05 1.56e-06 1.21e-07 5.81e-09 1.260136e-10 1.76e-12 9.91e-10 3.02e-08 5.64e-07 4.94e-06 4.94e-06 9.712e-05 4.858e-05 0.00049117 0.00217947 0.00532217 0.008751 0.00497267 0.00497267 0.00804978 0.00435859 0.00154631 0.000398 6.623e-05 7.74e-06 6.52e-07 3.57e-08 1.25e-09 3.05e-11 4.41e-13 1.62e-09 5.77433e-11 0.0 6.94e-12 1.26e-08 2.96e-07 5.31e-06 2.7115e-05 2.7115e-05 0.00039162 0.00152876 0.00442937 0.00138188 0.00674715 0.0105205 0.00875107 0.00503974 0.00210018 0.00057654 9.747e-05 1.08e-05 8.45e-07 1.1418e-07 3.31e-10 1.61e-09 2.74e-08 2.74e-08 1.3e-07 9.52e-07 3.08e-06 1.091e-05 0.00010488 1.43e-05 0.00034478 0.00034478 0.00280332 0.00364766 0.00364766 0.01245472 0.00658904 0.00658904 0.00756702 0.00210863 0.00042638 7.632e-05 1.701e-05 4.73e-06 1.3e-06 6.64e-06 2.13e-07 2.14e-09 8.69e-12 0.0 0.0 6.47e-13 3.17e-07 5.1e-06 5.506e-05 0.00038844 0.00197545 0.00639628 0.0152496 0.0242907 0.0270261 0.0178424 0.00730872 0.0014408 2.77e-06 1.09e-07 4.87e-09 3.08e-10 2.79e-11 2.28e-12 2.28962e-08 1.35e-10 2.64e-13 0.0 0.0 6.57e-12 3.64e-10 1.29e-08 0.0 0.0 0.0 1.35e-12 2.3e-12 1.16e-09 4.21e-08 7.17e-08 3.96e-06 3.92e-07 5.2e-07 4.14e-06 4.82667e-06 4.82667e-06 4.82667e-06 5.445e-05 9.531e-05 6.581e-05 6.581e-05 0.00068171 0.00080513 0.00111182 0.00537024 0.00448746 0.00619695 0.0146888 0.00512428 0.00454417 0.00347812 0.00033053 0.00033053 7.257e-05 4.98e-06 1.68e-07 3.810145e-09 7.87e-11 0.0 0.0 0.0 5.73e-13 2.89e-11 7.53e-10 3.42e-09 2.51e-08 5.92e-07 5.72e-06 7.8e-07 4.263e-05 2.348e-05 0.00017218 0.00054617 0.00092175 0.00018879 0.00161332 0.00155776 0.00096852 0.00040427 9.898e-05 1.61e-05 1.70972e-06 1.09979e-07 4.91e-09 1.4761e-10 2.4e-12 0.0 0.0 2.52e-14 2.63e-12 1.47e-10 5.16e-09 1.18e-07 1.77e-06 1.676e-05 9.129e-05 0.00035577 0.00082594 0.00139361 0.00152289 0.00123874 0.00068104 0.00027817 7.1e-05 1.449e-05 1.72e-06 1.04e-07 3.7e-09 1.160195e-10 2.84e-12 3.62e-14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.76e-08 2.13e-07 8.01e-07 2.008e-05 3.679e-05 0.00010471 0.00039777 0.00046523 0.00016346 0.00087524 0.00095257 0.00033469 0.00229957 0.00219258 0.00077037 0.00203819 0.00203819 0.00257122 0.0009034 0.0015418 0.0015418 0.000796955 0.000796955 0.00038474 4.51e-05 2.79e-06 8.96826e-08 1.9200466e-09 1.93e-11 4.57e-06 2.97942e-07 1.29e-08 4.08097e-10 7.19e-12 2.04e-12 1.41e-10 3.565e-09 3.565e-09 2.19e-07 3.57e-06 7.32e-07 4.835e-05 0.00035929 0.00177045 0.00551529 0.0115219 0.015767 0.0169998 0.0114819 0.00534557 0.00151116 0.00032593 4.57758e-05 0.0 7.62e-12 4.1e-09 4.39e-08 4.34e-09 4.19e-07 2.01e-06 1.98e-07 8.09e-06 2.013e-05 3.159e-05 2.558e-05 1.295e-05 6.06e-06 2.36e-06 5.4e-07 8.18e-08 7.46e-09 9.15e-10 9.0107e-11 4.11e-12 0.00015526 0.00097504 0.00193141 0.00193141 0.0111039 0.021852 0.0312545 0.0304733 0.0218249 0.00984816 0.00268251 0.00036927 2.482e-05 1.55e-12 1.12e-14 0.0 1.25824e-17 0.0 0.0 3.76891e-12 0.0 3.12e-11 6.38e-12 3.395e-08 3.395e-08 1.31e-06 1.47e-05 3.01e-06 0.0 0.0 0.0 1.1e-13 6.01e-11 5.15e-09 5.15e-09 1.35e-12 3.98e-12 1.5e-11 2.21e-10 5.28e-10 1.98e-09 3.64e-08 8.91e-08 2.54e-07 3.16e-06 0.00026732 0.00076083 0.00501029 0.00425059 0.0120978 0.0295829 0.00719941 0.0204907 0.0161442 0.00523805 0.00119311 0.00011999 8.74e-06 4.88e-07 1.94e-08 4.9782e-10 9.92e-12 1.99e-12 1.59e-11 8.83e-11 2.6e-10 1.11e-09 3.47e-09 5.13e-09 7.3e-09 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.395e-08 1.395e-08 9.19e-07 5.8e-06 1.652e-05 0.00032436 0.00059452 0.00169209 0.00283351 0.00766097 0.0069041 0.01965 0.0487247 0.0382922 0.0222787 0.00868982 0.00252478 0.00048226 7.565e-05 6.1788e-06 2.72955e-07 7.02e-09 1.43e-10 1.32616e-11 0.00086461 0.00015632 1.767e-05 1.40959e-06 7.38e-08 1.30794e-09 7.24157e-11 0.0 6.7e-13 3.54e-09 1.11e-07 4.18e-07 2.04e-06 1.6235e-05 1.6235e-05 0.000142495 0.000142495 0.00146382 0.00264228 0.00264228 0.013261 0.0225083 0.00222151 0.0224619 0.00895229 0.00895229 0.00460982 0.00460979 0.00345427 0.0 4.15e-14 3.36e-13 3.26e-12 4.44e-13 3.11e-11 1.11e-10 4.15e-10 1.56e-12 4.19e-11 3.8e-10 4.27e-10 2.08e-09 2.05e-08 1.36e-08 6.63e-08 3.25e-07 1.19e-06 2.11e-06 2.99e-06 2.1e-06 2.44e-06 1.45e-06 5.03e-07 9.37e-08 1.3e-08 9.560661e-10 4.37e-11 0.0171128 0.00748187 0.00227482 0.00046075 6.733e-05 6.74869e-06 4.39928e-07 6.42246e-09 4.49e-10 1.46741e-11 2.13e-14 2.38e-13 2.32e-11 1.45e-09 2.925e-08 2.925e-08 1.42e-06 2.087e-05 0.0001988 0.00134423 0.00549877 0.0153309 0.0252364 0.031449 0.0268845 - - - - - - - - - - - - 0.0253 500000.0 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 1.27e-13 8.52001e-13 2.46e-11 5.73e-11 5.02e-10 3.36e-09 5.8e-08 5.8e-08 1.23e-06 1.23e-06 6.75001e-06 1.574e-05 2.033e-05 0.00013606 5.121e-05 7.32001e-06 4.897e-05 7.61701e-05 8.83801e-05 8.83801e-05 8.22501e-05 8.22501e-05 9.36401e-05 9.36401e-05 9.09601e-05 9.09601e-05 0.00010993 2.0405e-05 2.0405e-05 5.81e-06 4.625e-11 4.625e-11 1.38e-11 1.52e-12 1.81e-13 1.97e-14 0.0 0.0 1.0049745589999999e-08 7.32001e-11 0.0 0.0 1.81e-13 9.06001e-12 4.14e-10 1.36e-08 2.32e-07 3.01e-06 1.942e-05 9.62501e-05 0.00031084 0.000869651 0.000674411 0.000674411 0.00146826 0.00086089 0.00034244 0.00011243 2.797e-05 3.41e-06 3.87914e-07 2.386945401e-08 2.25561e-10 1.18765e-11 0.0 1.85e-14 3.81e-12 5.44e-10 3.99e-08 5.9e-07 5.9e-07 1.6025e-05 1.6025e-05 6.443e-05 0.00015774 0.00144293 0.00622535 0.0248148 0.0235798 0.0207696 0.0247734 0.014631 0.00596694 0.00147893 0.000258488 4.457e-05 3.70964e-06 2.22e-07 2.43572e-08 1.48e-10 5.92138e-12 0.0 8.72001e-13 2.92e-12 1.85e-10 1.53e-09 5.14e-09 3.74e-08 1.12e-07 2.88e-06 1.94e-05 8.31001e-06 0.00017813 0.00021516 0.00038251 0.00141649 0.00270118 0.00408336 0.00335709 0.00273189 0.00114138 0.00034603 6.72301e-05 1.02855e-05 8.52001e-07 7.118910709999999e-08 1.4700396e-09 2.46245e-11 0.0 0.0 1.16e-13 1.18e-11 7.46001e-10 2.27e-08 1.11e-07 3.72e-07 1.13e-10 1.97e-10 6.61e-10 5.69e-09 1.1e-08 3.69e-08 1.76e-07 1.33e-07 4.47e-07 6.1e-05 6.88401e-05 6.88401e-05 0.0002169 6.07e-05 6.07e-05 7.73001e-06 2.98e-06 1.03e-06 3.5e-07 1.22e-07 6.77001e-08 7.64001e-09 6.79996e-06 3.72e-07 4.4100043769999994e-08 6.93001e-10 0.0 4.01e-11 2.25e-09 2.18e-08 5.34e-08 1.54e-06 1.72e-05 0.00017367 0.000815831 0.00401882 0.00750559 0.0111361 0.0126277 0.0141309 0.00812518 0.00375296 0.000783301 0.00017131 2.423e-05 1.44e-06 6.06941e-08 1.2500005223000002e-09 2.68e-11 1.92e-08 4.04e-08 5.65e-08 5.36e-08 2.82e-08 1.19e-08 3.41e-09 5.88204e-10 9.30122e-11 1.050004621e-11 3.530005635e-13 3.9300070980000003e-14 0.0 5.02e-14 8.57001e-10 4.93e-08 2.17e-06 2.266e-05 2.266e-05 0.000218385 0.000218385 0.00268731 0.00268731 0.00902371 0.010506 0.0101232 0.0417147 0.0228048 0.0230415 0.00777646 0.00401723 0.000883921 0.00014258 1.23e-05 3.81936e-07 4.590419e-08 3.65428e-10 2.67e-11 2.39938e-12 2.24e-10 2.81e-09 6.80001e-09 1.59e-08 1.28e-07 9.71001e-08 2.91e-07 9.79001e-07 1.85e-06 2.13e-06 2.38e-06 1.98e-06 8.83001e-07 2.74e-07 4.59e-08 5.73000036e-09 4.450261e-10 2.54e-11 0.0 3.1e-14 2.85e-12 1.3e-10 2.97e-09 4.98e-08 4.27e-07 2.6e-06 1.017e-05 2.007e-05 2.062e-05 3.08e-06 1.97e-05 1.34e-05 8.27001e-06 2.73e-06 7.44001e-07 1.57e-07 2.19926362e-08 0.0 4.21e-13 1.28e-11 2.02e-10 1.73e-09 9.96001e-09 3.87e-08 5.78e-09 1.78e-07 3.47e-07 5.41e-07 6.49e-07 5.53e-07 0.0 6.11e-13 1.23e-09 1.20667e-08 1.20667e-08 1.20667e-08 8.20001e-07 3.87e-06 3.87e-06 5.221e-05 0.00017563 0.00064134 0.00109495 0.00121732 0.00103057 0.00054017 0.00018905 5.374e-05 6.46e-06 5.57e-07 2.9025500046660002e-08 1.330391e-09 6.6e-09 5.38e-09 3.2e-09 1.08e-09 2.260930001e-10 3.3e-11 3.530005635e-12 0.0 0.0 5.27e-13 2.23e-11 4.59e-10 7.39001e-09 4.435e-08 4.435e-08 5.92e-07 1.88e-06 1.88e-06 1.795e-05 4.734e-05 9.57601e-05 0.00010571 9.18801e-05 5.199e-05 2.368e-05 5.98e-06 1e-06 8.541830000000001e-08 4.4699988140000005e-09 1.95e-10 0.0 0.0 4.03e-14 3.78e-12 2.49e-10 7.80001e-09 1.49e-07 1.6e-06 1.579e-05 7.91601e-05 0.00021536 0.00047572 0.00056536 0.00050552 0.00032388 0.00010436 2.161e-05 3.15e-06 3.65e-07 3.49423e-08 1.770816001e-09 0.0 0.0 0.0 0.0 3.72e-13 7.85001e-12 7.85001e-12 5.62e-10 7.67001e-09 1.8e-09 1.66e-07 2.45e-07 1.64e-06 1.29e-05 5.2e-05 7.77001e-06 0.00016454 0.000159075 0.000159075 0.0004622 0.00050694 0.00035081 0.00014703 3.62e-05 5.06e-06 6.58e-07 5.630004545e-08 2.5500046660000002e-09 8.66979e-11 0.0 0.0 1.53e-13 2.28e-14 1.86e-10 2.78e-11 1.05e-09 2.45e-09 3.48e-08 5.2e-09 6.16e-08 1.44e-07 6.75001e-07 4.03e-07 9.40001e-07 2.39e-06 3.39e-06 2.79e-06 8.44001e-07 8.44001e-07 9.09001e-07 3.04e-07 6.05e-14 0.0 0.0 0.0 0.0 9.00001e-14 3.24e-06 3.41e-05 1.462e-05 0.000710881 0.00187261 0.00187261 0.00738223 0.00738223 0.0189424 0.0185627 0.0301684 0.0059909 0.00855614 0.0149102 0.0045082 0.00110472 0.00014881 2.22056e-05 9.98001e-07 9.9611658e-08 4.16403e-10 2.98449e-11 0.0 1.2e-14 8.34001e-11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.26667e-14 2.26667e-14 2.26667e-14 6.79001e-13 1.59e-13 1.03e-06 1.715e-07 1.715e-07 1.184e-05 2.78e-06 2.962e-05 2.962e-05 2.962e-05 0.00016493 3.869e-05 0.000124707 0.000124707 0.000124707 0.00028469 6.67801e-05 0.000146875 0.000146875 0.00014062 0.00014062 0.00012684 0.00012684 0.00024492 0.00024492 8.72551e-05 8.72551e-05 0.00032213 0.00032213 3.146e-05 3.146e-05 3.146e-05 1.876e-05 1.876e-05 1.83e-06 2.0300036000000001e-07 5.870193e-09 5.12191e-11 1.66e-12 0.0 1.15e-14 5.55e-13 5.55e-13 7.45001e-11 3.38e-09 5.05e-10 1.09e-07 1.45e-06 3.4e-07 1.685e-05 8.00501e-05 1.878e-05 0.0004966 0.0016892 0.00352407 0.00643825 0.00695456 0.00501024 0.0027709 0.00110891 0.0002844 4.81688e-05 4.68e-06 2.49976e-07 8.46001e-09 1.1954e-10 0.0 1.78e-10 3.36e-07 5.94e-06 7.11901e-05 0.00049835 0.00211355 0.00858976 0.013221 0.0176407 0.018914 0.00547933 0.00547933 0.00536409 0.00231338 0.00056034 9.10301e-05 7.26565e-06 5.27e-07 2.64941e-08 4.750136e-10 5.97884e-12 0.0 0.0 3.29e-14 3.29e-14 5.3e-13 5.3e-13 1.52e-10 5.01e-11 1.04e-11 1.310002717e-12 0.00177835 0.00820933 0.0333032 0.0334874 0.0418861 0.0271356 0.0132873 0.00393432 0.00062247 7.60301e-05 4.17e-06 1.25988e-07 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.815e-14 3.815e-14 8.68001e-12 6.29e-10 3.43e-08 1.14e-06 2.297e-05 0.00024758 0.00988897 0.00988897 0.0331373 0.0216247 0.0216247 0.0193742 0.00432371 0.00432371 0.00216239 0.00035622 3.94079e-05 1.97e-06 8.31815e-08 1.38e-09 1.24108e-11 0.0 0.0 0.0 3.56e-13 3.19e-11 1.89e-09 4.44e-10 4.76e-08 3.31e-08 1.63e-06 3.82e-07 3.22e-05 0.00028709 6.73401e-05 0.00165565 0.00055199 0.000674561 0.00775713 0.0 3.13e-13 2.81e-11 1.11e-09 3.93e-08 6.17e-07 8.09001e-06 5.602e-05 0.00040405 0.00142529 0.00411541 0.00536747 0.00759718 0.00768483 0.00366102 0.00125942 0.0002346 4.519e-05 4.46e-06 2.59e-07 1.300129e-08 3.320007014e-10 7.93001e-09 3.69e-08 1.29e-07 2.81e-07 3.85e-07 3.88e-07 3.16e-07 1.38e-07 6.37e-08 1.89e-08 3.300615e-09 3.4000048939999997e-10 2.16e-11 0.0 5.18e-14 5.77e-12 4.44e-10 2.44e-08 6.81001e-07 3.45e-06 6.4e-06 0.00010086 0.00023049 0.00042805 0.00214173 0.00161351 0.00299651 0.0127335 0.00345714 0.00152229 0.00139039 0.000522 0.00026841 7.66101e-05 1.049e-05 9.16001e-07 2.6e-08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.94e-14 1.91e-12 9.55001e-11 3.42e-09 8.07001e-08 4.26e-07 9.94001e-07 1.471e-05 0.00011042 0.00040876 0.000465947 0.000465947 0.000465947 0.00382959 0.00220641 0.00220641 0.00408443 0.00191181 0.00105964 0.00027705 4.827e-05 6.41e-06 4.97e-07 2.430594e-08 9.610790001e-10 4.33e-12 2.44e-10 8.35001e-09 1.17e-07 1.17e-07 3.37e-06 3.9e-06 3.511e-05 0.00022228 0.000981121 0.00261539 0.00331551 0.00331551 0.00972385 0.0104559 0.00587881 0.00318157 0.00133602 0.00022108 2.934e-05 1.76e-06 1.23e-07 3.810286e-09 7.94001e-11 2.83e-08 2.67915e-10 1.09351e-11 0.0 2.87e-13 1.05e-09 2.6e-08 4.55e-07 2.89e-06 2.89e-06 5.476e-05 0.00028132 0.00137827 0.00062172 0.00264981 0.00496582 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1.92e-09 0.0 0.0 0.0 9.35001e-14 8.12001e-12 4.11e-10 1.84e-09 1.23e-08 2.62e-07 2.79e-06 4.16e-07 2.795e-05 2.411e-05 0.00016133 0.000729731 0.00142458 0.00033417 0.00253587 0.0023697600000000003 0.00204809 0.00123083 0.00043239 0.00012866 2.19661e-05 2.28978e-06 1.63988e-07 3.448439665e-08 2.33e-10 0.0 0.0 0.0 0.0 1.25e-13 8.41001e-12 4.57e-10 1.29e-08 3e-07 2.9e-06 3.061e-05 0.00021971 0.000737301 0.00165814 0.00212517 0.00279174 0.00199308 0.000849781 0.00031502 6.241e-05 9.00001e-06 9.26001e-07 4.3114600000000004e-08 1.280163e-09 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.1e-09 3.56e-08 1.19e-07 4.67e-06 1.23e-05 3.012e-05 0.00015924 0.00018199 7.43301e-05 0.00042805 0.00051943 0.00021216 0.00136642 0.00398824 0.0016357 0.00275934 0.00275934 0.00431026 0.00176003 0.00124336 0.00124336 0.0013715 0.0013715 0.00044472 0.00015383 1.55e-05 5.19949e-07 9.970357e-09 3.77e-10 4.228e-05 2.71973e-06 1.50989e-07 1.32058e-09 3.47e-11 4.8e-14 3.58e-12 8.60001e-11 8.60001e-11 6.49e-09 1.3e-07 3.04e-08 2.44e-06 2.899e-05 0.00019938 0.00125314 0.00299448 0.00732552 0.0131019 0.01603 0.0131802 0.00637983 0.00207632 0.000361985 0.0 0.0 2.37e-11 6.80001e-10 7.56001e-11 2.39e-08 3.01e-07 3.34e-08 2.73e-06 1.52e-05 4.962e-05 0.00010442 0.00016885 0.000123 6.395e-05 2.121e-05 6.52e-06 1.56e-06 2.23e-07 2.26701109e-08 1.942050001921e-09 9.51001e-06 0.00015002 0.000711901 0.000711901 0.00994248 0.0156783 0.028383 0.033785 0.0275048 0.0112793 0.00419165 0.0006422 6.595e-05 9.94001e-11 4.75e-13 0.0 1.20329e-16 0.0 2.2773e-19 4.72164e-13 0.0 0.0 0.0 1.015e-10 1.015e-10 1.24e-08 3.9e-07 9.14001e-08 0.0 0.0 0.0 0.0 3.83e-12 4.22e-10 4.22e-10 6.58e-14 1.69e-13 5.67e-13 9.53001e-12 2.75e-11 9.20001e-11 1.54e-09 1.32e-08 3.23e-08 6.51e-07 0.00014506 0.00035514 0.00285617 0.00266083 0.0113146 0.0371839 0.0100644 0.0246404 0.0306759 0.00974142 0.00177899 0.00063597 7.45101e-05 6.09e-06 3.1000044950000004e-07 7.444883307e-08 2.44e-10 2.47e-13 3.78e-12 5e-11 5.34e-10 2.99e-09 1.16e-08 3.78e-08 7.70001e-08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.70001e-10 8.70001e-10 8.19001e-08 8.80001e-07 2.15e-06 5.617e-05 0.00020294 0.00049684 0.00147284 0.00343663 0.0155489 0.00296642 0.0258672 0.0392272 0.0316144 0.018974 0.00713154 0.00272632 0.00037016 6.74004e-05 5.74959e-06 3.0297e-07 9.710003177000001e-09 8.5362e-11 0.00330794 0.000712751 0.0001178 5.90868e-06 2.89e-07 1.64984e-09 7.01005e-11 1.30139e-11 0.0 2.62e-11 1.09e-09 7.48001e-09 3.19e-08 3.505e-07 3.505e-07 3.61e-06 3.61e-06 8.72501e-05 0.00037278 0.00037278 0.00354796 0.00978149 0.0016632 0.0149689 0.0113427 0.0113427 0.00953374 0.0095335 0.0101158 0.0 0.0 1.46e-13 1.88e-12 2.81e-13 2.48e-11 2.09e-10 1.24e-09 6.42e-13 2.07e-11 4.72e-10 1.26e-09 5.39e-09 5.37e-08 5.87e-08 2.5e-07 1.5e-06 3.83e-06 1.075e-05 2.053e-05 2.446e-05 1.903e-05 9.05001e-06 2.91e-06 6.65001e-07 1.03e-07 1.0000822001000001e-08 5.68e-10 0.0294088 0.017253 0.00891595 0.00131769 0.00021476 1.67484e-05 9.67931e-07 1.3211e-07 5.67e-10 1.85909e-11 3.94e-14 0.0 4.22e-14 4.21e-12 1.13e-10 1.13e-10 7.51001e-09 2.37e-07 5.5e-06 6.66001e-05 0.00048133 0.00291273 0.0100733 0.023823 0.0312435 - - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 3.42e-13 2.51e-12 6.41e-11 1.73e-10 1.66e-09 1.22e-08 2.07e-07 2.07e-07 3.615e-06 3.615e-06 1.562e-05 4.223e-05 3.33e-05 0.00024417 0.00071687 2.05e-05 0.00015031 0.00026387 0.00018244 0.00018244 0.00019793 0.00019793 0.00020876 0.00020876 0.00021346 0.00021346 0.00031853 6.045e-05 6.045e-05 1.993e-05 6.15e-07 6.15e-07 3.06e-12 2.93e-13 3.45e-14 0.0 0.0 0.0 4.30385e-10 3.78e-11 0.0 1.96e-14 1.38e-12 5.94e-11 1.7790000000000002e-09 3.53e-08 4.65e-07 4.11e-06 2.233e-05 9.259e-05 0.00025439 0.00050363 0.00033267 0.00033267 0.00063343 0.00040148 0.00017949 4.744e-05 8.58e-06 8.7e-07 6.02905e-08 7.96369e-10 5.84318e-11 2.87447e-12 0.0 1.38e-13 2.7e-11 2.79e-09 1.64e-07 2.305e-06 2.305e-06 3.85e-05 3.85e-05 0.00015646 0.00044532 0.00302316 0.00937246 0.0199503 0.0263814 0.0286149 0.0201491 0.0104464 0.00345601 0.00077996 0.000106863 1.018e-05 5.58912e-07 2.1e-08 1.93202e-10 5.93e-12 0.0 0.0 3.03e-12 1.14e-11 5.75e-10 3.52e-09 1.32e-08 7.11e-08 2.52e-07 4.04e-06 2.33e-05 8.62e-06 0.00018272 0.00021856 0.00046443 0.00190138 0.0032461699999999996 0.00400878 0.0031061 0.00174934 0.00059517 0.00013302 1.873e-05 1.63974e-06 9.16e-08 1.14665e-09 6.84e-11 3.50276e-12 0.0 0.0 6.24e-13 6.66e-11 3.79e-09 1e-07 2.94e-07 1.1e-06 1.057e-05 1.43e-09 5.38e-09 3.96e-08 3.88e-08 1.46e-07 6.59e-07 3.96e-07 1.49e-06 5.4e-06 0.0001048 0.0001048 0.00035498 0.00012708 0.00012708 7.883e-05 1.5e-06 4.84e-07 1.79e-07 3.65e-08 7.21e-09 8.73e-10 2.73957e-06 2.78e-07 1.240004773e-08 2.67e-10 2.51e-14 3.28e-10 1.38e-08 9.01e-08 2.57e-07 5.41e-06 4.964e-05 0.00035218 0.00168503 0.00544961 0.0116433 0.0173301 0.0164648 0.0115263 0.00497066 0.0015517 0.00028289 3.438e-05 2.6e-06 1.28e-07 3.87939e-09 7.91e-11 1.1e-12 5.13e-08 6.94e-08 6.33e-08 4.61e-08 2.41e-08 8.25e-09 1.77e-09 2.7406139999999996e-10 2.94e-11 2.08e-12 2.599998394e-13 1.88e-14 0.0 3.99e-13 3.8e-09 2.08e-07 6.6e-06 5.5475e-05 5.5475e-05 0.00050818 0.00050818 0.00267808 0.00267808 0.0150723 0.0138828 0.0138835 0.0352205 0.0297877 0.0171565 0.00742317 0.00210168 0.00039941 4.939e-05 3.73e-06 2.47146e-07 5.52025e-09 8.78047e-11 1.22e-12 0.0 1.41e-09 1.1e-08 1.61e-08 4.35e-08 2.45e-07 1.63e-07 5.79e-07 1.54e-06 2.21e-06 2.31e-06 1.77e-06 9.25e-07 2.98e-07 7.04e-08 1e-08 1.050004928e-09 6.27e-11 2.77e-12 0.0 5.74e-13 2.15e-11 5.2e-10 7.57e-09 7.93e-08 4.68e-07 2.01e-06 5.52e-06 1.067e-05 1.214e-05 1.66e-06 1.257e-05 7.96e-06 3.53e-06 1.1e-06 2.62e-07 3.87e-08 4.1511e-09 3.12e-14 1.04e-12 2.15e-11 3.05e-10 2.91e-09 1.76e-08 6.18e-08 8.42e-09 1.86e-07 3.48e-07 4.66e-07 3.91e-07 2.58e-07 0.0 1.57e-11 2.16e-08 1.23333e-07 1.23333e-07 1.23333e-07 4.23e-06 1.4555e-05 1.4555e-05 0.00012033 0.00031839 0.00057628 0.00066097 0.00057073 0.00032627 0.00013076 3.678e-05 7.39e-06 9.65e-07 8.34e-08 4.82254e-09 1.980357e-10 1.14e-08 6.09e-09 2.23e-09 6e-10 1.220336e-10 1.47e-11 1.16e-12 0.0 9.5e-14 3.89e-12 1.1e-10 2.16e-09 2.76e-08 1.22e-07 1.22e-07 1.45e-06 3.115e-06 3.115e-06 1.874e-05 3.781e-05 5.644e-05 5.376e-05 3.958e-05 1.82e-05 6.09e-06 1.29e-06 1.77e-07 1.530197e-08 8.48e-10 2.67e-11 0.0 0.0 1.73e-12 9.49e-11 3.15e-09 6.75e-08 7.67e-07 5.9e-06 2.716e-05 8.297e-05 0.00016644 0.000239 0.00021503 0.00014844 6.918e-05 2.345e-05 5.14e-06 8.2e-07 8.75e-08 6.22491e-09 2.780799e-10 0.0 0.0 0.0 5.04e-14 2.87e-12 4.82e-11 4.82e-11 2.45e-09 3.05e-08 6.24e-09 4.19e-07 3.6e-07 2.64e-06 1.543e-05 4.57e-05 6.23e-06 0.00012044 0.00010105 0.00010105 0.00024165 0.00019578 0.00011238 4.062e-05 1.023e-05 1.59e-06 1.54e-07 9.20999666e-09 3.22e-10 9.4716e-12 0.0 0.0 7.66e-13 1.05e-13 3.81e-10 5.19e-11 1.32e-09 3.58e-09 3.33e-08 4.55e-09 5.01e-08 1.36e-07 6.57e-07 3.86e-07 1.04e-06 2.27e-06 2.28e-06 1.76e-06 4.77e-07 4.77e-07 3.56e-07 9.41e-08 1.3e-14 0.0 0.0 0.0 3.27e-14 1.63e-12 9.25e-06 8.475e-05 3.134e-05 0.00102562 0.00267519 0.00267519 0.00855311 0.00855311 0.0166039 0.0147246 0.0353798 0.0127829 0.0127791 0.0106327 0.00315061 0.00062152 8.13e-05 6.82893e-06 4.07e-07 5.0055462439999995e-08 2.37183e-10 9.51872e-12 0.0 1.11e-13 4.17e-10 1.51e-09 7.38e-09 0.0 0.0 0.0 0.0 3.05e-14 0.0 1.48e-13 1.48e-13 1.48e-13 3.56e-12 7.29e-13 2.96e-11 4.17e-12 4.17e-12 9.59e-06 1.96e-06 2.89e-05 2.89e-05 2.89e-05 0.00023116 4.735e-05 0.00011772 0.00011772 0.00011772 0.00030248 6.195e-05 0.000160135 0.000160135 0.00018146 0.00018146 0.00011595 0.00011595 7.983e-05 7.983e-05 3.696e-05 3.696e-05 0.00019553 0.00019553 3.625e-05 3.625e-05 3.625e-05 7.99e-06 7.99e-06 1.11e-06 4.4900052649999996e-08 9.520194e-10 2.51319e-11 1.6e-13 0.0 5.98e-14 2.36e-12 2.36e-12 2.72e-10 7.92e-09 1.08e-09 2.01e-07 2.5e-06 5.13e-07 3.112e-05 0.00018154 3.718e-05 0.00098567 0.00286894 0.00576487 0.00792097 0.00734714 0.00427502 0.00176036 0.00042805 7.36e-05 7.29885e-06 5.03e-07 2.22965e-08 5.98e-10 3.63362e-11 6.54e-14 1.23e-09 1.57e-06 2.153e-05 0.00019252 0.00103144 0.00360685 0.00918525 0.0168949 0.0205989 0.0180956 0.00517671 0.00517671 0.00405669 0.0010505 0.0001749 1.876e-05 1.22981e-06 4.89e-08 1.28e-09 1.99e-11 6.0945e-13 0.0 0.0 1e-13 1e-13 1.21e-12 1.21e-12 1.78e-10 3.73e-11 4.72e-12 4.800003958e-13 0.00387977 0.0126621 0.0280482 0.0407483 0.0413004 0.0262615 0.0107246 0.00235619 0.00030288 2.148e-05 9.67e-07 2.67958e-08 5.39e-10 0.0 0.0 0.0 0.0 0.0 4.9e-14 2.77e-12 2.77e-12 4.27e-10 1.88e-08 5.56e-07 9.24e-06 0.0001088 0.0007899 0.0131041 0.0131041 0.0353666 0.0156562 0.0156562 0.0193645 0.00371076 0.00371076 0.00178331 0.00024368 1.77472e-05 6.76e-07 1.49976e-08 2.03e-10 4.2096e-12 0.0 0.0 2.47e-13 2.52e-11 1.57e-09 4.99e-08 1.02e-08 9.19e-07 5.39e-07 1.833e-05 3.75e-06 0.00021023 0.0010566 0.00021641 0.00393305 0.0011094 0.00112991 0.0129937 3.66e-14 2.88e-12 1.63e-10 6.93e-09 1.82e-07 3.16e-06 3.596e-05 0.00024481 0.00115893 0.00325475 0.00656809 0.00793395 0.00671807 0.00376948 0.00146888 0.00035398 5.974e-05 6.71e-06 4.97e-07 2.59e-08 8.410504e-10 1.77e-11 3.07e-08 9.33e-08 2.13e-07 3.58e-07 4.82e-07 4.05e-07 2.45e-07 9.8e-08 3e-08 5.69e-09 6.840834e-10 5.6300043300000004e-11 2.99e-12 0.0 2.01e-13 1.83e-11 1.18e-09 5.32e-08 1.43e-06 7.31e-06 1.628e-05 0.00022095 0.00035313 0.00078599 0.00316096 0.00159833 0.00355758 0.00535045 0.0048414 0.00301601 0.0016862 0.00089425 0.00045876 0.00024735 5.944e-05 4.95e-06 1.51e-07 1.79e-09 0.0 0.0 0.0 0.0 0.0 0.0 2.26e-13 1.7e-11 9.41e-10 3.11e-08 6.55e-07 2.35e-06 6.34e-06 7.208e-05 0.00038326 0.00127249 0.0008807 0.0008807 0.0008807 0.00386309 0.00181501 0.00181501 0.00228603 0.00096261 0.00029553 5.689e-05 8.34e-06 7.33e-07 4.48e-08 1.8202669999999998e-09 5.05e-11 3.58e-11 1.55e-09 4.35e-08 4.075e-07 4.075e-07 1.186e-05 9.19e-06 9.294e-05 0.00062658 0.00237678 0.00602097 0.00507524 0.00507524 0.0114187 0.00876801 0.00441142 0.00141802 0.0003175 4.388e-05 4.02e-06 2.32e-07 9.67e-09 2.42e-10 4.69e-12 4.79e-09 1.29901e-10 6.40865e-12 0.0 1.15e-12 3.1e-09 8.66e-08 1.8e-06 1.075e-05 1.075e-05 0.00017525 0.00089624 0.00325708 0.00123095 0.00601014 0.0110032 0.0111787 0.0075362 0.00344296 0.00101525 0.00019694 2.641e-05 2.06e-06 2.31294e-07 2.29e-11 1.12e-10 2.97e-09 2.97e-09 2.25e-08 1.65e-07 8.38e-07 2.97e-06 4.94e-05 6.74e-06 0.00023021 0.00023021 0.00244646 0.00351497 0.00351497 0.01256614 0.00611284 0.00611284 0.00801315 0.00326879 0.00088759 0.00022015 4.878e-05 9.26e-06 1.84e-06 3.39e-07 8.28e-07 1.23e-08 6.86e-11 1.38e-13 0.0 1.53e-14 3.52e-08 9.15e-07 1.46e-05 0.00015147 0.00108047 0.00502089 0.0145338 0.0249639 0.0262719 0.01638858 0.00634252 0.00153048 0.00025226 3.52e-07 2.57e-08 1.5e-09 8.67e-11 5.16e-12 3.05e-13 9.900005039999998e-10 2.64e-12 0.0 0.0 1.34e-13 1.46e-11 9.09e-10 0.0 0.0 0.0 0.0 0.0 1.83e-11 1.54e-09 2.62e-09 3.54e-07 4.16e-06 5.52e-06 1.88e-06 2.9e-06 2.9e-06 2.9e-06 5.051e-05 7.596e-05 5.2445e-05 5.2445e-05 0.00069605 0.00095508 0.00131887 0.00627901 0.00479173 0.00661714 0.0134531 0.0047431 0.00420614 0.00368806 0.000421925 0.000421925 0.00011727 1.017e-05 5e-07 1.8400878999999998e-08 4.33e-10 0.0 0.0 0.0 6.64e-13 3.53e-11 1.37e-09 3.77e-09 2.76e-08 4.7e-07 4.24e-06 5.78e-07 3.509e-05 1.931e-05 0.00014161 0.0005212 0.00094556 0.00019367 0.00179224 0.00198414 0.00143727 0.00068166 0.0002074 4.075e-05 5.0692e-06 3.71942e-07 1.82971e-08 2.57602e-10 9.69e-12 0.0 0.0 0.0 2.14e-14 2.27e-12 1.41e-10 5.89e-09 1.41e-07 2.29e-06 2.1e-05 0.00012647 0.00047699 0.00120732 0.00179908 0.00192745 0.00137634 0.00068649 0.00024289 5.91e-05 9.24e-06 1.06e-06 7.82e-08 3.960879e-09 1.17e-10 0.0 6.31e-13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.26e-08 4.72e-08 2.47e-06 9.6e-06 2.731e-05 0.00021144 0.00031522 0.00011075 0.00077069 0.00125442 0.00044406 0.00256337 0.00304658 0.00107042 0.00294912 0.00294912 0.00396551 0.00139272 0.00206059 0.00206059 0.00086307 0.00086307 0.0004041 5.176e-05 3.85e-06 1.59975e-07 4.550104e-09 6.86e-11 1.19e-05 8.2687e-07 4.07936e-08 5.98454e-10 1.78e-11 2.49e-13 2.1e-11 6.15e-10 6.15e-10 4.48e-08 8.35e-07 1.71e-07 1.54e-05 0.0001499 0.00093071 0.00351587 0.00935339 0.0150981 0.0182762 0.0135738 0.00746083 0.00264141 0.00062187 0.000100009 0.0 1.32e-13 2.92e-10 5.75e-09 5.69e-10 8.51e-08 6.28e-07 6.21e-08 3.78e-06 1.361e-05 3.142e-05 4.984e-05 5.671e-05 4.321e-05 2.398e-05 8.98e-06 2.52e-06 4.48e-07 5.73e-08 5.1295e-09 2.99193e-10 3.86e-05 0.00035987 0.000992 0.000992 0.00768581 0.0190176 0.0329054 0.0340387 0.0221928 0.00817288 0.00187826 0.00024847 2.187e-05 1.24e-06 5.34e-14 0.0 7.48954e-17 0.0 0.0 0.0 0.0 7.49e-13 1.53e-13 2.01e-09 2.01e-09 1.37e-07 2.47e-06 5.06e-07 0.0 0.0 0.0 0.0 6e-13 1.43e-10 1.43e-10 4.45e-08 1.23e-12 4.62e-12 9.07e-11 3.09e-10 1.16e-09 2.01e-08 6.36e-08 1.81e-07 3.06e-06 0.00023218 0.00066082 0.00425179 0.00329124 0.00936737 0.0240057 0.00693587 0.0197406 0.0185937 0.0074141 0.0020211 0.00028787 3.214e-05 2.15e-06 1.040003358e-07 1.07282e-09 6.32e-11 7.56e-13 1.23e-11 1.4e-10 9.7e-10 5.14e-09 1.71e-08 3.74e-08 5.89e-08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.045e-09 4.045e-09 3.07e-07 2.09e-06 5.95e-06 0.00013767 0.00033271 0.00094696 0.00190454 0.00514931 0.00541186 0.0154028 0.0409217 0.0421345 0.0329516 0.0164038 0.00592166 0.00130301 0.00022547 2.164e-05 1.50976e-06 6.05905e-08 1.58e-09 3.88265e-11 0.00163224 0.00033712 4.528e-05 3.8094e-06 1.78e-07 1.81421e-09 8.34066e-11 0.0 4.75e-14 3.43e-10 1.52e-08 7.78e-08 3.8e-07 3.97e-06 3.97e-06 4.4115e-05 4.4115e-05 0.00061651 0.00136492 0.00136492 0.0080534 0.015566 0.00185944 0.0188011 0.00959124 0.00959124 0.00579951 0.00579944 0.0052661 0.0 2.23e-14 4.51e-13 5.77e-12 7.86e-13 6.22e-11 3.52e-10 1.55e-09 6.47e-12 1.31e-10 1.97e-09 3.36e-09 1.64e-08 1.52e-07 1.23e-07 5.99e-07 2.59e-06 6.1e-06 1.156e-05 1.43e-05 1.247e-05 7.41e-06 2.95e-06 8.13e-07 1.43e-07 1.7e-08 1.3700755000000001e-09 6.31e-11 0.0232822 0.0122734 0.00459502 0.00104734 0.00015082 1.15482e-05 5.30917e-07 2.78808e-09 1.3e-10 2.35612e-12 0.0 0.0 1.27e-12 1.12e-10 3.075e-09 3.075e-09 1.97e-07 4.2e-06 5.117e-05 0.00043581 0.00216065 0.00769965 0.0168786 0.0265556 0.0288153 - - - - - - - - - - - - 500000.0 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 1.81e-14 1.21e-13 4.48e-12 1.05e-11 1.37e-10 9.15e-10 2.335e-08 2.335e-08 6e-07 6e-07 5.03e-06 1.174e-05 1.59e-05 0.00010642 0.00047116 1.851e-05 0.0001239 0.0003076 0.000247 0.000247 0.00030783 0.00030783 0.00034657 0.00034657 0.00025078 0.00025078 0.00034642 4.8765e-05 4.8765e-05 1.504e-05 6.25e-07 6.25e-07 7.95e-12 1.15e-12 1.67e-13 1.75e-14 0.0 0.0 1.3816681889999999e-08 1.71e-10 0.0 0.0 2.32e-13 1.23e-11 4.19e-10 1.06e-08 1.53e-07 1.64e-06 1.096e-05 5.093e-05 0.00015925 0.00036868 0.00027901 0.00027901 0.00064407 0.00044001 0.00024767 7.893e-05 1.726e-05 2.3e-06 1.9197e-07 2.252625554e-08 1.42819e-10 7.57393e-12 0.0 0.0 1.93e-12 2.68e-10 2.19e-08 4.31e-07 4.31e-07 9.46e-06 9.46e-06 5.82e-05 0.00014248 0.00136801 0.00537996 0.014746 0.0262809 0.0323448 0.0278055 0.0161598 0.00674355 0.00177911 0.000297914 3.525e-05 2.4297e-06 1.12e-07 7.94323e-10 5.45e-11 1.3423e-12 0.0 5.26e-13 1.76e-12 1.17e-10 9.18e-10 3.07e-09 2.18e-08 6.55e-08 1.34e-06 8.84e-06 3.79e-06 8.67e-05 0.00012891 0.00022917 0.00121377 0.00238946 0.00355716 0.00343193 0.00232852 0.00097503 0.0002669 4.403e-05 4.86923e-06 3.54e-07 4.054030202e-08 4.02e-10 1.19595e-11 0.0 0.0 2.01e-14 3.25e-12 2.79e-10 1.39e-08 7.15e-08 2.39e-07 3.8e-06 6.99e-10 2.34e-09 3.13e-08 4.24e-08 1.42e-07 9.44e-07 8.15e-07 2.73e-06 9.83e-05 0.00016212 0.00016212 0.0004068 0.00012228 0.00012228 8.407e-05 2.6e-06 1.09e-06 4.21e-07 1.39e-07 2.68e-08 4.13e-09 7.27618e-06 8.31e-07 4.390005196e-08 1.23e-09 0.0 2.86e-11 1.81e-09 1.66e-08 4.07e-08 1.22e-06 1.594e-05 0.00014263 0.00083658 0.00315113 0.00869082 0.015218 0.0175763 0.0144271 0.00774909 0.00297861 0.00068752 0.00010938 1.01e-05 6.67e-07 2.53968e-08 6.78e-10 1.05e-11 4.14e-08 5.84e-08 6.11e-08 4.83e-08 2.69e-08 1.06e-08 2.75e-09 4.83143e-10 5.52e-11 3.99e-12 4.550001351999999e-13 3.3699985210000005e-14 0.0 2.38e-14 4.39e-10 2.93e-08 1.22e-06 1.3965e-05 1.3965e-05 0.000174225 0.000174225 0.00117407 0.00117407 0.00894933 0.0105336 0.0105346 0.033697 0.0358412 0.0274525 0.0131445 0.00467295 0.00100312 0.00015726 1.389e-05 5.65618e-07 3.170233e-08 3.01783e-10 1.07e-11 6.11e-10 5.17e-09 9.42e-09 2.2e-08 1.42e-07 1.16e-07 3.47e-07 1.11e-06 1.85e-06 2.2e-06 1.8e-06 1.03e-06 4.21e-07 1.05e-07 1.78e-08 2.030000572e-09 1.48e-10 7.48e-12 0.0 3.22e-14 1.84e-12 6.31e-11 1.23e-09 1.56e-08 1.25e-07 7e-07 2.49e-06 6.59e-06 9.44e-06 1.41e-06 1.234e-05 9.69e-06 5.47e-06 2.17e-06 6.21e-07 1.24e-07 1.69844362e-08 0.0 8.01e-14 2.25e-12 4.85e-11 5.91e-10 4.75e-09 2.17e-08 3.24e-09 8.88e-08 2.11e-07 3.5e-07 4.01e-07 3.19e-07 0.0 1.16e-12 2.99e-09 2.14667e-08 2.14667e-08 2.14667e-08 9.619999e-07 4.165e-06 4.165e-06 5.138e-05 0.00016413 0.000387 0.00058108 0.00061785 0.0004272 0.00021575 7.445e-05 1.842e-05 2.99e-06 3.47e-07 2.502300003541e-08 1.2803999999999998e-09 1.21e-08 6.67e-09 2.88e-09 8.35e-10 1.800641e-10 2.48e-11 2.400004999e-12 0.0 2e-14 9.13e-13 2.97e-11 6.57e-10 1.01e-08 5.1e-08 5.1e-08 7.14e-07 1.68e-06 1.68e-06 1.168e-05 2.727e-05 4.51e-05 5.121e-05 4.138e-05 2.242e-05 8.74e-06 2.12e-06 3.71e-07 3.4406119999999996e-08 2.5000047929999997e-09 9.87e-11 0.0 0.0 1.15e-13 7.78e-12 3.46e-10 9.29e-09 1.62e-07 1.55e-06 9.82e-06 4.061e-05 0.00010341 0.00017983 0.00020654 0.00017513 9.986e-05 4.262e-05 1.217e-05 2.4e-06 3.2e-07 2.93406e-08 1.7709130000000002e-09 0.0 0.0 0.0 0.0 5.58e-13 1.15e-11 1.15e-11 6.69e-10 9.76e-09 2.29e-09 1.54e-07 1.74e-07 1.16e-06 7.76e-06 2.617e-05 3.91e-06 8.384e-05 7.736e-05 7.736e-05 0.00021259 0.00020036 0.00013178 5.928e-05 1.613e-05 3.23e-06 3.85e-07 2.770004007e-08 1.3e-09 3.03201e-11 0.0 0.0 5.74e-14 0.0 4.77e-11 7.12e-12 2.48e-10 5.79e-10 7.37e-09 1.1e-09 1.81e-08 4.22e-08 2.73e-07 2.3e-07 5.37e-07 1.55e-06 2.03e-06 1.98e-06 6.43e-07 6.43e-07 6.43e-07 2.09e-07 2.84e-14 0.0 0.0 0.0 0.0 1.8e-13 2.1e-06 2.484e-05 1.065e-05 0.00039067 0.00123651 0.00123651 0.00489545 0.00489545 0.0132425 0.00959002 0.0330595 0.0145552 0.0145454 0.0166814 0.00593744 0.00154688 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2.79e-07 1.28e-07 4.19e-08 8.41e-09 1.3402209999999999e-09 1.119999093e-10 7.02e-12 0.0 1.38e-14 1.45e-12 1.11e-10 5.66e-09 1.96e-07 1.42e-06 2.63e-06 5.475e-05 0.0001412 0.00026222 0.00178993 0.001462 0.00271515 0.00595342 0.00656777 0.00464765 0.00280336 0.00146773 0.00070129 0.00033255 4.358e-05 2.64e-06 8.48e-08 1.69e-09 0.0 0.0 0.0 0.0 0.0 0.0 1.49e-14 1.43e-12 1.07e-10 4.53e-09 1.26e-07 5.91e-07 1.38e-06 2.183e-05 0.00015082 0.00065823 0.000567633 0.000567633 0.000567633 0.0030642 0.00178801 0.00178801 0.00302037 0.00153836 0.00057157 0.00014258 2.581e-05 2.91e-06 2.27e-07 1.1402899999999999e-08 3.900334e-10 2.92e-12 1.75e-10 6.88e-09 8.9e-08 8.9e-08 3.17e-06 3.35e-06 3.016e-05 0.00026759 0.00123376 0.00390973 0.00385067 0.00385067 0.0108641 0.0102329 0.00646344 0.00263338 0.0007179 0.00012971 1.538e-05 1.14e-06 5.5e-08 1.78012e-09 4.23e-11 2.06e-08 3.68845e-10 1.93645e-11 0.0 1.37e-13 5.43e-10 1.76e-08 4.44e-07 3.075e-06 3.075e-06 6.348e-05 0.00040207 0.00180816 0.00095139 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0.0 0.0 9.56e-14 6.76e-12 2.78e-10 1.05e-09 7.05e-09 1.53e-07 1.56e-06 2.33e-07 1.491e-05 1.063e-05 7.115e-05 0.00030881 0.00065465 0.00015356 0.00136285 0.00186574 0.00158345 0.00090685 0.00035371 8.408e-05 1.28575e-05 1.14986e-06 6.60924e-08 7.69056e-10 5.79e-11 0.0 0.0 0.0 0.0 1.61e-13 1.35e-11 6.61e-10 2.16e-08 4.68e-07 5.81e-06 4.387e-05 0.00021083 0.00066866 0.00139319 0.00179511 0.00158581 0.00101719 0.0004407 0.00013328 2.65e-05 3.77e-06 3.44e-07 2.110793e-08 8.58118e-10 0.0 5.68e-14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.52e-09 4.3e-08 1.44e-07 5.45e-06 1.517e-05 3.715e-05 0.00022622 0.00029297 0.00011966 0.00075343 0.00094577 0.0003863 0.00239699 0.00283651 0.00115857 0.00297158 0.00297158 0.00403251 0.00164598 0.00257764 0.00257764 0.0012805 0.0012805 0.00075415 0.00011747 1.106e-05 5.90927e-07 2.0400714e-08 4.55e-10 3.44e-05 2.96963e-06 1.68981e-07 2.0245e-09 1.23e-10 1.27831e-11 2.46e-14 2.44e-12 8.65e-11 8.65e-11 7.24e-09 1.71e-07 4e-08 3.99e-06 4.958e-05 0.00039541 0.00188244 0.00595876 0.0122013 0.017876 0.0158354 0.0111995 0.0047947 0.00137878 0.000261823 0.0 0.0 2.95e-11 7.09e-10 7.87e-11 1.54e-08 1.56e-07 1.73e-08 1.24e-06 5.45e-06 1.661e-05 3.332e-05 4.734e-05 4.685e-05 3.3e-05 1.518e-05 5.26e-06 1.2e-06 2.01e-07 2.19734128e-08 1.732009999623e-09 1.216e-05 0.00013227 0.00044135 0.00044135 0.00401052 0.0118835 0.0248038 0.0326422 0.0281124 0.0137771 0.00426163 0.00075538 8.716e-05 5.84e-06 3.73e-13 0.0 1.94379e-16 0.0 0.0 9.24684e-12 0.0 4.9e-14 1.15e-14 2.83e-10 2.83e-10 2.55e-08 5.71e-07 1.34e-07 0.0 0.0 0.0 0.0 2.44e-12 2.79e-10 2.79e-10 4.26e-08 6.13e-13 2.05e-12 3.2e-11 8.16e-11 2.73e-10 4.31e-09 1.92e-08 4.71e-08 8.15e-07 0.00010517 0.00025748 0.0022034 0.00229479 0.00561829 0.01805 0.0070229 0.017194 0.0212325 0.0108665 0.00363363 0.00073557 9.644e-05 8.72e-06 5.319999581e-07 6.951703333999999e-08 4.37e-10 7.1e-14 1.56e-12 2.4e-11 2.29e-10 1.54e-09 6.56e-09 2.03e-08 3.89e-08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.825e-10 4.825e-10 5.19e-08 5.04e-07 1.23e-06 3.626e-05 0.00012529 0.00030675 0.0009415 0.00219683 0.00353866 0.00866326 0.0296704 0.0412079 0.0394265 0.0251401 0.0114413 0.00337177 0.00063405 7.86208e-05 6.20929e-06 3.12961e-07 9.479996249999999e-09 8.93349e-11 0.00312324 0.00078512 0.00012223 1.2818e-05 7.72e-07 8.09352e-08 3.09374e-10 2.26722e-11 0.0 3.93e-11 2.15e-09 1.6e-08 6.83e-08 9.65e-07 9.65e-07 1.3885e-05 1.3885e-05 0.0002381 0.000690765 0.000690765 0.00493549 0.0115656 0.00198092 0.0178284 0.0113345 0.0113345 0.00832221 0.00832205 0.00888525 0.0 0.0 4.94e-14 7.9e-13 1.18e-13 1.13e-11 9.26e-11 5.01e-10 1.9e-12 4.22e-11 7.13e-10 1.53e-09 6.52e-09 6.83e-08 7.25e-08 3.09e-07 1.57e-06 4.25e-06 8.65e-06 1.195e-05 1.257e-05 8.1e-06 3.85e-06 1.16e-06 2.47e-07 3.4e-08 3.1002290000000003e-09 1.85e-10 0.0296167 0.018459 0.00799426 0.00221892 0.00040274 4.26291e-05 2.69969e-06 1.82538e-07 1.38e-09 1.84763e-11 5.57e-14 0.0 9.29e-14 1.09e-11 4.02e-10 4.02e-10 3.41e-08 9.15e-07 1.502e-05 0.00015573 0.00096609 0.00457387 0.0129006 0.0248682 0.0317195 - - - - - - - - - - - - - - - 0.0253 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 8.58001e-14 2e-13 3.79001e-12 2.53e-11 7.70001e-10 7.70001e-10 2.61e-08 2.61e-08 3.75001e-07 8.75002e-07 2.68e-06 1.795e-05 0.00108452 0.00010234 0.000684921 0.00016063 0.00016082 0.00016082 0.00014627 0.00014627 0.00020739 0.00020739 0.00018579 0.00018579 0.000297331 8.34101e-05 8.34101e-05 4.88601e-05 5.87501e-06 5.87501e-06 8.18001e-07 1.37e-11 1.77e-12 2.35e-13 2.79e-14 0.0 2.64809e-16 1.210028e-09 0.0 0.0 0.0 2.91e-13 1.616001e-11 4.77001e-10 1.49e-08 3.03001e-07 3.09001e-06 2.232e-05 9.36302e-05 0.000298551 0.000299401 0.000299401 0.0009809521 0.000740992 0.000434301 0.0001874 4.56801e-05 7.14001e-06 8.98903e-07 5.112072331e-08 5.683664826e-09 2.52233e-11 0.0 0.0 1.63e-14 5.20001e-12 7.22001e-10 2.295e-08 2.295e-08 5.60001e-07 5.60001e-07 6.95001e-06 1.702e-05 0.00020879 0.00108855 0.00536816 0.0134382 0.0236848 0.0278762 0.025869 0.0117178 0.00501429 0.00124959 0.00026099 2.89149e-05 2.35e-06 9.03749e-08 2.470002241e-09 3.30901e-11 0.0 1.29e-14 4.30001e-14 4.99001e-12 7.03001e-11 2.35e-10 2.59e-09 7.77001e-09 2.52e-07 2.52e-06 1.08e-06 3.95201e-05 6.84601e-05 0.0001217 0.000694191 0.001880924 0.00276832 0.00322196 0.00316454 0.00177808 0.000522871 0.00013758 2.60288e-05 1.93e-06 1.249067315e-07 4.740148999999999e-09 5.14456e-11 0.0 0.0 0.0 2.54e-14 2.58e-12 2.22e-10 2.71e-09 9.06002e-09 5.04001e-07 1.25e-06 4.17001e-06 3.58001e-09 6.97001e-09 2.33e-08 1.23e-07 1.36e-07 4.55001e-07 1.74e-06 3.15101e-05 3.15101e-05 0.00017252 9.90002e-05 9.90002e-05 0.00022379 6.83401e-05 3.42001e-06 1.22e-06 5.25001e-07 2.04e-07 4.40001e-08 6.75001e-09 4.03001e-06 3.300003972e-07 1.4200017741e-08 0.0 2.17e-13 1.93e-11 2.73e-10 6.69001e-10 4.55001e-08 9.62002e-07 1.436e-05 0.00012364 0.000713131 0.0023701 0.0065765 0.0110145 0.0146951 0.0118024 0.00785224 0.00218266 0.000712671 0.00013615 1.087e-05 5.72962e-07 1.5000063811e-08 4.55001e-10 5.88001e-09 1.13e-08 1.41e-08 1.72e-08 1.35e-08 6.97001e-09 1.83e-09 3.73133e-10 5.09001e-11 4.310003815e-12 1.960002871e-13 1.7000053789999998e-14 0.0 0.0 7.08001e-12 1.02e-09 9.08002e-08 1.565e-06 1.565e-06 3.02101e-05 3.02101e-05 0.000629131 0.000629131 0.00743312 0.0117933 0.00421199 0.018298 0.0286491 0.0300114 0.0187947 0.0098681 0.00328215 0.000617731 8.76002e-05 2.40019e-05 5.470941002e-07 3.515856511e-08 5.14001e-10 6.13417e-12 2.48e-11 3.22001e-10 7.76001e-10 1.81e-09 1.97e-08 2.41e-08 7.22001e-08 3.22001e-07 5.77001e-07 8.57001e-07 9.10002e-07 5.99001e-07 3.92001e-07 1.69e-07 3.65001e-08 5.480012437e-09 4.560273e-10 2.71e-11 0.0 0.0 0.0 6.91001e-13 2.89e-11 9.41002e-10 1.41e-08 1.49e-07 9.89002e-07 3.01001e-06 5.41001e-06 8.08001e-07 8.21001e-06 8.52001e-06 7.88001e-06 3.78001e-06 1.52e-06 4.82001e-07 9.14001660015143e-08 0.0 0.0 4.56001e-14 1.17e-12 2.22e-11 2.31e-10 1.65e-09 2.47e-10 1.31e-08 4.39001e-08 1.16e-07 2.31e-07 2.7e-07 0.0 0.0 8.32001e-12 1.77334e-10 1.77334e-10 1.77334e-10 2.12e-08 1.97e-07 1.97e-07 5.16001e-06 2.881e-05 0.00017279 0.000424411 0.000730911 0.000900162 0.000704421 0.000357831 0.00014388 2.552e-05 3.17001e-06 2.4753000052129996e-07 1.6010700026211002e-08 2.82e-09 2e-09 1.04e-09 4.32001e-10 1.3105230010000002e-10 2.16e-11 2.100000934e-12 0.0 0.0 1.37e-14 8.61001e-13 3.30001e-11 8.10001e-10 4.99501e-09 4.99501e-09 9.65002e-08 3.21001e-07 3.21001e-07 2.51e-06 1.048e-05 3.03801e-05 4.69001e-05 5.36901e-05 3.62001e-05 1.825e-05 5.54001e-06 1.29e-06 1.300304001e-07 9.53001767e-09 4.900134e-10 0.0 0.0 0.0 1.02e-14 1.25e-12 8.58001e-11 3.50001e-09 7.91001e-08 1.19e-06 9.43002e-06 4.19101e-05 0.00015015 0.00027333 0.000370051 0.000336351 0.00015745 4.77501e-05 1.056e-05 1.76e-06 2.5474618100000004e-07 1.81196000547e-08 0.0 0.0 0.0 0.0 1.11e-14 3.76001e-13 3.76001e-13 2.96001e-11 6.18001e-10 1.45e-10 1.55e-08 1.92e-08 1.28e-07 1.75e-06 1.026e-05 1.53e-06 4.85001e-05 6.27601e-05 6.27601e-05 0.00022888 0.00027496 0.00024287 0.00014552 4.54401e-05 9.02002e-06 1.5e-06 1.1699994081e-07 6.6900152600000004e-09 9.88305e-11 0.0 0.0 0.0 0.0 1.13e-12 1.69e-13 1.14e-11 2.67e-11 7.13001e-10 1.07e-10 1.98e-09 4.62001e-09 4.37001e-08 4.36001e-08 1.02e-07 4.43001e-07 9.87002e-07 1.29e-06 5.89001e-07 5.89001e-07 9.90002e-07 4.29001e-07 0.0 4.01001e-12 0.0 0.0 0.0 0.0 3.24001e-13 2.59e-06 1.11e-06 8.12101e-05 0.00108715 0.00108715 0.0051598 0.0051598 0.0203418 0.0100392 0.0343332 0.0124029 0.0149248 0.0119334 0.0104298 0.00435819 0.000833801 0.000122858 1.012e-05 4.2389460030000004e-07 4.555510222e-08 1.82571e-10 0.0 0.0 2.98001e-13 8.13001e-12 3.46001e-11 1.45e-09 3.40001e-10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.82e-13 6.63001e-14 2.5e-12 4.16501e-13 4.16501e-13 6.72001e-07 1.58e-07 3.66001e-06 3.66001e-06 3.66001e-06 4.97401e-05 1.167e-05 8.60801e-05 8.60801e-05 8.60801e-05 0.00028459 6.67501e-05 0.000193395 0.000193395 0.00019129 0.00019129 0.000203205 0.000203205 0.00027246 0.00027246 0.000178765 0.000178765 0.00012573 0.00012573 0.00018237 0.00018237 0.00018237 7.87601e-05 7.87601e-05 2.201e-05 1.240006318e-06 3.6501770000000004e-08 2.71481e-10 9.83002e-12 0.0 0.0 8.50001e-15 8.50001e-15 1.9e-12 1.11e-10 1.66e-11 5.89001e-09 1.2e-07 2.81e-08 2.6e-06 1.963e-05 4.60001e-06 0.00019007 0.000715841 0.00193705 0.00468689 0.00721072 0.00511249 0.00406495 0.00206319 0.000496201 0.000102725 1.254e-05 7.5795e-07 3.3900052210000004e-08 1.18578e-10 0.0 1.56e-12 8.88002e-09 2.25e-07 3.82001e-06 0.000335071 0.000404881 0.00269091 0.00602342 0.0131002 0.0157047 0.00786215 0.00786215 0.0113068 0.00566599 0.00192604 0.000434121 4.58897e-05 4.56001e-06 3.00967e-07 6.7103350010000004e-09 3.88896e-11 0.0 0.0 0.0 0.0 0.0 0.0 7.74001e-11 2.14e-11 3.61001e-12 5.570012332000001e-13 0.000410681 0.00264695 0.0106858 0.024447 0.0416915 0.0396958 0.0257847 0.0114869 0.00399024 0.000537421 8.54601e-05 4.97967e-06 1.19993e-07 5.56265e-10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.44e-13 1.78e-11 1.46e-09 5.81001e-08 1.99e-06 3.38201e-05 0.00528616 0.00528616 0.0256375 0.0169555 0.0169555 0.032717 0.00927455 0.00927455 0.00714496 0.00153913 0.000261575 2.92801e-05 1.40985e-06 6.44001e-08 3.37993e-10 6.91049e-12 0.0 0.0 0.0 8.52001e-13 7.55001e-11 1.77e-11 2.6e-09 1.81e-09 0.00028058 3.38001e-08 4.10001e-06 4.38601e-05 1.029e-05 0.000390801 0.00013027 0.00023952 0.00275225 0.0 0.0 1.09e-13 9.06002e-12 4.91001e-10 1.34e-08 3.28001e-07 4.68001e-06 4.54901e-05 0.00026233 0.00124195 0.00235291 0.00560822 0.00801044 0.00545145 0.00263833 0.000683561 0.00019449 2.662e-05 2.27e-06 1.540283e-07 5.680005503e-09 1.64e-09 7.24001e-09 2.4e-08 6.66001e-08 1.43e-07 1.8e-07 1.41e-07 7.50001e-08 3.22001e-08 7.24001e-09 1.800330001e-09 2.450006282e-10 1.930001144e-11 0.0 0.0 1.95e-14 3.27001e-12 2.54e-10 1.34e-08 1.36e-07 2.53e-07 7.14001e-06 2.472e-05 4.59101e-05 0.000571331 0.000920182 0.0017089 0.00943938 0.00978665 0.00858028 0.00476602 0.00181399 0.00100396 0.000470801 0.0001591 2.088e-05 1.16e-06 6.03001e-08 7.28001e-10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.94001e-13 2.78e-11 1.22e-09 1.07e-08 2.51e-08 6.82001e-07 8.99002e-06 5.68301e-05 0.00011596 0.00011596 0.00011596 0.00149282 0.0012853 0.0012853 0.00362556 0.00240401 0.00195835 0.000694631 0.00018302 3.36401e-05 3.92001e-06 2.7914700046535e-07 1.4702369999999999e-08 1.02e-14 1.47e-12 1.05e-10 2.25e-09 2.25e-09 1.29e-07 2.07e-07 1.86e-06 2.041e-05 0.00015192 0.000781351 0.00123577 0.00123577 0.00550192 0.00864393 0.0067897 0.00555213 0.00328825 0.000744051 0.00013551 1.073e-05 1.11e-06 4.580646001e-08 1.390002808e-09 1.31e-07 9.42038e-10 4.40544e-11 0.0 0.0 3.07001e-11 1.49e-09 4.46001e-08 3.18001e-07 3.18001e-07 1.11e-05 7.92001e-05 0.000525631 0.000388101 0.00166954 0.00290452 0.00692625 0.0100913 0.00609513 0.00399653 0.00136683 0.00028075 3.83301e-05 1.07851e-06 2.4e-14 1.02e-13 8.76002e-12 8.76002e-12 1.76e-10 1.18e-09 1.57e-08 4.71001e-08 1.39e-06 2.07e-07 1.179e-05 1.179e-05 0.00023102 0.000667531 0.000667531 0.01127175 0.00596192 0.00596192 0.0198304 0.0150666 0.00892285 0.00219513 0.000507641 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5.126437932e-08 6.22001e-10 0.0 0.0 0.0 0.0 0.0 2.3e-14 2.22e-12 1.22e-10 5.56001e-09 1.02e-07 2.14e-06 2.414e-05 0.00014779 0.000558031 0.00120165 0.00215201 0.00212879 0.00133239 0.000709901 0.00020728 4.47901e-05 6.28001e-06 4.5134102119999995e-07 1.770471001e-08 0.0 0.0 1.945e-14 1.945e-14 0.0 0.0 0.0 0.0 0.0 0.0 1.88e-10 6.29001e-10 6.94001e-08 5.45001e-07 1.33e-06 3.90001e-05 0.00014506 5.92501e-05 0.000319281 0.000389181 0.00015896 0.00119572 0.00327488 0.000927352 0.00276824 0.00276824 0.00712364 0.00290649 0.00373976 0.00373976 0.00294431 0.00294431 0.00291793 0.000547211 6.26701e-05 3.99974e-06 1.2000578000999998e-07 2e-09 0.00013818 1.38791e-05 7.64957e-07 7.620857228e-08 5.03001e-10 1.59785e-11 0.0 6.71001e-14 2.815e-12 2.815e-12 3.86001e-10 1.02e-08 2.39e-09 2.86e-07 5.33001e-06 6.26301e-05 0.000291381 0.0016015 0.00529659 0.00919842 0.015007 0.0152535 0.00828138 0.00376374 0.000887083 0.0 0.0 1.06e-13 6.78001e-12 7.54001e-13 3.93001e-10 8.16001e-09 9.07002e-10 1.32e-07 1.28e-06 7.03001e-06 2.354e-05 5.98101e-05 6.29001e-05 5.18401e-05 2.543e-05 1.156e-05 4.04001e-06 8.47001e-07 1.24010341001e-07 1.594170003019e-08 7.17001e-07 1.569e-05 9.48302e-05 9.48302e-05 0.00145363 0.00605362 0.0164921 0.0264489 0.0329815 0.0274329 0.0110973 0.00483701 0.000995942 7.67801e-05 5.05945e-06 5.13001e-13 3.54648e-15 0.0 5.02157e-18 2.02052e-19 0.0 0.0 0.0 1.4e-12 1.4e-12 3.03001e-10 1.52e-08 3.57001e-09 0.0 0.0 0.0 0.0 0.0 1.635e-13 1.635e-13 1.95e-10 6.12001e-09 2.05e-08 1.02e-12 3.12001e-12 1.05e-11 2.17e-10 1.51e-09 3.69001e-09 9.84002e-08 4.10001e-07 1e-06 0.000693301 0.00129319 0.00709232 0.0171612 0.00890161 0.0219193 0.0347438 0.0228233 0.00684768 0.00107128 0.000359741 4.85501e-05 3.32000119e-06 2.4197242270000003e-07 4.36001e-09 0.0 1.42e-14 3.84001e-13 7.32001e-12 7.84001e-11 5.55001e-10 3.40001e-09 1e-08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.67e-09 3.27001e-08 7.99001e-08 4.87001e-06 3.08801e-05 7.56001e-05 0.000332971 0.000776941 0.00291234 0.00689584 0.0140526 0.0420635 0.0349017 0.0349942 0.0200098 0.00763659 0.00197264 0.000348616 4.41545e-05 2.68982e-06 1.3699988947999998e-07 4.727240838e-08 0.00630359 0.00210219 0.000346841 3.86939e-05 2.48e-06 1.4533e-07 5.22318e-10 3.49653e-11 1.05961e-12 0.0 6.45001e-13 4.10001e-11 4.87001e-10 2.07e-09 4.23501e-08 4.23501e-08 5.35001e-07 5.35001e-07 1.958e-05 0.00010678 0.00010678 0.000918742 0.00416958 0.00107557 0.00968025 0.00830721 0.00830721 0.00961017 0.0096096 0.0141556 0.0 0.0 0.0 0.0 0.0 2.22e-13 3.75001e-12 3.52001e-11 3.32001e-14 1.17e-12 2.92001e-11 1.08e-10 4.62001e-10 8.14001e-09 1.23e-08 5.26001e-08 3.15001e-07 1.06e-06 3.00001e-06 4.47001e-06 8.27001e-06 8.94002e-06 5.87001e-06 2.34e-06 6.22001e-07 1.02e-07 1.1500936002e-08 8.380018752e-10 0.0320102 0.0272003 0.014429 0.00461552 0.000999192 0.000119535 9.06949e-06 5.15145e-07 1.8399e-08 1.55007e-10 3.83001e-12 0.0 0.0 8.16001e-14 4.58501e-12 4.58501e-12 3.32001e-10 2.09e-08 6.54001e-07 1.054e-05 0.00012284 0.000957272 0.00378602 0.0129856 0.0238773 - - - - - - - - - - - - 500000.0 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 1.24e-14 5.65e-14 5.55e-13 6.38e-12 2.535e-10 2.535e-10 1.17e-08 1.17e-08 1.27e-07 5.8e-07 9.22e-07 1.061e-05 0.00010222 3.818e-05 0.00043909 0.00013585 0.0001634 0.0001634 0.00027021 0.00027021 0.00034632 0.00034632 0.00033416 0.00033416 0.00060924 0.000209805 0.000209805 0.00020253 2.245e-05 2.245e-05 4.33e-06 5.95e-11 7.14e-12 9.28e-13 7.89e-14 0.0 3.35531e-16 3.7101319999999997e-09 0.0 0.0 0.0 4.93e-13 2.4889999999999998e-11 8.2e-10 1.83e-08 2.46e-07 2.37e-06 1.502e-05 6.55e-05 0.00019364 0.00020086 0.00020086 0.00055625 0.00052644 0.00045899 0.00019387 6.173e-05 1.254e-05 1.58982e-06 1.171923493e-07 1.6109335e-08 8.32678e-11 0.0 0.0 0.0 2.3e-12 3.4e-10 1.26e-08 1.26e-08 4.67e-07 4.67e-07 3.21e-06 1.46e-05 0.00018537 0.00117886 0.00487297 0.0124873 0.0237833 0.0296378 0.027288 0.0156777 0.00617786 0.00157792 0.0002702 2.8437e-05 1.95e-06 1.05666e-07 2.439997939e-09 2.82495e-11 0.0 0.0 4e-14 3.7e-12 2.62e-11 1.61e-10 9.36e-10 5.3e-09 1.34e-07 1.48e-06 3.24e-07 1.605e-05 2.047e-05 7.257e-05 0.0004837 0.00121926 0.002501 0.00345018 0.0033354 0.00208451 0.00083871 0.00021365 3.52343e-05 3.65e-06 2.262747748e-07 1.0000379e-08 1.60612e-10 0.0 0.0 0.0 0.0 9.96e-13 1.08e-10 8.73e-10 5.36e-09 1.93e-07 4.07e-07 2.5e-06 2.15e-09 3.33e-09 2.04e-08 1.63e-07 1.08e-07 6.62e-07 2.28e-06 3.01e-06 3.01e-06 0.00014107 0.000156215 0.000156215 0.00031096 0.00014377 7.13e-06 2.75e-06 1.16e-06 3.48e-07 6.34e-08 6.72e-09 6.69e-06 5.620000171e-07 2.4900036339999997e-08 0.0 1.92e-13 1.9e-11 1.91e-10 8.69e-10 3.72e-08 7.38e-07 1.147e-05 0.00010365 0.0006819 0.00277202 0.00749347 0.01354 0.0161461 0.0129647 0.00725796 0.00270723 0.00065641 0.00010049 1.061e-05 7.28917e-07 2.949996033e-08 8.180001779e-10 2.38e-08 4.27e-08 5.67e-08 5.93e-08 4.16e-08 1.99e-08 7.01e-09 1.6608659996455e-09 2.4504389999999996e-10 2.359998826e-11 1.410000612e-12 1.369996056e-13 0.0 0.0 4.09e-12 5.38e-10 4.06e-08 8.4e-07 8.4e-07 1.8095e-05 1.8095e-05 0.00020428 0.00020428 0.00254088 0.0046138 0.00461492 0.0215975 0.0341135 0.0354439 0.0267949 0.0136031 0.00489634 0.00103436 0.00014382 4.2608e-05 7.05123e-07 5.299713687e-08 5.22e-10 1.20377e-11 8.74e-11 1.02e-09 1.42e-09 6.49e-09 4.94e-08 3.1e-08 1.76e-07 6.03e-07 1.36e-06 2.08e-06 2.22e-06 1.63e-06 8.44e-07 2.95e-07 6.75e-08 9.94000226e-09 9.710812e-10 6.08e-11 0.0 0.0 0.0 4.95e-13 1.87e-11 4.74e-10 6.97e-09 6.31e-08 4.1e-07 1.75e-06 4.5e-06 3.39e-07 9.07e-06 1.161e-05 9.94e-06 6.09e-06 2.82e-06 8.84e-07 1.93414459998036e-07 0.0 0.0 2.02e-14 7.23e-13 1.63e-11 2.4e-10 2.04e-09 1.53e-10 1.33e-08 5.46e-08 1.6e-07 2.88e-07 3.68e-07 0.0 0.0 4.18e-11 5.54667e-10 5.54667e-10 5.54667e-10 4.01e-08 2.87e-07 2.87e-07 6.02e-06 3.302e-05 0.00012637 0.00030785 0.00052516 0.00059523 0.00045501 0.00024883 9.668e-05 2.588e-05 4.61e-06 5.57489999725e-07 4.80463999854e-08 1.16e-08 8.49e-09 4.59e-09 1.84e-09 4.78331e-10 8.23197e-11 1.029997327e-11 0.0 0.0 4.13e-14 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7.11e-08 1.79e-07 3.1e-07 3.77e-07 3.52e-07 2.21e-07 9.12e-08 2.51e-08 4.711529999999999e-09 6.010004595e-10 4.799996765e-11 0.0 0.0 0.0 8.93e-13 6.86e-11 3.58e-09 2.42e-08 9.1e-08 2.56e-06 7.44e-06 2.8e-05 0.00030874 0.00031224 0.00117462 0.00403847 0.00635452 0.0074131 0.00549932 0.00332218 0.00178409 0.00082859 0.00036642 0.00013386 1.31e-05 5.58e-07 9.84e-09 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.26e-13 5.71e-11 2.81e-09 1.51e-08 6.87e-08 1.54e-06 1.827e-05 0.00012636 0.000183567 0.000183567 0.000183567 0.00151317 0.00136987 0.00136987 0.00364818 0.00288318 0.00167912 0.00064826 0.00018605 3.326e-05 4.03e-06 3.472460000217e-07 1.900465e-08 1.41e-14 1.51e-12 8.89e-11 2.105e-09 2.105e-09 1.13e-07 1.31e-07 2.05e-06 2.732e-05 0.00021574 0.00109524 0.00176011 0.00176011 0.00735942 0.0105762 0.0100051 0.00634322 0.00264114 0.00076723 0.00015363 1.808e-05 1.48e-06 7.53139e-08 2.840003503e-09 3.9e-07 5.2234449989999996e-08 2.52879e-10 0.0 0.0 1.36e-11 6.38e-10 2.57e-08 2.385e-07 2.385e-07 7.16e-06 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0.00387873 0.0115018 0.0225719 - - - - - - - - - - - - - - - - - - - - - - 500000.0 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 0.0 0.0 2.32e-14 1.435e-12 1.435e-12 9.4e-11 9.4e-11 2.68e-09 6.24e-09 4.04e-08 2.71e-07 5.94e-06 8.6e-06 5.755e-05 0.00040605 7.43e-05 7.43e-05 0.00020724 0.00020724 0.00036871 0.00036871 0.00045339 0.00045339 0.00086395 0.000371175 0.000371175 0.0004384 9.994e-05 9.994e-05 4.356e-05 4.99e-06 1.76e-10 2.85e-11 3.08e-12 1.59e-13 3.37983e-15 0.0 0.0 0.0 0.0 1.26e-14 8.5e-13 4.23e-11 1.23e-09 2.61e-08 3.38e-07 2.96e-06 1.691e-05 6.707e-05 9.211e-05 9.211e-05 0.00034874 0.00034671 0.0005001 0.0002559 0.00012019 3.088e-05 5.97921e-06 1.88862497e-07 4.383392646e-08 4.568764224e-09 0.0 0.0 0.0 0.0 2.46e-12 1.805e-10 1.805e-10 1.255e-08 1.255e-08 2.63e-07 6.43e-07 1.509e-05 0.00016367 0.00103319 0.00438031 0.012217 0.0237357 0.0305055 0.0252626 0.014687 0.00545315 0.00132204 0.00021315 2.138e-05 6.12365e-07 5.280001418e-08 6.2174041459999995e-09 0.0 0.0 0.0 7.24e-14 1.3e-12 4.34e-12 6.51e-11 1.95e-10 7.98e-09 1.18e-07 5.06e-08 2.18e-06 5.28e-06 9.39e-06 0.00012979 0.00041796 0.00118553 0.00210497 0.00286351 0.00278561 0.00153694 0.00057678 0.000139788 2.23e-05 6.000462252000001e-07 9.890713000000001e-08 1.132961825e-08 0.0 0.0 0.0 0.0 0.0 2.17e-13 6.4e-12 2.14e-11 1.93e-09 1.81e-08 6.08e-08 1.79e-06 5.41e-10 1.81e-09 3.51e-08 6.86e-08 2.3e-07 1.44e-06 2.21e-06 2.21e-06 1.08e-05 7.8485e-05 7.8485e-05 0.00031963 0.00031245 0.00016309 1.231e-05 6.37e-06 2.4e-06 5.52e-07 8.24e-08 7.61e-09 4.200001124473e-06 2.610004053e-07 0.0 0.0 8.68e-14 2.68e-12 6.55e-12 5.27e-10 1.96e-08 4.85e-07 8.36e-06 9.34e-05 0.00061965 0.00258268 0.00713091 0.0124191 0.014595 0.011971 0.00623768 0.0022271 0.00054544 8.426e-05 8.49964e-06 5.470001905e-07 2.359997201e-08 1.05e-08 2.22e-08 3.55e-08 4.11e-08 3.64e-08 2.23e-08 9.9e-09 2.612059997218e-09 4.84131e-10 5.680000622e-11 2.479996878e-12 2.8700011729999995e-13 0.0 0.0 1.82e-14 4.59e-12 6.37e-10 2.555e-08 2.555e-08 1.03e-06 1.03e-06 2.045e-05 2.045e-05 0.00045453 0.00125735 0.00125735 0.00931982 0.0213881 0.0342834 0.0367043 0.0275239 0.013405 0.0040931 0.00086471 0.000107056 8.593490002623001e-06 2.597056332e-07 1.3900524e-08 4.07928e-11 1.02e-11 1.47e-10 4.73e-10 1.1e-09 1.12e-08 1.49e-08 4.46e-08 2.31e-07 6.44e-07 1.19e-06 1.56e-06 1.46e-06 9.25e-07 4.03e-07 1.2e-07 2.170000660492e-08 2.7703600000000003e-09 2.350001947e-10 0.0 0.0 0.0 0.0 1.01e-13 4.48e-12 1.16e-10 2.15e-09 2.34e-08 1.77e-07 7.33e-07 1.1e-07 2.67e-06 5.78e-06 8.23e-06 8.15e-06 5.68e-06 2.76e-06 9.973240003632e-07 0.0 0.0 0.0 0.0 1.2e-13 3.22e-12 5.23e-11 7.82e-12 6.68e-10 5.15e-09 2.49e-08 8.26e-08 1.74e-07 0.0 0.0 2.09e-13 5.24e-12 5.24e-12 5.24e-12 7.62e-10 9.8e-09 9.8e-09 3.21e-07 3.32e-06 2.034e-05 8.31e-05 0.00022031 0.00038704 0.00046105 0.00039205 0.00022907 9.231e-05 2.681e-05 5.394399979934109e-06 6.81949999891e-07 8.42e-09 7.35e-09 4.61e-09 2.08e-09 6.8873e-10 1.4805239997193e-10 2.319998047e-11 0.0 0.0 0.0 8.81e-14 3.67e-12 1.12e-10 1.09e-09 1.09e-09 2.77e-08 1.13e-07 1.13e-07 1.33e-06 5.08e-06 1.438e-05 2.785e-05 3.679e-05 3.469e-05 2.297e-05 9.85e-06 2.92e-06 4.0722699952879996e-07 8.120001452000001e-08 5.130319e-09 0.0 0.0 0.0 0.0 1.34e-14 1.09e-12 5.62e-11 1.81e-09 3.33e-08 3.99e-07 3.03e-06 1.476e-05 4.577e-05 0.00010001 0.0001421 0.00014078 9.911e-05 4.925e-05 1.664e-05 4.0202600000115705e-06 6.18290004726e-07 0.0 0.0 0.0 0.0 0.0 1.92e-14 1.92e-14 2.19e-12 6.66e-11 1.56e-11 1.89e-09 4.08e-09 2.73e-08 3.18e-07 2.05e-06 3.06e-07 1.125e-05 1.8325e-05 1.8325e-05 8.315e-05 0.00013509 0.00015084 0.00011816 4.622e-05 2.592e-05 4.83e-06 8.220003187326e-07 7.04999911e-08 1.0794819053e-08 0.0 0.0 0.0 0.0 0.0 0.0 3.86e-14 9e-14 3.61e-12 5.39e-13 2.84e-11 6.63e-11 1.31e-09 3.55e-09 8.28e-09 7.33e-08 2.82e-07 7.47e-07 6.53e-07 6.53e-07 1.58e-06 1.3e-06 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.13e-13 4.83e-14 3.17e-06 3.0425e-05 3.0425e-05 0.000332285 0.000332285 0.00240337 0.00174107 0.0146588 0.0146509 0.0145193 0.0393793 0.0312656 0.0173624 0.00633372 0.00157734 0.00023855 5.938101312823e-05 5.252055617e-07 6.430284674999999e-08 0.0 0.0 0.0 0.0 2.46e-14 3.81e-12 8.95e-13 1.03e-10 3.09e-10 0.0 0.0 0.0 0.0 0.0 4.08e-14 0.0 8.17e-13 1.36e-13 1.36e-13 1.03e-11 2.41e-12 3.57333e-11 3.57333e-11 3.57333e-11 3.34e-06 7.84e-07 1.42333e-05 1.42333e-05 1.42333e-05 0.00015197 3.565e-05 0.000198555 0.000198555 0.00031515 0.00031515 0.00042505 0.00042505 0.00047317 0.00047317 0.0004184 0.0004184 0.00027254 0.00027254 8.211e-05 8.211e-05 8.211e-05 0.00031568 0.00031568 0.00013553 1.6639987697499998e-05 1.03015e-06 6.642025575e-08 8.28e-10 0.0 0.0 0.0 0.0 1.41e-14 1.12e-12 1.67e-13 8.22e-11 2.51e-09 5.88e-10 6.12e-08 1.23e-06 2.88e-07 1.36e-05 0.00010112 0.00047689 0.0015879 0.00380803 0.00548177 0.00557672 0.00380368 0.00182123 0.00049248 8.072e-05 9.29867e-06 6.519999857e-07 1.2163610227e-07 0.0 0.0 9.18e-11 4.79e-09 1.61e-07 3e-06 3.68e-05 0.00029685 0.00164717 0.00591485 0.0131696 0.00985963 0.00985963 0.0199161 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 0.0253 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 2.85944e-13 1.91962e-12 2.90943e-11 6.77867e-11 5.44893e-10 3.64929e-09 4.26417e-08 4.26417e-08 8.89826e-07 8.89826e-07 5.38895e-06 1.25675e-05 2.34754e-05 0.000157099 0.00166287 0.000375277 0.00251899 0.00506866 0.00499532 0.00512208 0.00581074 0.00608749 0.00592027 0.00592027 0.00271132 0.00271132 0.00376982 0.000975314 0.000975314 0.000527277 5.6384e-05 5.6384e-05 2.50051e-05 1.91962e-06 2.16958e-07 1.21976e-08 3.33935e-10 5.67889e-11 1.85136e-11 3.26936e-12 0.0 7.00863e-13 1.63968e-11 3.89924e-10 5.85886e-09 7.1586e-08 4.88904e-07 4.32915e-06 1.46271e-05 7.29457e-05 0.000137103 0.000273706 0.000146471 0.000146471 0.000378556 0.00020014 0.000115867 3.15238e-05 8.59832e-06 1.26975e-06 1.38881e-07 2.16545312e-08 3.49559e-10 3.14559e-11 0.0 1.62968e-11 8.28838e-10 4.12919e-08 9.86807e-07 6.50373e-06 6.50373e-06 7.56552e-05 7.56552e-05 0.000218287 0.000534435 0.0030153 0.00845647 0.0209985 0.0241731 0.0235808 0.0230734 0.0142944 0.00510007 0.00192176 0.00034692 5.36295e-05 5.1667e-06 4.80906e-07 1.02892e-08 9.78809e-10 6.88149e-11 1.46971e-13 5.27897e-11 1.76965e-10 3.96922e-09 2.15958e-08 7.24858e-08 2.0296e-07 6.08881e-07 7.49853e-06 2.42453e-05 1.0388e-05 0.000197071 0.000170747 0.000303551 0.00126239 0.001614504 0.00214124 0.00168078 0.000787806 0.000221507 0.000114238 1.68367e-05 4.78589e-06 3.88924e-07 7.85898117e-08 3.959749898e-09 2.14432e-10 0.0 0.0 2.64948e-13 1.97961e-11 7.42855e-10 2.84944e-08 1.16977e-07 3.91923e-07 7.38855e-06 1.09379e-05 3.66028e-05 0.000292683 0.000194442 0.000650953 0.0011265 0.000578597 0.00192346 0.00212598 0.00091956 0.00091956 0.00155224 0.000550172 0.000550172 0.00096886 0.000405501 0.000128415 3.0434e-05 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Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 7.47189e-14 1.74044e-13 3.04077e-12 2.03051e-11 7.40187e-10 7.40187e-10 2.83572e-08 2.83572e-08 4.16105e-07 9.71245e-07 2.51063e-06 1.67942e-05 0.000192969 0.000128182 0.000857857 0.00421397 0.0060105 0.00600547 0.00885305 0.00885305 0.00875799 0.00875799 0.00486792 0.00486792 0.00356272 0.000514345 0.000514345 0.000273779 2.84672e-05 2.84672e-05 1.30233e-05 1.07027e-11 3.14079e-12 4.92124e-13 4.03102e-14 0.0 2.94834e-16 6.5819630769999995e-09 0.0 0.0 5.72144e-14 2.62066e-12 7.771959999999999e-11 1.77045e-09 2.46062e-08 2.51063e-07 1.72043e-06 7.02177e-06 2.42861e-05 5.5424e-05 4.68518e-05 4.68518e-05 0.00011739 8.50014e-05 7.27884e-05 2.7567e-05 8.17206e-06 1.70043e-06 2.43013e-07 1.571422409e-08 3.94622712e-09 1.50025e-11 0.0 0.0 0.0 8.90225e-13 1.15029e-10 3.84597e-09 3.84597e-09 1.74044e-07 1.74044e-07 2.3706e-06 5.80146e-06 0.000101096 0.000770715 0.00288132 0.00962652 0.0220287 0.0294335 0.0289033 0.0189435 0.00733574 0.00179566 0.00035824 4.63377e-05 3.5509e-06 2.98191e-07 5.821467984e-09 1.10504e-10 0.0 2.23056e-14 7.48189e-14 5.25133e-12 3.5709e-11 1.2003e-10 9.83248e-10 2.95074e-09 6.22157e-08 4.81121e-07 2.06052e-07 5.08128e-06 7.85198e-06 1.39535e-05 0.000108377 0.00023972 0.000428638 0.000566923 0.000571924 0.000569504 0.000302036 7.05778e-05 1.10688e-05 1.2003e-06 6.9086605e-08 6.011774064000001e-09 6.77327e-11 0.0 0.0 0.0 2.72069e-14 3.28083e-12 2.52064e-10 2.43061e-09 8.14206e-09 3.1508e-07 1.09028e-06 3.64092e-06 5.9345e-05 0.000113499 0.000379976 0.00203758 0.00124663 0.00417352 0.00752017 0.0043536 0.0043536 0.00630398 0.00207112 0.00207112 0.00216435 0.00126206 4.12104e-06 3.09078e-06 1.15029e-06 2.85072e-07 5.87148e-08 8.80222e-09 8.93225e-06 1.0502630881e-06 8.47214009e-08 0.0 6.26158e-14 7.95201e-12 1.76044e-10 4.31109e-10 2.33059e-08 5.80146e-07 1.04926e-05 0.000103856 0.000743328 0.00351127 0.00907867 0.0151881 0.0197034 0.0170439 0.00925897 0.00366289 0.000906369 0.000162441 1.93849e-05 1.54025e-06 8.212073204e-08 2.850722668e-09 5.59141e-07 7.72195e-07 6.83172e-07 4.97125e-07 2.23056e-07 6.52165e-08 1.47037e-08 2.49178029e-09 2.370983097e-10 1.7404427400000003e-11 2.110537035e-11 1.620413622e-12 0.0 0.0 2.51063e-12 2.99076e-10 1.9805e-08 3.83597e-07 3.83597e-07 7.61692e-06 7.61692e-06 0.000103556 0.000103556 0.00153204 0.00315283 0.00315361 0.0165546 0.0229069 0.0297039 0.0272875 0.0147003 0.0055908 0.00133525 0.000185507 3.43137e-05 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2.43061e-08 2.430971091e-09 1.810460466e-10 0.0203975 0.01789 0.0100919 0.00357967 0.000923793 0.000164954 1.87158e-05 1.49655e-06 5.67106e-08 9.45489e-10 2.29058e-11 0.0 0.0 3.32084e-14 2.67568e-12 2.67568e-12 4.91124e-10 1.67042e-08 4.24107e-07 5.72144e-06 5.85548e-05 0.000455835 0.00194931 0.00641757 0.0170697 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 0.0253 - - Ag103 Ag105 Ag105_m1 Ag106 Ag106_m1 Ag107 Ag107_m1 Ag108 Ag108_m1 Ag109 Ag109_m1 Ag110 Ag110_m1 Ag111 Ag111_m1 Ag112 Ag113 Ag113_m1 Ag114 Ag115 Ag115_m1 Ag116 Ag116_m1 Ag117 Ag117_m1 Ag118 Ag118_m1 Ag119 Ag120 Ag120_m1 Ag121 Ag122 Ag122_m1 Ag123 Ag124 Ag125 Ag126 Ag127 Ag128 Ag129 Ag130 As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 4.28e-14 1e-13 1.83e-12 1.22e-11 3.51e-10 3.51e-10 1.325e-08 1.325e-08 1.85e-07 4.32e-07 1.19e-06 7.99e-06 9.857e-05 8.107e-05 0.00054254 0.00361196 0.0063676 0.00636328 0.0124159 0.0124159 0.0152276 0.0152276 0.0120182 0.0120182 0.0134153 0.00258854 0.00258854 0.00131768 2.335e-06 2.335e-06 4.96e-07 5.02e-08 8.53e-09 4.66e-13 9.23e-14 2.08e-14 2.00175e-15 0.0 0.0 0.0 0.0 0.0 1.2010000000000001e-13 3.28e-12 6.92e-11 9.73e-10 8.58e-09 5.86e-08 2.7e-07 1.15e-06 1.28e-06 1.28e-06 4.66e-06 4.9e-06 5.81e-06 3.53e-06 1.57e-06 5.08e-07 9.8784e-08 1.0616518270000001e-08 4.070087198e-09 2.79112e-11 0.0 0.0 0.0 5.79e-13 8.39e-11 2.835e-09 2.835e-09 9.9e-08 9.9e-08 1.23e-06 3e-06 5.031e-05 0.0003807 0.00196215 0.00777785 0.0177466 0.0278623 0.0222555 0.0138268 0.00578089 0.00161559 0.00029739 3.28247e-05 2.54e-06 1.773e-07 4.370001223e-09 8.9293e-11 0.0 0.0 0.0 2.49e-14 2.99e-13 1e-12 1.09e-11 3.28e-11 1.23e-09 1.25e-08 5.36e-09 2.02e-07 4.53e-07 8.05e-07 8.27e-06 2.93e-05 7.854e-05 0.00015323 0.00019995 0.00023999 0.00020781 6.921e-05 1.5102e-05 2.34e-06 1.297549516e-07 1.2200807000000001e-08 1.5566e-10 0.0 0.0 0.0 1.15e-14 1.34e-12 9.23e-11 9.05e-10 3.03e-09 1.26e-07 5.56e-07 1.86e-06 4.134e-05 0.00010093 0.00033791 0.00241521 0.00184359 0.00617203 0.0158451 0.0107133 0.0107133 0.0202583 0.00634099 0.00634099 0.00572532 0.00226613 0.00070539 0.0002562 1.38e-06 5.58e-07 2.57e-07 4.9e-08 3.44e-09 3.720002122e-06 2.200001989e-07 0.0 2.2e-14 2.55e-12 5.33e-11 1.3e-10 9.25e-09 3.43e-07 7.26e-06 9.364e-05 0.00057288 0.00244876 0.00683786 0.0125095 0.0156076 0.0127026 0.00733706 0.00286703 0.000845 0.00017818 1.962e-05 1.43984e-06 7.33000299e-08 2.880001321e-09 5.61e-11 1.03e-10 1.37e-10 1.3e-10 8.39e-11 3.27e-11 3.25e-12 2.37e-12 3.65e-13 3.209998319e-14 0.0 0.0 0.0 0.0 2.14e-12 2.32e-10 1.52e-08 3.135e-07 3.135e-07 6.44e-06 6.44e-06 6.921e-05 6.921e-05 0.00095505 0.00192952 0.00192995 0.0102357 0.0187454 0.027606 0.0235232 0.0142239 0.00430237 0.00099828 0.00014988 3.76533e-05 9.23186e-07 5.5299329200000003e-08 8.650181e-10 1.91677e-11 3.23e-13 3.7e-12 8.77e-12 2.05e-11 1.61e-10 1.45e-10 4.34e-10 1.35e-09 8.47e-10 4.12e-09 4.26e-09 2.78e-09 1.45e-09 5.64e-10 1.24e-10 2.079995858e-11 2.25e-12 2.36e-13 0.0 0.0 1.66e-14 1.3e-12 4.72e-11 1.29e-09 2.19e-08 2.23e-07 1.15e-06 3.95e-06 6.78e-06 1.01e-06 1.156e-05 1.176e-05 9.82e-06 5.9e-06 2.56e-06 6.49e-07 1.4916484999529302e-07 0.0 0.0 6.17e-14 1.71e-12 2.66e-11 3e-10 1.84e-09 2.75e-10 1.22e-08 4.78e-08 1.28e-07 1.83e-07 2.38e-07 0.0 0.0 4.48e-11 6.66667e-10 6.66667e-10 6.66667e-10 5.43e-08 4.255e-07 4.255e-07 7.77e-06 4.955e-05 0.00024801 0.00080626 0.00139138 0.00191074 0.00176509 0.00104707 0.00033536 7.502e-05 9.89e-06 9.880600009480002e-07 7.078060001265001e-08 2.22e-11 1.69e-11 8.96e-12 3.28e-12 8.15e-13 1.16e-13 0.0 0.0 0.0 0.0 1.19e-14 2.9e-13 4.54e-12 9e-12 9e-12 5.26e-10 1.515e-09 1.515e-09 1.15e-08 3.55e-08 8.16e-08 1.18e-07 1.37e-07 1.09e-07 8.25e-08 3.01e-08 8.52e-09 1.310389e-09 2.269996537e-10 1.82e-11 0.0 0.0 0.0 5.76e-14 4.65e-12 2.22e-10 5.92e-09 1.09e-07 1.55e-06 1.397e-05 6.321e-05 0.00022 0.00049809 0.00070611 0.00053563 0.00028682 9.296e-05 2.353e-05 4.32e-06 6.1671149e-07 6.02310001526e-08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.93e-14 3.86e-12 9.05e-13 7.15e-11 9.14e-11 6.12e-10 5.45e-09 2.58e-08 3.86e-09 1.02e-07 1.405e-07 1.405e-07 5.38e-07 9.87e-07 9.23e-07 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As69 As71 As72 As73 As74 As75 As76 As77 As78 As79 As80 As81 As82 As82_m1 As83 As84 As85 As86 As87 As88 As89 As90 As91 As92 Ba129 Ba131 Ba132 Ba133 Ba134 Ba135 Ba135_m1 Ba136 Ba136_m1 Ba137 Ba137_m1 Ba138 Ba139 Ba140 Ba141 Ba142 Ba143 Ba144 Ba145 Ba146 Ba147 Ba148 Ba149 Ba150 Ba151 Ba152 Ba153 Br75 Br77 Br77_m1 Br78 Br79 Br79_m1 Br80 Br80_m1 Br81 Br82 Br82_m1 Br83 Br84 Br84_m1 Br85 Br86 Br87 Br88 Br89 Br90 Br91 Br92 Br93 Br94 Br95 Br96 Br97 Cd105 Cd106 Cd107 Cd108 Cd109 Cd110 Cd111 Cd111_m1 Cd112 Cd113 Cd113_m1 Cd114 Cd115 Cd115_m1 Cd116 Cd117 Cd117_m1 Cd118 Cd119 Cd119_m1 Cd120 Cd121 Cd121_m1 Cd122 Cd123 Cd124 Cd125 Cd126 Cd127 Cd128 Cd129 Cd130 Cd131 Cd132 Ce135 Ce137 Ce138 Ce139 Ce139_m1 Ce140 Ce141 Ce142 Ce143 Ce144 Ce145 Ce146 Ce147 Ce148 Ce149 Ce150 Ce151 Ce152 Ce153 Ce154 Ce155 Ce156 Ce157 Co66 Co67 Co68 Co69 Co70 Co71 Co72 Co73 Co74 Co75 Cr66 Cr67 Cs127 Cs129 Cs131 Cs132 Cs133 Cs134 Cs134_m1 Cs135 Cs135_m1 Cs136 Cs136_m1 Cs137 Cs138 Cs138_m1 Cs139 Cs140 Cs141 Cs142 Cs143 Cs144 Cs145 Cs146 Cs147 Cs148 Cs149 Cs150 Cs151 Cu66 Cu67 Cu68 Cu68_m1 Cu69 Cu70 Cu70_m1 Cu71 Cu72 Cu73 Cu74 Cu75 Cu76 Cu77 Cu78 Cu79 Cu80 Cu81 Dy155 Dy156 Dy157 Dy158 Dy159 Dy160 Dy161 Dy162 Dy163 Dy164 Dy165 Dy165_m1 Dy166 Dy167 Dy168 Dy169 Dy170 Dy171 Dy172 Er161 Er162 Er163 Er164 Er165 Er166 Er167 Er167_m1 Er168 Er169 Er170 Er171 Er172 Eu147 Eu149 Eu151 Eu152 Eu152_m1 Eu152_m2 Eu153 Eu154 Eu154_m1 Eu155 Eu156 Eu157 Eu158 Eu159 Eu160 Eu161 Eu162 Eu163 Eu164 Eu165 Eu166 Eu167 Fe66 Fe67 Fe68 Fe69 Fe70 Fe71 Fe72 Ga66 Ga67 Ga68 Ga69 Ga70 Ga71 Ga72 Ga72_m1 Ga73 Ga74 Ga74_m1 Ga75 Ga76 Ga77 Ga78 Ga79 Ga80 Ga81 Ga82 Ga83 Ga84 Ga85 Ga86 Gd147 Gd149 Gd151 Gd152 Gd153 Gd154 Gd155 Gd156 Gd157 Gd158 Gd159 Gd160 Gd161 Gd162 Gd163 Gd164 Gd165 Gd166 Gd167 Gd168 Gd169 Ge66 Ge67 Ge68 Ge69 Ge70 Ge71 Ge71_m1 Ge72 Ge73 Ge73_m1 Ge74 Ge75 Ge75_m1 Ge76 Ge77 Ge77_m1 Ge78 Ge79 Ge79_m1 Ge80 Ge81 Ge82 Ge83 Ge84 Ge85 Ge86 Ge87 Ge88 Ge89 Hf171 Hf172 Ho159 Ho159_m1 Ho161 Ho161_m1 Ho162 Ho162_m1 Ho163 Ho163_m1 Ho164 Ho164_m1 Ho165 Ho166 Ho166_m1 Ho167 Ho168 Ho169 Ho170 Ho170_m1 Ho171 Ho172 I121 I123 I125 I126 I127 I128 I129 I130 I130_m1 I131 I132 I132_m1 I133 I133_m1 I134 I134_m1 I135 I136 I136_m1 I137 I138 I139 I140 I141 I142 I143 I144 I145 In107 In109 In111 In112 In112_m1 In113 In113_m1 In114 In114_m1 In115 In115_m1 In116 In116_m1 In116_m2 In117 In117_m1 In118 In118_m1 In118_m2 In119 In119_m1 In120 In120_m1 In120_m2 In121 In121_m1 In122 In122_m1 In122_m2 In123 In123_m1 In124 In124_m1 In125 In125_m1 In126 In126_m1 In127 In127_m1 In128 In128_m1 In129 In129_m1 In130 In130_m1 In130_m2 In131 In131_m1 In132 In133 In134 In135 Kr100 Kr77 Kr78 Kr79 Kr79_m1 Kr80 Kr81 Kr81_m1 Kr82 Kr83 Kr83_m1 Kr84 Kr85 Kr85_m1 Kr86 Kr87 Kr88 Kr89 Kr90 Kr91 Kr92 Kr93 Kr94 Kr95 Kr96 Kr97 Kr98 Kr99 La133 La135 La137 La138 La139 La140 La141 La142 La143 La144 La145 La146 La146_m1 La147 La148 La149 La150 La151 La152 La153 La154 La155 Lu169 Lu169_m1 Lu171 Lu171_m1 Lu172 Lu172_m1 Mn66 Mn67 Mn68 Mn69 Mo100 Mo101 Mo102 Mo103 Mo104 Mo105 Mo106 Mo107 Mo108 Mo109 Mo110 Mo111 Mo112 Mo113 Mo114 Mo115 Mo90 Mo91 Mo92 Mo93 Mo93_m1 Mo94 Mo95 Mo96 Mo97 Mo98 Mo99 Nb100 Nb100_m1 Nb101 Nb102 Nb102_m1 Nb103 Nb104 Nb104_m1 Nb105 Nb106 Nb107 Nb108 Nb109 Nb110 Nb111 Nb112 Nb113 Nb89 Nb90 Nb91 Nb92 Nb93 Nb93_m1 Nb94 Nb94_m1 Nb95 Nb95_m1 Nb96 Nb97 Nb97_m1 Nb98 Nb98_m1 Nb99 Nb99_m1 Nd140 Nd141 Nd142 Nd143 Nd144 Nd145 Nd146 Nd147 Nd148 Nd149 Nd150 Nd151 Nd152 Nd153 Nd154 Nd155 Nd156 Nd157 Nd158 Nd159 Nd160 Nd161 Ni66 Ni67 Ni68 Ni69 Ni70 Ni71 Ni72 Ni73 Ni74 Ni75 Ni76 Ni77 Ni78 Pd101 Pd102 Pd103 Pd104 Pd105 Pd106 Pd107 Pd107_m1 Pd108 Pd109 Pd109_m1 Pd110 Pd111 Pd111_m1 Pd112 Pd113 Pd114 Pd115 Pd116 Pd117 Pd118 Pd119 Pd120 Pd121 Pd122 Pd123 Pd124 Pd125 Pd126 Pd99 Pm141 Pm143 Pm144 Pm145 Pm146 Pm147 Pm148 Pm148_m1 Pm149 Pm150 Pm151 Pm152 Pm152_m1 Pm152_m2 Pm153 Pm154 Pm154_m1 Pm155 Pm156 Pm157 Pm158 Pm159 Pm160 Pm161 Pm162 Pm163 Pr139 Pr140 Pr141 Pr142 Pr142_m1 Pr143 Pr144 Pr144_m1 Pr145 Pr146 Pr147 Pr148 Pr148_m1 Pr149 Pr150 Pr151 Pr152 Pr153 Pr154 Pr155 Pr156 Pr157 Pr158 Pr159 Rb100 Rb101 Rb102 Rb79 Rb81 Rb83 Rb84 Rb85 Rb86 Rb86_m1 Rb87 Rb88 Rb89 Rb90 Rb90_m1 Rb91 Rb92 Rb93 Rb94 Rb95 Rb96 Rb97 Rb98 Rb99 Rh101 Rh101_m1 Rh102 Rh102_m1 Rh103 Rh103_m1 Rh104 Rh104_m1 Rh105 Rh105_m1 Rh106 Rh106_m1 Rh107 Rh108 Rh108_m1 Rh109 Rh110 Rh110_m1 Rh111 Rh112 Rh113 Rh114 Rh115 Rh116 Rh117 Rh118 Rh119 Rh120 Rh121 Rh122 Rh123 Rh99 Ru100 Ru101 Ru102 Ru103 Ru104 Ru105 Ru106 Ru107 Ru108 Ru109 Ru110 Ru111 Ru112 Ru113 Ru114 Ru115 Ru116 Ru117 Ru118 Ru119 Ru120 Ru95 Ru96 Ru97 Ru98 Ru99 Sb113 Sb115 Sb117 Sb118 Sb118_m1 Sb119 Sb120 Sb120_m1 Sb121 Sb122 Sb122_m1 Sb123 Sb124 Sb124_m1 Sb124_m2 Sb125 Sb126 Sb126_m1 Sb126_m2 Sb127 Sb128 Sb128_m1 Sb129 Sb130 Sb130_m1 Sb131 Sb132 Sb132_m1 Sb133 Sb134 Sb134_m1 Sb135 Sb136 Sb137 Sb138 Sb139 Se72 Se73 Se73_m1 Se74 Se75 Se76 Se77 Se77_m1 Se78 Se79 Se79_m1 Se80 Se81 Se81_m1 Se82 Se83 Se83_m1 Se84 Se85 Se86 Se87 Se88 Se89 Se90 Se91 Se92 Se93 Se94 Sm143 Sm143_m1 Sm144 Sm145 Sm146 Sm147 Sm148 Sm149 Sm150 Sm151 Sm152 Sm153 Sm154 Sm155 Sm156 Sm157 Sm158 Sm159 Sm160 Sm161 Sm162 Sm163 Sm164 Sm165 Sn111 Sn112 Sn113 Sn113_m1 Sn114 Sn115 Sn116 Sn117 Sn117_m1 Sn118 Sn119 Sn119_m1 Sn120 Sn121 Sn121_m1 Sn122 Sn123 Sn123_m1 Sn124 Sn125 Sn125_m1 Sn126 Sn127 Sn127_m1 Sn128 Sn128_m1 Sn129 Sn129_m1 Sn130 Sn130_m1 Sn131 Sn131_m1 Sn132 Sn133 Sn134 Sn135 Sn136 Sn137 Sr100 Sr101 Sr102 Sr103 Sr104 Sr105 Sr83 Sr84 Sr85 Sr85_m1 Sr86 Sr87 Sr87_m1 Sr88 Sr89 Sr90 Sr91 Sr92 Sr93 Sr94 Sr95 Sr96 Sr97 Sr98 Sr99 Tb151 Tb153 Tb155 Tb156 Tb156_m1 Tb157 Tb158 Tb158_m1 Tb159 Tb160 Tb161 Tb162 Tb163 Tb164 Tb165 Tb166 Tb167 Tb168 Tb169 Tb170 Tb171 Tc100 Tc101 Tc102 Tc102_m1 Tc103 Tc104 Tc105 Tc106 Tc107 Tc108 Tc109 Tc110 Tc111 Tc112 Tc113 Tc114 Tc115 Tc116 Tc117 Tc118 Tc93 Tc95 Tc95_m1 Tc97 Tc97_m1 Tc98 Tc99 Tc99_m1 Te115 Te117 Te118 Te119 Te120 Te121 Te121_m1 Te122 Te123 Te123_m1 Te124 Te125 Te125_m1 Te126 Te127 Te127_m1 Te128 Te129 Te129_m1 Te130 Te131 Te131_m1 Te132 Te133 Te133_m1 Te134 Te135 Te136 Te137 Te138 Te139 Te140 Te141 Te142 Tm165 Tm166 Tm167 Tm168 Tm169 Tm170 Tm171 Tm172 Xe124 Xe125 Xe125_m1 Xe126 Xe127 Xe127_m1 Xe128 Xe129 Xe129_m1 Xe130 Xe131 Xe131_m1 Xe132 Xe133 Xe133_m1 Xe134 Xe134_m1 Xe135 Xe135_m1 Xe136 Xe137 Xe138 Xe139 Xe140 Xe141 Xe142 Xe143 Xe144 Xe145 Xe146 Xe147 Y100 Y101 Y102 Y103 Y104 Y105 Y106 Y107 Y108 Y85 Y87 Y88 Y89 Y89_m1 Y90 Y90_m1 Y91 Y91_m1 Y92 Y93 Y93_m1 Y94 Y95 Y96 Y96_m1 Y97 Y97_m1 Y98 Y98_m1 Y99 Yb166 Yb167 Yb168 Yb169 Yb169_m1 Yb170 Yb171 Yb172 Zn66 Zn67 Zn68 Zn69 Zn69_m1 Zn70 Zn71 Zn71_m1 Zn72 Zn73 Zn74 Zn75 Zn76 Zn77 Zn78 Zn79 Zn80 Zn81 Zn82 Zn83 Zr100 Zr101 Zr102 Zr103 Zr104 Zr105 Zr106 Zr107 Zr108 Zr109 Zr110 Zr87 Zr88 Zr89 Zr90 Zr90_m1 Zr91 Zr92 Zr93 Zr94 Zr95 Zr96 Zr97 Zr98 Zr99 - 0.0 0.0 0.0 0.0 1.64e-14 4.43001e-13 2.97001e-12 1.435e-10 1.435e-10 8.30002e-09 8.30002e-09 1.67e-07 3.89001e-07 1.26e-06 8.44002e-06 0.00012214 0.00012941 0.000866042 0.00486487 0.00787177 0.00787177 0.0145287 0.0145287 0.0188055 0.0188055 0.0162199 0.0162199 0.0189561 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openmc_msre_notebook +# OpenMC MSRE_notebooks [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) - + -List of Jupyter Notebooks `Openmc` simulations of the Molten Salt Reactor Experiment (MSRE), operated at ORNL in the 1960s. -All scripts are set up using `.h5m` files (surface mesh) of detailed CAD models of the MSRE (designed with `OnShape` CAE tool and available to downlaod [here](https://github.com/openmsr/msre/tree/deplete/step_files)). -The surface mesh was created with `Cubit` and is available in the [/h5m](https://github.com/openmsr/openmc_msre_notebooks/tree/main/h5m) folder. Soon a `gmsh` version will also be available. - -## Extra libraries required: - - **Pandas** - - **Numpy** - - **Scypy** - - **Seaborn** - -## Depletion msr -Some notebooks use a different branch of Openmc: [openmsr/msr](https://github.com/openmsr/openmc/tree/msr_13.2), where msr functionalities have been added. +`OpenMC` simulation examples of the Molten Salt Reactor Experiment (MSRE), operated at ORNL in the 1960s. +All scripts are set up using the [h5m meshed files](https://github.com/openmsr/msre/tree/master/h5m) obtained with the open source meshing tool [CAD-to-OpenMC](https://github.com/openmsr/CAD_to_OpenMC) from a CAD version of the the MSRE, designed with the CAE tool `OnShape` and available for export here: [onshape msre model]((https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/)). diff --git a/openmc_notebooks/msre_cad.ipynb b/openmc_notebooks/msre_cad.ipynb deleted file mode 100644 index fb14879..0000000 --- a/openmc_notebooks/msre_cad.ipynb +++ /dev/null @@ -1,1336 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "84287fea", - "metadata": {}, - "source": [ - "# MSRE CAD - Notebook\n", - "The following notebook aims at providing general Openmc modeling capabilities using a pre-generated meshed CAD geometry of the Molten Salt Reactor Experiment (MSRE), operated at ORNL in the 1960s. \n", - "\n", - "The geometry was designed using `Onshape` cad tool and is free to donwload as a step file from the [msr/msre](https://github.com/openmsr/msre/tree/deplete) repository. " - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "f8a4de08", - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 62, - "metadata": { - "image/jpeg": { - "width": 400 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(\"./images/msre_assembly.jpg\", width=400)" - ] - }, - { - "cell_type": "markdown", - "id": "9a9f360a", - "metadata": {}, - "source": [ - "Fig. 1: Section view of entire assembly" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "d951f7c7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 64, - "metadata": { - "image/jpeg": { - "width": 400 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"./images/control_rod.jpg\", width=400)" - ] - }, - { - "cell_type": "markdown", - "id": "c7ee2ce3", - "metadata": {}, - "source": [ - "Fig. 3: Section view of control rod bottom end" - ] - }, - { - "cell_type": "markdown", - "id": "ce7fc65d", - "metadata": {}, - "source": [ - "**Note:** Only one control rod is exported from Onshape as a separate step file from the rest of the geometry. \n", - "\n", - "The h5m surface mesh files were created using `Coreform Cubit`.\n", - "The other two control rods are created directly in Openmc, as offset duplicates of the imported one.\n", - "The reason to have a separate export of the control rods is to give the user the freedom to position it as needed using Openmc functionailities.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "328a02ad", - "metadata": {}, - "source": [ - "Materials definition are based on ORNL documentation and the [Molten Salt Reactor Experiment\n", - "Benchmark Evaluation, Fratoni et. al.](https://www.osti.gov/servlets/purl/1617123) report." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "787fab93", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib inline\n", - "\n", - "import openmc\n", - "import numpy as np\n", - "from math import log10\n", - "import matplotlib.pyplot as plt\n", - "import os \n", - "\n", - "os.system('rm *.xml *.h5')" - ] - }, - { - "cell_type": "markdown", - "id": "ceb51601", - "metadata": {}, - "source": [ - "In this example we will set the fuel salt temperature and density to $638.3 ^\\circ C$ and $2.3275 g/cm³$, respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e1001bc3", - "metadata": {}, - "outputs": [], - "source": [ - "# Define materials \n", - "hot_temp = 638.3 + 273.15 # in K\n", - "\n", - "# Fuel salt \n", - "salt = openmc.Material(name=\"salt\", temperature = hot_temp)\n", - "salt.add_nuclide('Li6',1.31480070E-05)\n", - "salt.add_nuclide('Li7', 0.262960140146177)\n", - "salt.add_nuclide('Be9',1.1863E-01)\n", - "salt.add_nuclide('Zr90',1.0543E-02)\n", - "salt.add_nuclide('Zr91',2.2991E-03)\n", - "salt.add_nuclide('Zr92',3.5142E-03)\n", - "salt.add_nuclide('Zr94',3.5613E-03)\n", - "salt.add_nuclide('Zr96',5.7375E-04)\n", - "salt.add_nuclide('Hf174',8.3786E-10)\n", - "salt.add_nuclide('Hf176',2.7545E-08)\n", - "salt.add_nuclide('Hf177',9.7401E-08)\n", - "salt.add_nuclide('Hf178',1.4285E-07)\n", - "salt.add_nuclide('Hf179',7.1323E-08)\n", - "salt.add_nuclide('Hf180',1.8370E-07)\n", - "salt.add_nuclide('U234',1.034276246E-05)\n", - "salt.add_nuclide('U235',1.009695816E-03)\n", - "salt.add_nuclide('U236',4.227809892E-06)\n", - "salt.add_nuclide('U238',2.168267822E-03)\n", - "salt.add_nuclide('Fe54',2.8551E-06)\n", - "salt.add_nuclide('Fe56',4.4818E-05)\n", - "salt.add_nuclide('Fe57',1.0350E-06)\n", - "salt.add_nuclide('Fe58',1.3775E-07)\n", - "salt.add_nuclide('Cr50',2.1224E-06)\n", - "salt.add_nuclide('Cr52',4.0928E-05)\n", - "salt.add_nuclide('Cr53',4.6409E-06)\n", - "salt.add_nuclide('Cr54',1.1552E-06)\n", - "salt.add_nuclide('Ni58',5.8597E-06)\n", - "salt.add_nuclide('Ni60',2.2571E-06)\n", - "salt.add_nuclide('Ni61',9.8117E-08)\n", - "salt.add_nuclide('Ni62',3.1284E-07)\n", - "salt.add_nuclide('Ni64',7.9671E-08)\n", - "salt.add_nuclide('O16',5.1437E-04)\n", - "salt.add_nuclide('O17',1.8927E-07)\n", - "salt.add_nuclide('O18',9.6440E-07)\n", - "salt.add_nuclide('F19',5.9409E-01)\n", - "salt.set_density('g/cm3', 2.3275)\n", - "\n", - "#Moderator graphite block\n", - "graphite = openmc.Material(name='graphite', temperature = hot_temp)\n", - "graphite.set_density('g/cm3',1.86)\n", - "graphite.add_nuclide('C12',1)\n", - "graphite.add_s_alpha_beta('c_Graphite')\n", - "\n", - "#inor-8\n", - "inor = openmc.Material(name='inor-8', temperature = hot_temp)\n", - "inor.set_density('g/cm3',8.7745)\n", - "inor.add_element('Ni',68.5,'wo')\n", - "inor.add_element('Mo',16.5,'wo')\n", - "inor.add_element('Cr',7,'wo')\n", - "inor.add_element('Fe',5,'wo')\n", - "inor.add_element('C',0.06,'wo')\n", - "inor.add_element('Al',0.25,'wo')\n", - "inor.add_element('Ti',0.25,'wo')\n", - "inor.add_element('S',0.02,'wo')\n", - "inor.add_element('Mn',1.0,'wo')\n", - "inor.add_element('Si',1.0,'wo')\n", - "inor.add_element('Cu',0.35,'wo')\n", - "inor.add_element('B',0.010,'wo')\n", - "inor.add_element('W',0.5,'wo')\n", - "inor.add_element('P',0.015,'wo')\n", - "inor.add_element('Co',0.2,'wo')\n", - "\n", - "#helium\n", - "helium = openmc.Material(name='helium')\n", - "helium.add_element('He',1.0)\n", - "helium.set_density('g/cm3',1.03*(10**-4))\n", - "\n", - "#Control rods inconel clad\n", - "trace = 0.01\n", - "inconel = openmc.Material(name='inconel', temperature = 65.6 + 273.15)\n", - "inconel.add_element('Ni',78.5,percent_type='wo')\n", - "inconel.add_element('Cr',14.0,percent_type='wo')\n", - "inconel.add_element('Fe',6.5,percent_type='wo')\n", - "inconel.add_element('Mn',0.25,percent_type='wo')\n", - "inconel.add_element('Si',0.25,percent_type='wo')\n", - "inconel.add_element('Cu',0.2,percent_type='wo')\n", - "inconel.add_element('Co',0.2,percent_type='wo')\n", - "inconel.add_element('Al',0.2,percent_type='wo')\n", - "inconel.add_element('Ti',0.2,percent_type='wo')\n", - "inconel.add_element('Ta',0.5,percent_type='wo')\n", - "inconel.add_element('W',0.5,percent_type='wo')\n", - "inconel.add_element('Zn',0.2,percent_type='wo')\n", - "inconel.add_element('Zr',0.1,percent_type='wo')\n", - "inconel.add_element('C',trace,percent_type='wo')\n", - "inconel.add_element('Mo',trace,percent_type='wo')\n", - "inconel.add_element('Ag',trace,percent_type='wo')\n", - "inconel.add_element('B',trace,percent_type='wo')\n", - "inconel.add_element('Ba',trace,percent_type='wo')\n", - "inconel.add_element('Be',trace,percent_type='wo')\n", - "inconel.add_element('Ca',trace,percent_type='wo')\n", - "inconel.add_element('Cd',trace,percent_type='wo')\n", - "inconel.add_element('V',trace,percent_type='wo')\n", - "inconel.add_element('Sn',trace,percent_type='wo')\n", - "inconel.add_element('Mg',trace,percent_type='wo')\n", - "inconel.set_density('g/cm3',8.5)\n", - "\n", - "# SS316 control rod flexible hose\n", - "ss316 = openmc.Material(name='ss316', temperature = 65.6 + 273.15)\n", - "ss316.add_element('C',0.026,'wo')\n", - "ss316.add_element('Si',0.37,'wo')\n", - "ss316.add_element('Mn',0.16,'wo')\n", - "ss316.add_element('Cr',16.55,'wo')\n", - "ss316.add_element('Cu',0.16,'wo')\n", - "ss316.add_element('Ni',10,'wo')\n", - "ss316.add_element('P',0.029,'wo')\n", - "ss316.add_element('S',0.027,'wo')\n", - "ss316.add_element('Mo',2.02,'wo')\n", - "ss316.add_element('N',0.036,'wo')\n", - "ss316.add_element('Fe',70.622,'wo')\n", - "ss316.set_density('g/cm3',7.99)\n", - "\n", - "#Control rods bushing posion material\n", - "Gd2O3 = openmc.Material()\n", - "Gd2O3.add_element('Gd',2)\n", - "Gd2O3.add_element('O',3)\n", - "Gd2O3.set_density('g/cm3',7.41)\n", - "Al2O3 = openmc.Material()\n", - "Al2O3.add_element('Al',2)\n", - "Al2O3.add_element('O',3)\n", - "Al2O3.set_density('g/cm3',3.95)\n", - "bush = openmc.Material.mix_materials([Gd2O3,Al2O3],[0.7,0.3],'wo')\n", - "bush.name='bush'\n", - "bush.temperature = 65.6 +273.15\n", - "\n", - "#Concrete block\n", - "concrete = openmc.Material(name='concrete')\n", - "concrete.add_element('H',0.005,'wo')\n", - "concrete.add_element('O',0.496,'wo')\n", - "concrete.add_element('Si',0.314,'wo')\n", - "concrete.add_element('Ca',0.083,'wo')\n", - "concrete.add_element('Na',0.017,'wo')\n", - "concrete.add_element('Mn',0.002,'wo')\n", - "concrete.add_element('Al',0.046,'wo')\n", - "concrete.add_element('S',0.001,'wo')\n", - "concrete.add_element('K',0.019,'wo')\n", - "concrete.add_element('Fe',0.012,'wo')\n", - "concrete.set_density('g/cm3',2.35)\n", - "\n", - "#Thermal shielding as material mix of water and SS304 50-50 vo\n", - "water = openmc.Material()\n", - "water.add_element('H',2)\n", - "water.add_element('O',1)\n", - "water.set_density('g/cm3',0.997)\n", - "\n", - "#stainless steel 304\n", - "ss304 = openmc.Material()\n", - "ss304.add_element('C',0.08,'wo')\n", - "ss304.add_element('Mn',2,'wo')\n", - "ss304.add_element('P',0.045,'wo')\n", - "ss304.add_element('S',0.03,'wo')\n", - "ss304.add_element('Si',0.75,'wo')\n", - "ss304.add_element('Cr',19,'wo')\n", - "ss304.add_element('Ni',10,'wo')\n", - "ss304.add_element('N',0.1,'wo')\n", - "ss304.add_element('Fe',67.995, 'wo')\n", - "ss304.set_density('g/cm3',7.93)\n", - "shield = openmc.Material.mix_materials([water,ss304],[0.5,0.5],'vo')\n", - "shield.temperature = 32.2 + 273.15\n", - "shield.name='steelwater'\n", - "\n", - "# \"Careytemp 1600\" by Philip Carey Manufacturing Compamy (Cincinnati) \n", - "insulation=openmc.Material(name='insulation')\n", - "insulation.add_element('Si',1)\n", - "insulation.add_element('O',2)\n", - "insulation.set_density('g/cm3',0.16) \n", - "\n", - "# Sand water (not sure about this material)\n", - "sandwater=openmc.Material(name='sandwater')\n", - "sandwater.add_element('Fe',3)\n", - "sandwater.add_element('O',4)\n", - "sandwater.set_density('g/cm3',6)\n", - "\n", - "#Vessel anular steel\n", - "steel = openmc.Material(name='steel')\n", - "steel.add_element('Fe',1)\n", - "steel.set_density('g/cm3',7.85)\n", - "\n", - "mats = openmc.Materials([salt,graphite,inor,helium,inconel,shield,concrete,steel,ss316,sandwater,insulation,bush])\n", - "mats.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "id": "31f8365e", - "metadata": {}, - "source": [ - "Let's import the h5m mesh files: the full msre reactor and one of the control rods. \\\n", - "We will create two `Dagmc` universes that will be used to fill the core and control rod cells, respectively. \\\n", - "As previously anticipated, we will create the other two control rods as conveniently translated replica of the first one. \\\n", - "All three control rods are now objects and the `translation` attribute value can be set accordinly using the `setattr()` function. \n", - "\n", - "In this example, we will start by setting all three control rods at the extreme top position (fully withdrawn)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6e9faef6", - "metadata": {}, - "outputs": [], - "source": [ - "# Import h5m files\n", - "core_h5m = '../h5m/msre_reactor_1e-2.h5m'\n", - "cr_h5m = '../h5m/msre_control_rod_1e-2.h5m'\n", - "\n", - "# Create DAGMC universes out of h5m files\n", - "core = openmc.DAGMCUniverse(filename=core_h5m, auto_geom_ids=True, universe_id=1)\n", - "cr = openmc.DAGMCUniverse(filename=cr_h5m, auto_geom_ids=True, universe_id=2)\n", - "\n", - "# Create regions\n", - "core_region = core.bounding_region()\n", - "cr1_region = cr.bounding_region(boundary_type='transmission', starting_id=20000)\n", - "\n", - "# Extend control rod region, to include upwards translations\n", - "cr1_region = cr1_region | cr1_region.translate([0,0,150])\n", - "\n", - "# Create control rod region 2 and 3 as translated region of control rod 1\n", - "offset = 10.163255\n", - "cr2_region = cr1_region.translate([-offset,0,0])\n", - "cr3_region = cr1_region.translate([-offset,offset,0])\n", - "\n", - "# Create openmc Cells \n", - "core_cell = openmc.Cell(region=~(cr1_region | cr2_region | cr3_region) & core_region , fill=core)\n", - "cr1_cell = openmc.Cell(name='CR1', region=cr1_region, fill=cr)\n", - "cr2_cell = openmc.Cell(name='CR2', region=cr2_region, fill=cr)\n", - "cr3_cell = openmc.Cell(name='CR3', region=cr3_region, fill=cr)\n", - " \n", - "#translate control rods at top position (fully withdrawn)\n", - "inch_to_cm = 2.54\n", - "start_pos = 19.2 #cm\n", - "top_pos = 51 #inches\n", - "setattr(cr1_cell, 'translation', [0, 0, start_pos + top_pos*inch_to_cm])\n", - "setattr(cr2_cell, 'translation', [-offset, 0, start_pos + top_pos*2.54])\n", - "setattr(cr3_cell, 'translation', [-offset, offset, start_pos + top_pos*2.54])\n", - "\n", - "# Create openmc Geometry object\n", - "geometry = openmc.Geometry([core_cell,cr1_cell,cr2_cell,cr3_cell])\n", - "geometry.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "id": "6fc3dcec", - "metadata": {}, - "source": [ - "Let's initialize some settings and create some generic tallies:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "92335e2b", - "metadata": {}, - "outputs": [], - "source": [ - "settings = openmc.Settings()\n", - "settings.temperature = {'method':'interpolation','range':(293.15,923.15)}\n", - "settings.batches = 50\n", - "settings.inactive = 20\n", - "settings.particles = 30000\n", - "settings.photon_transport = False\n", - "source_area = openmc.stats.Box([-100., -100., 0.],[ 100., 100., 200.],only_fissionable = True)\n", - "settings.source = openmc.Source(space=source_area)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "edfde381", - "metadata": {}, - "outputs": [], - "source": [ - "tally = dict()\n", - "tally['heating'] = openmc.Tally(name=\"heating\")\n", - "tally['heating'].scores.append('heating-local')\n", - "e_min, e_max = 1e-5, 20e6\n", - "groups = 500\n", - "energies = np.logspace(log10(e_min), log10(e_max), groups + 1)\n", - "energy_filter = openmc.EnergyFilter(energies)\n", - "particle_filter = openmc.ParticleFilter(['neutron'])\n", - "cell_filter = openmc.MaterialFilter([salt])\n", - "tally['flux']= openmc.Tally(name=\"flux\")\n", - "tally['flux'].filters = [energy_filter, particle_filter] \n", - "tally['flux'].scores = ['flux']\n", - "mesh = openmc.RegularMesh()\n", - "mesh.dimension = [500, 500, 1]\n", - "mesh.lower_left = [-100, -100, 50]\n", - "mesh.upper_right = [100, 100, 200]\n", - "mesh_filter = openmc.MeshFilter(mesh)\n", - "tally['mesh'] = openmc.Tally(name=\"Mesh\")\n", - "tally['mesh'].scores = ['flux','absorption','fission']\n", - "tally['mesh'].filters = [mesh_filter]\n", - "tally['mesh'].filters.append(particle_filter)\n", - "tallies = openmc.Tallies(tally.values())" - ] - }, - { - "cell_type": "markdown", - "id": "41ca034e", - "metadata": {}, - "source": [ - "We can now plot the geometry to see if the control rods are positioned as set:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c2d50f58", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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4tZRXWgVgVy8gQwrWCFReS4AFstCVvrR3AIuujGTugzWognXAcqrAq46tTtmPcHtY3kMnW0g5rQUWCNj6PeyGBsImJu/CvgHLU3iA01JUwYJgOfXGa0GwhCEqPMBpNaScFgXLqQgvKCFsHbBSnsA4Uk5Lg+UkxAvoC7Najo9m5SB+d7A2Uk4GwHIK8YJawkAAme72MGo220n0sN+DV0fK6YIb3o8NtqAQL0geaZm13xLbJJ/Qov4YRQoArue64AYAS2xBghfIoEm/7cgfL7+zQNFpegqYQgoArucCF7GcbLHlVBrA/p4bH1zx42m6Nu2GKC9HFYRggU22AAtg0EBJ3QMBdN1ZRAoCqiACC8yy5aRLWFftxJNTSBWkYIFxtrwWhGw/mLwiqgAFC3ZhywklzKsfavyUugdPTilVQIEFe7EViucsUtEKU87UPiSFQqkCgP8D0bE/wKb3bfUAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "colors = {salt:'yellow', graphite:'black', inor: 'grey', helium: 'cyan', inconel: 'grey', \n", - " bush: 'blue', ss316: 'grey', concrete: 'brown', shield: 'red', insulation: 'green',\n", - " sandwater: 'lightgreen', steel: 'grey'}\n", - "plot = openmc.Plot.from_geometry(geometry)\n", - "plot.basis = 'xy'\n", - "plot.width = (150,150)\n", - "plot.pixels = (200,200)\n", - "plot.origin = (0,0,150)\n", - "plot.color_by = 'material'\n", - "plot.colors = colors\n", - "openmc.plot_inline(plot)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "76287483", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot.basis = 'xz'\n", - "plot.width = (150,200)\n", - "plot.pixels = (150,200)\n", - "plot.origin = (0,-5,150)\n", - "plot.color_by = 'material'\n", - "plot.colors = colors\n", - "openmc.plot_inline(plot)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b8bde700", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.2\n", - " Git SHA1 | 030f73a8690ed19e91806e46c8caf338d252e74a\n", - " Date/Time | 2023-01-12 15:41:35\n", - " MPI Processes | 1\n", - " OpenMP Threads | 56\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_reactor_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_control_rod_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - " Reading Li6 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li6.h5\n", - " Reading Li7 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li7.h5\n", - " Reading Be9 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Be9.h5\n", - " Reading Zr90 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr96.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n", - " Reading Hf174 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf174.h5\n", - " Reading Hf176 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf176.h5\n", - " Reading Hf177 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf177.h5\n", - " Reading Hf178 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf178.h5\n", - " Reading Hf179 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf179.h5\n", - " Reading Hf180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf180.h5\n", - " Reading U234 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U234.h5\n", - " Reading U235 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U235.h5\n", - " Reading U236 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U236.h5\n", - " Reading U238 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U238.h5\n", - " Reading Fe54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe58.h5\n", - " Reading Cr50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr50.h5\n", - " Reading Cr52 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr52.h5\n", - " Reading Cr53 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr53.h5\n", - " Reading Cr54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr54.h5\n", - " Reading Ni58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni58.h5\n", - " Reading Ni60 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni60.h5\n", - " Reading Ni61 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni61.h5\n", - " Reading Ni62 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni62.h5\n", - " Reading Ni64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni64.h5\n", - " Reading O16 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O16.h5\n", - " Reading O17 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O17.h5\n", - " Reading O18 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O18.h5\n", - " Reading F19 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/F19.h5\n", - " Reading C12 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C12.h5\n", - " Reading Mo100 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo100.h5\n", - " Reading Mo92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo92.h5\n", - " Reading Mo94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo94.h5\n", - " Reading Mo95 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo95.h5\n", - " Reading Mo96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo96.h5\n", - " Reading Mo97 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo97.h5\n", - " Reading Mo98 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo98.h5\n", - " Reading C13 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C13.h5\n", - " Reading Al27 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Al27.h5\n", - " Reading Ti46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti46.h5\n", - " Reading Ti47 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti47.h5\n", - " Reading Ti48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti48.h5\n", - " Reading Ti49 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti49.h5\n", - " Reading Ti50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti50.h5\n", - " Reading S32 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S32.h5\n", - " Reading S33 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S33.h5\n", - " Reading S34 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S34.h5\n", - " Reading S36 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S36.h5\n", - " Reading Mn55 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mn55.h5\n", - " Reading Si28 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si28.h5\n", - " Reading Si29 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si29.h5\n", - " Reading Si30 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si30.h5\n", - " Reading Cu63 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu63.h5\n", - " Reading Cu65 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu65.h5\n", - " Reading B10 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B10.h5\n", - " Reading B11 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B11.h5\n", - " Reading W180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W180.h5\n", - " Reading W182 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W182.h5\n", - " Reading W183 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W183.h5\n", - " Reading W184 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W184.h5\n", - " Reading W186 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W186.h5\n", - " Reading P31 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/P31.h5\n", - " Reading Co59 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Co59.h5\n", - " Reading He3 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He3.h5\n", - " Reading He4 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He4.h5\n", - " Reading Ta180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta180.h5\n", - " Reading Ta181 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta181.h5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Zn64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn64.h5\n", - " Reading Zn66 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn66.h5\n", - " Reading Zn67 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn67.h5\n", - " Reading Zn68 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn68.h5\n", - " Reading Zn70 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn70.h5\n", - " Reading Ag107 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag107.h5\n", - " Reading Ag109 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag109.h5\n", - " Reading Ba130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba130.h5\n", - " Reading Ba132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba132.h5\n", - " Reading Ba134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba134.h5\n", - " Reading Ba135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba135.h5\n", - " Reading Ba136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba136.h5\n", - " Reading Ba137 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba137.h5\n", - " Reading Ba138 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba138.h5\n", - " Reading Ca40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca40.h5\n", - " Reading Ca42 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca42.h5\n", - " Reading Ca43 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca43.h5\n", - " Reading Ca44 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca44.h5\n", - " Reading Ca46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca46.h5\n", - " Reading Ca48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca48.h5\n", - " Reading Cd106 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd106.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n", - " 1200K\n", - " Reading Cd108 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd108.h5\n", - " Reading Cd110 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd110.h5\n", - " Reading Cd111 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd111.h5\n", - " Reading Cd112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd112.h5\n", - " Reading Cd113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd113.h5\n", - " Reading Cd114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd114.h5\n", - " Reading Cd116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd116.h5\n", - " Reading V50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V50.h5\n", - " Reading V51 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V51.h5\n", - " Reading Sn112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn112.h5\n", - " Reading Sn114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn114.h5\n", - " Reading Sn115 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn115.h5\n", - " Reading Sn116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn116.h5\n", - " Reading Sn117 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn117.h5\n", - " Reading Sn118 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn118.h5\n", - " Reading Sn119 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn119.h5\n", - " Reading Sn120 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn120.h5\n", - " Reading Sn122 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn122.h5\n", - " Reading Sn124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn124.h5\n", - " Reading Mg24 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg24.h5\n", - " Reading Mg25 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg25.h5\n", - " Reading Mg26 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg26.h5\n", - " Reading N14 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N14.h5\n", - " Reading N15 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N15.h5\n", - " Reading Gd152 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd152.h5\n", - " Reading Gd154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd154.h5\n", - " Reading Gd155 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd155.h5\n", - " Reading Gd156 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd156.h5\n", - " Reading Gd157 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd157.h5\n", - " Reading Gd158 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd158.h5\n", - " Reading Gd160 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd160.h5\n", - " Reading H1 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H1.h5\n", - " Reading H2 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H2.h5\n", - " Reading Na23 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Na23.h5\n", - " Reading K39 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K39.h5\n", - " Reading K40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K40.h5\n", - " Reading K41 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K41.h5\n", - " Reading c_Graphite from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/c_Graphite.h5\n", - " Minimum neutron data temperature: 250 K\n", - " Maximum neutron data temperature: 1200 K\n", - " Reading tallies XML file...\n", - " Preparing distributed cell instances...\n", - " Reading plot XML file...\n", - " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000 eV for Li6\n", - " Initializing source particles...\n", - "\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - "\n", - " Bat./Gen. k Average k\n", - " ========= ======== ====================\n", - " 1/1 0.60680\n", - " 2/1 0.89503\n", - " 3/1 0.97822\n", - " 4/1 0.97629\n", - " 5/1 0.99493\n", - " 6/1 1.00876\n", - " 7/1 1.01365\n", - " 8/1 1.02006\n", - " 9/1 1.00658\n", - " 10/1 1.00865\n", - " 11/1 1.00286\n", - " 12/1 1.00007\n", - " 13/1 1.01010\n", - " 14/1 1.00743\n", - " 15/1 1.00569\n", - " 16/1 0.99394\n", - " 17/1 1.00821\n", - " 18/1 1.00439\n", - " 19/1 0.99024\n", - " 20/1 0.99914\n", - " 21/1 1.01217\n", - " 22/1 1.00916 1.01066 +/- 0.00150\n", - " 23/1 1.01148 1.01094 +/- 0.00091\n", - " 24/1 1.00735 1.01004 +/- 0.00110\n", - " 25/1 1.01509 1.01105 +/- 0.00132\n", - " 26/1 0.99109 1.00773 +/- 0.00350\n", - " 27/1 1.00911 1.00792 +/- 0.00296\n", - " 28/1 1.01895 1.00930 +/- 0.00291\n", - " WARNING: No intersection found with DAGMC cell 22, material 33\n", - " 29/1 1.01086 1.00947 +/- 0.00257\n", - " 30/1 1.00364 1.00889 +/- 0.00238\n", - " 31/1 0.99094 1.00726 +/- 0.00270\n", - " 32/1 1.00587 1.00714 +/- 0.00247\n", - " 33/1 1.00917 1.00730 +/- 0.00227\n", - " 34/1 0.99504 1.00642 +/- 0.00228\n", - " 35/1 1.00676 1.00645 +/- 0.00212\n", - " 36/1 1.00402 1.00629 +/- 0.00199\n", - " 37/1 1.00018 1.00593 +/- 0.00190\n", - " 38/1 1.00436 1.00585 +/- 0.00180\n", - " 39/1 1.00841 1.00598 +/- 0.00171\n", - " 40/1 0.99722 1.00554 +/- 0.00168\n", - " 41/1 1.01386 1.00594 +/- 0.00164\n", - " 42/1 1.00322 1.00582 +/- 0.00157\n", - " 43/1 0.99819 1.00548 +/- 0.00154\n", - " 44/1 1.01448 1.00586 +/- 0.00152\n", - " 45/1 1.00663 1.00589 +/- 0.00146\n", - " 46/1 1.01666 1.00630 +/- 0.00146\n", - " 47/1 1.00919 1.00641 +/- 0.00141\n", - " 48/1 1.00408 1.00633 +/- 0.00136\n", - " 49/1 1.01247 1.00654 +/- 0.00133\n", - " 50/1 1.00580 1.00651 +/- 0.00128\n", - " Creating state point statepoint.50.h5...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 1.0746e+02 seconds\n", - " Reading cross sections = 1.4857e+01 seconds\n", - " Total time in simulation = 1.1420e+02 seconds\n", - " Time in transport only = 1.1383e+02 seconds\n", - " Time in inactive batches = 3.9231e+01 seconds\n", - " Time in active batches = 7.4969e+01 seconds\n", - " Time synchronizing fission bank = 1.5400e-01 seconds\n", - " Sampling source sites = 1.3872e-01 seconds\n", - " SEND/RECV source sites = 1.4818e-02 seconds\n", - " Time accumulating tallies = 5.6165e-02 seconds\n", - " Time writing statepoints = 3.6797e-02 seconds\n", - " Total time for finalization = 7.5855e-01 seconds\n", - " Total time elapsed = 2.2292e+02 seconds\n", - " Calculation Rate (inactive) = 15294 particles/second\n", - " Calculation Rate (active) = 12005 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 1.00713 +/- 0.00128\n", - " k-effective (Track-length) = 1.00651 +/- 0.00128\n", - " k-effective (Absorption) = 1.00651 +/- 0.00107\n", - " Combined k-effective = 1.00662 +/- 0.00099\n", - " Leakage Fraction = 0.00001 +/- 0.00000\n", - "\n" - ] - } - ], - "source": [ - "model = openmc.model.Model(geometry,mats,settings,tallies)\n", - "results=model.run()" - ] - }, - { - "cell_type": "markdown", - "id": "8ab1fdd2", - "metadata": {}, - "source": [ - "We can now post-process the result file and plot the tallies:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "44bd9b3c", - "metadata": {}, - "outputs": [], - "source": [ - "with openmc.StatePoint(results) as sp:\n", - " heating = sp.get_tally(name=\"heating\").get_pandas_dataframe()[\"mean\"].sum()*openmc.data.JOULE_PER_EV\n", - " t = sp.get_tally(name=\"flux\")\n", - " flux = sp.get_tally(name=\"Mesh\").get_slice(scores=['flux']).get_pandas_dataframe()[\"mean\"]\n", - " fission = sp.get_tally(name=\"Mesh\").get_slice(scores=['fission']).get_pandas_dataframe()[\"mean\"]\n", - " absorption = sp.get_tally(name=\"Mesh\").get_slice(scores=['absorption']).get_pandas_dataframe()[\"mean\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "7232a2ac", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot neutron flux spectrum\n", - "power = 8e6 #Thermal power in W\n", - "volume = 1.65e6 #Fuel salt volume in cm3\n", - "\n", - "energy_filter = t.filters[0]\n", - "energies = energy_filter.bins[:, 0]\n", - "mean = t.mean.ravel()\n", - "uncertainty = t.get_values(value='std_dev').ravel()\n", - "fig, ax = plt.subplots()\n", - "ax.plot(energies, mean*power/heating/volume, drawstyle='steps-post')\n", - "ax.set_xlabel('Energy [eV]')\n", - "ax.set_ylabel(r'Flux [neutrons/cm$^2$-s]')\n", - "ax.set_xscale('log')\n", - "ax.grid(True, which='both')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "99831db8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "flux = flux.values.reshape(500,500)\n", - "fig, ax = plt.subplots()\n", - "pos = ax.imshow(flux*power/heating/volume, aspect='auto', origin='lower')\n", - "cbar = plt.colorbar(pos,ax=ax,label=r'Flux [neutrons/cm$^2$-s]')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "bdbb596d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "fission = fission.values.reshape(500,500)\n", - "fig, ax = plt.subplots()\n", - "pos = ax.imshow(fission*power/heating/volume, aspect='auto', origin='lower')\n", - "cbar = plt.colorbar(pos,ax=ax,label=r'Fission [reactions/cm$^2$-s]')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "2ece2684", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "absorption = absorption.values.reshape(500,500)\n", - "fig, ax = plt.subplots()\n", - "pos = ax.imshow(absorption*power/heating/volume, aspect='auto', origin='lower')\n", - "cbar = plt.colorbar(pos,ax=ax,label=r'Absorption [reactions/cm$^2$-s]')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "5a35516e", - "metadata": {}, - "source": [ - "The multiplication factor turns out to be slightly above one, as expected with all control rods fully withdrawn. \\\n", - "\n", - "Now, let's see what happens by fully inserting one of the control rods and re-running the simulation:" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "09406423", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.2\n", - " Git SHA1 | 030f73a8690ed19e91806e46c8caf338d252e74a\n", - " Date/Time | 2023-01-12 12:24:12\n", - " MPI Processes | 1\n", - " OpenMP Threads | 56\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_reactor_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_control_rod_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - " Reading Li6 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li6.h5\n", - " Reading Li7 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li7.h5\n", - " Reading Be9 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Be9.h5\n", - " Reading Zr90 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr96.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n", - " Reading Hf174 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf174.h5\n", - " Reading Hf176 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf176.h5\n", - " Reading Hf177 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf177.h5\n", - " Reading Hf178 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf178.h5\n", - " Reading Hf179 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf179.h5\n", - " Reading Hf180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf180.h5\n", - " Reading U234 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U234.h5\n", - " Reading U235 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U235.h5\n", - " Reading U236 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U236.h5\n", - " Reading U238 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U238.h5\n", - " Reading Fe54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe58.h5\n", - " Reading Cr50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr50.h5\n", - " Reading Cr52 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr52.h5\n", - " Reading Cr53 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr53.h5\n", - " Reading Cr54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr54.h5\n", - " Reading Ni58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni58.h5\n", - " Reading Ni60 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni60.h5\n", - " Reading Ni61 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni61.h5\n", - " Reading Ni62 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni62.h5\n", - " Reading Ni64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni64.h5\n", - " Reading O16 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O16.h5\n", - " Reading O17 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O17.h5\n", - " Reading O18 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O18.h5\n", - " Reading F19 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/F19.h5\n", - " Reading C12 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C12.h5\n", - " Reading Mo100 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo100.h5\n", - " Reading Mo92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo92.h5\n", - " Reading Mo94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo94.h5\n", - " Reading Mo95 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo95.h5\n", - " Reading Mo96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo96.h5\n", - " Reading Mo97 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo97.h5\n", - " Reading Mo98 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo98.h5\n", - " Reading C13 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C13.h5\n", - " Reading Al27 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Al27.h5\n", - " Reading Ti46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti46.h5\n", - " Reading Ti47 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti47.h5\n", - " Reading Ti48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti48.h5\n", - " Reading Ti49 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti49.h5\n", - " Reading Ti50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti50.h5\n", - " Reading S32 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S32.h5\n", - " Reading S33 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S33.h5\n", - " Reading S34 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S34.h5\n", - " Reading S36 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S36.h5\n", - " Reading Mn55 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mn55.h5\n", - " Reading Si28 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si28.h5\n", - " Reading Si29 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si29.h5\n", - " Reading Si30 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si30.h5\n", - " Reading Cu63 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu63.h5\n", - " Reading Cu65 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu65.h5\n", - " Reading B10 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B10.h5\n", - " Reading B11 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B11.h5\n", - " Reading W180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W180.h5\n", - " Reading W182 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W182.h5\n", - " Reading W183 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W183.h5\n", - " Reading W184 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W184.h5\n", - " Reading W186 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W186.h5\n", - " Reading P31 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/P31.h5\n", - " Reading Co59 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Co59.h5\n", - " Reading He3 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He3.h5\n", - " Reading He4 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He4.h5\n", - " Reading Ta180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta180.h5\n", - " Reading Ta181 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta181.h5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Zn64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn64.h5\n", - " Reading Zn66 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn66.h5\n", - " Reading Zn67 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn67.h5\n", - " Reading Zn68 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn68.h5\n", - " Reading Zn70 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn70.h5\n", - " Reading Ag107 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag107.h5\n", - " Reading Ag109 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag109.h5\n", - " Reading Ba130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba130.h5\n", - " Reading Ba132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba132.h5\n", - " Reading Ba134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba134.h5\n", - " Reading Ba135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba135.h5\n", - " Reading Ba136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba136.h5\n", - " Reading Ba137 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba137.h5\n", - " Reading Ba138 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba138.h5\n", - " Reading Ca40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca40.h5\n", - " Reading Ca42 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca42.h5\n", - " Reading Ca43 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca43.h5\n", - " Reading Ca44 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca44.h5\n", - " Reading Ca46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca46.h5\n", - " Reading Ca48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca48.h5\n", - " Reading Cd106 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd106.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n", - " 1200K\n", - " Reading Cd108 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd108.h5\n", - " Reading Cd110 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd110.h5\n", - " Reading Cd111 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd111.h5\n", - " Reading Cd112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd112.h5\n", - " Reading Cd113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd113.h5\n", - " Reading Cd114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd114.h5\n", - " Reading Cd116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd116.h5\n", - " Reading V50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V50.h5\n", - " Reading V51 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V51.h5\n", - " Reading Sn112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn112.h5\n", - " Reading Sn114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn114.h5\n", - " Reading Sn115 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn115.h5\n", - " Reading Sn116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn116.h5\n", - " Reading Sn117 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn117.h5\n", - " Reading Sn118 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn118.h5\n", - " Reading Sn119 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn119.h5\n", - " Reading Sn120 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn120.h5\n", - " Reading Sn122 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn122.h5\n", - " Reading Sn124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn124.h5\n", - " Reading Mg24 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg24.h5\n", - " Reading Mg25 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg25.h5\n", - " Reading Mg26 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg26.h5\n", - " Reading N14 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N14.h5\n", - " Reading N15 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N15.h5\n", - " Reading Gd152 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd152.h5\n", - " Reading Gd154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd154.h5\n", - " Reading Gd155 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd155.h5\n", - " Reading Gd156 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd156.h5\n", - " Reading Gd157 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd157.h5\n", - " Reading Gd158 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd158.h5\n", - " Reading Gd160 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd160.h5\n", - " Reading H1 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H1.h5\n", - " Reading H2 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H2.h5\n", - " Reading Na23 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Na23.h5\n", - " Reading K39 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K39.h5\n", - " Reading K40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K40.h5\n", - " Reading K41 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K41.h5\n", - " Reading c_Graphite from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/c_Graphite.h5\n", - " Minimum neutron data temperature: 250 K\n", - " Maximum neutron data temperature: 1200 K\n", - " Reading tallies XML file...\n", - " Preparing distributed cell instances...\n", - " Reading plot XML file...\n", - " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000 eV for Li6\n", - " Initializing source particles...\n", - "\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - "\n", - " Bat./Gen. k Average k\n", - " ========= ======== ====================\n", - " 1/1 0.60143\n", - " 2/1 0.87902\n", - " 3/1 0.94407\n", - " 4/1 0.96780\n", - " 5/1 0.98270\n", - " 6/1 0.98736\n", - " 7/1 0.98152\n", - " 8/1 0.99125\n", - " 9/1 0.99058\n", - " 10/1 0.99338\n", - " 11/1 0.99214\n", - " 12/1 0.98799\n", - " 13/1 0.98786\n", - " 14/1 0.99696\n", - " 15/1 0.98684\n", - " 16/1 0.98929\n", - " 17/1 0.99445\n", - " 18/1 0.98696\n", - " 19/1 0.99719\n", - " 20/1 0.98706\n", - " 21/1 1.00109\n", - " 22/1 0.98444 0.99277 +/- 0.00833\n", - " 23/1 0.99031 0.99195 +/- 0.00488\n", - " 24/1 0.98193 0.98944 +/- 0.00426\n", - " 25/1 0.98344 0.98824 +/- 0.00351\n", - " 26/1 0.99143 0.98877 +/- 0.00292\n", - " 27/1 0.98918 0.98883 +/- 0.00247\n", - " 28/1 0.98168 0.98794 +/- 0.00231\n", - " 29/1 0.97684 0.98670 +/- 0.00238\n", - " 30/1 0.99536 0.98757 +/- 0.00230\n", - " 31/1 0.98886 0.98769 +/- 0.00209\n", - " 32/1 0.97517 0.98664 +/- 0.00217\n", - " 33/1 0.99694 0.98744 +/- 0.00215\n", - " 34/1 0.99354 0.98787 +/- 0.00204\n", - " 35/1 0.98582 0.98773 +/- 0.00190\n", - " 36/1 0.99392 0.98812 +/- 0.00182\n", - " 37/1 0.99090 0.98828 +/- 0.00172\n", - " 38/1 0.99378 0.98859 +/- 0.00165\n", - " 39/1 0.99599 0.98898 +/- 0.00161\n", - " 40/1 0.97795 0.98843 +/- 0.00162\n", - " 41/1 0.97967 0.98801 +/- 0.00160\n", - " 42/1 0.97884 0.98759 +/- 0.00158\n", - " 43/1 0.98422 0.98745 +/- 0.00152\n", - " 44/1 0.98107 0.98718 +/- 0.00147\n", - " WARNING: No intersection found with DAGMC cell 246, material 19\n", - " 45/1 1.00148 0.98775 +/- 0.00153\n", - " 46/1 1.00561 0.98844 +/- 0.00162\n", - " 47/1 0.97446 0.98792 +/- 0.00164\n", - " 48/1 0.98380 0.98778 +/- 0.00159\n", - " 49/1 1.00076 0.98822 +/- 0.00160\n", - " 50/1 0.99268 0.98837 +/- 0.00155\n", - " Creating state point statepoint.50.h5...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 1.0612e+02 seconds\n", - " Reading cross sections = 1.5880e+01 seconds\n", - " Total time in simulation = 1.1580e+02 seconds\n", - " Time in transport only = 1.1540e+02 seconds\n", - " Time in inactive batches = 3.9903e+01 seconds\n", - " Time in active batches = 7.5898e+01 seconds\n", - " Time synchronizing fission bank = 1.3660e-01 seconds\n", - " Sampling source sites = 1.2398e-01 seconds\n", - " SEND/RECV source sites = 1.2082e-02 seconds\n", - " Time accumulating tallies = 1.2340e-01 seconds\n", - " Time writing statepoints = 3.9311e-02 seconds\n", - " Total time for finalization = 7.9916e-01 seconds\n", - " Total time elapsed = 2.2324e+02 seconds\n", - " Calculation Rate (inactive) = 15036.3 particles/second\n", - " Calculation Rate (active) = 11858 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 0.98850 +/- 0.00142\n", - " k-effective (Track-length) = 0.98837 +/- 0.00155\n", - " k-effective (Absorption) = 0.98878 +/- 0.00104\n", - " Combined k-effective = 0.98879 +/- 0.00108\n", - " Leakage Fraction = 0.00001 +/- 0.00000\n", - "\n" - ] - } - ], - "source": [ - "setattr(cr1_cell, 'translation', [0, 0, start_pos])\n", - "results = model.run()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openmc_notebooks/msre_depletion_fp-removal-rates.ipynb b/openmc_notebooks/msre_depletion_fp-removal-rates.ipynb deleted file mode 100644 index c30b3bf..0000000 --- a/openmc_notebooks/msre_depletion_fp-removal-rates.ipynb +++ /dev/null @@ -1,1904 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fafca9f7", - "metadata": {}, - "source": [ - "# MSRE depletion with fission products removal rates\n", - "We will run a depletion simuation of the MSRE, introducing continuous removal rates to model fission products removal. \n", - "\n", - "**Note:** to run the following script this [branch](https://github.com/openmsr/openmc/tree/msr_13.2_cont) of OpenMC is needed." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "079fc782", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib inline\n", - "\n", - "import openmc\n", - "import openmc.deplete\n", - "import numpy as np\n", - "from math import log10\n", - "import matplotlib.pyplot as plt\n", - "import os \n", - "\n", - "os.system('rm *.xml *.h5')" - ] - }, - { - "cell_type": "markdown", - "id": "bfca4d07", - "metadata": {}, - "source": [ - "Let's start by defining materials and geometry in the same fashion of the other notebooks:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4bc6b094", - "metadata": {}, - "outputs": [], - "source": [ - "# Define materials \n", - "hot_temp = 638.3 + 273.15 # in K\n", - "salt_density = 2.3275 # g/cm3\n", - "# Fuel salt \n", - "salt = openmc.Material(name=\"salt\", temperature = hot_temp)\n", - "salt.add_nuclide('Li6',1.31480070E-05)\n", - "salt.add_nuclide('Li7', 0.262960140146177)\n", - "salt.add_nuclide('Be9',1.1863E-01)\n", - "salt.add_nuclide('Zr90',1.0543E-02)\n", - "salt.add_nuclide('Zr91',2.2991E-03)\n", - "salt.add_nuclide('Zr92',3.5142E-03)\n", - "salt.add_nuclide('Zr94',3.5613E-03)\n", - "salt.add_nuclide('Zr96',5.7375E-04)\n", - "salt.add_nuclide('Hf174',8.3786E-10)\n", - "salt.add_nuclide('Hf176',2.7545E-08)\n", - "salt.add_nuclide('Hf177',9.7401E-08)\n", - "salt.add_nuclide('Hf178',1.4285E-07)\n", - "salt.add_nuclide('Hf179',7.1323E-08)\n", - "salt.add_nuclide('Hf180',1.8370E-07)\n", - "salt.add_nuclide('U234',1.034276246E-05)\n", - "salt.add_nuclide('U235',1.009695816E-03)\n", - "salt.add_nuclide('U236',4.227809892E-06)\n", - "salt.add_nuclide('U238',2.168267822E-03)\n", - "salt.add_nuclide('Fe54',2.8551E-06)\n", - "salt.add_nuclide('Fe56',4.4818E-05)\n", - "salt.add_nuclide('Fe57',1.0350E-06)\n", - "salt.add_nuclide('Fe58',1.3775E-07)\n", - "salt.add_nuclide('Cr50',2.1224E-06)\n", - "salt.add_nuclide('Cr52',4.0928E-05)\n", - "salt.add_nuclide('Cr53',4.6409E-06)\n", - "salt.add_nuclide('Cr54',1.1552E-06)\n", - "salt.add_nuclide('Ni58',5.8597E-06)\n", - "salt.add_nuclide('Ni60',2.2571E-06)\n", - "salt.add_nuclide('Ni61',9.8117E-08)\n", - "salt.add_nuclide('Ni62',3.1284E-07)\n", - "salt.add_nuclide('Ni64',7.9671E-08)\n", - "salt.add_nuclide('O16',5.1437E-04)\n", - "salt.add_nuclide('O17',1.8927E-07)\n", - "salt.add_nuclide('O18',9.6440E-07)\n", - "salt.add_nuclide('F19',5.9409E-01)\n", - "salt.set_density('g/cm3', salt_density)\n", - "\n", - "#Moderator graphite block\n", - "graphite = openmc.Material(name='graphite', temperature = hot_temp)\n", - "graphite.set_density('g/cm3',1.86)\n", - "graphite.add_nuclide('C12',1)\n", - "graphite.add_s_alpha_beta('c_Graphite')\n", - "\n", - "#inor-8\n", - "inor = openmc.Material(name='inor-8', temperature = hot_temp)\n", - "inor.set_density('g/cm3',8.7745)\n", - "inor.add_element('Ni',68.5,'wo')\n", - "inor.add_element('Mo',16.5,'wo')\n", - "inor.add_element('Cr',7,'wo')\n", - "inor.add_element('Fe',5,'wo')\n", - "inor.add_element('C',0.06,'wo')\n", - "inor.add_element('Al',0.25,'wo')\n", - "inor.add_element('Ti',0.25,'wo')\n", - "inor.add_element('S',0.02,'wo')\n", - "inor.add_element('Mn',1.0,'wo')\n", - "inor.add_element('Si',1.0,'wo')\n", - "inor.add_element('Cu',0.35,'wo')\n", - "inor.add_element('B',0.010,'wo')\n", - "inor.add_element('W',0.5,'wo')\n", - "inor.add_element('P',0.015,'wo')\n", - "inor.add_element('Co',0.2,'wo')\n", - "\n", - "#helium\n", - "helium = openmc.Material(name='helium')\n", - "helium.add_element('He',1.0)\n", - "helium.set_density('g/cm3',1.03*(10**-4))\n", - "\n", - "#Control rods inconel clad\n", - "trace = 0.01\n", - "inconel = openmc.Material(name='inconel', temperature = 65.6 + 273.15)\n", - "inconel.add_element('Ni',78.5,percent_type='wo')\n", - "inconel.add_element('Cr',14.0,percent_type='wo')\n", - "inconel.add_element('Fe',6.5,percent_type='wo')\n", - "inconel.add_element('Mn',0.25,percent_type='wo')\n", - "inconel.add_element('Si',0.25,percent_type='wo')\n", - "inconel.add_element('Cu',0.2,percent_type='wo')\n", - "inconel.add_element('Co',0.2,percent_type='wo')\n", - "inconel.add_element('Al',0.2,percent_type='wo')\n", - "inconel.add_element('Ti',0.2,percent_type='wo')\n", - "inconel.add_element('Ta',0.5,percent_type='wo')\n", - "inconel.add_element('W',0.5,percent_type='wo')\n", - "inconel.add_element('Zn',0.2,percent_type='wo')\n", - "inconel.add_element('Zr',0.1,percent_type='wo')\n", - "inconel.add_element('C',trace,percent_type='wo')\n", - "inconel.add_element('Mo',trace,percent_type='wo')\n", - "inconel.add_element('Ag',trace,percent_type='wo')\n", - "inconel.add_element('B',trace,percent_type='wo')\n", - "inconel.add_element('Ba',trace,percent_type='wo')\n", - "inconel.add_element('Be',trace,percent_type='wo')\n", - "inconel.add_element('Ca',trace,percent_type='wo')\n", - "inconel.add_element('Cd',trace,percent_type='wo')\n", - "inconel.add_element('V',trace,percent_type='wo')\n", - "inconel.add_element('Sn',trace,percent_type='wo')\n", - "inconel.add_element('Mg',trace,percent_type='wo')\n", - "inconel.set_density('g/cm3',8.5)\n", - "\n", - "# SS316 control rod flexible hose\n", - "ss316 = openmc.Material(name='ss316', temperature = 65.6 + 273.15)\n", - "ss316.add_element('C',0.026,'wo')\n", - "ss316.add_element('Si',0.37,'wo')\n", - "ss316.add_element('Mn',0.16,'wo')\n", - "ss316.add_element('Cr',16.55,'wo')\n", - "ss316.add_element('Cu',0.16,'wo')\n", - "ss316.add_element('Ni',10,'wo')\n", - "ss316.add_element('P',0.029,'wo')\n", - "ss316.add_element('S',0.027,'wo')\n", - "ss316.add_element('Mo',2.02,'wo')\n", - "ss316.add_element('N',0.036,'wo')\n", - "ss316.add_element('Fe',70.622,'wo')\n", - "ss316.set_density('g/cm3',7.99)\n", - "\n", - "#Control rods bushing posion material\n", - "Gd2O3 = openmc.Material()\n", - "Gd2O3.add_element('Gd',2)\n", - "Gd2O3.add_element('O',3)\n", - "Gd2O3.set_density('g/cm3',7.41)\n", - "Al2O3 = openmc.Material()\n", - "Al2O3.add_element('Al',2)\n", - "Al2O3.add_element('O',3)\n", - "Al2O3.set_density('g/cm3',3.95)\n", - "bush = openmc.Material.mix_materials([Gd2O3,Al2O3],[0.7,0.3],'wo')\n", - "bush.name='bush'\n", - "bush.temperature = 65.6 +273.15\n", - "\n", - "#Concrete block\n", - "concrete = openmc.Material(name='concrete')\n", - "concrete.add_element('H',0.005,'wo')\n", - "concrete.add_element('O',0.496,'wo')\n", - "concrete.add_element('Si',0.314,'wo')\n", - "concrete.add_element('Ca',0.083,'wo')\n", - "concrete.add_element('Na',0.017,'wo')\n", - "concrete.add_element('Mn',0.002,'wo')\n", - "concrete.add_element('Al',0.046,'wo')\n", - "concrete.add_element('S',0.001,'wo')\n", - "concrete.add_element('K',0.019,'wo')\n", - "concrete.add_element('Fe',0.012,'wo')\n", - "concrete.set_density('g/cm3',2.35)\n", - "\n", - "#Thermal shielding as material mix of water and SS304 50-50 vo\n", - "water = openmc.Material()\n", - "water.add_element('H',2)\n", - "water.add_element('O',1)\n", - "water.set_density('g/cm3',0.997)\n", - "\n", - "#stainless steel 304\n", - "ss304 = openmc.Material()\n", - "ss304.add_element('C',0.08,'wo')\n", - "ss304.add_element('Mn',2,'wo')\n", - "ss304.add_element('P',0.045,'wo')\n", - "ss304.add_element('S',0.03,'wo')\n", - "ss304.add_element('Si',0.75,'wo')\n", - "ss304.add_element('Cr',19,'wo')\n", - "ss304.add_element('Ni',10,'wo')\n", - "ss304.add_element('N',0.1,'wo')\n", - "ss304.add_element('Fe',67.995, 'wo')\n", - "ss304.set_density('g/cm3',7.93)\n", - "shield = openmc.Material.mix_materials([water,ss304],[0.5,0.5],'vo')\n", - "shield.temperature = 32.2 + 273.15\n", - "shield.name='steelwater'\n", - "\n", - "# \"Careytemp 1600\" by Philip Carey Manufacturing Compamy (Cincinnati) \n", - "insulation=openmc.Material(name='insulation')\n", - "insulation.add_element('Si',1)\n", - "insulation.add_element('O',2)\n", - "insulation.set_density('g/cm3',0.16) \n", - "\n", - "# Sand water (not sure about this material)\n", - "sandwater=openmc.Material(name='sandwater')\n", - "sandwater.add_element('Fe',3)\n", - "sandwater.add_element('O',4)\n", - "sandwater.set_density('g/cm3',6)\n", - "\n", - "#Vessel anular steel\n", - "steel = openmc.Material(name='steel')\n", - "steel.add_element('Fe',1)\n", - "steel.set_density('g/cm3',7.85)\n", - "\n", - "mats = openmc.Materials([salt,graphite,inor,helium,inconel,shield,concrete,steel,ss316,sandwater,insulation,bush])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "edfa5bd1", - "metadata": {}, - "outputs": [], - "source": [ - "# Import h5m files\n", - "core_h5m = '../h5m/msre_reactor_1e-2.h5m'\n", - "cr_h5m = '../h5m/msre_control_rod_1e-2.h5m'\n", - "\n", - "# Create DAGMC universes out of h5m files\n", - "core = openmc.DAGMCUniverse(filename=core_h5m, auto_geom_ids=True, universe_id=1)\n", - "cr = openmc.DAGMCUniverse(filename=cr_h5m, auto_geom_ids=True, universe_id=2)\n", - "\n", - "# Create regions\n", - "core_region = core.bounding_region()\n", - "cr1_region = cr.bounding_region(boundary_type='transmission', starting_id=20000)\n", - "\n", - "# Extend control rod region, to include upwards translations\n", - "cr1_region = cr1_region | cr1_region.translate([0,0,150])\n", - "\n", - "# Create control rod region 2 and 3 as translated region of control rod 1\n", - "offset = 10.163255\n", - "cr2_region = cr1_region.translate([-offset,0,0])\n", - "cr3_region = cr1_region.translate([-offset,offset,0])\n", - "\n", - "# Create openmc Cells \n", - "core_cell = openmc.Cell(region=~(cr1_region | cr2_region | cr3_region) & core_region , fill=core)\n", - "cr1_cell = openmc.Cell(name='CR1', region=cr1_region, fill=cr)\n", - "cr2_cell = openmc.Cell(name='CR2', region=cr2_region, fill=cr)\n", - "cr3_cell = openmc.Cell(name='CR3', region=cr3_region, fill=cr)\n", - " \n", - "#translate control rods at top position (fully withdrawn)\n", - "inch_to_cm = 2.54\n", - "start_pos = 19.2 #inches\n", - "top_pos = 51 #inches\n", - "setattr(cr1_cell, 'translation', [0, 0, start_pos + top_pos*inch_to_cm])\n", - "setattr(cr2_cell, 'translation', [-offset, 0, start_pos + top_pos*2.54])\n", - "setattr(cr3_cell, 'translation', [-offset, offset, start_pos + top_pos*2.54])\n", - "\n", - "# Create openmc Geometry object\n", - "geometry = openmc.Geometry([core_cell,cr1_cell,cr2_cell,cr3_cell])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ba99fb4b", - "metadata": {}, - "outputs": [], - "source": [ - "settings = openmc.Settings()\n", - "settings.temperature = {'method':'interpolation','range':(293.15,923.15)}\n", - "settings.batches = 50\n", - "settings.inactive = 20\n", - "settings.particles = 30000\n", - "settings.photon_transport = False\n", - "source_area = openmc.stats.Box([-100., -100., 0.],[ 100., 100., 200.],only_fissionable = True)\n", - "settings.source = openmc.Source(space=source_area)" - ] - }, - { - "cell_type": "markdown", - "id": "c16520ed", - "metadata": {}, - "source": [ - "First of all, we will set up and run a standard depletion analysis that will be used as the reference to compare with the next case.\n", - "\n", - "Let's define some general settings (timestep, power, depletable volume, integration scheme) and run the analysis:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b9f8eada", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.2\n", - " Git SHA1 | 030f73a8690ed19e91806e46c8caf338d252e74a\n", - " Date/Time | 2023-01-20 11:06:11\n", - " MPI Processes | 1\n", - " OpenMP Threads | 56\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_reactor_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_control_rod_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - " Reading Li6 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li6.h5\n", - " Reading Li7 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li7.h5\n", - " Reading Be9 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Be9.h5\n", - " Reading O16 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O16.h5\n", - " Reading O17 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O17.h5\n", - " Reading O18 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O18.h5\n", - " Reading F19 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/F19.h5\n", - " Reading Cr50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr50.h5\n", - " Reading Cr52 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr52.h5\n", - " Reading Cr53 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr53.h5\n", - " Reading Cr54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr54.h5\n", - " Reading Fe54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe58.h5\n", - " Reading Ni58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni58.h5\n", - " Reading Ni60 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni60.h5\n", - " Reading Ni61 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni61.h5\n", - " Reading Ni62 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni62.h5\n", - " Reading Ni64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni64.h5\n", - " Reading Zr90 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr96.h5\n", - " Reading Hf174 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf174.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Hf176 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf176.h5\n", - " Reading Hf177 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf177.h5\n", - " Reading Hf178 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf178.h5\n", - " Reading Hf179 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf179.h5\n", - " Reading Hf180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf180.h5\n", - " Reading U234 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U234.h5\n", - " Reading U235 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U235.h5\n", - " Reading U236 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U236.h5\n", - " Reading U238 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U238.h5\n", - " Reading C12 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C12.h5\n", - " Reading B10 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B10.h5\n", - " Reading B11 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B11.h5\n", - " Reading C13 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C13.h5\n", - " Reading Al27 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Al27.h5\n", - " Reading Si28 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si28.h5\n", - " Reading Si29 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si29.h5\n", - " Reading Si30 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si30.h5\n", - " Reading P31 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/P31.h5\n", - " Reading S32 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S32.h5\n", - " Reading S33 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S33.h5\n", - " Reading S34 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S34.h5\n", - " Reading S36 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S36.h5\n", - " Reading Ti46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti46.h5\n", - " Reading Ti47 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti47.h5\n", - " Reading Ti48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti48.h5\n", - " Reading Ti49 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti49.h5\n", - " Reading Ti50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti50.h5\n", - " Reading Mn55 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mn55.h5\n", - " Reading Co59 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Co59.h5\n", - " Reading Cu63 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu63.h5\n", - " Reading Cu65 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu65.h5\n", - " Reading Mo92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo92.h5\n", - " Reading Mo94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo94.h5\n", - " Reading Mo95 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo95.h5\n", - " Reading Mo96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo96.h5\n", - " Reading Mo97 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo97.h5\n", - " Reading Mo98 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo98.h5\n", - " Reading Mo100 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo100.h5\n", - " Reading W180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W180.h5\n", - " Reading W182 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W182.h5\n", - " Reading W183 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W183.h5\n", - " Reading W184 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W184.h5\n", - " Reading W186 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W186.h5\n", - " Reading He3 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He3.h5\n", - " Reading He4 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He4.h5\n", - " Reading Mg24 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg24.h5\n", - " Reading Mg25 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg25.h5\n", - " Reading Mg26 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg26.h5\n", - " Reading Ca40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca40.h5\n", - " Reading Ca42 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca42.h5\n", - " Reading Ca43 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca43.h5\n", - " Reading Ca44 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca44.h5\n", - " Reading Ca46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca46.h5\n", - " Reading Ca48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca48.h5\n", - " Reading V50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V50.h5\n", - " Reading V51 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V51.h5\n", - " Reading Zn64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn64.h5\n", - " Reading Zn66 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn66.h5\n", - " Reading Zn67 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn67.h5\n", - " Reading Zn68 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn68.h5\n", - " Reading Zn70 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn70.h5\n", - " Reading Ag107 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag107.h5\n", - " Reading Ag109 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag109.h5\n", - " Reading Cd106 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd106.h5\n", - " Reading Cd108 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd108.h5\n", - " Reading Cd110 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd110.h5\n", - " Reading Cd111 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd111.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n", - " 1200K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Cd112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd112.h5\n", - " Reading Cd113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd113.h5\n", - " Reading Cd114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd114.h5\n", - " Reading Cd116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd116.h5\n", - " Reading Sn112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn112.h5\n", - " Reading Sn114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn114.h5\n", - " Reading Sn115 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn115.h5\n", - " Reading Sn116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn116.h5\n", - " Reading Sn117 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn117.h5\n", - " Reading Sn118 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn118.h5\n", - " Reading Sn119 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn119.h5\n", - " Reading Sn120 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn120.h5\n", - " Reading Sn122 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn122.h5\n", - " Reading Sn124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn124.h5\n", - " Reading Ba130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba130.h5\n", - " Reading Ba132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba132.h5\n", - " Reading Ba134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba134.h5\n", - " Reading Ba135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba135.h5\n", - " Reading Ba136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba136.h5\n", - " Reading Ba137 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba137.h5\n", - " Reading Ba138 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba138.h5\n", - " Reading Ta180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta180.h5\n", - " Reading Ta181 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta181.h5\n", - " Reading N14 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N14.h5\n", - " Reading N15 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N15.h5\n", - " Reading Gd152 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd152.h5\n", - " Reading Gd154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd154.h5\n", - " Reading Gd155 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd155.h5\n", - " Reading Gd156 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd156.h5\n", - " Reading Gd157 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd157.h5\n", - " Reading Gd158 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd158.h5\n", - " Reading Gd160 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd160.h5\n", - " Reading H1 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H1.h5\n", - " Reading H2 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H2.h5\n", - " Reading Na23 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Na23.h5\n", - " Reading K39 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K39.h5\n", - " Reading K40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K40.h5\n", - " Reading K41 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K41.h5\n", - " Reading c_Graphite from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/c_Graphite.h5\n", - " Minimum neutron data temperature: 250 K\n", - " Maximum neutron data temperature: 1200 K\n", - " Preparing distributed cell instances...\n", - " Reading plot XML file...\n", - " Writing summary.h5 file...\n", - "[openmc.deplete] t=0.0 s, dt=432000 s, source=8000000.0\n", - " Reading H3 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H3.h5\n", - " Reading Be7 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Be7.h5\n", - " Reading Ne20 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ne20.h5\n", - " Reading Ne21 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ne21.h5\n", - " Reading Ne22 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ne22.h5\n", - " Reading Na22 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Na22.h5\n", - " Reading Al26_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Al26_m1.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Na22 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Na22 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Na22 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Na22 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Na22 at 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Na22 at 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Si31 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si31.h5\n", - " Reading Si32 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si32.h5\n", - " Reading S35 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S35.h5\n", - " Reading Cl35 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cl35.h5\n", - " Reading Cl36 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cl36.h5\n", - " Reading Cl37 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cl37.h5\n", - " Reading Ar36 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar36.h5\n", - " Reading Ar37 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar37.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Ar38 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar38.h5\n", - " Reading Ar39 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar39.h5\n", - " Reading Ar40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar40.h5\n", - " Reading Ar41 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar41.h5\n", - " Reading Ca41 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca41.h5\n", - " Reading Ca45 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca45.h5\n", - " Reading Ca47 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca47.h5\n", - " Reading Sc45 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sc45.h5\n", - " Reading V49 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V49.h5\n", - " Reading Cr51 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr51.h5\n", - " Reading Mn54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mn54.h5\n", - " Reading Fe55 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe55.h5\n", - " Reading Co58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Co58.h5\n", - " Reading Co58_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Co58_m1.h5\n", - " Reading Ni59 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni59.h5\n", - " Reading Ni63 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni63.h5\n", - " Reading Cu64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu64.h5\n", - " Reading Zn65 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn65.h5\n", - " Reading Zn69 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn69.h5\n", - " Reading Ga69 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ga69.h5\n", - " Reading Ga70 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ga70.h5\n", - " Reading Ga71 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ga71.h5\n", - " Reading Ge70 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge70.h5\n", - " Reading Ge71 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge71.h5\n", - " Reading Ge72 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge72.h5\n", - " Reading Ge73 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge73.h5\n", - " Reading Ge74 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge74.h5\n", - " Reading Ge75 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge75.h5\n", - " Reading Ge76 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge76.h5\n", - " Reading As73 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/As73.h5\n", - " Reading As74 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/As74.h5\n", - " Reading As75 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/As75.h5\n", - " Reading Se74 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se74.h5\n", - " Reading Se75 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se75.h5\n", - " Reading Se76 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se76.h5\n", - " Reading Se77 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se77.h5\n", - " Reading Se78 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se78.h5\n", - " Reading Se79 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se79.h5\n", - " Reading Se80 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se80.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Se79 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Se79 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Se79 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Se79 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Se79 at 1200K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Se81 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se81.h5\n", - " Reading Se82 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se82.h5\n", - " Reading Br79 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Br79.h5\n", - " Reading Br80 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Br80.h5\n", - " Reading Br81 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Br81.h5\n", - " Reading Kr78 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr78.h5\n", - " Reading Kr79 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr79.h5\n", - " Reading Kr80 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr80.h5\n", - " Reading Kr81 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr81.h5\n", - " Reading Kr82 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr82.h5\n", - " Reading Kr83 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr83.h5\n", - " Reading Kr84 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr84.h5\n", - " Reading Kr85 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr85.h5\n", - " Reading Kr86 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr86.h5\n", - " Reading Rb85 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rb85.h5\n", - " Reading Rb86 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rb86.h5\n", - " Reading Rb87 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rb87.h5\n", - " Reading Sr84 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr84.h5\n", - " Reading Sr85 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr85.h5\n", - " Reading Sr86 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr86.h5\n", - " Reading Sr87 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr87.h5\n", - " Reading Sr88 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr88.h5\n", - " Reading Sr89 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr89.h5\n", - " Reading Sr90 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr90.h5\n", - " Reading Y89 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Y89.h5\n", - " Reading Y90 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Y90.h5\n", - " Reading Y91 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Y91.h5\n", - " Reading Zr93 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr93.h5\n", - " Reading Zr95 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr95.h5\n", - " Reading Nb93 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nb93.h5\n", - " Reading Nb94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nb94.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 2500K\n", - " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 1200K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Nb95 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nb95.h5\n", - " Reading Mo93 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo93.h5\n", - " Reading Mo99 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo99.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Tc98 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tc98.h5\n", - " Reading Tc99 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tc99.h5\n", - " Reading Ru96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru96.h5\n", - " Reading Ru97 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru97.h5\n", - " Reading Ru98 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru98.h5\n", - " Reading Ru99 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru99.h5\n", - " Reading Ru100 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru100.h5\n", - " Reading Ru101 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru101.h5\n", - " Reading Ru102 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru102.h5\n", - " Reading Ru103 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru103.h5\n", - " Reading Ru104 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru104.h5\n", - " Reading Ru105 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru105.h5\n", - " Reading Ru106 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru106.h5\n", - " Reading Rh103 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rh103.h5\n", - " Reading Rh104 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rh104.h5\n", - " Reading Rh105 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rh105.h5\n", - " Reading Pd102 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd102.h5\n", - " Reading Pd103 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd103.h5\n", - " Reading Pd104 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd104.h5\n", - " Reading Pd105 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd105.h5\n", - " Reading Pd106 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd106.h5\n", - " Reading Pd107 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd107.h5\n", - " Reading Pd108 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd108.h5\n", - " Reading Pd109 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd109.h5\n", - " Reading Pd110 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd110.h5\n", - " Reading Ag108 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag108.h5\n", - " Reading Ag110_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag110_m1.h5\n", - " Reading Ag111 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag111.h5\n", - " Reading Ag112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag112.h5\n", - " Reading Ag113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag113.h5\n", - " Reading Ag114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag114.h5\n", - " Reading Ag115 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag115.h5\n", - " Reading Ag116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag116.h5\n", - " Reading Ag117 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag117.h5\n", - " Reading Ag118_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag118_m1.h5\n", - " Reading Cd107 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd107.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", - " 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", - " 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", - " 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", - " 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", - " 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Cd109 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd109.h5\n", - " Reading Cd115_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd115_m1.h5\n", - " Reading In113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/In113.h5\n", - " Reading In114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/In114.h5\n", - " Reading In115 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/In115.h5\n", - " Reading Sn113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn113.h5\n", - " Reading Sn121_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn121_m1.h5\n", - " Reading Sn123 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn123.h5\n", - " Reading Sn125 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn125.h5\n", - " Reading Sn126 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn126.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Sn123 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Sn123 at\n", - " 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Sb121 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb121.h5\n", - " Reading Sb122 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb122.h5\n", - " Reading Sb123 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb123.h5\n", - " Reading Sb124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb124.h5\n", - " Reading Sb125 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb125.h5\n", - " Reading Sb126 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb126.h5\n", - " Reading Te120 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te120.h5\n", - " Reading Te121 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te121.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Te120 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Te120 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Te120 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Te120 at\n", - " 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Te121_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te121_m1.h5\n", - " Reading Te122 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te122.h5\n", - " Reading Te123 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te123.h5\n", - " Reading Te124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te124.h5\n", - " Reading Te125 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te125.h5\n", - " Reading Te126 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te126.h5\n", - " Reading Te127_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te127_m1.h5\n", - " Reading Te128 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te128.h5\n", - " Reading Te129_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te129_m1.h5\n", - " Reading Te130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te130.h5\n", - " Reading Te131 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te131.h5\n", - " Reading Te131_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te131_m1.h5\n", - " Reading Te132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te132.h5\n", - " Reading I127 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I127.h5\n", - " Reading I128 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I128.h5\n", - " Reading I129 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I129.h5\n", - " Reading I130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I130.h5\n", - " Reading I131 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I131.h5\n", - " Reading I132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I132.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide I131 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide I131 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide I131 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide I131 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide I131 at 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide I131 at 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading I132_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I132_m1.h5\n", - " Reading I133 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I133.h5\n", - " Reading I134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I134.h5\n", - " Reading I135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I135.h5\n", - " Reading Xe123 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe123.h5\n", - " Reading Xe124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe124.h5\n", - " Reading Xe125 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe125.h5\n", - " Reading Xe126 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe126.h5\n", - " Reading Xe127 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe127.h5\n", - " Reading Xe128 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe128.h5\n", - " Reading Xe129 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe129.h5\n", - " Reading Xe130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe130.h5\n", - " Reading Xe131 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe131.h5\n", - " Reading Xe132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe132.h5\n", - " Reading Xe133 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe133.h5\n", - " Reading Xe134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe134.h5\n", - " Reading Xe135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe135.h5\n", - " Reading Xe136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe136.h5\n", - " Reading Cs133 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cs133.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Xe133 at\n", - " 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Cs134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cs134.h5\n", - " Reading Cs135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cs135.h5\n", - " Reading Cs136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cs136.h5\n", - " Reading Cs137 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cs137.h5\n", - " Reading Ba131 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba131.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cs136 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cs136 at\n", - " 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Ba133 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba133.h5\n", - " Reading Ba139 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba139.h5\n", - " Reading Ba140 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba140.h5\n", - " Reading La138 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/La138.h5\n", - " Reading La139 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/La139.h5\n", - " Reading La140 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/La140.h5\n", - " Reading Ce136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce136.h5\n", - " Reading Ce137 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce137.h5\n", - " Reading Ce137_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce137_m1.h5\n", - " Reading Ce138 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce138.h5\n", - " Reading Ce139 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce139.h5\n", - " Reading Ce140 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce140.h5\n", - " Reading Ce141 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce141.h5\n", - " Reading Ce142 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce142.h5\n", - " Reading Ce143 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce143.h5\n", - " Reading Ce144 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce144.h5\n", - " Reading Pr141 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pr141.h5\n", - " Reading Pr142 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pr142.h5\n", - " Reading Pr143 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pr143.h5\n", - " Reading Nd142 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd142.h5\n", - " Reading Nd143 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd143.h5\n", - " Reading Nd144 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd144.h5\n", - " Reading Nd145 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd145.h5\n", - " Reading Nd146 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd146.h5\n", - " Reading Nd147 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd147.h5\n", - " Reading Nd148 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd148.h5\n", - " Reading Nd149 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd149.h5\n", - " Reading Nd150 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd150.h5\n", - " Reading Pm143 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm143.h5\n", - " Reading Pm144 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm144.h5\n", - " Reading Pm145 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm145.h5\n", - " Reading Pm146 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm146.h5\n", - " Reading Pm147 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm147.h5\n", - " Reading Pm148 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm148.h5\n", - " Reading Pm148_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm148_m1.h5\n", - " Reading Pm149 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm149.h5\n", - " Reading Pm150 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm150.h5\n", - " Reading Pm151 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm151.h5\n", - " Reading Sm144 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm144.h5\n", - " Reading Sm145 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm145.h5\n", - " Reading Sm146 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm146.h5\n", - " Reading Sm147 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm147.h5\n", - " Reading Sm148 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm148.h5\n", - " Reading Sm149 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm149.h5\n", - " Reading Sm150 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm150.h5\n", - " Reading Sm151 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm151.h5\n", - " Reading Sm152 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm152.h5\n", - " Reading Sm153 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm153.h5\n", - " Reading Sm154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm154.h5\n", - " Reading Eu151 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu151.h5\n", - " Reading Eu152 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu152.h5\n", - " Reading Eu153 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu153.h5\n", - " Reading Eu154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu154.h5\n", - " Reading Eu155 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu155.h5\n", - " Reading Eu156 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu156.h5\n", - " Reading Eu157 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu157.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Eu156 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Eu156 at\n", - " 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Gd153 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd153.h5\n", - " Reading Gd159 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd159.h5\n", - " Reading Tb158 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tb158.h5\n", - " Reading Tb159 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tb159.h5\n", - " Reading Tb160 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tb160.h5\n", - " Reading Tb161 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tb161.h5\n", - " Reading Dy154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy154.h5\n", - " Reading Dy155 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy155.h5\n", - " Reading Dy156 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy156.h5\n", - " Reading Dy157 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy157.h5\n", - " Reading Dy158 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy158.h5\n", - " Reading Dy159 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy159.h5\n", - " Reading Dy160 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy160.h5\n", - " Reading Dy161 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy161.h5\n", - " Reading Dy162 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy162.h5\n", - " Reading Dy163 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy163.h5\n", - " Reading Dy164 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy164.h5\n", - " Reading Ho165 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ho165.h5\n", - " Reading Ho166_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ho166_m1.h5\n", - " Reading Er162 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er162.h5\n", - " Reading Er163 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er163.h5\n", - " Reading Er164 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er164.h5\n", - " Reading Er165 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er165.h5\n", - " Reading Er166 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er166.h5\n", - " Reading Er167 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er167.h5\n", - " Reading Er168 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er168.h5\n", - " Reading Er169 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er169.h5\n", - " Reading Er170 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er170.h5\n", - " Reading Tm168 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tm168.h5\n", - " Reading Tm169 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tm169.h5\n", - " Reading Tm170 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tm170.h5\n", - " Reading Tm171 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tm171.h5\n", - " Reading Yb168 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb168.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb168 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb168 at\n", - " 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Yb169 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb169.h5\n", - " Reading Yb170 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb170.h5\n", - " Reading Yb171 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb171.h5\n", - " Reading Yb172 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb172.h5\n", - " Reading Yb173 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb173.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb170 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb170 at\n", - " 2500K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb171 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb171 at\n", - " 2500K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb172 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb172 at\n", - " 2500K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb173 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb173 at\n", - " 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Yb174 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb174.h5\n", - " Reading Yb175 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb175.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb174 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb174 at\n", - " 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Yb176 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb176.h5\n", - " Reading Lu175 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Lu175.h5\n", - " Reading Lu176 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Lu176.h5\n", - " Reading Hf175 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf175.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb176 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Yb176 at\n", - " 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Hf181 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf181.h5\n", - " Reading Hf182 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf182.h5\n", - " Reading Ta182 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta182.h5\n", - " Reading W181 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W181.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Hf181 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Hf181 at\n", - " 2500K\n", - " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Hf182 at\n", - " 1200K\n", - " WARNING: Negative value(s) found on probability table for nuclide Hf182 at\n", - " 2500K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading W185 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W185.h5\n", - " Reading Re185 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Re185.h5\n", - " Reading Re186_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Re186_m1.h5\n", - " Reading Re187 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Re187.h5\n", - " Reading Os184 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os184.h5\n", - " Reading Os185 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os185.h5\n", - " Reading Os186 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os186.h5\n", - " Reading Os187 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os187.h5\n", - " Reading Os188 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os188.h5\n", - " Reading Os189 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os189.h5\n", - " Reading Os190 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os190.h5\n", - " Reading Os191 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os191.h5\n", - " Reading Os192 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os192.h5\n", - " Reading Ir191 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ir191.h5\n", - " Reading Ir192 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ir192.h5\n", - " Reading Ir193 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ir193.h5\n", - " Reading Ir194_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ir194_m1.h5\n", - " Reading Pt190 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt190.h5\n", - " Reading Pt191 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt191.h5\n", - " Reading Pt192 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt192.h5\n", - " Reading Pt193 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt193.h5\n", - " Reading Pt194 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt194.h5\n", - " Reading Pt195 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt195.h5\n", - " Reading Pt196 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt196.h5\n", - " Reading Pt197 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt197.h5\n", - " Reading Pt198 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt198.h5\n", - " Reading Au197 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Au197.h5\n", - " Reading Hg196 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg196.h5\n", - " Reading Hg197 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg197.h5\n", - " Reading Hg197_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg197_m1.h5\n", - " Reading Hg198 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg198.h5\n", - " Reading Hg199 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg199.h5\n", - " Reading Hg200 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg200.h5\n", - " Reading Hg201 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg201.h5\n", - " Reading Hg202 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg202.h5\n", - " Reading Hg203 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg203.h5\n", - " Reading Hg204 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg204.h5\n", - " Reading Tl203 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tl203.h5\n", - " Reading Tl204 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tl204.h5\n", - " Reading Tl205 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tl205.h5\n", - " Reading Pb204 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pb204.h5\n", - " Reading Pb205 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pb205.h5\n", - " Reading Pb206 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pb206.h5\n", - " Reading Pb207 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pb207.h5\n", - " Reading Pb208 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pb208.h5\n", - " Reading Bi209 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bi209.h5\n", - " Reading Bi210_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bi210_m1.h5\n", - " Reading Po208 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Po208.h5\n", - " Reading Po209 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Po209.h5\n", - " Reading Po210 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Po210.h5\n", - " Reading Ra223 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ra223.h5\n", - " Reading Ra224 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ra224.h5\n", - " Reading Ra225 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ra225.h5\n", - " Reading Ra226 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ra226.h5\n", - " Reading Ac225 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ac225.h5\n", - " Reading Ac226 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ac226.h5\n", - " Reading Ac227 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ac227.h5\n", - " Reading Th227 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th227.h5\n", - " Reading Th228 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th228.h5\n", - " Reading Th229 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th229.h5\n", - " Reading Th230 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th230.h5\n", - " Reading Th231 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th231.h5\n", - " Reading Th232 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th232.h5\n", - " Reading Th233 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th233.h5\n", - " Reading Th234 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th234.h5\n", - " Reading Pa229 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pa229.h5\n", - " Reading Pa230 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pa230.h5\n", - " Reading Pa231 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pa231.h5\n", - " Reading Pa232 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pa232.h5\n", - " Reading Pa233 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pa233.h5\n", - " Reading U230 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U230.h5\n", - " Reading U231 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U231.h5\n", - " Reading U232 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U232.h5\n", - " Reading U233 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U233.h5\n", - " Reading U237 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U237.h5\n", - " Reading U239 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U239.h5\n", - " Reading U240 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U240.h5\n", - " Reading U241 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U241.h5\n", - " Reading Np234 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np234.h5\n", - " Reading Np235 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np235.h5\n", - " Reading Np236 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np236.h5\n", - " Reading Np236_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np236_m1.h5\n", - " Reading Np237 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np237.h5\n", - " Reading Np238 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np238.h5\n", - " Reading Np239 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np239.h5\n", - " Reading Pu236 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu236.h5\n", - " Reading Pu237 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu237.h5\n", - " Reading Pu238 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu238.h5\n", - " Reading Pu239 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu239.h5\n", - " Reading Pu240 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu240.h5\n", - " Reading Pu241 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu241.h5\n", - " Reading Pu242 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu242.h5\n", - " Reading Pu243 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu243.h5\n", - " Reading Pu244 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu244.h5\n", - " Reading Pu245 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu245.h5\n", - " Reading Pu246 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu246.h5\n", - " Reading Am240 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am240.h5\n", - " Reading Am241 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am241.h5\n", - " Reading Am242 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am242.h5\n", - " Reading Am242_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am242_m1.h5\n", - " Reading Am243 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am243.h5\n", - " Reading Am244 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am244.h5\n", - " Reading Am244_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am244_m1.h5\n", - " Reading Cm240 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm240.h5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Cm241 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm241.h5\n", - " Reading Cm242 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm242.h5\n", - " Reading Cm243 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm243.h5\n", - " Reading Cm244 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm244.h5\n", - " Reading Cm245 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm245.h5\n", - " Reading Cm246 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm246.h5\n", - " Reading Cm247 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm247.h5\n", - " Reading Cm248 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm248.h5\n", - " Reading Cm249 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm249.h5\n", - " Reading Cm250 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm250.h5\n", - " Reading Bk245 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk245.h5\n", - " Reading Bk246 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk246.h5\n", - " Reading Bk247 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk247.h5\n", - " Reading Bk248 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk248.h5\n", - " Reading Bk249 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk249.h5\n", - " Reading Bk250 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk250.h5\n", - " Reading Cf246 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf246.h5\n", - " Reading Cf247 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf247.h5\n", - " Reading Cf248 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf248.h5\n", - " Reading Cf249 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf249.h5\n", - " Reading Cf250 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf250.h5\n", - " Reading Cf251 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf251.h5\n", - " Reading Cf252 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf252.h5\n", - " Reading Cf253 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf253.h5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cf250 at\n", - " 1200K\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Cf254 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf254.h5\n", - " Reading Es251 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es251.h5\n", - " Reading Es252 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es252.h5\n", - " Reading Es253 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es253.h5\n", - " Reading Es254 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es254.h5\n", - " Reading Es254_m1 from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es254_m1.h5\n", - " Reading Es255 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es255.h5\n", - " Reading Fm255 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fm255.h5\n", - "Timestep: 0 --> keff: 1.00505\n", - "[openmc.deplete] t=432000.0 s, dt=432000 s, source=8000000.0\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.15837032197505e-16 atom/b-cm)\n", - "Timestep: 1 --> keff: 0.99648\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.158335355509685e-16 atom/b-cm)\n", - "[openmc.deplete] t=864000.0 s, dt=2592000 s, source=8000000.0\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.158332857910594e-16 atom/b-cm)\n", - "Timestep: 2 --> keff: 0.99569\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.158297094883484e-16 atom/b-cm)\n", - "[openmc.deplete] t=3456000.0 s, dt=2592000 s, source=8000000.0\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.158233980121496e-16 atom/b-cm)\n", - "Timestep: 3 --> keff: 0.99108\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.158125377264131e-16 atom/b-cm)\n", - "[openmc.deplete] t=6048000.0 s, dt=2592000 s, source=8000000.0\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.158023599019991e-16 atom/b-cm)\n", - "Timestep: 4 --> keff: 0.98848\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.15791131104636e-16 atom/b-cm)\n", - "[openmc.deplete] t=8640000.0 s, dt=15552000 s, source=8000000.0\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.15776668121522e-16 atom/b-cm)\n", - "Timestep: 5 --> keff: 0.98764\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.155893001929469e-16 atom/b-cm)\n", - "[openmc.deplete] t=24192000.0 s, dt=8208000 s, source=8000000.0\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.151049790950367e-16 atom/b-cm)\n", - "Timestep: 6 --> keff: 0.97911\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.149211007095395e-16 atom/b-cm)\n", - "[openmc.deplete] t=32400000.0 (final operator evaluation)\n", - "WARNING: nuclide B11 in material1 is negative (density = -8.145479262213239e-16 atom/b-cm)\n" - ] - } - ], - "source": [ - "model = openmc.model.Model(geometry,mats,settings)\n", - "\n", - "#Depletion general settings\n", - "depletion_days = [5,5,30,30,30,180,95]\n", - "power = 8e6 #total thermal power [W]\n", - "salt_mass = 4590 * 10**3 #grams\n", - "salt_volume =salt_mass/salt_density #cm3\n", - "salt.volume = salt_volume\n", - "\n", - "# Initialize depletion operator\n", - "op = openmc.deplete.CoupledOperator(model, normalization_mode = \"energy-deposition\", chain_file='ENDF-B-VIII.0_chain_msr.xml')\n", - "\n", - "# Initialize integrator object and start depletion calculation \n", - "integrator = openmc.deplete.CECMIntegrator(op, depletion_days, timestep_units='d', power=power)\n", - "integrator.integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "41d076d2", - "metadata": {}, - "outputs": [], - "source": [ - "# Let's store some results that will be used later for comparison\n", - "results = openmc.deplete.Results('depletion_results.h5')\n", - "t, k_ref = results.get_keff()\n", - "n_xe_ref = 0\n", - "n_kr_ref = 0\n", - "for nuc,_ in openmc.data.isotopes('Xe'):\n", - " n_xe_ref += results.get_atoms(str(salt.id), nuc)[1]\n", - "for nuc,_ in openmc.data.isotopes('Kr'):\n", - " n_kr_ref += results.get_atoms(str(salt.id), nuc)[1]" - ] - }, - { - "cell_type": "markdown", - "id": "dd3cd25c", - "metadata": {}, - "source": [ - "# Removal rates theory\n", - "The possibility to remove fission products from the fuel salt is one of the key characteristics of the MSRE and msr reactor in general. \n", - "\n", - "Mathematically nuclides removal can be thought as an additional term to the Bateman equation:\n", - "\n", - "$\\frac{dn_i(t)}{dt} = \\underbrace{\\sum_j \\gamma_{j\\rightarrow i} n_j\\overline{\\sigma_j\\phi} - n_i \\overline{\\sigma_i \\phi}}_\\textbf{R} + \\underbrace{\\sum_j \\gamma_{j\\rightarrow i} n_j\\lambda_{i\\rightarrow j} + \\lambda_{j\\rightarrow i}n_i}_\\textbf{D} - \\underbrace{\\epsilon_i \\lambda_i n_i}_\\textbf{T}$,\n", - "\n", - "Where $ \\epsilon_i \\lambda_i n_i $ is the removal term, $\\epsilon_i$ is the removal efficiency and\n", - "$\\lambda_i$ is the removal rate coefficient for the continuous removal of the nuclide $i$, expressed in ($s^{-1}$) units.\n", - "\n", - "Another way to characterize $\\lambda_i$ is through the concept of cycle time: $T_{cyc,i} = \\frac{1}{\\lambda_i}$, as the time needed to process the removal elements at a specific volumetric rate.\n", - "\n", - "For simplicity, we can combine $\\epsilon_i$ and $\\lambda_i$ in one single parameter, that we can call again $\\lambda_i$. Thus, setting a removal rate coefficient of, for example, $1\\ (s^{-1})$ at $100\\ \\%$ efficiency is the same as setting $10\\ (s^{-1})$ at $10\\ \\%$.\n", - "\n", - "The capability to model removal rates has been deployed under a different [branch](https://github.com/openmsr/openmc/tree/msr_13.2_cont) of OpenMC.\n", - "\n", - "In this example we will set removal rates to gasseous fission products, Xe and Kr, and noble metals fission products. The values and their explanation can be found [here](https://info.ornl.gov/sites/publications/Files/Pub173113.pdf)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "250cacc3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[openmc.deplete] t=0.0 s, dt=432000 s, source=8000000.0\n", - "Timestep: 0 --> keff: 1.00478\n", - "WARNING: nuclide B11 in material1 is negative (density = -7.810566471511284e-16 atom/b-cm)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: No intersection found with DAGMC cell 22, material 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[openmc.deplete] t=432000.0 s, dt=432000 s, source=8000000.0\n", - "Timestep: 1 --> keff: 1.00248\n", - "WARNING: nuclide B11 in material1 is negative (density = -5.467815330683659e-16 atom/b-cm)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: No intersection found with DAGMC cell 22, material 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[openmc.deplete] t=864000.0 s, dt=2592000 s, source=8000000.0\n", - "Timestep: 2 --> keff: 1.00147\n", - "[openmc.deplete] t=3456000.0 s, dt=2592000 s, source=8000000.0\n", - "Timestep: 3 --> keff: 0.99758\n", - "[openmc.deplete] t=6048000.0 s, dt=2592000 s, source=8000000.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: No intersection found with DAGMC cell 22, material 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Timestep: 4 --> keff: 0.99451\n", - "WARNING: nuclide B11 in material1 is negative (density = -1.0411576378416223e-16 atom/b-cm)\n", - "[openmc.deplete] t=8640000.0 s, dt=15552000 s, source=8000000.0\n", - "Timestep: 5 --> keff: 0.99462\n", - "[openmc.deplete] t=24192000.0 s, dt=8208000 s, source=8000000.0\n", - "Timestep: 6 --> keff: 0.98593\n", - "[openmc.deplete] t=32400000.0 (final operator evaluation)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " WARNING: No intersection found with DAGMC cell 22, material 1\n" - ] - } - ], - "source": [ - "# Re-initialize depletion operator\n", - "op = openmc.deplete.CoupledOperator(model, normalization_mode = \"energy-deposition\", chain_file='ENDF-B-VIII.0_chain_msr.xml')\n", - "\n", - "# Initialize msr continuous object\n", - "msr = openmc.deplete.msr.MsrContinuous(op, model)\n", - "\n", - "# Set removal rates constants in 1/s\n", - "msr.set_removal_rate('salt', ['Xe','Kr'], 4.067e-5)\n", - "msr.set_removal_rate('salt', ['Se','Nb','Mo','Tc','Ru','Rh','Pd','Ag','Sb','Te'], 8.777e-3)\n", - "\n", - "# Initialize integrator object and start depletion calculation \n", - "integrator = openmc.deplete.CECMIntegrator(op, depletion_days, msr_continuous=msr, timestep_units='d', power=power)\n", - "integrator.integrate()" - ] - }, - { - "cell_type": "markdown", - "id": "415f0334", - "metadata": {}, - "source": [ - "# Critical Factor\n", - "As we've set removal rates to fission products, we except keff to drop less rapidly than in the first case, let's verify that: " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "9877db52", - "metadata": {}, - "outputs": [], - "source": [ - "results = openmc.deplete.Results('depletion_results.h5')\n", - "t, k = results.get_keff()\n", - "n_xe = 0\n", - "n_kr = 0\n", - "for nuc,_ in openmc.data.isotopes('Xe'):\n", - " n_xe += results.get_atoms(str(salt.id), nuc)[1]\n", - "for nuc,_ in openmc.data.isotopes('Kr'):\n", - " n_kr += results.get_atoms(str(salt.id), nuc)[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "826c26f9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Let's convert time from sec to days \n", - "t /= (3600 * 24)\n", - "\n", - "plt.figure()\n", - "ax = plt.subplot()\n", - "k1, = ax.plot(t, [k[0] for k in k_ref], '--', c='red', label='keff wo removal rates')\n", - "k2, = ax.plot(t, [k[0] for k in k], c='red', label='keff w removal rates')\n", - "ax1 = ax.twinx()\n", - "n1, = ax1.plot(t, n_xe_ref, '--', c='green', label='Xe wo removal rates')\n", - "n2, = ax1.plot(t, n_xe, c='green', label='Xe w removal rates')\n", - "n3, = ax1.plot(t, n_kr_ref, '--', c='blue', label='Kr wo removal rates')\n", - "n4, = ax1.plot(t, n_kr, c='blue', label='Kr w removal rates')\n", - "ax.set_xlabel('Time[d]')\n", - "ax.set_ylabel(r'$k_{eff}$', color='r')\n", - "ax.tick_params(axis='y', colors='red')\n", - "ax1.set_yscale('log')\n", - "ax1.set_ylabel('Nuclides [atoms]')\n", - "ax1.legend(handles=[k1, k2, n1, n2, n3, n4])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cc827041", - "metadata": {}, - "source": [ - "# Fission products\n", - "The removal rate for gaseous fission products has been tuned (see [here](https://info.ornl.gov/sites/publications/Files/Pub173113.pdf)) to obtain a Xenon poison fractions matching the measurements reported during the MSRE U235 operation, of 0.3%-0.4%. \n", - "\n", - "The Xenon poison fraction is defined as:\n", - "$FP = \\frac{\\Sigma_a^{135}Xe}{\\Sigma_a^{235}U}$\n", - "\n", - "Let's plot the same quantity and see if we obtain values that matches the reference :" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "dc07279e", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Microscopic absorption cross section at 0.0253 eV\n", - "xs_xe135 = 2664214.0\n", - "xs_u235 = 686.006994850397\n", - "_, n_xe135 = results.get_atoms(str(salt.id), 'Xe135')\n", - "_, n_u235 = results.get_atoms(str(salt.id), 'U235')\n", - "# Poison fraction\n", - "pf = (xs_xe135*n_xe135)/(xs_u235*n_u235)*100\n", - "plt.figure()\n", - "plt.plot(t, pf)\n", - "plt.xlabel('Time [d]')\n", - "plt.ylabel('Xe posion fraction [%]')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9d28beee", - "metadata": {}, - "source": [ - "# Inventory \n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "aa32c623", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inventory = dict()\n", - "for nuc,_ in openmc.data.isotopes('U'):\n", - " inventory[nuc] = results.get_atoms(str(salt.id), nuc)[1] / openmc.data.AVOGADRO * openmc.data.atomic_mass(nuc) / 1000\n", - "\n", - "for nuc in ['Pu238','Pu239','Pu240','Pu241','Pu242']:\n", - " inventory[nuc] = results.get_atoms(str(salt.id), nuc)[1] / openmc.data.AVOGADRO * openmc.data.atomic_mass(nuc) / 1000\n", - "\n", - "plt.figure()\n", - "for nuc, mass in inventory.items():\n", - " plt.plot(t, mass, label=nuc)\n", - "plt.xlabel('Time [y]')\n", - "plt.ylabel('Mass [g]')\n", - "plt.yscale('log')\n", - "plt.ylim(1e-5)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "7434b75d", - "metadata": {}, - "source": [ - "# Neutron absorption in the fuel" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "0c5bd844", - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "import seaborn as sns\n", - "regex = re.compile(r'(\\d+|\\s+)')\n", - "\n", - "# All nuclides present in the fuel at last time-step\n", - "nucs = results.export_to_materials(-1)[0].get_nuclides()\n", - "\n", - "# Let's begin by making some useful groupings\n", - "gaseos = ['H', 'He', 'Ne', 'Ar', 'Kr', 'Xe', 'Rn'] #gaseous fission products\n", - "noble_metals = ['Se','Nb','Mo','Tc','Ru','Rh','Pd','Ag','Sb','Te'] # noble metals fission products\n", - "metals = ['Cr','Mn','Fe','Co','Ni','Cu','Zn','Hf','Zr','W',]\n", - "halogens = ['F','Cl','Br','I','At']\n", - "alkali_metals = ['Li','Na','K','Rb','Cs']\n", - "alkali_earths= ['Be','Mg','Ca','Sr','Ba','Ra']\n", - "lanthanides = ['Y','La','Ce','Pr','Nd','Pm','Sm','Eu','Gd','Tb','Dy','Ho','Er','Tm','Yb','Lu']\n", - "m_a = ['Ac','Th','Pa','Np','Am','Cm','Bk','Cf','Es','Fm','Md','No','Lr']\n", - "# Get fissile nuclides in the fuel, based on Ronen's rule for determining fissile isotopes\n", - "fissile = []\n", - "\n", - "for nuc in nucs:\n", - " elm = regex.split(nuc)[0]\n", - " a = round(openmc.data.atomic_mass(nuc))\n", - " z = openmc.data.ATOMIC_NUMBER[elm]\n", - " if 90 <= z <= 100:\n", - " ronen = 2*z -(a-z)\n", - " if ronen in [41,43,45]:\n", - " fissile.append(nuc) \n", - "\n", - "# Calculate totat absorption rate of fissile nuclides\n", - "tot_abs_rate = 0\n", - "for nuc in fissile:\n", - " tot_abs_rate += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", - " tot_abs_rate += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", - "\n", - "nuclides_stack = dict()\n", - "groups_stack = {'Gaseos':0, 'Noble metals':0, 'Metals':0, 'Halogens':0 , 'Alkali metals':0, 'Alkali earths':0, 'Lanthanides':0, 'MA':0, 'Others':0}\n", - "for nuc in nucs:\n", - " \n", - " if regex.split(nuc)[0] in ['U','Pu']:\n", - " nuclides_stack[nuc] = results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", - " nuclides_stack[nuc] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", - " nuclides_stack[nuc] /= tot_abs_rate\n", - " \n", - " \n", - " elif regex.split(nuc)[0] in gaseos:\n", - " groups_stack['Gaseos'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", - " groups_stack['Gaseos'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", - " elif regex.split(nuc)[0] in noble_metals:\n", - " groups_stack['Noble metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", - " groups_stack['Noble metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", - " elif regex.split(nuc)[0] in metals:\n", - " groups_stack['Metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", - " groups_stack['Metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", - " elif regex.split(nuc)[0] in halogens:\n", - " groups_stack['Halogens'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", - " groups_stack['Halogens'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", - " elif regex.split(nuc)[0] in alkali_metals:\n", - " groups_stack['Alkali metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", - " groups_stack['Alkali metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", - " elif regex.split(nuc)[0] in alkali_earths:\n", - " groups_stack['Alkali earths'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", - " groups_stack['Alkali earths'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", - " elif regex.split(nuc)[0] in lanthanides:\n", - " groups_stack['Lanthanides'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", - " groups_stack['Lanthanides'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", - " elif regex.split(nuc)[0] in m_a:\n", - " groups_stack['MA'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", - " groups_stack['MA'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", - " else:\n", - " groups_stack['Others'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", - " groups_stack['Others'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", - "\n", - "# Divide each array by the total absorption reaction rate of fissile nuclides\n", - "for g in groups_stack.keys():\n", - " groups_stack[g] /= tot_abs_rate\n", - "\n", - "# Sort dictionary groups\n", - "groups_stack=dict(reversed(sorted(groups_stack.items(), key=lambda item: item[1][len(item)])))\n", - "\n", - "# Create red color palette for groups_stack\n", - "colors = list(reversed(sns.color_palette(\"Reds\", len(groups_stack))))\n", - "\n", - "# Order uranium series\n", - "u_series = {key:value for key,value in nuclides_stack.items() if key.startswith('U')}\n", - "u_series = dict(reversed(sorted(u_series.items(), key=lambda item: item[1][len(item)])))\n", - "# Create green color palette for Uranium isotopes\n", - "colors += list(reversed(sns.color_palette(\"Greens\", len(u_series))))\n", - "\n", - "# Order plutionium series\n", - "pu_series = {key:value for key,value in nuclides_stack.items() if key.startswith('Pu')}\n", - "pu_series = dict(reversed(sorted(pu_series.items(), key=lambda item: item[1][len(item)])))\n", - "# Create blue color palette for plutonium isotopes\n", - "colors += list(reversed(sns.color_palette(\"Blues\", len(pu_series))))\n", - "# Add uramium and plutonium series to the stack\n", - "groups_stack.update(u_series)\n", - "groups_stack.update(pu_series)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "cc4b980e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15,10))\n", - "plt.stackplot(t, groups_stack.values(), labels=groups_stack.keys(), \n", - " edgecolor=\"black\", linewidth=0.5,colors=colors, alpha=0.8)\n", - "handles, labels = plt.gca().get_legend_handles_labels()\n", - "legend = plt.legend([handles[idx] for idx in list(reversed(np.arange(0,len(handles),1)))],\n", - " [labels[idx] for idx in list(reversed(np.arange(0,len(handles),1)))],\n", - " bbox_to_anchor=(1.05,1), loc='upper left',\n", - " borderaxespad=0, ncol=2,\n", - " fontsize=15)\n", - "plt.xlabel('Time [d]',weight='bold',fontsize=17)\n", - "plt.title('Neutrons absorption distribution per neutron absorbed in fissile isotopes',\n", - " weight='bold', fontsize=17)\n", - "plt.xticks(fontsize=13)\n", - "plt.yticks(fontsize=13)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c0c7c200", - "metadata": {}, - "source": [ - "Here we can visualize the neutrons absorption for each nuclide present in the fuel per neutrons absorption by fissile isotopes. In other words we can see, out of the total neutrons generated, where and how many we end up losing.\n", - "\n", - "It is useful to group together those isotopes with similar characteristics that individually wouldn't represent a big contribution to capture.\n", - "\n", - "Due to the low burnup and relatively short simulation time, we are far from equilibrium. However, we can notice the quick increase of absorption in the Pu isotopes, created from neutron capture of U238, and in particular of Pu239, due to its higher absorption cross section than U235.\n", - "\n", - "**Note**: Neutrons lost to leakage out of the core and capture in other isotopes other than fuel are not represented. Thus, approximately 1 neutron is missing from the counting (we know that a fission event releases approximately 2.3 neutrons). This is simply due to the fact that we have defined the fuel salt as our only depletable material." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 8d9dc76154c8b6ccf9f1a861ac50f5fa0df5c6ae Mon Sep 17 00:00:00 2001 From: church89 Date: Fri, 19 Apr 2024 14:13:21 +0200 Subject: [PATCH 24/32] update repo --- .../msre_control_rod.h5m | 0 h5m/msre_control_rod_1e-2.h5m | 3 - h5m/msre_reactor_1e-2_swap.h5m | 3 - openmc_notebooks/README.md | 4 +- openmc_notebooks/images/lat.png | Bin 0 -> 44481 bytes openmc_notebooks/msre_criticality_test.ipynb | 1054 +++++++++++++ .../msre_iso-temperature_feedback.ipynb | 1303 ----------------- step_files/.gitattributes | 1 - step_files/msre_control_rod.step | 3 - step_files/msre_reactor.step | 3 - 10 files changed, 1056 insertions(+), 1318 deletions(-) rename msre_control_rod.h5m => h5m/msre_control_rod.h5m (100%) delete mode 100644 h5m/msre_control_rod_1e-2.h5m delete mode 100644 h5m/msre_reactor_1e-2_swap.h5m create mode 100644 openmc_notebooks/images/lat.png create mode 100644 openmc_notebooks/msre_criticality_test.ipynb delete mode 100644 openmc_notebooks/msre_iso-temperature_feedback.ipynb delete mode 100644 step_files/.gitattributes delete mode 100644 step_files/msre_control_rod.step delete mode 100644 step_files/msre_reactor.step diff --git a/msre_control_rod.h5m b/h5m/msre_control_rod.h5m similarity index 100% rename from msre_control_rod.h5m rename to h5m/msre_control_rod.h5m diff --git a/h5m/msre_control_rod_1e-2.h5m b/h5m/msre_control_rod_1e-2.h5m deleted file mode 100644 index f979ff5..0000000 --- a/h5m/msre_control_rod_1e-2.h5m +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:6fc41a1a1029ff5d88cde69a12510cd6f7546eed642274f049df1a3e220694a4 -size 146320328 diff --git a/h5m/msre_reactor_1e-2_swap.h5m b/h5m/msre_reactor_1e-2_swap.h5m deleted file mode 100644 index b2c598b..0000000 --- a/h5m/msre_reactor_1e-2_swap.h5m +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:329389c2a06e7634e9d87b6e7aa0bc25ad55720a26329def5ad78694b3eb4b68 -size 240522156 diff --git a/openmc_notebooks/README.md b/openmc_notebooks/README.md index 872641e..d298c88 100644 --- a/openmc_notebooks/README.md +++ b/openmc_notebooks/README.md @@ -1,7 +1,7 @@ -# OpenMC MSRE_notebooks +# OpenMC MSRE notebooks [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) `OpenMC` simulation examples of the Molten Salt Reactor Experiment (MSRE), operated at ORNL in the 1960s. -All scripts are set up using the [h5m meshed files](https://github.com/openmsr/msre/tree/master/h5m) obtained with the open source meshing tool [CAD-to-OpenMC](https://github.com/openmsr/CAD_to_OpenMC) from a CAD version of the the MSRE, designed with the CAE tool `OnShape` and available for export here: [onshape msre model]((https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/)). +All scripts are set up using the [h5m meshed files](https://github.com/openmsr/msre/tree/master/h5m) obtained with the open source meshing tool [CAD-to-OpenMC](https://github.com/openmsr/CAD_to_OpenMC) from a CAD version of the the MSRE, designed with the CAE tool `OnShape` and available for export here: [onshape msre model](https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/). diff --git a/openmc_notebooks/images/lat.png b/openmc_notebooks/images/lat.png new file mode 100644 index 0000000000000000000000000000000000000000..a9bc9d3911e738f5016a741b7a63ed8abeb285f5 GIT binary patch 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[Copenhagen Atomics](https://www.copenhaagenatomics.com) using the CAE tool OnShape and made available for export here: [onshape msre model]((https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/)). \n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f8a4de08", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": { + "image/jpeg": { + "width": 400 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(\"./images/msre_assembly.jpg\", width=400)" + ] + }, + { + "cell_type": "markdown", + "id": "9a9f360a", + "metadata": {}, + "source": [ + "Fig. 1: Section view of entire assembly" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d951f7c7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": { + "image/jpeg": { + "width": 400 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"./images/control_rod.jpg\", width=400)" + ] + }, + { + "cell_type": "markdown", + "id": "c7ee2ce3", + "metadata": {}, + "source": [ + "Fig. 3: Section view of control rod bottom end" + ] + }, + { + "cell_type": "markdown", + "id": "ce7fc65d", + "metadata": {}, + "source": [ + "Teh CAD geometry has been surface meshed and converted into h5m files, readable by OpenMC, using the open-source meshing tool \n", + "[CAD-to-OpenMC](https://github.com/openmsr/CAD_to_OpenMC). \n", + "Two step files have been exported from OnShape:\n", + "- the entire vessel and shielding\n", + "- one of the the control rod \n", + "\n", + "**Note**: only one control rod has been meshed. The other two can be created directly within the OpenMC environment, as DAGMC universe duplicates of the imported one.\n", + "The reason to have a separate export of the control rods is to paramterize their position within Openmc directly.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "787fab93", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import openmc\n", + "import numpy as np\n", + "from math import log10\n", + "import matplotlib.pyplot as plt\n", + "import os \n", + "\n", + "os.system('rm *.xml *.h5')" + ] + }, + { + "cell_type": "markdown", + "id": "ceb51601", + "metadata": {}, + "source": [ + "Materials definition during the first criticality test comes from the MSRE benchmark evaluation [Fratoni et al](https://www.osti.gov/servlets/purl/1617123) and available in the International Reactor Physics Experiment Evaulation Project (IRPhEP) handbook. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e1001bc3", + "metadata": {}, + "outputs": [], + "source": [ + "salt_temp = 648.9\n", + "salt = openmc.Material(name=\"salt\", temperature = salt_temp + 273.15)\n", + "salt.add_nuclide('Li6', 0.0000131541127279649)\n", + "salt.add_nuclide('Li7', 0.263082254559299)\n", + "salt.add_nuclide('Be9', 0.118688703357854)\n", + "salt.add_nuclide('Zr90', 0.0105474637491834)\n", + "salt.add_nuclide('Zr91', 0.00230014661352455)\n", + "salt.add_nuclide('Zr92', 0.00351582124972781)\n", + "salt.add_nuclide('Zr94', 0.00356297220526352)\n", + "salt.add_nuclide('Zr96', 0.000574011632608622)\n", + "salt.add_nuclide('Hf174', 0.000000000838247756705461)\n", + "salt.add_nuclide('Hf176', 0.000000027557395001692)\n", + "salt.add_nuclide('Hf177', 0.0000000974463017170099)\n", + "salt.add_nuclide('Hf178', 0.000000142921242518281)\n", + "salt.add_nuclide('Hf179', 0.0000000713558402895524)\n", + "salt.add_nuclide('Hf180', 0.000000183785820657672)\n", + "salt.add_nuclide('U234', 0.000010347565467384)\n", + "salt.add_nuclide('U235', 0.00101016470221956)\n", + "salt.add_nuclide('U236', 0.00000422977321829143)\n", + "salt.add_nuclide('U238', 0.00216927473057667)\n", + "salt.add_nuclide('Fe54', 0.00000285638012142825)\n", + "salt.add_nuclide('Fe56', 0.0000448390593090724)\n", + "salt.add_nuclide('Fe57', 0.00000103552942297801)\n", + "salt.add_nuclide('Fe58', 0.000000137809956243416)\n", + "salt.add_nuclide('Cr50', 0.00000212334843928242)\n", + "salt.add_nuclide('Cr52', 0.000040946661076878)\n", + "salt.add_nuclide('Cr53', 0.00000464302267471169)\n", + "salt.add_nuclide('Cr54', 0.00000115574661884993)\n", + "salt.add_nuclide('Ni58', 0.00000586242358522768)\n", + "salt.add_nuclide('Ni60', 0.00000225819506936691)\n", + "salt.add_nuclide('Ni61', 0.0000000981621760803009)\n", + "salt.add_nuclide('Ni62', 0.00000031298397136929)\n", + "salt.add_nuclide('Ni64', 0.0000000797077903148755)\n", + "salt.add_nuclide('O16', 0.0000514608160260434)\n", + "salt.add_nuclide('O17', 0.0000000189357310970321)\n", + "salt.add_nuclide('O18', 0.0000000964845153990466)\n", + "salt.add_nuclide('F19', 0.594363006576997)\n", + "salt.set_density('g/cm3',2.3278)\n", + "\n", + "#moderator blocks170\n", + "graphite = openmc.Material(name='graphite',temperature= salt_temp + 273.15)\n", + "graphite.set_density('g/cm3',1.8492)\n", + "graphite.add_element('C',0.999992212250888) #endfb71 does not have C12 cross sections\n", + "graphite.add_nuclide('B10', 1.76873036539477E-07)\n", + "graphite.add_nuclide('B11', 7.1193229306592E-07)\n", + "graphite.add_nuclide('V51', 2.12200224802724E-06)\n", + "graphite.add_nuclide('S32', 1.77900760271203E-06)\n", + "graphite.add_nuclide('S33', 1.40462754188233E-08)\n", + "graphite.add_nuclide('S34', 7.95955607066653E-08)\n", + "graphite.add_nuclide('S36', 1.87283672250977E-10)\n", + "graphite.add_nuclide('O16', 1.85674385782835E-06)\n", + "graphite.add_nuclide('O17', 6.81675170610618E-10)\n", + "graphite.add_nuclide('Si28', 5.31911333979174E-07)\n", + "graphite.add_nuclide('Si29', 2.70215087309286E-08)\n", + "graphite.add_nuclide('Si30', 1.78336189959512E-08)\n", + "graphite.add_nuclide('Al27', 4.0117589478321E-07)\n", + "graphite.add_nuclide('Fe54', 2.31047953307142E-09)\n", + "graphite.add_nuclide('Fe56', 3.62695875239409E-08)\n", + "graphite.add_nuclide('Fe57', 8.37622947917593E-10)\n", + "graphite.add_nuclide('Fe58', 1.11472237523719E-10)\n", + "graphite.add_nuclide('Ti46', 1.12809907219147E-09)\n", + "graphite.add_nuclide('Ti47', 1.01734025419449E-09)\n", + "graphite.add_nuclide('Ti48', 1.008041983054E-08)\n", + "graphite.add_nuclide('Ti49', 7.39759512794646E-10)\n", + "graphite.add_nuclide('Ti50', 7.08309478054763E-10)\n", + "graphite.add_nuclide('Mg24', 8.53578076978748E-09)\n", + "graphite.add_nuclide('Mg25', 1.0806153652092E-09)\n", + "graphite.add_nuclide('Mg26', 1.18975751709533E-09)\n", + "graphite.add_nuclide('Ca40', 4.58305905846326E-09)\n", + "graphite.add_nuclide('Ca42', 3.05880815220158E-11)\n", + "graphite.add_nuclide('Ca43', 6.38236631448551E-12)\n", + "graphite.add_nuclide('Ca44', 9.86193787556798E-11)\n", + "graphite.add_nuclide('Ca48', 8.8407592652503E-12)\n", + "graphite.add_s_alpha_beta('c_Graphite')\n", + "\n", + "#inor-8\n", + "inor = openmc.Material(name='inor-8',temperature= salt_temp + 273.15)\n", + "inor.set_density('g/cm3',8.7745)\n", + "inor.add_element('Ni',(66+71)/2,'wo')\n", + "inor.add_element('Mo',(15+18)/2,'wo')\n", + "inor.add_element('Cr',(6+8)/2,'wo')\n", + "inor.add_element('Fe',5,'wo')\n", + "inor.add_element('C',(0.04+0.08)/2,'wo')\n", + "inor.add_element('Al',0.25,'wo')\n", + "inor.add_element('Ti',0.25,'wo')\n", + "inor.add_element('S',0.02,'wo')\n", + "inor.add_element('Mn',1.0,'wo')\n", + "inor.add_element('Si',1.0,'wo')\n", + "inor.add_element('Cu',0.35,'wo')\n", + "inor.add_element('B',0.010,'wo')\n", + "inor.add_element('W',0.5,'wo')\n", + "inor.add_element('P',0.015,'wo')\n", + "inor.add_element('Co',0.2,'wo')\n", + "\n", + "#helium\n", + "helium = openmc.Material(name='helium')\n", + "helium.add_element('He',1.0)\n", + "helium.set_density('g/cm3',1.03*(10**-4))\n", + "\n", + "#Control rods inconel clad\n", + "trace = 0.01\n", + "inconel = openmc.Material(name='inconel-600', temperature = 65.6 + 273.15)\n", + "inconel.add_element('Ni',78.5,percent_type='wo')\n", + "inconel.add_element('Cr',14.0,percent_type='wo')\n", + "inconel.add_element('Fe',6.5,percent_type='wo')\n", + "inconel.add_element('Mn',0.25,percent_type='wo')\n", + "inconel.add_element('Si',0.25,percent_type='wo')\n", + "inconel.add_element('Cu',0.2,percent_type='wo')\n", + "inconel.add_element('Co',0.2,percent_type='wo')\n", + "inconel.add_element('Al',0.2,percent_type='wo')\n", + "inconel.add_element('Ti',0.2,percent_type='wo')\n", + "inconel.add_element('Ta',0.5,percent_type='wo')\n", + "inconel.add_element('W',0.5,percent_type='wo')\n", + "inconel.add_element('Zn',0.2,percent_type='wo')\n", + "inconel.add_element('Zr',0.1,percent_type='wo')\n", + "inconel.add_element('C',trace,percent_type='wo')\n", + "inconel.add_element('Mo',trace,percent_type='wo')\n", + "inconel.add_element('Ag',trace,percent_type='wo')\n", + "inconel.add_element('B',trace,percent_type='wo')\n", + "inconel.add_element('Ba',trace,percent_type='wo')\n", + "inconel.add_element('Be',trace,percent_type='wo')\n", + "inconel.add_element('Ca',trace,percent_type='wo')\n", + "inconel.add_element('Cd',trace,percent_type='wo')\n", + "inconel.add_element('V',trace,percent_type='wo')\n", + "inconel.add_element('Sn',trace,percent_type='wo')\n", + "inconel.add_element('Mg',trace,percent_type='wo')\n", + "inconel.set_density('g/cm3',8.5)\n", + "\n", + "#Control rods bushing posion material\n", + "Gd2O3 = openmc.Material()\n", + "Gd2O3.add_element('Gd',2)\n", + "Gd2O3.add_element('O',3)\n", + "Gd2O3.set_density('g/cm3',7.41)\n", + "Al2O3 = openmc.Material()\n", + "Al2O3.add_element('Al',2)\n", + "Al2O3.add_element('O',3)\n", + "Al2O3.set_density('g/cm3',3.95)\n", + "bush = openmc.Material.mix_materials([Gd2O3,Al2O3],[0.7,0.3],'wo')\n", + "bush.name='gd2o3-al2o3'\n", + "bush.temperature = 65.6 +273.15\n", + "\n", + "#Concrete block\n", + "concrete = openmc.Material(name='concrete')\n", + "concrete.add_element('H',0.005,'wo')\n", + "concrete.add_element('O',0.496,'wo')\n", + "concrete.add_element('Si',0.314,'wo')\n", + "concrete.add_element('Ca',0.083,'wo')\n", + "concrete.add_element('Na',0.017,'wo')\n", + "concrete.add_element('Mn',0.002,'wo')\n", + "concrete.add_element('Al',0.046,'wo')\n", + "concrete.add_element('S',0.001,'wo')\n", + "concrete.add_element('K',0.019,'wo')\n", + "concrete.add_element('Fe',0.012,'wo')\n", + "concrete.set_density('g/cm3',2.35)\n", + "\n", + "#Thermal shielding as water and SS305 (50-50)\n", + "water = openmc.Material()\n", + "water.add_element('H',2)\n", + "water.add_element('O',1)\n", + "water.set_density('g/cm3',0.997)\n", + "\n", + "#stainless steel 304\n", + "ss304 = openmc.Material()\n", + "ss304.add_element('C',0.08,'wo')\n", + "ss304.add_element('Mn',2,'wo')\n", + "ss304.add_element('P',0.045,'wo')\n", + "ss304.add_element('S',0.03,'wo')\n", + "ss304.add_element('Si',0.75,'wo')\n", + "ss304.add_element('Cr',19,'wo')\n", + "ss304.add_element('Ni',10,'wo')\n", + "ss304.add_element('N',0.1,'wo')\n", + "ss304.add_element('Fe',67.995, 'wo')\n", + "ss304.set_density('g/cm3',7.93)\n", + "shield = openmc.Material.mix_materials([water,ss304],[0.5,0.5],'vo')\n", + "shield.temperature = 32.2 + 273.15\n", + "shield.name='steelwater'\n", + "\n", + "# \"Careytemp 1600\" by Philip Carey Manufacturing Compamy (Cincinnati)\n", + "# http://moltensalt.org/references/static/downloads/pdf/ORNL-TM-0728.pdf\n", + "insulation=openmc.Material(name='insulation')\n", + "insulation.add_element('Si',1)\n", + "insulation.add_element('O',2)\n", + "insulation.set_density('g/cm3',0.16) #https://www.osti.gov/servlets/purl/1411211\n", + "\n", + "# sand water, not sure about this material\n", + "sandwater=openmc.Material(name='watersand')\n", + "sandwater.add_element('Fe',3)\n", + "sandwater.add_element('O',4)\n", + "sandwater.set_density('g/cm3',6)\n", + "\n", + "#Vessel anular steel\n", + "steel = openmc.Material(name='steel')\n", + "steel.add_element('Fe',1)\n", + "steel.set_density('g/cm3',7.85)\n", + "\n", + "mats = openmc.Materials([salt, graphite, inor, helium, inconel, shield, concrete,\n", + " steel, sandwater, insulation, bush])" + ] + }, + { + "cell_type": "markdown", + "id": "31f8365e", + "metadata": {}, + "source": [ + "Let's now import the h5m mesh files: \n", + "- MSRE vessel + shielding \n", + "- Control rod \n", + "\n", + "We will create two `DAGMC` universes that will be used to fill the core and control rod cells, respectively. \\\n", + "As previously mentioned, we will create the other two control rods as conveniently translated replica of the first one. \\\n", + "All three control rods are now objects and the `translation` attribute value can be set accordinly using the `setattr()` function. \n", + "\n", + "All three control rods at set as fully withdrawn (56 inches from bottom end)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "6e9faef6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another UniverseBase instance already exists with id=1.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another UniverseBase instance already exists with id=2.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10000.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10001.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10002.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10003.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10004.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10005.\n", + " warn(msg, IDWarning)\n" + ] + } + ], + "source": [ + "# CAD h5m files\n", + "core_h5m = 'msre_full.h5m'\n", + "control_rod_h5m = 'msre_control_rod.h5m'\n", + "\n", + "#Geometry\n", + "core = openmc.DAGMCUniverse(filename = core_h5m, auto_geom_ids = True,\n", + " universe_id = 1)\n", + "control_rod = openmc.DAGMCUniverse(filename = control_rod_h5m,\n", + " auto_geom_ids = True, universe_id=2)\n", + "core_region = core.bounding_region()\n", + "cr_region = control_rod.bounding_region(boundary_type = 'transmission')\n", + "\n", + "_offset_xy = 10.163255 #cm, _offset_xy between cr1, cr2 and cr3 (from Onshape model)\n", + "_lower_rod = 61.728 # distance to lower limit (from OnShape model)\n", + "_upper_rod = 51 * 2.54 # escursion to upper rod limit (from ORNL)\n", + "start_rod = 56 * 2.54 # rod iniitial position (user input)\n", + "\n", + "# Create control rod regions w\n", + "cr1_region = cr_region.translate([_offset_xy/2, _offset_xy/2, _lower_rod + _upper_rod])\n", + "cr2_region = cr_region.translate([_offset_xy/2, -_offset_xy/2, _lower_rod + _upper_rod])\n", + "cr3_region = cr_region.translate([-_offset_xy/2, -_offset_xy/2, _lower_rod + _upper_rod])\n", + "\n", + "# Extend control rod region1 to include downloads translations\n", + "cr1_region = cr1_region | cr1_region.translate([0, 0, -_upper_rod])\n", + "\n", + "#Define cells\n", + "core_cell = openmc.Cell(region=~(cr1_region | cr2_region | cr3_region) & core_region ,\n", + " fill=core)\n", + "\n", + "cr1_cell = openmc.Cell(name='CR1', region=cr1_region, fill=control_rod)\n", + "cr2_cell = openmc.Cell(name='CR2', region=cr2_region, fill=control_rod)\n", + "cr3_cell = openmc.Cell(name='CR3', region=cr3_region, fill=control_rod)\n", + "\n", + "#translate cells to regions\n", + "setattr(cr1_cell, 'translation', [_offset_xy/2, _offset_xy/2 , _lower_rod + start_rod])\n", + "setattr(cr2_cell, 'translation', [_offset_xy/2, -_offset_xy/2, _lower_rod + start_rod])\n", + "setattr(cr3_cell, 'translation', [-_offset_xy/2, -_offset_xy/2, _lower_rod + start_rod])\n", + "\n", + "root = openmc.Universe(cells=[core_cell,cr1_cell,cr2_cell,cr3_cell])\n", + "geometry = openmc.Geometry(root)" + ] + }, + { + "cell_type": "markdown", + "id": "6fc3dcec", + "metadata": {}, + "source": [ + "Let's initialize settings and tallies:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "92335e2b", + "metadata": {}, + "outputs": [], + "source": [ + "settings = openmc.Settings()\n", + "settings.temperature = {'method':'interpolation','range':(293.15,923.15)}\n", + "settings.batches = 50\n", + "settings.inactive = 20\n", + "settings.particles = 30000\n", + "settings.photon_transport = False\n", + "source_area = openmc.stats.Box([-100., -100., 0.],[ 100., 100., 200.],only_fissionable = True)\n", + "settings.source = openmc.IndependentSource(space=source_area)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "edfde381", + "metadata": {}, + "outputs": [], + "source": [ + "tally = dict()\n", + "tally['heating'] = openmc.Tally(name=\"heating\")\n", + "tally['heating'].scores.append('heating-local')\n", + "e_min, e_max = 1e-5, 20e6\n", + "groups = 500\n", + "energies = np.logspace(log10(e_min), log10(e_max), groups + 1)\n", + "energy_filter = openmc.EnergyFilter(energies)\n", + "particle_filter = openmc.ParticleFilter(['neutron'])\n", + "cell_filter = openmc.MaterialFilter([salt])\n", + "tally['flux']= openmc.Tally(name=\"flux\")\n", + "tally['flux'].filters = [energy_filter, particle_filter] \n", + "tally['flux'].scores = ['flux']\n", + "mesh = openmc.RegularMesh()\n", + "mesh.dimension = [500, 500, 1]\n", + "mesh.lower_left = [-100, -100, 50]\n", + "mesh.upper_right = [100, 100, 200]\n", + "mesh_filter = openmc.MeshFilter(mesh)\n", + "tally['mesh'] = openmc.Tally(name=\"Mesh\")\n", + "tally['mesh'].scores = ['flux','absorption','fission']\n", + "tally['mesh'].filters = [mesh_filter]\n", + "tally['mesh'].filters.append(particle_filter)\n", + "tallies = openmc.Tallies(tally.values())\n", + "\n", + "#Build the model\n", + "model = openmc.Model(geometry=geometry, materials=mats, settings=settings, tallies=tallies)\n", + "model.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "id": "41ca034e", + "metadata": {}, + "source": [ + "We can now plot the geometry to see if the control rods are positioned as set and run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "c2d50f58", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = {salt:'yellow', graphite:'black', inor: 'grey', helium: 'cyan', inconel: 'grey',\n", + " bush: 'blue', concrete: 'brown', shield: 'red', insulation: 'green',\n", + " sandwater: 'lightgreen', steel: 'grey'}\n", + "plot=openmc.Plot()\n", + "plot.basis = 'xy'\n", + "plot.width = (20,20)\n", + "plot.pixels = (200,200)\n", + "plot.origin = (0,0,220)\n", + "plot.color_by = 'material'\n", + "plot.colors = colors\n", + "openmc.plot_inline(plot)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "76287483", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot=openmc.Plot()\n", + "plot.basis = 'xz'\n", + "plot.width = (20,30)\n", + "plot.pixels = (150,200)\n", + "plot.origin = (0,-5,200)\n", + "plot.color_by = 'material'\n", + "plot.colors = colors\n", + "openmc.plot_inline(plot)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "b8bde700", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ca-ws:1462097] mca_base_component_repository_open: unable to open mca_accelerator_rocm: libamdhip64.so.6: cannot open shared object file: No such file or directory (ignored)\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2024 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.14.1-dev\n", + " Git SHA1 | d93ac83697e7fdb4fd4cc0f2705795398bf804bd\n", + " Date/Time | 2024-04-19 13:18:31\n", + " MPI Processes | 1\n", + " OpenMP Threads | 56\n", + "\n", + " Reading model XML file 'model.xml' ...\n", + " WARNING: Other XML file input(s) are present. These files may be ignored in\n", + " favor of the model.xml file.\n", + " Reading cross sections XML file...\n", + "Using the DOUBLE-DOWN interface to Embree.\n", + "Loading file msre_full.h5m\n", + "Initializing the GeomQueryTool...\n", + "Using faceting tolerance: 0.01\n", + "Building acceleration data structures...\n", + "Using the DOUBLE-DOWN interface to Embree.\n", + "Loading file msre_control_rod.h5m\n", + "Initializing the GeomQueryTool...\n", + "Using faceting tolerance: 4.67638e-310\n", + "Building acceleration data structures...\n", + " Reading Li6 from /home/lorenzo/nuclear_data/endfb80_hdf5/Li6.h5\n", + " Reading Li7 from /home/lorenzo/nuclear_data/endfb80_hdf5/Li7.h5\n", + " Reading Be9 from /home/lorenzo/nuclear_data/endfb80_hdf5/Be9.h5\n", + " Reading Zr90 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr90.h5\n", + " Reading Zr91 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr91.h5\n", + " Reading Zr92 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr92.h5\n", + " Reading Zr94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr94.h5\n", + " Reading Zr96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr96.h5\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n", + " Reading Hf174 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf174.h5\n", + " Reading Hf176 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf176.h5\n", + " Reading Hf177 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf177.h5\n", + " Reading Hf178 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf178.h5\n", + " Reading Hf179 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf179.h5\n", + " Reading Hf180 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf180.h5\n", + " Reading U234 from /home/lorenzo/nuclear_data/endfb80_hdf5/U234.h5\n", + " Reading U235 from /home/lorenzo/nuclear_data/endfb80_hdf5/U235.h5\n", + " Reading U236 from /home/lorenzo/nuclear_data/endfb80_hdf5/U236.h5\n", + " Reading U238 from /home/lorenzo/nuclear_data/endfb80_hdf5/U238.h5\n", + " Reading Fe54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe54.h5\n", + " Reading Fe56 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe56.h5\n", + " Reading Fe57 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe57.h5\n", + " Reading Fe58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe58.h5\n", + " Reading Cr50 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr50.h5\n", + " Reading Cr52 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr52.h5\n", + " Reading Cr53 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr53.h5\n", + " Reading Cr54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr54.h5\n", + " Reading Ni58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni58.h5\n", + " Reading Ni60 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni60.h5\n", + " Reading Ni61 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni61.h5\n", + " Reading Ni62 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni62.h5\n", + " Reading Ni64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni64.h5\n", + " Reading O16 from /home/lorenzo/nuclear_data/endfb80_hdf5/O16.h5\n", + " Reading O17 from /home/lorenzo/nuclear_data/endfb80_hdf5/O17.h5\n", + " Reading O18 from /home/lorenzo/nuclear_data/endfb80_hdf5/O18.h5\n", + " Reading F19 from /home/lorenzo/nuclear_data/endfb80_hdf5/F19.h5\n", + " Reading C12 from /home/lorenzo/nuclear_data/endfb80_hdf5/C12.h5\n", + " Reading C13 from /home/lorenzo/nuclear_data/endfb80_hdf5/C13.h5\n", + " Reading B10 from /home/lorenzo/nuclear_data/endfb80_hdf5/B10.h5\n", + " Reading B11 from /home/lorenzo/nuclear_data/endfb80_hdf5/B11.h5\n", + " Reading V51 from /home/lorenzo/nuclear_data/endfb80_hdf5/V51.h5\n", + " Reading S32 from /home/lorenzo/nuclear_data/endfb80_hdf5/S32.h5\n", + " Reading S33 from /home/lorenzo/nuclear_data/endfb80_hdf5/S33.h5\n", + " Reading S34 from /home/lorenzo/nuclear_data/endfb80_hdf5/S34.h5\n", + " Reading S36 from /home/lorenzo/nuclear_data/endfb80_hdf5/S36.h5\n", + " Reading Si28 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si28.h5\n", + " Reading Si29 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si29.h5\n", + " Reading Si30 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si30.h5\n", + " Reading Al27 from /home/lorenzo/nuclear_data/endfb80_hdf5/Al27.h5\n", + " Reading Ti46 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti46.h5\n", + " Reading Ti47 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti47.h5\n", + " Reading Ti48 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti48.h5\n", + " Reading Ti49 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti49.h5\n", + " Reading Ti50 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti50.h5\n", + " Reading Mg24 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg24.h5\n", + " Reading Mg25 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg25.h5\n", + " Reading Mg26 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg26.h5\n", + " Reading Ca40 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca40.h5\n", + " Reading Ca42 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca42.h5\n", + " Reading Ca43 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca43.h5\n", + " Reading Ca44 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca44.h5\n", + " Reading Ca48 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca48.h5\n", + " Reading Mo92 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo92.h5\n", + " Reading Mo94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo94.h5\n", + " Reading Mo95 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo95.h5\n", + " Reading Mo96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo96.h5\n", + " Reading Mo97 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo97.h5\n", + " Reading Mo98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo98.h5\n", + " Reading Mo100 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo100.h5\n", + " Reading Mn55 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mn55.h5\n", + " Reading Cu63 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu63.h5\n", + " Reading Cu65 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu65.h5\n", + " Reading W180 from /home/lorenzo/nuclear_data/endfb80_hdf5/W180.h5\n", + " Reading W182 from /home/lorenzo/nuclear_data/endfb80_hdf5/W182.h5\n", + " Reading W183 from /home/lorenzo/nuclear_data/endfb80_hdf5/W183.h5\n", + " Reading W184 from /home/lorenzo/nuclear_data/endfb80_hdf5/W184.h5\n", + " Reading W186 from /home/lorenzo/nuclear_data/endfb80_hdf5/W186.h5\n", + " Reading P31 from /home/lorenzo/nuclear_data/endfb80_hdf5/P31.h5\n", + " Reading Co59 from /home/lorenzo/nuclear_data/endfb80_hdf5/Co59.h5\n", + " Reading He3 from /home/lorenzo/nuclear_data/endfb80_hdf5/He3.h5\n", + " Reading He4 from /home/lorenzo/nuclear_data/endfb80_hdf5/He4.h5\n", + " Reading Ta180 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta180.h5\n", + " Reading Ta181 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta181.h5\n", + " Reading Zn64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn64.h5\n", + " Reading Zn66 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn66.h5\n", + " Reading Zn67 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn67.h5\n", + " Reading Zn68 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn68.h5\n", + " Reading Zn70 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn70.h5\n", + " Reading Ag107 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag107.h5\n", + " Reading Ag109 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag109.h5\n", + " Reading Ba130 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba130.h5\n", + " Reading Ba132 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba132.h5\n", + " Reading Ba134 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba134.h5\n", + " Reading Ba135 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba135.h5\n", + " Reading Ba136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba136.h5\n", + " Reading Ba137 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba137.h5\n", + " Reading Ba138 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba138.h5\n", + " Reading Ca46 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca46.h5\n", + " Reading Cd106 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd106.h5\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n", + " 1200K\n", + " Reading Cd108 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd108.h5\n", + " Reading Cd110 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd110.h5\n", + " Reading Cd111 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd111.h5\n", + " Reading Cd112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd112.h5\n", + " Reading Cd113 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd113.h5\n", + " Reading Cd114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd114.h5\n", + " Reading Cd116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd116.h5\n", + " Reading V50 from /home/lorenzo/nuclear_data/endfb80_hdf5/V50.h5\n", + " Reading Sn112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn112.h5\n", + " Reading Sn114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn114.h5\n", + " Reading Sn115 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn115.h5\n", + " Reading Sn116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn116.h5\n", + " Reading Sn117 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn117.h5\n", + " Reading Sn118 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn118.h5\n", + " Reading Sn119 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn119.h5\n", + " Reading Sn120 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn120.h5\n", + " Reading Sn122 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn122.h5\n", + " Reading Sn124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn124.h5\n", + " Reading Gd152 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd152.h5\n", + " Reading Gd154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd154.h5\n", + " Reading Gd155 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd155.h5\n", + " Reading Gd156 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd156.h5\n", + " Reading Gd157 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd157.h5\n", + " Reading Gd158 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd158.h5\n", + " Reading Gd160 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd160.h5\n", + " Reading H1 from /home/lorenzo/nuclear_data/endfb80_hdf5/H1.h5\n", + " Reading H2 from /home/lorenzo/nuclear_data/endfb80_hdf5/H2.h5\n", + " Reading Na23 from /home/lorenzo/nuclear_data/endfb80_hdf5/Na23.h5\n", + " Reading K39 from /home/lorenzo/nuclear_data/endfb80_hdf5/K39.h5\n", + " Reading K40 from /home/lorenzo/nuclear_data/endfb80_hdf5/K40.h5\n", + " Reading K41 from /home/lorenzo/nuclear_data/endfb80_hdf5/K41.h5\n", + " Reading N14 from /home/lorenzo/nuclear_data/endfb80_hdf5/N14.h5\n", + " Reading N15 from /home/lorenzo/nuclear_data/endfb80_hdf5/N15.h5\n", + " Reading c_Graphite from /home/lorenzo/nuclear_data/endfb80_hdf5/c_Graphite.h5\n", + " Minimum neutron data temperature: 250 K\n", + " Maximum neutron data temperature: 2500 K\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.60880\n", + " 2/1 0.88232\n", + " 3/1 0.96334\n", + " 4/1 1.00418\n", + " 5/1 0.98436\n", + " 6/1 1.00206\n", + " 7/1 0.99280\n", + " 8/1 0.98498\n", + " 9/1 0.99798\n", + " 10/1 1.00342\n", + " 11/1 1.00202\n", + " 12/1 1.01708\n", + " 13/1 0.99542\n", + " 14/1 1.01309\n", + " 15/1 1.00049\n", + " 16/1 1.00931\n", + " 17/1 0.99363\n", + " 18/1 1.00903\n", + " 19/1 1.00821\n", + " 20/1 1.01369\n", + " 21/1 1.00872\n", + " 22/1 0.99225 1.00049 +/- 0.00824\n", + " 23/1 1.00850 1.00316 +/- 0.00545\n", + " 24/1 0.99733 1.00170 +/- 0.00412\n", + " 25/1 1.01087 1.00353 +/- 0.00368\n", + " 26/1 1.01232 1.00500 +/- 0.00334\n", + " 27/1 1.01345 1.00620 +/- 0.00307\n", + " 28/1 1.00635 1.00622 +/- 0.00266\n", + " 29/1 1.01336 1.00702 +/- 0.00248\n", + " 30/1 1.01235 1.00755 +/- 0.00228\n", + " 31/1 0.99936 1.00680 +/- 0.00219\n", + " 32/1 1.01775 1.00772 +/- 0.00220\n", + " 33/1 1.00712 1.00767 +/- 0.00202\n", + " 34/1 1.00815 1.00771 +/- 0.00187\n", + " 35/1 1.01133 1.00795 +/- 0.00176\n", + " 36/1 1.00568 1.00781 +/- 0.00165\n", + " 37/1 1.01406 1.00817 +/- 0.00160\n", + " 38/1 0.99515 1.00745 +/- 0.00167\n", + " 39/1 1.01624 1.00791 +/- 0.00165\n", + " 40/1 1.00469 1.00775 +/- 0.00157\n", + " 41/1 1.01875 1.00827 +/- 0.00158\n", + " 42/1 1.00781 1.00825 +/- 0.00151\n", + " 43/1 1.00939 1.00830 +/- 0.00144\n", + " 44/1 1.00363 1.00811 +/- 0.00139\n", + " 45/1 1.00444 1.00796 +/- 0.00135\n", + " 46/1 1.00185 1.00773 +/- 0.00131\n", + " 47/1 1.01499 1.00800 +/- 0.00129\n", + " 48/1 1.01605 1.00828 +/- 0.00128\n", + " 49/1 0.99991 1.00799 +/- 0.00127\n", + " 50/1 1.00888 1.00802 +/- 0.00122\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 3.7033e+01 seconds\n", + " Reading cross sections = 1.4467e+01 seconds\n", + " Total time in simulation = 1.5251e+02 seconds\n", + " Time in transport only = 1.5208e+02 seconds\n", + " Time in inactive batches = 5.1562e+01 seconds\n", + " Time in active batches = 1.0095e+02 seconds\n", + " Time synchronizing fission bank = 1.9172e-01 seconds\n", + " Sampling source sites = 1.5614e-01 seconds\n", + " SEND/RECV source sites = 3.4788e-02 seconds\n", + " Time accumulating tallies = 8.5964e-02 seconds\n", + " Time writing statepoints = 3.0541e-02 seconds\n", + " Total time for finalization = 8.4758e-01 seconds\n", + " Total time elapsed = 1.9094e+02 seconds\n", + " Calculation Rate (inactive) = 11636.5 particles/second\n", + " Calculation Rate (active) = 8915.68 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00758 +/- 0.00121\n", + " k-effective (Track-length) = 1.00802 +/- 0.00122\n", + " k-effective (Absorption) = 1.00818 +/- 0.00096\n", + " Combined k-effective = 1.00803 +/- 0.00101\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n" + ] + } + ], + "source": [ + "results=model.run()" + ] + }, + { + "cell_type": "markdown", + "id": "8ab1fdd2", + "metadata": {}, + "source": [ + "We can now post-process the result file and plot the tallies:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "44bd9b3c", + "metadata": {}, + "outputs": [], + "source": [ + "with openmc.StatePoint(results) as sp:\n", + " heating = sp.get_tally(name=\"heating\").get_pandas_dataframe()[\"mean\"].sum()*openmc.data.JOULE_PER_EV\n", + " t = sp.get_tally(name=\"flux\")\n", + " flux = sp.get_tally(name=\"Mesh\").get_slice(scores=['flux']).get_pandas_dataframe()[\"mean\"]\n", + " fission = sp.get_tally(name=\"Mesh\").get_slice(scores=['fission']).get_pandas_dataframe()[\"mean\"]\n", + " absorption = sp.get_tally(name=\"Mesh\").get_slice(scores=['absorption']).get_pandas_dataframe()[\"mean\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "7232a2ac", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot neutron flux spectrum\n", + "power = 8e6 #Thermal power in W\n", + "volume = 1.65e6 #Fuel salt volume in cm3\n", + "\n", + "energy_filter = t.filters[0]\n", + "energies = energy_filter.bins[:, 0]\n", + "mean = t.mean.ravel()\n", + "uncertainty = t.get_values(value='std_dev').ravel()\n", + "fig, ax = plt.subplots()\n", + "ax.plot(energies, mean*power/heating/volume, drawstyle='steps-post')\n", + "ax.set_xlabel('Energy [eV]')\n", + "ax.set_ylabel(r'Flux [neutrons/cm$^2$-s]')\n", + "ax.set_xscale('log')\n", + "ax.grid(True, which='both')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "99831db8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zbB/f7p7Z2fCSsLmxe2eDDgWy62AJ8JgzS2nPCRXLgd+6MkqrbZtyQ7Q69Dmfceu4Wj2tTrwFdFrX32pjzZZGxpR12Lwbtdl2WRaYDgeuHb/UzVK2qbTW87aUwZi6h+UzsGQQOctAU7JT5fby81QbllyTrdM+nzdjEC3f3ymQVNrUNZ/HwG6elc0oRUIZ9ZOiHs/LlgDMnd0R9vHJpMxRqXOHN142pT83XlpLn7dm92ft5GozWjtYzLkytqXnp9qwjc3R3rVtS8DIaTut2gxySp9QinTt/hoY2UaYOmUxcpK709a1IYbcQnxac6uU+5ZazfV0VptyAdj3b5t3qwV4p9iyJW1sucbK67DnrNVTK7PNNdby5LSehcpvlFw/M0DkXNw/U66fbYH8TbDhjNE9Zzn2TrSPT5Ay1aHMLDXedPOUVs5Kp8SSLVpXP9eO0e9T7hFb39RxdlurY7O0emtf65xz2V9b7MvcbHCqM2/ZEnfQlDuqtKkZ/NSAfxqwUoKBOTfNVISPfSYVZNWioFptnXqHlg7YrdDnVjtqgEbZK6D+LtSe5/J3L49fCnrL41vP7xQjWXONncZ1VNq2A3PNzdN6FsptYjo521igcC7qZ6bPrZyo3YfZ/XbbLbQh4oyrIJ9fW+4E+/gEKUtsiQ5Fsym67N6ZXSSwBQBSvQtmP63Obxs2YRKoVTrrFq0+d57WMa2BYKkLbG5/CaZa+UdqM9ptzzXXhqXRM2X5bYBMi5FoDTBatwKCubDh0qbu2xImaar+uWuey8nSAiW6r/XctXQaZZ3l+1E79xwzqiBz6pwtOwtDMldXq+xUHdXf2YL1BWDltEnfgDrIs39vse00KedrH58gZbYTmHqp3fizq+hPWlbrMMqXaw48lGXmZhT2e60uZUeWajxqdbTK2+NaAGpuRj5V75SVA73+nRp0bpa1Zsy17TWwUNtemj2+pO2XHG+PLcsOQ/seLXEJ1NidpS6wuXtUExkD7UHsNANXjcFs7WuxOVPHLXHrlPtabILWp8fPuc+W3I+lLjj5LUbJ30bX6eQP/17br6DcYFym7gU3Qsosuoqd3Wb28SecPeWAtKFDMULZkU3NmpeKU5fMbFLDFtDQNZvaN9VO7XRbHWtrRtq6pqnfY9vOu1b/XEjxFEN0VluqVWm1YSL8M9mSAaQGjltgwX6eyudTs9pvs+QaynPX2jnHClqzbsnTgJIlLtOlx5X7lzzTrUlFyf7VJjjb2tLnfgt9ymaZcoIyFtXaz1V3ugYwTDIxM/f+FrIqAYThDP8CbvKk6YLZxxdIOcuMuRLpU43gqc1i56zGoOi2s0YtzJWrnaNVx5x76GZQrqcBKrWZ9ZJ6l56nFQFkZ6+tMos6dXPsaZ4nYAxCloogW/qQVvumWMEpa0WZLY2ssu2z7JhtgwU3S92I9hm3IKcFWpaAktr1TIGMOffP1DFlubn3Fdh8NqauaQnL5xp9ZG1pkNpaP5uF+G8LoMyBs8fB5RPi2f/tLNudBVKWdBzV41wVhPCuAt1bBqXW2c698FNMRM3mOi3bUZUUcs3Fss25lwwYtQ7Xts2W0+8tjcpch9LaP3Wc99N1TtVVc3cpqCtFqdZ0f2uQX6pLKQeQpbqRGktS3t/aM1vex6n6t3EftYCObed5MVlLQenc9zmhcAsclO1o3VvroijfydZ7OuVmsm1aonNpuahbbFj5veVSLN+LDSCq/cW4v50NT2654Kf6s8dJk3Kr7R3veAde+MIX4ilPeQqICG95y1sWH/trv/Zr6LoOz372s29a+85qdxZIUWsNzksPL+P6JxYKrM4G1bZF+DWQsWS2dhpbMmOaG0znZonlNdhZaknH18otbfNZGDLbBufGYKp2nm3dU+V55gbjKXAzZyUDYwePgiWorslif58WCFvKBE2BqyWC2W2v/7QAd+5cteehBh5qLKL9vMSFtA1w2KZPa4HtpTbFDLb6CrN9ik2ZsuqSI9um0n8c7CyuHv23jV27dg3Petaz8NrXvnar4z760Y/iJS95Cb74i794q+Nutd3ZwtnmoFZ/0CfX4RlV26Bxt2mPfm51gFNCsFq9NXHgWQdvtVpbagPpUpGmPbbVuWt9NXfXFKMyNSOei2KxLoKlkTVLIlVa2o6SzSjvX+myaZUrz7fgHBvpzEube76nfvspgKJlLcNQuljscUsFzvpcaO6YufdnSqxcq/usZsWuZb22LTV3lZaxf20dpxWBT71L1lrMyRIrxLQpl4r6NLZZnLC5EGFFr/I4siinARrl8dvY/fffj/vvv3/r87z0pS/Fi1/8Ynjvt2JfbrXd/rB0qc3Rt9YqD/vcSsbVTLJqpf97idnZTEtDMUXfllaji8tjW/WV28vPU1YbWMpObIpCnjrPti6AEjwuFZW2tteEpC1x6WnPUfu7tH7bphaDsoS1mLOlQuSpNrbOrb+ZApdWpE4L1JfWEtpu0z9MWWtgr7Gg9tluvZ+ttiwR/k6BjDl2aI61nKurZPym3o3aM2AWX+V2ZJbEstiLstS2In7Oa5L2ONkjjzwy+nd8fHxudb/uda/DH/zBH+C7vuu7zq3Om2V3Bkg5w8NYVZTPJWmbEhtOzeBa7pyW22RuJmg/14BG2RnaemszshrTUw4Q9jg7qEzpTaaYp206y/IYtZb/e8rNMDf4l+XLY0/jBqm1xeo2pgSltuwSBmWqniXtmqq31Tb9PvV+LD0vUH8Hlrrblg7e2864a8BpzuZY05opG9Rq4xyDMnXft2lHq86lAN0eK27G5mTPNtGoRzfc7uXnKTtN/3JGC5HO/A8AnvrUp+Luu+9O/77/+7//XNr3+7//+/i7f/fv4qd+6qfQdbe/M+X2b+ESK6nUhQ/kLEo3+1O6+yk3h23Pxskq52pRt0tcPVOuo9bxU64kO/iVnX35oteOt99bHaSeY6krpVauPHfNnVEeV7rCatoLazVGqKUVKQfnJYO6PaYFCmoDQAFiRqvQ1tpVDCST5aeuq9V+rdf7tvuoLN8CMHOuwqXPTGpU4/2puQ9Law3+5cSgdWyrL9pmoLTHLwFSc+zOadpQmnPL2jMFUs3vnBYntOEs4rpp9s2n0aQQ4VbmSDkvd88HP/hBXL16NW3f398/e9uGAS9+8Yvxyle+Ek9/+tPPXN+tsDsDpADzA/s2D3ctXK7mN699n6KDl8wKawBhTqthWZMpa4G4uRlRTe+yZLAv62+VqXXI2w5KU+WVyVEB6dLjSjq7Fb0wp3sp91Xux8ay9wvEs1VgUAEnG+XL62q1tfW92DcJUOxnC/S2AEGpza1ztEDfElZz7h3cZl/NrTPXjiX12Ppq7Mrc9iX9Q4tdtW3Sv1OM5NQz25qgaH8bVK/ilmlUttGzXEC7evXqCKSchz366KP4zd/8Tfz2b/82Xv7ylwMAQgiIMaLrOrztbW/D85///HM951ntYoOUrTQgASDXXuCqFn7cAia1dizVjtRMX9yljNBptBq27rITXCoctHXYgX9qkF7CFpQahak2zbEoc9dTc0+U+1uD5LYDeu26G4P9aKBfSKNvAJu5YyugKNUxNfAsAEwbVt7H2udtXW5zVmNJpgDCHIhogY6Wtcpuy1603ENlv1BOGJb2P7a/mWrf1CRpidVYlMl3s96O2QUJrXD2NgAuAxyGMygphnNsS2lXr17Fe9/73tG2H/3RH8Wv/uqv4md+5mfwaZ/2aTfx7Keziw1SYgSwcGBvxeSX4EQFW1ZHscTXPddJzPmfbf1TjMu2NHI5i5qbWS6pW6+11KOoO8za3CAzt78ERqe10zA9rYG21IiUn+fqrbWtZFnKfYVZgGIjJ0ZRFAtck9VyRj8wGwVUaVNeEXdGmzLlPmu57iauZStrPUstt2dpNXaiBiLm6qidp6YnO82z3/rt7ITIWq0PK9+/cv+czmvBb1R9zgzYmF05eSOy5/FlVqLRlZz2+G3ssccew/ve9770/f3vfz/e85734J577sHTnvY0fPu3fzv+8A//EG94wxvgnMPnfM7njI5/0pOehIODg43tt4tdbJACzA++S908Nj1zq9No0fHb+IxPO9CWnUpLZ1HanPtom/ZsM1tbYktmya3f9zSi0DlruTRKsFFzqbRYnSVtm3K9LLiuFliJxbFkEtvVmJt0vHyv6VemgItur+6vgbAW0zK1bZsyS4HtkjLl+7vkfStZv5Y4uHaeWpnSLdpiXJYca883V0/NlaS2DYislCsXZm09W3ZiuQFYbgP2xNqtDkH+zd/8TTzvec9L31/xilcAAB588EG8/vWvx4c+9CE89NBDp27P420Ut5kq3Sb2yCOP4O6778Zz8eXo3N5mAbtCccXI0XifG78ozVlty287DMu1KS1aeWq/lpnqrMoOeYmmY659tU6+1ta5GeHNsJauYUaYOxJ5LnE7tFxTtcG2Vc8UA2PbVgCAKUBQ2zdXHjAApsKgLGFOFrErS91DNRdd7ft5aZO2qcO2Abi5z3Lr3W6Va723S7e3Ji61CUxtMlSWXfJ7T7l4zbb8biowMv24gBAFKSOGRcs1gMo6rvD2+BZ87GMfO3edh5qOS29776fi8pXTT5quPRrwZc/4wE1t60Wyi82kNAf0xgBVzRjbWCSwBjqq54r1F7TVodR0J/Z7q0MpAVRrf+0arFumNdNbCqiWbFs6UJxHx1+79xPnHg3OS0SzDcZmI1LGikLn2lipY8NVom1ttC0W4ELL23pG11zeg1q7yuuq1L/hUqqUqd6jmrV+u9pvMzO4bRx/VqBSApSabfP82jbV+oAlVnt3a6CibKMt7xxPqmplps5Z9k8VJm6xbmtCq5VdhcRApQw7bkX+2MUIgceVVRmiwxDPoEm5cLTBzbVz4MhvQ6stBljTobiis60NSi3TQcm6AID6wD81sMc4zaSU+0sdiJptu+00aoCmZqV/fRtXVmlLfPrl/rnBfQF1PGvbDF7FAFjmdihdLFUzx9bKlnVslJuh0cs2tc5TK1PbryCktBrIsW2fAi0t29i/jbvOPuundROV9UyZnUi0XB5zz2/5HpYuF/t3yqYA09QkpNWOVn9VY1VOay1dV8NlOkqjP5Mm4lxDlc/JAggB7gz/bhJjd0HtzgIp57W2Q61zKTuBliaifLFbFGoNwLQ6hiXsRQ0wtWxiJj2qr3XuuWOXlJvqkGs08rbgwn6eAnAVRqJWXzkAz/nQa1Yr2xrMp5ZemAMAVSsB+RZ1LQE/5fcS8JRJvKogzzIoJWNSY6rmtD+177XnoHb+ltVAwBQbV1ptUrLt71ljYst21Vw19q9tT6strXa12qz31O6rictrk6jCHTQC4BasXJD1e3Z2fnax3T3WLM03tbpmZT2ejQHBvlj2xSs7mLmBugQipZYkN2D82XYuUx1YOXPS70s72m0ja+z55sRypci0LF8DWbV9c8LIlrXEqBP6kZoAtbZfXR6TydHMtjmdyGgtk4m22PK0LZEkOSimjpu67haYKfdNuZnKbRuM0RIXj9pUJNQSsL4tA1NjLm2UzNy7NPdOLmUtbL/Qcv3a+krGxvY9NRBTtqkERFPupVrZEkTaMtr+GpgsWMipyUA1+qd095DDrUrodquFs3e63RkgZVtkPQVQgLbGhA8Yf9/Gys6nHPhr56sdpy9xq1PaVnRYK1fzQS+Z8Z22s96GMSkHoiltQ+s8jbJzSdVq7MFkB0r1UOHx+WdATHleW94AnCkwUbUKOJprc6l5GTMh4/pq+pjyejZClWsDV+33nmNQzqpJ2Wxw3e3aut/l+7vU7aq25H0Yhmm2o2yHfrf7au/1lEup9X1pHaVt5eYzz9ec9qSctMabmX1kbGfXpOxEKdbuHN6sTOaDCRZFv8694NXzLJjxTA34lo0pt9fqr52j1nHVyi7tqOeiU9RO+/JM1V8bcGr+6vJ4nZVp/a3Z88Q9aCXrG1PNrvqclLqMZt2V4+ogbVMwm86xcPkGBtz1tgLYBDcT7a2tr1J3h5lzTgCe1v2acrFtbKs8E7V2zrpf5p6r1nswN5koy9p3UustwU6r7tLlNQUQam0qWd+pY62QdorhXdovTt0TYJlrumBCN1w/hftnYw22dM47Z4j7eLU7g0kBNgFIK2FbLVnb6MBGJ1Ir17LSRQTM19liWcp9cyCpxbpYV5BaOROrifvKGVnNxdRqi7U0ULaV/U3qt/a9pleZYmiqTSoZjQCYEOVyMLXRLaUbphriWzkfEaVzjFiG0GAnZF+aRW6kEK8AjwqjMb4nDVBhj5uoYzRgNNoy6+I6rRXPSMnwNJ+Blr5pjsWZM/t+tECMMin6eapci92YM3tM690ty2vZmp7O1lnri8qyS1jimmvUfi773srvfBGMhbOnf853wtmx3TkgxVh11cyai6cFBGq+VmATcCwZmJf4bafObeuptaV1TrWlIkA1S5WX7aqdd0ln2gIoto1TM9glgslSj1C4aKqApCzTcvnMWM39YUFNrc6tB+saoNC/us+CmAl2Y6OOJce1AFGlfa1cL3N6FXuMLTMCIG4mE24JYudYPPv5NC6iciCf+l2XTDymjq2db8mkYaoPmmI9ascscfFse21TZfU3D6E+AbChypXw5NmU+jfBwhnT4odbuRriBbCLz4UtofPKZG1qNf9sy2oD7dSAPTcItUBHDQi16lrSAZSsTot1mKurdX+m9Dv23K1rqIETpYOXDBql/qZSXzOSZMaWRsJs6DLMdsuyzIX6bgCFKbO++TJUUzvt2rvRyBWUyleE5RsMy4wtcRXNhlnX6tmGFWsJp3VbGWGiTMeUlXUtAey1wX4OyCyxpYN/i5lptWNK6zZnS/vSsvyUVfRgkwC/NR7cJtlod7a9XWwmpbYoYK0TNTPFlBq8xRLUOp4GBblhSzqtltvEzsRKpsXWZUWzpR+41emU7WlpWlr7pq6pLD8VXVGbrdoZ7xIB7NJIj4pNuWLKMtXvjkBKxQYDfEo2o7SG62RS6FqCjikmQzNtWpCjnXJNl6X7LINSy9hZApXSWmyL/CXvRm4svWbAABCbAdiUKe/rtuxW06aYFev6qLmGahFqZd+wlI2puWdq9dbAhS0zxaJMuZDmmKha/1Qe12pzq3xpS3/Pso8IQZhPjJ9Bm9AthjGLciuje3bC2XO1iw1SCpvSoczS6zXXjG5PdVb2L9l3GitBi362L2ytE5nq4JZYq6OdYnMsgNqm/iWdeg2gtOqYiMLhptYzpraEr80onFIsWuo4lgpda5feYjoAwKy9s9ngwrU5RXOXoEZtarZZMimlPsb+LdtebG9FCJW/jd6fMuR7a7BiQew2kUAttrHFmM49yzW3rd2+7Xu7hK2dc0FNWYuBsftrn6e2ncbK3270zpvnqwiemF2Y8CaZJmU7/fE7kGLtjgEpc0LZ9GBrh1VjK5a4PKb21Tqe8hzl+VpAoyxrbQlDMeVeadXT2jZl23SAJZiYCiMur6l2za1jjZWDYSl+nRK41nQlfM6G66O28FmL0dBtNlRSQYgCgPQcGBdMCYIqoChrOCrMS4jCmnRjUKJtTW5NLVcBNOW1TOlfKgAFALMsxf4SPJai5NG1LbUpoXZqv9t8Frd9D7Y9Zu6dnXuv7PGnAQhl/bXyU20479n+HKM6ZbUU+o+jDZEwnGEV5LMceyfaHQNSkk0xKDXx5TYv21K2pAZ6SvpzCkS0Oh9dyLDmcmkJXkvdzVwnMBUJMWdzocytc7cieGz4YeEWmBLDtga1plbCMBtTycm4TWNmYMM1YQfwEgTYOiwAaWmmSuZEn7+WDkjcKwBANQbQsnIhANEV+1y6JgCbM1QAGMImKKkBsUak0chC3LjfVdePua6WNcFLKYZuCbCXMCxTg/YSgfo2rMucTWloygnREobmtJO1bRnkWvmpyUpZZikYNELax4NN2dn52R0BUlrRPKM8GLWIEDXdt8SPWpuBlJ3BZGNnOo6p2VFNuJdm9xMvbsnYTJUt9SRT7pmlHUarTHE95Uy6NDsQTQ1IteNymQmXjGRkrWkiNgbmGrCwzEj6vsmGjI6xGqlSS+CKbTYiTcpE70BDMMdgXL6lRSifnSGkc1IwZex9DgHRWz2L1innt9qZ0nXUcA21XD9VU60L1SOGpqwKaKdsSgt1nkxLi1VdYnP91dIJVe285bPYqm+ur2wxNHZ7Sx9k+9bKpGkETEt20VblCLh1udwwnDG6Z9i5e0Z24UHKJEApXTxLrHypai9oy1dbAo8WEGn5ims+6xobs43NUbYlkCu3zwGaGnBBZWa7oGNvDSTRsCPVeo3NDlpT+olaHS3XBiAsSMhsCFH6TPZ3tODCmbLl81EBKjGxPBGxU3dQ2DguenMfRnU0XJumHA3CVNnIpMH8VmYgpRDHA6sWWa+lbVEoKQvwggFpoekCStbS9MyEU1sNSz6mwe4t6Q+mmJZt7azMyZSd1XWjx8yBlda9rPWvU8CpxQTXji/Z3wI4zgrhQ2wvQniTLESHcAbhbDhvV9oFtwsNUjYevursuNF5A/WOo/Uy17bXNCU1RqUFapacozUbmXuQl9DVFqDYv61Z4xzlarZH+/20Hb1V8sfNga2Vf2MSZMwlRNNjWgOicc/A+VR+BEq8N/fWABIBK9Fso+I8G2BDykVqA7PY+c1ZZ2I0DKOidRbgKHo3Bkgh5G32mLK95lmnzufzDQO3WdkVy7ToPdF7GTdBy4ZVEsxtPBOVdPy1e9U+R6GJWBJpZq32TswB8yXsybYRQ9vY1ASoNsGaunZbz1J3UcmSWJvrNwpGJVdt328HclyOHAE3ESfu7ObZhQYpMURgIuABwPilKV+e2qDcesFKMFLWXdu2xKa0Hy2qfqqMWumzXsrC1ISGU2VabjRlsGplUpNkkCxmS61BZs4dZMvwsQXr0XJBiJXhxuk4/V4yJcqSKCgY1V1cr5RPACV9R53yt+6eIWS9iXUFWbdM2QZ1/QTzXY/V4wGuQ0GOfjftS9cu545k2lACR22fAC0KMT8HCloEwKTjgwMwbLqDys/pvuZySavSiCDiW5PdQ5PPzxJ2YAqI1FjIKSvLWYA0JRhvtaXGAM/1Q3OM72ms5VarudNPwxhMCehrVnvvb7Lt3D3naxcapFCpAahpDRod6shqmpXymNI3WwMtU+eodV5zs68lrM5S1maug7Wzlm1Eai0auNXpYnMAIaLRazkpkDW0ftW1U8tZMpfvw2gdkq7E6kG8B9n7Y102JTi1szpn9tX+epcZhhbI0UE/CpgIwZwjAwI+n4P2jTQomIFhPIpBMUbAm+uIUY6n3CYtZwGP/Z2ty0fZnugYoHiMwHICLbocQ4yICsD0PnhgJMRtsSxLwr31WXGc3yYOm8/ylLugCWpqDOHcO7tZ+fh5KdnKjWuZeRfL+loC/LIfs7a0XysnZy134lQ7gHk3/FQ/tARAPk4WcLYIndvjKm4fu9AgpbkmD7DZkS4BKkB+caZ8qrZOu63axs3BK9kcS7HkxWu5cMr2tV7yuU5iGyA1J06WbUtmsrUIm1ruDLUqCwIYtmHmPnpv2BPKwKRkSoqImxEQIcogouZ6GV2veU5p81mKI0YF+fMgz3mMiL28vn4TKEUHYB0S6KDBggllWYjrMeciATSxN9ep2y2Lom1NwEUYHz2HH/8WFCLgIhAdgy2tegjZPTQEcQ2Z++UmfrcZrUEc2FU4604a1Znfi8nndAKEL7ZtmIsWw9liS235GvConX8b5mWqntKWuImWWusYud4q6LyFidx2dv52sUFKYSOxbGlLqUV9CaZATW32UAICOxOeOtdUR1cCgRpAmqrXtrdmUxEMrTJT1gAlo78T9SUwMgwjsWy5X20jKmQqEVqqxNC/ljER8JHcON7zDL8QlI7Yk5rZ58ebzw4bTEoCB368fVSmZDo6ATFFeyJhg12gdcigRQGUtSD1CEtCMfL3GPn+B3N8BJhhwZitgWF2Op+jhIr3I/q8jULMgmDKi1xSFzf1LGFANVpozgyLMpe1dlJ8WXNDlgBhW91I6/lp7SvZiFqZFoDYxpaCmbI/KssumRTassA8U6LbKiL96r6Nc53e/bKtnT2Z261r60WwOwOkTOVDAdovTG0m0XoBE2WOzbrKY2rnL+sH5ineufbW3FTWfTMXmaN/G6zKVC6SjXPW6NdyX6U9G1lGBWg2U8ebnCbltsVmw34tY0LEA60O/Nblo+yagqEhVH4PGj9rBtBE50ZgZARQLCDR+0KE5J3ROiPG+U9MOQrCiHSmjt6n8nw+x99DHAt0wYxGdNwujeyJIgxOYCcA5PI7ED1A0SzMGEL+LfT3sefRc7vMyMQOfP/12fAOGGQd2BiBwWWGBeD7bgFLcktR/m5/j0a22/LzhpnnfCPTrba37HOWsKBTAGJqktU6fu77aUFL7biWK7m1v+wXy+NGbtxKX2atdEOXZvbpYoSzmZdvkp09Lf4OpFi7M0CKWusBn/Kplqjfvmg1wGLLz81q9Fgtd9oZTtkua1PCsakOz+5rHFsKWkfnK63VwSxgUEqbTKaW6q3dd4eNGXcpfLWMiffcJsN4pDBfddXI9qRVsc9CQ3OigzYc+D56EyJs2p2AiHmuIuV6GHjo9ToBEg6RgCz/0HNGBjyhOK64HVEYHusypwhEcpkxEnAS02UJcOlzYqwkklXwpuzLINu7im5FGSXVvOg994SUn8Xed3lmiNZIieYojNmVmmvPDE5TgGRyZWzzrFbrqL13tfdtW5CwbYTQNlZjPrYxC9hLkFbrN6fqmHP5TvUXdhI2wcqOIn1uYZ6UnZ2vXXyQch4x8OULY79P+V8taJmqr5whzXUWts5ah6A210FOdXi1WUkL6EzZKfzyU26brawmnLS0rp3FK2OibhxuyHgw0rDbGMflGueNVp/iM6Og7EgkIHb5XNFx3VQ+bgaYwFMCLzHyMSQsSvQ+a0hizMyHRuRE8BsdMnhBiIDoUWKnTIq6kZCO13ppiIjIQJwiuFxn7xmDFdbF8DkUYMQeGahYQB8MC2RnuZZtMkJb1rCIXgdddhV5YVesOyj9JuXzTqiuj2SsKY5dAqrnJgDnCTBaLp/E7jXe41bfNeXKLsuWNsUQaTun+s05N9A2VmN20ynNb3sLc6UEEAJOf76zHHsn2sUGKXZWGqPQzQtm7qXbpuVHtdtaM4Cl9Ger4yhf5ppLqJy11YBRCTqmNC5znW+5X0VplRVrm8dsux+o07O1rK92nzVda8YR4DyHqCpjoq4cIAMGa7aj99zpRRoLS0nZDk8MJjpn2BNhS7TamNsXCQl8EOONqsWyXY7riaYeErFrEAAVPSHaaJzAjEr0fC4aIshFqZvdM2UDFOxEAkhBEcA52QLG5YWtAQERJCAmAs7cpyEIO5MZH1oNiGsG2yrOha8893qPEfj83uXIKrUQmNkJAQgcPh3XayTBrV3lubAyHHm03WpPzDmbCxvWQEjDdboVYJlzHU2xEVPnmHLXnMYtVJts1XR0rf51ypaKbafYWzHWVi077XnYzt1zvnahQQpPwiqDdQNZN63l+in31Y5rzRb0+9zLXytTvvxlx9N0gUxcc4tpqYnPSrGgbF8iLhw3s56EbbEl90P5G5uZkbp4SnCiwESBirPujKKtyZ2TZ5EjgOJkGyiH+RJldgFI+pXkRjHXqayFAo4R6AHyuSyoUfO5Tors9QgKdsy5YifgI+lepF/umE1QF5BiimZSOwKCjZTrhBmR9oMAOOQoqxKwhIjYOT63/v5EQOfgViKKDQANhn9XliWxAnIvnQAWCqP3j1RYKPoVCOiIIbDwdsCYVZnIcDuZkl+e6+pzb/uZ1oRoRo81srIPsO9iy2xftaT+bfZN9VvbMCElM1P2r3MTp9a9Ldmj28jOnifl9ryux8suNEhJttQlUftsj5+aZdhtpS6ldqxaWtl24SxqrmNYQsPaznOpNTqDjRTjpYBt4t4vXa12Y3XhZkGjOdGBVb8LMCHngK6D5vjIWhCnjcoDYYhj1w8wErRGcR2NXDYEdneIS2bjGfFkwIYBOlpMfz6XGRBmPdjVEroS7OTLjzF/j8DIFRT6DEbsdVDkOhlA5GNVYEsCMqAuJqnXqdtHromZIT0Y0mYGL0kfEwyRRAqq8v0MTtw2EaDBSZsCu5hiBAYBG0QgBSbqEtLfKEaWpzif3Vzy+5OImUe6FQCair8GkE/lZgQWMoNu2SDMDZlmCVrv8lz759w6rTqWTKy2tZK5BuoThiV1b3N+5zZcrDu7OHaxQYpzGCdzmxBvWp+p3Q5sAg41HdBqL2yrbHFeIkIcZlRbUx3Cefm1bR3b+Ntr9PWWAtitFoNrKfKt5kCBnzAnADgtu+pI5PeONhlZOaMr9CyJMXGOmYCSJdEmOR0o5blwQPAug4MkZJWBfDDHAQieUghu0nt4SWanjAuJ68fzZ7cydROQ2GBhMACk86Tze3CbA7t5WG9i7qee23FFZBEQxC/us3tKmRgaooA0sOsoRDme63RDgIYpB/N7U4zCAMWkiaEhggYg9pLHZZ2Zg6iRSA7CKJhnQp6/iCG1mfS3JQJ5dgNFPS4MgphCzp2yxEq3QYUZTOWmQmOB5e9cWf60776tZ2qCU9s3xaJMaeRaVusr1eZAySn6HQDbsVjnbCESwlmSuZ3h2DvRLjZIsVYLqbU2JYybQ+Wtl3zmRd0AKFM+7NqLFON4pjWixBsd2FR9LWp67iUu3T+Weq9Q56d28SzVn5SaE2V0vOPonBKQttbDcTwQcvSNBzqXxK4b2WLTfQcA0XCIa4XZDwNU9BBPGYxAWA7C2K0jwIOFsYZxUZARpX4+bWZ6tD5IfS6m/cqeuDWzDul83bgdzGjwuUb15rE//R4JqCnw9MKcJOqEywa4vFSFnIOvlY+PIBAxOIFDCnOOHiBPiOA1gGgIDFpiZPeOk99gCIYF8+N3Qn9rR0CQPC/DwG0PEVivQWSerxA39SmyfW7hOqACWrZ1WSw12/9sA1xKfchUvdaWsLUtK9vX0vC13OhT/fTUtlo7pr7fRAtndPfs8qSM7eKDFB28p3zCWqZ8uMv9NZsSts4g9Q0GZUrM2rKSBWj5oEs9Set+1KxGS8+EY9rvraRri6J25lT3yngYzQkcgbpuBEKiEcXm36f4nVSUKSxJdC4lS0vAomMXkQIQ1YrEIhmUshkWiMTEMNjrM64ZM/grK8JfuByDHf6u561pVVSbAlLgIO1QFgb8OahORdqbIl2I0veoEdfa7ggGPJC6okTI6DVZHY2TtgVtm5bNzyytQ2JNIH/13iaXkR6TwsTBglnPAlysBgYsRJnr0Z9j4Hdfk9WlBRA1u60A0QRW1gJYJHyZbDh0YRsL1yEDk41nXti9SabFjfP/jPap1QZge64lAnlbZ+liqbmw7XGtOuesBQJa73/Zp25T77aAw3sg7mKQL6pdfJCy5AG37EO5r1bH1AtkX/a5kOKGC2gxMKmBp9pLP1dnA3Bs1DH1fca0022xLFxnY0CwrhcriAV4Vq7MSedHrImcZMyeWIBidSaO64zOMWNic5coeCl/PiNytVqSvA9psEyXLbggp5rPACGBjR4ZXOj1ypiaE7rpc7b5nGrY8NArkMogw/6NUOBhQIf8BsETS3wE2JAGQMQICrlebVcKgxY3GoWYUvQzSJKPwYChCMSe9QAJDHUZsChTlDQxkpDOrRhAROc51JmYZWHtCrF2RRmUxJIJ2FQWRyYIpO4+70Brzm6bFjrUxHD2uTQi4pIZtM/2pOC2ZuqiWgIOyr8122Zf+W5YTV0NAJVtm4o0Ko+d6gt1e6tPPavV9D/buKXOyUJ0CGeI0DnLsXeiXXyQYl+2KV/naV+CuYfcnrt86Ze8IDUgs6QO+9K3/OHltpoQb0lkQmpKnQKfXBBwoy3tmWuuxOWyxCzKhuYEyHlN+MT5nmh4rs6iNYeJunPEJZDzkzCIsS6P0JFhUwxIoQxOrDg1u4fknogLJTEx6r6hzMJkACBalWj2AYI4zMeEPLi8lk2RNnL+dHejAhWdvXO7FISEdG65zVHbDMmXIqAjAkHZInsfOsC6dPRmRZOfhAJH88CAsdQ2ym3TtYUiEcKeS+XcKiBCfi+TgyXGCFoHzoarGgkFeFFvbI4MohhTNmH+3DG7sl6LCylsPJcWhLSe+xrbUtaxVEA+qW3Z1rWbG9DeNnJzVcBHje2ZC3Geaof2kdscVx5fbivb2TrmPEDQQhtAGM6Q6+Qsx96JdvFBSglQzoLUWwzKEhBRzlLK9gH1jmApCJpqq7WW5qU028Et9JnXBLA15qS5OnG1vbWZJaEaSmwE0Em4qv8MK2KTqgXnU8SNghPEmHKXAJkd0L4hdDQCHTHN9g0bYscP5zBKTU95MNf8I4l9IBqBFC4DCS3OdaaoG22DKx7JyO1khsbcOwENKTlb1PV4DMPiTd0CQmybkz5GAZEwLm7g++sGOacCFL31xA2wbIp+jh7s7pLyUVkUBSvE+1moS+l+BxHQ5uWWxKWTkr0NoFW+1zRIVE/5jiqjYtlV5/gSvDAr6zWqJqBrm2i1lj6rVi6ZZQI0N5GWqQGSkr3Qaz2N1cBHOQHSc9VYmNr5a5NHLTPFULfuWauPbbE2c/Xt7ELYxQcpQJ0xKbctefht2Vr9JXKfYjlq+1qzkFb5qXNYKwWxDR/3hj98Km/KBGgpqe6SDh+tRmxp9I12l+CP3TjUdQI8GtE6qTwZtmW8Ho4mWhslWUssiDNaEkl0JsBE3R8KSGwkTcpzIgwEkAGGMjIj8awBJApUUl3AiBXR7en4pBUxxxjmRYFT9BjpTbJuRX9r+Z2EVRm5tBKgKX6GEDOTMTDTwnUBFDh/SShZJlJmhC8sASL7G3sIS6Pn0Tpjuv9uiKNnJXZ6XwGnywIMMTMqvgP1HlgHk2vF9gH8bpSuPUSNi+pzkj7dbyPJTFvmROO1z1NWTQ5nPi9iYab6jrLebViQKbAx1UfWAFMJQKb64dq+EpzMscu1a7iFtnP3nK9dbJBSo/pr34Ht0HSrrvIlmZtV2DYuKXNa4dqc60b2xXLfHJ08amIBcMRqnfaIBl+SjrqWiM05oPPZpWNDiTWJlwkx1gid2GdXUE7GZnKX+MyaRAJSiK7RmFi3T2IUgMwYSJmcC8SAA2FStFzSXKAACFq3ZTAIo5wqCkpGYEJBgRPwoODFG7BRAQ66qwQzICCt76Pl1eUTIid01fBpicqhFOnDLItea3YHEQLG6f9tOyhdp4Qg6zPSMZukjIo9Fo5ZFae5UDxSArlIUSKDpO3DIAno5KQjsJxDkmLn8+KGxIuUpoRwGl5dpNpPq20XUWjpvsYJsXhlwqDH1MrG8v20E4gycmcqcrBldrJiWMoN13WNLda/rT64PEeLjZ5zx7TK1fpd24byuFsYhjzgbC6bncR3bBcbpKiVD+4UYGl9b71ENbO5AvTzHLtSvjS1tgzDuFytriUi2da2qcRSCwS9NXePBSbaQW+VIEvYE3ifI3ZsIjYrkoUBiCYtPJxn8NF7hM4Z4aiUVxCiQATIAlhIhlQyoGGDPYFhS5QhgGg1TFlgFC0zAjgGqKQBzfwM6RzmvNxO+VwAlKRvIdNOrTvm/VnIK8yFBSfgbaHXRpj2ADlvS2JVNCoopmPtNSXRKpgdcoO2R3QqojtJUUeRWA8jgCgBFkJ27yTgxWxLgJOVmKWOlA1XgMO+Rwyc2I2GKO5MgCgIsDV+M2G6UgZb1+eEcMqorNfYWLQSyOB7wSq7c4sb1hiajQmFtVZkX61cK+1APlldyzFu7Hh7C7QsYULK+mv9sT3vVH1L+hmt/xaGIO/sfO1ig5QYk8Ygfa+VmXoRat/tcbX9Nb/w1AtTvpytMMNyBlOzbfMUbGNLKeaKbeSamCysmhGXs8Q6B/Q5rDiFFCtAGaXCpxw63LmkNVHWxbpKyuRqFmikwU2YkSDRMhZoJBeRAQQ51DefxwIaIA+uiQkBMvvQmW2mLSquVT1K0nEAgAMGn4EBnLkGIOk9Ul1u3AbVp4yAReS2jNbZicggCPl4GphwUZEvCGO3jQFZDsxgBZLzCTIKxh1mgRWfUpkpjuBJ1zkw/ZKinjy7hGjgRQh1jaII8BQ0ID0XDHwihy+v5Fapnwxg0KLXp0BFmDoamFGJAGwyuGTFKst8KFW3baycPGPVLM+lG3ZqwmGtJXid03WUVgMuS7ZNnWOKeSnrWOpyr00Gb7Ht3D3na2e6Gz/wAz8AIsK3fMu3pG1HR0d42ctehic+8Ym466678FVf9VX48Ic/PDruoYcewgMPPIDDw0M86UlPwrd927dh3RKtTZkFEfaf3QfMsxxlqPJU2daxc+0cDeLmfGUby3ZYq/mwyw7Mdmp2+5TWxHRgTb3KqBmVxdZSmwgocoqk2Sa5zJx0HUj+Ya8H+g5R3DsbidTUxeM4hDh2DrH3/M+7dExy17js0kmiUwU1XsCAA0JPCHsMTmLH5ULPxwbP20OXB+D8j/UvoSMMe1KngA8tEzzEVTL+FzwzF6olCZ18NyxK6AxQEpeOgo/QSXvlmqLndoz+7cn2Xsp6vj79HvS4JBxG+quuq+hNO/R7n7elsi5fi34epH1BzsFCZLlWaWP0xOVG7c+i5XT9yqyYbLz828hv7gmhcwidMwLprEVihs0h7nvWKdl3wzxf6blTZqHn55I6z5FAXWeWuBiDEP1nmUTLKLbYxUnGcQqELJmE2He+Zkv6t1q/aZmoKX1JWU+t7BzrPNWOVrm5vv4WmC4weJZ/O8t2aiblN37jN/DjP/7jeOYznzna/q3f+q34hV/4Bbz5zW/G3XffjZe//OV40YtehF/7tV8DAAzDgAceeAD33nsv3vnOd+JDH/oQXvKSl6Dve7zqVa8629UAdVZlietmiQCtfAFasxtb3rqELMsx54KqtVOt1KC0/NVleXtMzVrJ7qbKpPa1Ohw39uurMNboTgBkYaPeLxu9A2ErfGZOYuegUSw8iGUxrE20NmJOIIPeiCWgBGSUIeDtmYHgdukBm4OzjWax7EY+BxLQUBdIPl7Eux6wotxYuGxoHUftCD2l/doem9dEy9ZcPMqUcLZZO9OV9ko9FCOC48y1un90Hp/PpewPDQxm3Dpfp4qQVb/j1nHMSkUV54pAFpFddZ7Lpt/Qus9CROgz2KeBV4amFAIdmYWBNFhdiOsgq+JGUHRZXyLu29hxFlvS6CGfdStxzeeFB0y4UfpM9rUrQu1boL4lxi3dPRuMTI3NTeeusC1lFtiWa2apzq4FOFpsytJ+2U5MynNsM4mMcdxX3UJNSgTxshJnOH5n2U4F2R577DF87dd+LX7iJ34Cn/AJn5C2f+xjH8NP/uRP4tWvfjWe//zn4/M+7/Pwute9Du985zvx67/+6wCAt73tbfi93/s9/NRP/RSe/exn4/7778f3fM/34LWvfS1OTk5OdxUtpF7ur9m27pqa60f/lTblJqoxPVPtsOdpCO2qnVVrRlWWbx2bmrgFZV3pwOF5Rkp9D/Q9g5MkZnUZnBCJYNYza6IuHWVOep8jdzqHIGxK6AWkdMocKGiRgTIxD8j5PYDMTshsP5JlQDKAscyBZWgAZgiGPcMy+E0mIp9L9xkBr7IJPbct1aNlvbAghpFg1sewMAY4JdcR7LWbn0evpctMTqpztI3vV9gzbEfP7NGwT/nc5l7o92Cuo2SU9FpCRynfS2q3G7MtlmlJLJjcsxGzJd+HfZdAkWbhVbYlemJGZa/j5yi9HzR+/lQPJc8ihE2hvZ5ZPxV322d99O6MAUrzNTGMZEuYrvsmk8CV++be7Vzx+G+5r8b0lv2PBSc1UDF3ziWgo+wr585VApQ73N7xjnfghS98IZ7ylKeAiPCWt7xlsvzP/uzP4ku/9EvxSZ/0Sbh69Sruu+8+vPWtb701jT2FnepXfNnLXoYHHngAX/IlXzLa/u53vxur1Wq0/bM+67PwtKc9De9617sAAO9617vwjGc8A09+8pNTmRe84AV45JFH8Lu/+7vV8x0fH+ORRx4Z/RtZ6yWbogNLawEFSwtb2+YFmOsktrFWe6bO1Tp/6Q5qXFMt7f3msvax3jGTdOgaudN13MnbwUDPK26dUXSNgJLYZ0CS3DkOLKbsXR7clE0xjIO6cGIHdtMYVwgfYwZuAyrsttKVYzUf3Pb8L4UlA8ndEXx2EWn6ewVLI9eJAAwFDqGHXGfeboHQSPNSsCppUJd9CTQZQKHXpiBnBJC6vD+1Rz8LiBkBmARKMoCw9YVKmxPIML9ZpPwvuYHETQUyIEd+Q84mTFmfYlxD8JT+pfNItuGRC0hBs3UBOWFWvE9ghfb2mAm07h81R5lR9D5/dwTybvOdwfjdWhJBl89lGAIFWLUyte1LtSitcqVrvTxmzrXTcmXPsTut+mptKvvJWwhYbrW759q1a3jWs56F1772tYvKv+Md78CXfumX4hd/8Rfx7ne/G8973vPwwhe+EL/92799msu96ba1u+dNb3oTfuu3fgu/8Ru/sbHv4Ycfxt7eHp7whCeMtj/5yU/Gww8/nMpYgKL7dV/Nvv/7vx+vfOUrN3cI1b9hlq6cc6XYctamaEJF6lpujkq0L5ktX3P/tARp2vbSbTSn9J+LBJoJiSw70fGCaooE4vi7FcaSG4MTASZ5jRWXwEkyK4rVdXbIiGXN2i+6SF/KHiuzaN4v16MuoMSSyH5lARK40OOx6SIZ1Sd/9VxmcE3lCCNXS0rOJmBh9FnOo1qPFKFjmBoroEXIg31KrGbOn5Kl2UFf2j5oNM0aiWVByHWQOU6jfmiNnA03GoBmH60IYCBO+a+/l4OsroyUvRayoKEmkdP1jDShW+hYGKttZlcapXMEDTuW39GteTFFcnK/pd5IXDeHU4vItnegtUnkJ7oV6mPKXJsigoD0Hkbx49AgP47bAw1BMvwSC3BHC4kSaiJbazVwMmVNpmWpcLa0sp+wEYtzNgUoWuWBdr9W1lvuK9mWJfq9Vh23wG71Ksj3338/7r///sXl/8k/+Sej76961avw8z//8/j3//7f4znPec5W574VthVI+eAHP4hv/uZvxi//8i/j4ODgZrVpw779278dr3jFK9L3Rx55BE996lP5Sw2MLBFoOTfuWOxxCgZaZutrgZils5W5MmWHYKnMli+61gGVVmyrrVy8KJus7YDLNXjUn991KSV5Op/VoJj09hpKrGBklOeknHVr/g6fBy5EYQCMRiZFsngDbAijJGpcEDn3iHznNiFpT3S/RstYVmKUbI0AG4Fj67AuqHQeGpdVgDNKlW+A08i9AgN89BGOyDoZK75Fxj+pL/TgyBhnQJI5BzqkrLU2rb2eP5qfXO+DtoUCgziSbLU2lb6TdCRu0GuQ35NMQynmdomeyKWoIrsII/+2NMgrM+RrV7DCyeCkPTLZJzlP7L0sPjjk3Cnpovj9i45GUUAEMOhOJwuZUWwAeJroVqZsMuKu1VfNTVBsnUs0G60JXcvlsy1jo8dPTdJqdbXATa2OC2Slx2B/fx/7+/vnfp4QAh599FHcc8895173edhWIOXd7343PvKRj+BzP/dz07ZhGPCOd7wDr3nNa/DWt74VJycn+OhHPzpiUz784Q/j3nvvBQDce++9+K//9b+O6tXoHy1T2uSPM/VwTiFpnTXUXowlItop29Yd07Jau5ZYybK0/LOG0ZmjoqtJqixAscJZR+zDV4CiocFWDJtExZITwkFYFkLYGwMajeQAIBlIdUA3ET3IwGSUOVa+az6TEQih8SBrtynrogNpBgWUgARCzvOR2iEsgYbXhi4PqsmNogM5MAoDTiHEUtbmYRkxwHptWt4CioicXdYkeLP5VNJ9MNfr7DnIHCP1aFsTEAuGTVFGShkg2x5nAIN5RggxrRmUlgMwYxFJvSq4pcAiXq4nwq0hzxyLbBP75DnfSkrKlx7RiMG5VBfWMTNden1wOevsmkAYMojW/CmO9xPAvaeAFV5heY1RKq4SwDfM6lKIiFdPn3G9jhnNBWxuvTJtwHzZpX2ifbdrx9i2btPHTgET/X6b2ACH4QyBs3psmoiLfdd3fRe++7u/+yxNq9o//sf/GI899hj+8l/+y+de93nYViDli7/4i/He9753tO2v/bW/hs/6rM/C3/k7fwdPfepT0fc9fuVXfgVf9VVfBQD4H//jf+Chhx7CfffdBwC477778H3f9334yEc+gic96UkAgF/+5V/G1atX8dmf/dnbX8HUw9l6UVrHtGYEdl/t/OV5lswGgDybadU9NROZcjctVbS3onQWiP5G5QGkrLFEOXpH/Pmj1Oh6HX2XPwt7ErscBqragSSCNICD/yIxGhZYqGtnE6ggJ1gz3xNTYQb7lDNEQIZNhw9kF40yFENfABXLJpB4RZxpg07GA5LLpIwKigK80sw7ASNTzlybXRcn6CAvYEWBEOQzSYTRKGOtwwjopHolcql0f2n70hNkXFBkwQpMIWU4CJItNub7GPO57LaoQNGRYUdIRKcAOVlXR3Ky0IC0phFnrtX1lPgdjQADHNHmkM3V5gEQAxXyxLlVJAEdJOongRXJzaMZaOlkhegEddVWVwYy0K8kgqvlU5lKw59sTiA6F+nDFbePL9mTcuJn+8raRK9kt6fytbRAU6svvY2AibXzcvd88IMfxNWrV9P2m8Gi/Jt/82/wyle+Ej//8z+fxuPbzbYCKVeuXMHnfM7njLZdvnwZT3ziE9P2v/7X/zpe8YpX4J577sHVq1fxTd/0TbjvvvvwBV/wBQCAL/uyL8Nnf/Zn4+u+7uvwgz/4g3j44Yfx9//+38fLXvay0/8IU/7R0zAi9pjWLKV8+eeQ/nn7RLVdrfDiGQBkO8Ayhf1GZwqAUHSsI+DhxonZCv3JqA36nWgUVhx6E+2T1tHJIbmj1PYKQlRfQnnwL0FMKm/AjAUEQdkPvsiUDTU9TVquG39P5zQ6FQtwdGCPjm/PiPEQtsO2c0MDo20Hxm4jZV0sWFGg5YDopOKB0kVwOyNrKnSssO4hC1CCARpA0snoPgp8mDIxQeogBTs63gSMgVfMnxMwdASY8GareykBm15Hqlu3RYAT0NIIaOXsuAKMDGoKgo8pEMjFHK4sPx67j7w5Wcxgxbsx8Og839c98LM/DIhYc13DMAIkLYCyxKpLTgBjgFKmI1iqR7G2VD9S63NrgGPOauWmGJIlrp2pNl4gu3r16giknLe96U1vwjd8wzfgzW9+80YQzO1k555x9od/+IfhnMNXfdVX4fj4GC94wQvwoz/6o2m/9x7/4T/8B3zjN34j7rvvPly+fBkPPvgg/sE/+AenO+EcCzE1c2i9kNZq+QV0e5kErtTHnNZttJQuLa3WYTVPUQAUAHEI4ygEC0jUz16CE0eAY4BBXZcZkjTbdGNRLMDuHS9uHklJbwfqoAsCQmbN3NARe5Lyishf68ZJgtbUTh68kovDsigCNJhZoPHAraACue5SO8InHA+uI8ASxuUtGMg3JIOVBG4KFwvfNwYhERn4UKAEeIKPuQ6HfIEkQtQuwg0GxNkmeHkexPcSjdB3BC4UtOijD0i+FXlcg4AAwwBhMMBHGBgoi5RS5ANDAhgGBFC+B8rCkDBbiTmCyaSrzMro3db3CWklakAWNQxyKfq+SdZaFtr6LPJeDZJ3BSBdGXHgv6xV8el85D3iiSzNXOreZkS0o98k1ldSbupTSq3aEhZlad9VY4lLEDDHiNRsDlCV92aqP7XteRwBSoBDOIO75yzHLrU3vvGN+Pqv/3q86U1vwgMPPHDTz3cWOzNIefvb3z76fnBwgNe+9rWT4VCf+qmfil/8xV8866mzTT2YJcg46zlqYrPa7KFsSy2hmz2mBEzl+Sw4snVOmdnfStltjYimZ3gWtNhFATUrZ8oKS7nDE4vOLPgnUTtRQE9aqViZEBJGI2kAgOTK8eazDNRZzGrAiQUTChx8AUgAmTVnADRKUqY/hcvjvQUnGy6QglXJKwFjNNgnhkUZET+uK3jzG+jHmK9Lz8F/Y3JXJFHqWgZZD5BEyvDvBgSKnCRtID5WB31tu8/beLCmpNlQN9Io7DnjIIwYEAM64c1vqGUE0CSmg5DcORqizcAkjoGiMCFube6N1wbIMQpiAESKgCf4VUzn4XvlcpXa9ijfie8NDZHBYBKDR1AIiDKI0OhdiezilGeeiBBXBqgscKOO3tFG5F3Vai6f2uA/Fc1TAxZTItQlLHFt+1R/1qp/rg21vvlxZFCGSBjO4O7Z9tjHHnsM73vf+9L397///XjPe96De+65B0972tPw7d/+7fjDP/xDvOENbwDALp4HH3wQP/IjP4LP//zPT1G1ly5dwt13333qdt8su/mQ7VZaS32uZvfp521oyTkKtHaMmhWxWj9uqX+Zmpksamb9nK01RsoU9/UVWRvXSZRXLVaAolE8+m8UZcPak7DnU3hx0qB4QpAcKCk1ehr8Nb27fLe6E7MoYBnRo3k6bBp6y1rElIZdymnSMdErWHChOT5S0rOKm2eU8E23dUDsYgIf+ZoU8AnQ6CKiiwxQ5HhIuxhcSRkjvh2Brx4jAJNyluxlcBYdTIK4IusrkABHMsfnDT23LTog9BFhLyaAk91MSABOzz9yt8l+TQA3ykdj7rv+DSk/S3b92WuzieTSs0UKcO3vyc/XRm6cvvYMWS2UyafiJUdPLyHx2mvafD+61pSCA+9kTSoB8Arql9qEmH0jT1EtS3TpCi5zIU0xF62+seynlgKa2r65Y5eCjAvszjkv+83f/E085znPSeHDr3jFK/Cc5zwH3/md3wkA+NCHPoSHHnoolf8X/+JfYL1e42Uvexn+xJ/4E+nfN3/zNz8u7Z+zi73AIFAf1HX7tgh/iYtlyl+61OYoS6176hpK37N8nkr81KKPS5tcbl5ZlAROfHLxJJCgHTbZzt7ntPYCXlJuEwdodlFlQ5LAVcOJtck6MMvgGjqd+UrbDcuh4lebA0QHp3StlhXQ88lgOwq5NYBA608DrTIZPZImJBLgIhkgIYyA0WCEbsyYRBW3Wj0HZd3HSICqGJdku1ar2+2brXUpW+AMGBEGIwuP5fzmcGVBQh9TPbETJiGxEEY/VLiytE2DrrZMmZFJIc+6fW2uyYhx3RopRBlRnnN1MSmWj8iRPmDWzp8oE8NMBgUg7PHz5AAg8iKFkDB2twrpejXvCoDk6ozewRFxXpVV5Gc9sG8sSn1wxAsU7vVjncoAwIW6G9VsmwpTnnw3rZXAxf6tlT1twsm5idS2/WWrzyuFuLa+lmvncco6e6vzpDz3uc+d7NNf//rXj76X3o/b3S4+SFHTB7IFNGpmH/LWy6bUa1le/25zrlq5lv+29kKWx0+JZivZJpu5TpDBTQIx5axPU9wLpU19nyN4iHLek1IY6yDsiY7AAkI6Z2bayqTkGW2alZMJM1ZQY1w0ClCsADVKRE6ZtyRlQtVLS3UiD8SFQDbY22CYAgteEpjoxr9h9NG4UeRvUX86wrAOo4geF8dsB2DW9iFWjepnijJQyna9AMc3IHai3ViTYT8iKAHGmNqASNnlE3P7R24rIEcJqS5kIMQ+ShtpYx2iNPDLb5pWMNZrtFocKW8T1qmuJYADhlNSOgMWGdAxEAl9Bn0O+fcnKZfWLiIAg6wHFMU1JCsuJ7dTx4gxEDiqiAgUAmiN8SRG3abq/hFWJGK1CVQAVMXohbV0KZsJFiuDcit/Ujr/BCqytlRr0po41vrWkrGZAjvlRLLVp9b6v1u5dk882yrI8QzH3ol254CUUsSqn4H5l2tKQNtC6fa4JQLcpTOSGnBqHV92SFNJnZxrz8KK2dtGmZRB1mOU3l5WLa5RxBagRBXCKuDQ9Oaa9dPoSDIQoLR4oGoUciI0Ls8p53lAsyHAnNyNQ1R1TZeczM1eVwYpozBbYKwlgexP7oiIEjWEbvQ1ASQrrm2KcuWYxMIYDYSNBkpATOuLmdnQ0Z7bmdkapDqlkjUBXQYjrNGJG9cPRNBawKKCPgFNFGic8M2APL43yOc0AAoxH5fOD/PoO8iigsjoLY7vU/q9IWDH/p56XxR8RpgonygYRt7nwKxauk+RH3O3jgmZRk+gowAXQgIz5CJiJEA0LZAkdRQpR/44AQ4xguDSBRKASAMQBrkfKmjdfCfLd9WusnzutiRlQQ0QLAEUteOX9JdluRowmeqbrd1CgAIAAwgDTv87neXYO9EuNkgpB/Ip0FBjKnT7EgChx7coxNJHXIsKKl/UtPR7hanRsq3ZQE3FX6bLL9rR7OAs5WypZ/Wj63aboK1XZkTaqOViZNeOASi63o66dlL0TtIAyOUrwLEsiYCI4IFoBpVgZ9zIgz6PhzIIm0UDx1E8SAOTundSBInWpz+dTXqmgzDAOhNjo7BYAUsWBKWQ4U4Hb2IQouO3ntcjAQ4boaM4JDEbzpSFnG+g7Pohcx4gAxNIdFCgDCjMfeb7L4+F+kT0UuU6YseDvFtRKp9cRDG3k4EAWIAauK1pJWNCyu2SRcAZDKqNw4gNWAMkQiyXdYMBW9pWvV/E95ubQUlUrHXy78eLJ7pVvu7YESeB09coAOTlWk3YPNQFpL9H54EhIOoP7x2zL3JRcS3uHw9sLFJYTBoUnJTid8uMzmakbbk9LFM8ZS2A0mJnWv1ry21Tm0iWk7QlGsAY6yLiHTtxYe1igxRgGmS0UHeLdpyqT4/3vv1SqVnQ0XIlEW2+3N5jRBtPWSmQs0CltEKEV87IYowgUO5wy5mdrsHjnKwC67N7RwEKwMwK0Tg5G0m9aeE/mZ2re8dlrcEoMscha1QsUHDmswINHeQ0AVhic/IYbV1BZbhv2qeDmw7yBlAoezJyLRl3RTossQjmr4spwiWFFmvUio7Za6nEAgWlh5QNUZZAL66LMpMnRBdGYEevOTUqFPUr2xKV3UJmcwhAIIxdPMygKDijaHRAAloSAwHkhG7gupNeRTZGJ+vtrPNvASiokdcm5FuQ2m2BpEllAgCBiAGRAk4FctoWYkSYbpEnZm0Cg5KUAK8DMBCzLx2lDLaQ8OUYCUQRWEt0jLKJkVkYBcnj7QGx7/i9W0vu/hilTlcFKghxEnzUmJUa4xKXaE6WhCfXrJaPqabhs/1va8Jov9c0eUuZl8fZeGWEmbbOHL+zbBcfpMw9xLUXrHSptF6eso6l9OLNsjJZk26zNrcf45nXKCdKyyduk7SRcc8oQFFQ4AlwjqN3BMTwWjxuBE5UW2KjKrgxMG6WDFByOKnZD9mHMXDR7RZsWE2HZTtGa9q4Yr9Dcr8QVHwZc31F+aRdGaQZxMdbQAOAgcWa8rEuim5E2pPAg15P+V1AgbSFFKmBgQdFyp8H2WcfgQTsoqyrY64Jst3UmY4hcDgvzGdTLBJALh8z6qOLxHIcesy6Iu2BRoDDIYlmM8Aw59JLUjCpIIeQRcCxaJuwO7zmkjZSf2Nun2atjdAE+ZTbGSK7eNZB7jugzGCE49+RetBqQFwHWddJLmRd6FW8Aw0OnKE2SBbbOlDh+0HVkat8jzVSr56occY1XAMo+UTliae/12wOmJTbpyaeur/WF9vjNEfNLRbPhjNqUs5y7J1oFx+kAGPErzbltyyRevlitJiVGhMz9YLO1VfatqK20t1Tfi4sxsh6EgCzGhQFH97L0vQ+C5OLWVPsHJRmjZ1mmQUDjW4cVsz1S3s8kmYkMx4SEmyyyQJIob8A0sC/sUCg1lmCFjNLtmG2KYJHz09jcDLSk8CUse4IQhapyizcupf4HsmAronUeqFwEtOQtSfJjWTEs8pkJHeOiwxm1mSEuHyONM5FBiroYhr0lSHJbTcARm9loDEgMsCD2RbKdas5cFuQwUBqt+KptH6PlB/A7hYBo8ldJucOB7zNrcxPEPmeuhWfQtdVImGXKCKt7VTmh9HVl8noXdjtR6JD4R8zOsgqzCQRTMj5VSJ4kcPA+8lFBO/YzaMuJPmJ4hqiqSJxN5j+xvvNDLXD1Hs+7r9aKyJboJJ+49akam69nyn3dWm1Pk2PmQMStXMs0azM6QR3dsfYnQFSgM2Hs8aWbMO6LH25WsfXjluifynbPmU1DQo2Z1i5GZUZWTlLU5EskBkU57KLx4IU44+Pe50M2ITEjmhYsbp6igRqaSBMA2J2p6TP6iYx7MkInBAPNhsr95rPiZHpMGJUkrvG5XOkiByZlQcFMsjH5c9xBGSSziOBnsw8gBicWKbDzvbZfSJfvLIUFn1R0pUAYMDSycAXyYCaOAYj6jZSlkaBjrpnFJQooSDtIhvWVDITRpdDkhQOvTAwq1y/zXwbOz2f+U16pDDmxDh1FtBkYXRNSBsN6FQiQn8Pp/eZ5HkjpCghBTQp6quj1A438PPlYkxJ4TQEmoKIZonBEAMR8MnX8lt0jjEaETAM4ioiZlNsn+Jc7n11uyanLRmVdIz5TcIpRLS1CMDWiurlcVNWtqOVrHKqH6wBmiVuqtIlVd5jLXsLsUsAIZxB/HqWY+9EuzNAyhTTUZZRm1Kr19iXsm47SzoP9XiNBar5eq1tkX22tU7PRhZZYLwOz16PlEVW2+ZdalsUt03QaAeTEMsmZEvuFC/CWSAzKMa1w2UqQk4LQKS+VEaYF6sHSaHIev2U67F1BhGfpjBnPReQFqErdR6hz2nYk0CVgGjyiAAyiK8FuJCeJ7MuAGW9SRptI29zAFSnosyGbgfGTEtEZljU+gCsXQYt8tPlyB85t22H/lZmczIfWaeCCBv2nDQsJM+YB+sylDywTE1iTvhak9tFr0GBkjJfQXQw2pQCLGr6/STa1XT8MYNbC3hizHqVkr1J6fSD/JaeoHlVMjCShop7DpH4msRtlTUt9kEdQOoOgjwrMXIeFcfbSd2oNUbFTiIWrKTctKkV0Zf0X0snWEvqmHK/2+9TAKWVMbdkl8/a5lPYrc44e6fbxQYpMaIKOu0La1/KbURY9oWxL2gJSmqzhlZb587X8tu2AEiZNttE4hBy52aFdCNRXQOgsHunS2CF09e7DfAUOzfOgeIUbLB7J30366wkTYRHKmvFq+n3lMFqIweKMjUGdGh20yiD0ohBEffCBlhSvUmgnGlWXRFmtp8GKJdn/KU7iWfWMqDqAO7Abhwt42PWn0Dqs4AliTWQAA3ty+83ZDCTKnQAdSHvHygdBwDkI+AHBhY6NvgADI4fMwtuQsxaGLVIwujEXHdn3FSBEkBM16UgyGdxKq1cFgGDWN9DzG4EB6AD3JoyEAOSoJkICLLekLImAFKUlr3/CbQZMJzcNMLg2HBuy95AI4JdZlUCkFZYVl2KG2SdngEpJX6KVtLfUVmn3uf2hIHdrHrjg+azkXdRB1vLqMRQByXl5KKwUhA/PtbVP5fWmsDVXDK6b6OdjbLlcVOTxZrpPrsmUjmRK/vunV1ou9ggZQlKtpTmUjcKMC5rH/ZBQw8m6qixK0vOOUeHNjqWBDwkGqDMWGlBzGhf6epxJAnasgYlhQ6rictHU9xHCcO0mhIevDaTs4XOhBu7rD1JuhPjxknAwyHnPSHa0Jxo+vc0Y67lQtHvIYOTXLZI7+51RM9rxtgcLMnV0SMPTgnMyGA/GrSUaZC/en4FIra8bhdgoSd3ewExEoMRBSEu/yUXEeC4LgVBgRiUdDG7dIiBjSZaQyBECYMdiV4HA1CcXkg+Pq752AS8yvbLw0AWXDkgdAE0ELuIBChoRBGJXid0EW5NieWgCAQRtGqZBGi1vRGyHhFMyDEy6yTnpxU/g26I6XkB5Lc32lZ2S+brUrATSUBWioSiDFbkmScBIjEg51LpATqx71n6DxFDyrFC6Pndiqvs+pkxG568mE2ZWoRQb0Jptu+Zc4vb+pZOzpa60mv1lfseZ2CyE86er11skGKnV3OgQW2O7Wi5iuYefPuyTy2D3jr/lMBNrZEKnw+vd1bjtXwmZmYA50FpARQiSd7G26JzzKDo8T6HFwdx9dQASnIBKcCQjj5lg3UYuWkSQNE0+Q4G6CAPROXAVbznrIHIA+YGG+KQonE0mkfdDiENSobNiDFF08RysFbtiJ3hy2fSfCVRgIH+Nbc6IBEPIIrwHYOUQfxYrueROAZKj6brdZTl7WpEgOsCiCJCpOyN0pM4FoASAUGijrSNlrWJlqnxAPzAic106WI9ZURCCdG6kJzZbliQ2EXQMeUkdLb8yB3D98JqiBxRXrU48u/khIUZian1vnYikoVhY7SMAKJhD5JMTn5jAV3Mvgj6ifzsupUyK3wfIyICyarJ4g4iR6xTcR3c0TqfUBks5zKJJDoWBsku51ApM9Ka0GQi2pxslFYAEpL3d7R/LjtrCWLmwIItMwVoSmBh+yvbX5Z/W+740lJ9CyaJ52QBZ0yLv9OkjOxig5TWA11z91hG5DQalpa7x57H2mlozCkGpTxvJRy5Jpat7qulvPeSS0XONXLxFOVTBI9mi3XKjFiRLPJfZVf0+OSO0dkzjcCHZU7ggCDuIqspsdoS1bPYRF82gmcUoQLD3qQyZuVfyoMgsymUy6RBNA+mkUJy7UB28e/DAz+6iDiQABJhN+RcSaNMkZsjI6xzSJ2cE4UnUYTrGGI4x6Oy6yJCIMTgmGnRscwIbGMEnA9wElrsXMQwEEL0cN2AMPj8WgirE9bMyrgu5KSoPRCEySAnQEiuJw6irfCyfSBBCxgLdQfWo1BirJgdiR4pcVz07GpxxzRyDxK4DkMwIXRRcq2wbsWtMBI9K4sWHUYrLFNEiv5RD5uGs7sVEgtj2TkGTOL+kVws0TNASStqG7BAISKqiNblB5NWQ2Zx9L5YZnavl0te8Y8SHN9LayUjquc0Lt28b3Ni03QD1fq1Gngp+ymrNbGgoyxTWoybAGkOfJRgpWxDzZ3UOv/OLoRdbJDS0qRYK9Pl1x76ls9U91ubAyOloKva5sY+BQnleUqg0lLlT/iZR3kUAjLwIMcMik11rwwKEbt3pG2qQUkgBUgARTtqm9tkHAmD5P4pfzOrPcn1UNYXEMYL5tnzFDPmJMC1xzok1wMzJbo9poEMyBNlHrSEMRAh5egfYNwYut+yJ5TcIUTagTMI0URgJMyCghC9kBgJzgd48IDvvDmGBoSBp/xOQRCYYYjykyaMZNxCzjGwiZEwCPPhXODjSeoRkEOOWRlyDJxYr6QgCukYRMqgxQt9Ia4neDBYUd2KmrrKtN3K+AijYzVAsTcgYjAgVU9vo64UqEp0jyZfs7qUHA6uryClzPTqslGQm5Zf0PbbZ0A+MngV4DnEtE+vz0mYcnJ/IrJrlC9cstMSEBixKABixrLj6kIA1msGKlMRPxXtWb3shBthakmN0pZoRkqw0mJfSg2JrafFyJR2mwGQiLNF98QdkzKyiw1SWrbUd2lReetBX+IvVZtLGjSV1bGWyrkl0K2dq8KoANjotEbfdS2evT67eLzLdRkGJdossjqgmCyyeXl7ARj2dpKwJdoXOSSGQvOfjLQmitM02ZphUGohxrqdIh8bOx48dLYeZRsDmDg6JutJkNOjC0DhWb+4LUwq+6T7iEBawA9Io68yClH3U8waEvmc7qwAjhCAGBzIsWsGAPr9AcPgmDkBPyrODeaRCQAc41Vx3zhC4e7R8+qjxnRRhLAzKa1+4PMraNKfQuoLwUE1Npnd4eyrYSAZ7IVZschRdTL6WwlbpUsCjMSm+o/G6fOj1doQYBO8RYecS6V8Hvj2jFPpm2gdipCMt5K0DcgLHAbk6CFbXtsvbY3eHC9AhpxEu2kbo2hV1tE0MLKbShhJDIGft3Tje9DJCrHrgJNVPq7UkBkrFxCNw4SwpWRYrNXClG27+ST2xJt9q5atsdYttqTFjJ/GznLsGe1Wr4J8p9vFByktN8kUUCk/W7rV7luyrfYytPy7U7OSVmK22vbWuTaqNm3TiJ+005m1eBqJ2sSic4gS5cOrDlMGKJqoTUWyRJKPRL+PBw/VrGR3CUaz0AQaFKBY8JJAhIAbMgOQTNxZa0AIXRwxLXYV4nycsCVD3sezcnaNJL2JtkuiaZI4VtmDLuTBU3Umcpzr85yK2Q6tr2BPXITr1vK76cwYaf8wEDo9PwjOBWFIBqzXHr4fEkvi+wHrtWMizIcEcgCX6mZgRAiBzw0gnT9GgveBXUlyjR1x/TEC3vOxCNzWGFwa3BWgkJNBPxCoD3wtawW/MQGVFBWUaAiw+0Z+I124kULktYI08sc8UtFLplhPY0AyAFgXeNlGf0HwUQSLdQdphTI2+qyRpMaPkfVJJBlpNWeKAiKA9SyKDUJEhEQFqY/PEeJ+B0cEWkk2uhiZsRxCcqPyr4wMVNZrIIZNVy0w0qqke8KIdrNsYSN9ig1TnrOS9ai5Ycoytc9qZRtaYGbOhbTk/DfZdsLZ87WLDVJqPsfay9DyU5ZlasfWZhItxD/FdizNpzKRRXZKa2KtzDxZzSirGhTNg2LcOsnFAwYo6Bzivk/sR9pHOZPsOLW9akzG4cXlYn08UOV9Cdgos0K5vI4oQcsrMEmuopjcAEHr1p+2z6HAycWDvF9Zk3QuAmtN9KAkSsVYCOuD3E7RWaTwXx5PyEcDEMBRHJTdNP3emgd7MQUL/DNEDAODCnb35DLODfCiU1kHh64b0Ml5QmDXUNcFASAh7dP2hSi5R4jQ9wN7HQSsKLsS5Jwk7XXC8ITgEiOT3DYOIIECYU1JCAwCA5RBBL4Kstbm2S6S2gEMDPknJ9hcLqPIqxRBxL9fcstYF5L+0AJuQcCwL4yILG6o5w0d4KR89IBbxww+opxDo5WIgQs80grSEPYwcMoTBisKhgOYSfGULjPseTh9dwez3o/Nk+I90MX0+CegUhq5cUhuuZsqkT/Sp6TtU+6emoBVbcrVMgUMan1wLSKyBVRuMxfPzm6eXWyQso21kPychmRJHpQWNVnTlbTEaQsAzNSCYva73U6+QOUKUDQXigKU5D834AwAPPFaPHaNns6lPCiw2hELQioAJXgaZZDNyduQ3T/GxaMCxtAhDTQ2aRug9QjwiOa41A7RNgzEbiDt8UVQS0GSizmIawJ5qq5aCwLIh5FgNAaJivExgQvygBPgEgaXBasyqMYY4X1IoEAHe+ciOhewDgpGub6u40Xbh+DQdVxPJ2Ch9wGrwWG/GzAEwhDULeTQ+4HZF0fwUtcQHJwAG08RQ3DY69bpXOuBfwwtw64fgnchgZbOB0QfEIJLLIv3gZfmkd/Eeb5/IVJya0WXxcMxECDAxeZGwUAZYKj7R0GbYT9GYdFKhUiWWwJGg1fw/L64FXKYsfz+qldRMExRpCcpUojb4ld5yYKEbEc6JQHWMcKtkaLVotyHxK4JALJJAMN+B0cDKMqKyDpBCBEIgevqPLLAVFA5gHHUj7oD48b7r9ZKpb/I1B1dm6wt0Ym0gI3d3lrhvTbRbGlXpuwWgpqdu+d87WKDlJpwdopRqR1/lmyxc+BiG/Zkzp1TKPM30t1vHEcpa6d+V4ACL4uMdD53NFaHQhJOLDqUtM1lt07KgwKkwcKGCQfzZDHzQelzYlC0vzUMil3oLyV8U+1IitCAieaJIyCUNAMkg6MCIQ0/1vFCRZw2H4ge3IWspfABpAJY8G5yPGAkfQnFBE60z/TdAOeUBYnyGdjrhsSAUG4OOh9GnRtvG5i5UMGt3M/ehwRWhuAQIwOQzgcAAzoFKcGh8wPWg8d+f4LjVZfa411A5wO8C/AUsZY6TwbP4MMNWA/KqDA4Wg0eBKD3A45Dl64PYBcQP6YEcgFh7VN7u25AIMfgRsOcPd8PjSiKcJLTBUAgQ6zwcn+IAhYCJTGsHfAVeNhIHj0+9Pou6AMpf7V85GeLU/cDWPOroq4lZxgXfhYZ1USSnCtWt6KiW0n6BsS8MKEANxspxKtBO8RgpZZ50I3egcBCWhAhrk1WWit+x1CdwACo9hkjW6JP0c+tVdpbfd0Sl/nSepa4j24DhmWXFv987WKDFKCOtFsuHLUyG+ISf+VU1M1SoLI0H0ENsJhts2t21JK06d8y3X2K0tEOT0BLIZQFwOuVmDBjQBiTLutMFJBoZIYVySbwYBfoKzLIRgtS9DJlJE8J3hRgICd3S0VTDpW8Bk8KQdbHQoGJHqSiWGVQCDygiNZEE6YhSvIzYsaEJ9JjgKLuEdaHKHOSmRKNslEXjDcuHgUX2a0igAIABIR4cd/0fkAcJDFIBwyBWQ9rCkB8t8YgTAj/1BEnaw+nbhNp3xApu1E07Fk+exexNoO/9wHrtRcAhqRh0ce067iwMi4pSknvtXF7kSbBg/zmmrQuPS8xAQxeTJAyoJTfl7PAMnOhifZSVlpJrx86CNshoEKeCVojJ3+LADrB3cf6/ORncMSo6GNj9tvVmKHgPEbEPQe3lqR7Q2T3juZX6QXQrYbMpgxhPOHyThoWN1PnV1xANda1XDE52VQm2jlNXFlOTUHFXLSk1r2NHmaq/6u5gsQVt7OLaRcbpFgasubCsVaG7bZU6C3bJkRPz1fun8pHsJDRmUzWluqqvJESP1oFKMbfHJ1jgLLXictGE7MZgJIAxTgPSrlAIBDzNpN6PotZCaX2RMGNWnILJe1KBhfRAg09XpkVdQMp+FCXUHlMWsyPRy3qs2uCfI7K4dvDuUnU3ZNYFfAgrbfSuYC+G9hF4kJiRzoXGD9Kj9mnY7JLxjsGKieG8lVwYm0IedDwLsA7oJcyJ4NPLiYFR54iBjAL0ps2KauT2ifiWWZhAlZrjxAcer9G5zNK6eT3U92LghFljbKmBqN9ykoxECOJghLQEYkZFnXLrYTGYJQmOVUiM12Sf4UkpX7SJYm7jhRMsAcMYU+YHvnxNbJHxbE2nFmTw0UPDJ7gVpGz2ULqk2P4OSVe78dRZvZS2BJSODTAQD4Rl2sI8AjMyOiNDUF0NrmviD6zaDTIe6up8wGkTNNz7GrFmplqaxlpFwhxZ05W396KeNyGFbfH1s658H6ch+3cPedrFxukAGNGBID0inm/PvAtpH6ah9cCnZq/tnae1rZavRPWnA2N6qFNoOIdqO9lTZ6xxiSluleRbFq92Il7hhA7YU5MxtjYZZYkmMiK7KIxgCZtQ6rH+jtsOvuoYckKUFIPHcd1aBWSiIszwMJEDOWBY5TNFCg+a3p62ZhCimUyL0nWQiR2XQzsutBcIzGaQVgAh3cBe44ZC2VIYqT0s3jRoWi0z0qAQpTzeMcumSE47HfrEdhxAiws2NDt3gXsC9BQ8LHfrZMmRcsfdGuESKNtvYAQdS1F+bzXrRNrFCNhJWHRPQB4oO8GrAdmftbCAAEQd5D4AsEAUVmVMDhmX0ivW/8DaG9AOPGcZ6YPDAjV1aip8V0GnLSSbUFZkphznBgQTBE5G/CasrtH1u4ZAZUBvGjgwGBscIDTfCgCpBPrJ88uDdk1lPKeRH3ABYyKhkt1WA7gjMsdAzq+lmEMVEaMipdEh2EU8VMuhSEPBHS15HL9LrWyD0n9ivZFJXO8hBlZGgXZKm+BhbIxU/oXW0cL3NxC24GU87WLDVKi9Aith7HmqzwNsm7lLKmxN63MsLV6WueasTIfgtpoNmX91UmP4oG+Q1QxHlEWv6qLp3MIvc9UtWOmJPjMorCYlRJgGblopPwoFJQy2BixJm78z2pL0rUGpLDhdJyCEOjnDEBCH9PMVTUFaXXiVKkONjLIKaDxQRKoxZQNVt03oAgP/kyO6XoSnUkK4XURvR/gXUxgpPNDYk8ygOFyFhQ4ioAL6MXtQmDQoCAmREpMiYpbHUX0Zn8CMXIu7wNWwaOjAOczGDled9jv1lgNPm0bIsEFt8HYKEDpXACCwzoSes/gKQApekm1Nqp7GWIObyYKKTy668UNNPA9dcrOREk0p6+puOSSm03DlVPkUODvQ86/Q4KsovxGyXUkFvnmSG6UyO6fgXJ6Fl1oEEjgxgVIenp5ZYJ5PuW7AnRdiFDQ3SiDsV0QMRIAWWU5dsTszACAXEq/H8HXRwCHJjtKa/wAHQhrxChalwpzqkkb7XIZLbMTnllhbc29Pj7xGCgssSmgs6SuqnvHgJup9u7streLDVKA5aBjmxenhsRbrppSfDvFqEy5hlpMz2yCuNzG5mzKJGxTH3m6NudEd8JunmAWDEzsCeVkbQm86IDQcX01EWxy64xACAOfYF043bhsyi4q9QSbq4SQ2JLoDbMi3zeThsnfVIeZDetKxEkdCXFH5MGXb1E0rAl36urSADDSneyJm8cTgwxlK+R2cXnKSdSUIdn368Sc3Fj3CcA4ivAUeN0eAJ5Yk3Kpi3CIWIto9njdYQ2HjphJubHu4Smgd1JP5LoCCHftHWOILrUPAHo51xAcjgcv540JtGg7vRvL+pQlOujWOB58un4Xsu5GGZ312qVHz3dZf6NEJ8V8n3hwFYaLAuLKYbQ+kuMfOXZZ2xJFdJt+fmHRdGHCDYGsAhqpL0odBCA4xvUxIoNdTYsf+NlJafgTe5I/p5T68j7YLHCk4VCO8jxLJluhc/zInhhqR5iNSMQACxAqJoo+RU5m0/Ib9qTGlpS2aIFCNefyGkPWzgIEpiaRS/r2VjseB3CyY1LO1y42SFnqnywZlTzaLDt+Kmz4PCjFKeYFMx3I1MJiqkPpfD1hmwKWyIN76H2K0IFk/EwaFLMmT3bZ6F/uiG2IMZDZkxRWTJTdOjUgk/JdIJUbARFLyevxohHR4Ju0qJ2OCUBe0Ve1KSrgFEGs6koQWV8BiilaRTUW3gUTicNRN0OkFO4LsHZEQ3wVhChAUZeL1tkZELTv16msMgvqWtEQ4ANap4XLHDFAOehWOBk8joYe+90aJOCCWZesiVH3UIgED25b57K7hx+jXLdGDe136wSE1sZd5Ez7tY0ptBmQsGcesdUNRBSxtyd6F3GXebmPnHAuP7YxAtSZn5qUWcn7E7pwMbEsBEJUnY+stExFh6+HBdWUaIRgJEnnz8+fpslRzZRTl5ACYhJ2Rx6t9B5A2DVlf1KbifOiJEBsJxe8zg9EUMtspQNFSfZm+quo+Y1C5Kgg7zASzgpQafUXS7Uqk4xvK+igxlos6XuXMCVmUrUoUOFxZE52IOV87WKDFGAZ2FDbBtSc1q85JZgtt+u+QpRG3qMVNji7eGAq6DbzoVjXjr0+1dXodxXFkrp3hB3ptHNGDt2MeYXirB3JACZ4cAhozKAkARIZy0auHAUwkjFWGRcrZrSdPwURU2qeDR10dPaq7EoE4GTRvChlA8F1AV4jUdK6NlncSYTEJuhgGxXMBJdcHxp1sw4OewI0vIT0XupX8JT1J6wnySzGYNwsHQXAY6QV2XPrVKftwDoKWJNjIIKIgwR2QspaedCt4BBxEvxIg2LBiX4+CR2AgVkZAUIqcHUU0fs1jocuARM1RxFH646ZFx2xJYppv18bwBVTipHoOfJoJSSAN+n0QyAEhEQS8mrPJInqwGn4B+QBX8vlpx+awdau2RQjOGttRGJEMqDN70NiRJCfc312E5Ni2EMbKZTYRh8xeEqMil/l55JZyQjN25JcROl5lpWUo0NEAGnj+SHkxtn8KTHmZG6kgprNyY3tHyzLsohFqfVhS9zsrf3bABNrc+7yxxmg7Oz87eKDlNoDXwqvpo7TAdqyIhbx1/yayk9PhSWrLV1h1JjtOGYjeTbqzCxJAig2H4rqUEDZ1bPX8VojRoOSaGpJe7/RRulYgxW8KgBxSHlSbO4Um+LeJnS1ae8BpHV2VLgaAeMiimmGm9gRATWQcnyCmFw8iTUh0ZpE/Y7ksmFdSUjbUoI2w4hoLhLVg/h+LUnOhjQ459BiHnQPewYJDAQYyHhyOOxOcBI6Pp4COpdByJ4Amj3Z1rlBUm0zM9K7AQ6Rc5VQxIFfjcDHWrQje46ZEK23cwEdBZwE/sH23DBiZzp3guvrPXgX0NOAdXAYokvtOPDrdB9UIEvSzl7ap0BjGHxyXzkCum4t4NBhv19jbRY6zLlWgmTglWUC5DeI4hYhYWCIYnbjAYgnWcuSjABaEWcbjgxWWN/EolqSZyWlyVeyzYhhaQCHJ5tnVplBBxKQEZnIIMj6RfxvcMTARB9tBfvgcOlIfO8g7KIuUsg6MGL5DRFohQygBu5b0iKhfcdgiX9gbsAwzPYZVn9SAzKjNX+W5j9ZYmX/mhtUr68mmF0yKa2e59axE9KTnOn4nWW7+CClBVBKv2TtwbYApVbH3MtR06fUtgPTgKYCVlozm2YyN5uynhzgPAtlvQk1tiZAJPYeoc8sS5CymtaeZ5Y0YklUFxJGWpLsylH3kC2f6kyzyQKk6D6dtRrxLAAJJ9Z95t54E7ljZtWcVVY7YwYhUZkWyE/vh9FP2nVB1qfhZGbJXeLGi+apC2aQAdMRDxR7hdB1HRz2/TqV0e3qguloSAOxAggbwaNghBscsIocSrzn1giR2RhHQRgQYWHE9txaQAWzKp0LDCYEmOy5NdYqqnW5jgO/QnAKXDzWYZwNVwHLKjII6SgggIHWSfTJlaXAx1MERPsSjStJtSsxUsqxpm42DefOmW0hOpaAuCJEUAplDivHyxYEkrBcCKOGJHjlwT+mB5Ega+moKDYIWKHxUBadcQuZ54qc4BPicw17gBs4THnMIhJ8iLwmkLCBfCExa0usCbOoyd5C59hFFEREq5OjQFmM2+uKyZGjfVzY1KYVKyWPTjnzvRp6vDRJZcvKfrTl+mn12TU7a5vO0XbunvO1iw9Syge8dNPMUX9LH+wlM4eyTI0irW1rJUyayE1QjeQJUdb76GR1VZezy2r9mgIf4HBjk8QtLRKYFg9EAigpL4p12ZDZpiyJstpDzNFAQMoSCxiwAoxADEQkm0SzkGN0Ru3BM2PHCwCmnCe6/otZMwYDjX4OUhAjg4y6cvSvpq0Pgdez8S7ARRqF14ZI2OtWSWdSumCG4OD8kMSqyqooAAEhCV1DJOz5gV020cGBQcnx0CXmZBU8HAX0wqz0MpzfGHqE6LDv1gBhA9xc6Y+xChnlaXv4FjPI6Smg9yHN+DoKcBRwNPSZWZHstUHYFC17MngEsO5FAckggGyQa1Fti0YkKaA7kQR0yjwp2JNUI5KULuJ4ze3vu0Gy6iKxKL7TZQY4IistUkhILhREcJhxegBg3ISUs8QSP3cp03GMKSmcfT6j/HOQc4hXJelRyADrwBoWBTm6JlBKlx+IQ6HXApp1QUFZkJDSwouiVYketBoyA5Qe7JgmOeQ58zAGAM5E/FRWSh4BkZr7uRbN2IperNlcwso5m+pra9GSKVt2RY+yjRzgnGwHUs7XLj5IsbbE1wnU0fqcoHZKo1K6hCxDM/XCz1kly+wGfWsBilDF5B2wv8f5UDqPGmCLnUM46IQBMblRTIccHWeTBZD0KbpdxbCJFUm6DwEkyr7I/tAjDx5pdovMoAhAGaXLtwDFYRRKnNbbUbeODkgEzhQrAwZp6nbirKmap4Nvb561K9uyt7fOkTd+k0EBxowJ33pKaeMvCYgBAIesEeFyDgd+hT03oHMD1sEzgAi5jIpo990KgAASijgeOlztjzBEHkCdG7Dv1onFUKDD58uuEnst6+gyKDEuJIeIS36FdeTooECU3E0nwWPPr5LwFgCCIxy6E6kn4GTocCL3QaOP9DwquOV7TEm3oq4gbhun7idRpg5B1hwSxim6kBZb9F3+vdZrD3I8qIdBfutjn4BrdEG0SumnyhqmCF4GJwKIlEKQQ4e0aGACK5mEyWAI0l3IYx49YRC3jVNXJkFC9c35IwBJckgOcCsAa36uQ++g2XGduJ+jg6znAxAGATgeWA+sXRmC7Pd5kmaXBSiF9bIaehzMQD9nFgTYv60kbEv6tlZ/OgcoatGSc2n0dxqVC213FkgBxqwB0H5hWi/H1Esyta8U3U7NPlri2aXMSTqOxp+9z24eorGbR3UoJLlQ9rsspHVFnapHIWRtCrILx4YTK3BJbIsuFkgKVjDi0G2HPabG4wiw8H6zuFsJTmR/brdcnpmFKkAhYT5cAToYmIy9xxEiJo2EECP2ugFe7rMVi3Yka+dEZhA04kaBCsBAQDUc7P5h0LCOTONnQJKBkTInnRuSC2atrh1EdAJOuGzEWtiWPcPxr4MfgSBmNrKYFlCfeRh1AHuO0+dbU1GuXseeG9CF7OZhEMJ+E3VjOUSEgr1xPqbPJ4bl0Xuqf20umX3HiygiOHHXxCSeXa+diG2BYe1ZEB0I2BsQ1w6k33Xlwchshj4rUTMLB34QOP9J8X6pywhILpSg8q4I1qBYlkXcPSPXpSZ5M9qXmCYc/GA7gIFFkMdc86esokQsiZA2UO5fvENap4AYuFAIiM7LhEUbXDAqJkQ5XWapSbGD+lmyzE6xGEvZjRaYOY+6b5J9PDAp/+7f/butj/nSL/1SXLp0aevj7jyQojaF5uce4iVKdvuSbDMbmFtIcMI2gIqyJ5p5zPkUbhw77V1JGBY3euGjdfsAKTFbvgYwRR1jwaJgNKsMHcbsh9WvaN0J8Mh3WdU4hRib/eprD5oaHWManSBuni5mVkb7VJlIOplt88Aog6MAlBgIvgtJHEvgmbxdQ0dN176J4rY46FaioeCG8do3MQ3kXRew59dJt7GODicSFqyiUzVlTjoKCXSoVkTtsj/GDewBocMlfzICJVkoG7BvAIijgEt+JR2lQ0fqknLo3ICe+N8j60sAAb2ENqv1FADHYcSqcbkx9OjA9XZuQFgdpPMn/UnIWpk90cKohUi45NcsxA0uZ891AT0xsPN+wMng4V12FQG8dEBwrOtgXYwARlmNOUYA8jktYSAAEuoSFN1JyosykKTVNytkO2ZQVMiqeVhCz8+jRgW5tXmWvXh+5CcLHbt2gohjSR4//RsdUiARyarH0UWEjrUr/BNyu5gx4XeWVtIux7UnU70YiCP5guSZicGwKcOGy2eRtSZO1tVSTr5aLvZW8EHNWmX1s/dYlPqh1oZbZB8PIOUrvuIrtipPRPj93/99fPqnf/rW57pzQQrQRttlWvuynE1UNCe6VWslYdtG0FVhUjZmPGXKe501eQUoXa7DHkuUaOnYJQd8DjV2SJ2Z1ZqMMs2mupC0LCmfhO5XhnWUzj7/TXoUCSVOOhUFIg4INssogTvtBIJicgEl9xGBVyuW8r5jwUEMDiRJ2DQh23o9VuSmybVoI3gdnLHGw4voFEAa0Pc7HnQv98eprs4FHPiV/CzsHjnwa3SOo39cZJZkFR2DgXRuBiqr6GVA5zo8AuBPsO9WCcg8FvcBgMGG49WIB3A0Tzda/pcZmn23xnHoUrtDJAzkcMmfpOigECnVDwBOmA4FGgp6AAYvV/qjVPbG0ONo6HHYnSS3k+1kD7sTHA19uj8hEnoKOB66xEApGNLkcieDh5d2RYoIg09RVQCyHoWyK09ZNQUqUQS30PB0dQk6+d7ld0jzncCEJwP5GY5QcB7ZFXZiyoh7U4EIgBy5I/oY1qUAAPHCgiHXq3cqdMWqySECa3BZ75LmNqADhQBam8mTMrdlWLJOYtSmcirpvaiwKLV0+s3cTnNWm+zZ7XMMt9WctOra2S2xhx9+GE960pMWlb1y5cqpz3NngpTWw1r6TGsvRmt2UNs/l/Coxo6UocutcGURw/F3Q9Fqx2P/AuzmEWFsYkmUFibuHKMuHNg5aLjjaJVjsy4Pz/zGrEheJNDkkjDsSl4ZGeMcJ6kCuaVKgljQophQzyfAJCY3kHHzaDRP4MEnpVBP0TzEM22Eke6EKK/OS8RMiSZmC5KwTVcDVnGsBSg2CZp+18RmqgdZB499v0aIHAlzuTtOLMclzSIrbphB2I4QXQIMCgj25TwKMhxFrKJn7UhwOPQnuD7sJVfRyq2T/kNDhAf5vO/WuEQrBj1iB6KrCcIM3eWPsYoeq+jZbSVAKoCw705SGwHJmCvXe0IcAp1BGX8+CT65fvgRJmFZhtE2RxFBXWKBkk4lRE6tr98VmGjCOB44o8lPKLoVfXbMM5dcfImZk/0BOdkbgTVPDhmsqAvGvub2mQfyGj7iqgkeEv3D7Mgo30oARxXJqs4jTEkKigzy1gUTgeTyib0DncQ06UgPc36oQbHjaJ+w5sY0Vkm2VgUitbJL1ilraQPtsXMaP7uvdJuX9dtQ5dsAqOizepbjb3d78MEHt3Ld/NW/+ldx9erVU53rzgQp1lpumalkQeWLaMtuo1afy40y5/qZmvnoDMlzGvsUzWPNXDcLY31iTvjYHMVjZ1zREUJPSV+iuU+saDYBE89si7p8ynwoNjEWbyi2RSB2yGGeErHDdcmgEQByHDjKsc/EYacKpoqfklwY5dnQW6G6Bxta7Ajo+3Vy7QA849dQ2nVgsanqMtSl4SRa57A7SedQMDJYNoQielpjFZ1E1Qw4pk7Kdbja3UjHdy7g0J0ggIWpjw4HADBiOQ7cCoO49/bdOqXMP3ArrJAzzgYiIAZ4BBx4dvEMMV/LgVsJGGFg8tiwj0N/Ao8gkU3MpnQUcehO8Mj6Eno3YM+tcRK6pHHR+7GOMelZ2B2Vs9QqgxJkAGEglcOe9/wgKzXzGkO9aENWg8dKhLYhEo7XHYgCsOYIoSjurhAJJydddqeJmpV8RBiEaRnkmdG1fATgxgjQmlk7UvG1rsm0osx6aARZp6HsyHlVCMx6KDUygF1BAnQS26jPvWqmFNjHiKyZiak8OWm7vAPRCRvaOVAw6EjzpwiwBgAaBon2qQOUrdLgt2wKdFjAUPadU3qSGnipTQpHL3zl8+MIVgKyS/i0x9/u9rrXvW6r8j/2Yz926nPdGSDFum2m9re+l7aEyqykr2/WZRmSou5qMqWojIBoUCxzYk0XD5SVjbPoldKsagRUeicsCqHq5vEZoAQjgFW3D2BYD5e3J42J1aaYBG9JhKv7iTUn6vvPs0W5bqt7sYMEkKl7gDt6B5APKaxY76S6ArKLQDQmLmIINIrc8S6kcOKUPVYateeGxLAAGk7MLpxLwhrogLsOhMvdCYfmmmnypeQCInbT+AGr6BN7cRw63OWPoev0rEKPo9Cn43sacOBWOAq8Ho/aFc9ul5XebAesgscAh7v8cWI+hujS2j/KXvQxJ3LTdq0Ci189AjphXRRo7LvVSASsdfcUsAKwRyGxSEMk7LsBx8GPtC3X1ntJbMsi21W6lhD7kZZnJdE+e55DkHvPzNPxqsN+v8Zq4DKOOPV+1w1Jo8IzWX34ONeKunf4eRdXSCRZ2E8a4YWxA+Ukb/p8Kvk68PNO64SFcuZ6ASFZgyJ9EiEnhdP9clz03Bx9XEi0M27gNgbP+VEcjOi7c6C1y1lmvUsTGgYykoYAQIwrEeVm5rWMFKyBlY19c2uK1frfbYWtc5GVtWNr/XOtHY8/wbKzU9qdAVK2UXPXgMwU9TgHULTMnAundhzqHcRmG6YYFS+ZZX29/SRgRDPKQjpPcdukU3jKa+tQZlCSSNBiPJ2sSCdedgApiCTm47RDtkJZ9cuX0T/RIa8gq2nthWGBB7t4iMEJa1CyCyeGnB9llN4eHIbLokxec+dk3aUF81aDTysTd5R1KWmFYoCTryUXypAAihc3DQBc8sys8ArFQ9KZHPqTUe4SrSdno8XGQoKH/hir0CVgcuBWGZCAwYengH23wkpAg6OIISKl4dew4CE6rKJPTE0AA5OBXAonVmbFU4CLnNG2k+yzzAwwOAnRJYDlVHsB1uk4irgx9FhHh31JNpdFvXK9Io49GnKbFchYTUsKXRbAuS+ZfdeSxXZfcth4z7+hrtvHHl3Ces1hywpiQZz8LQbi50gYjwika4ieFwCMhJQ1WaN2Rknd7PNbPLfJuyIPd4xgYaySf463k+l3go9wAmTIsCbQdQT4oZAq5X0OzIsx7ZOvgcs6EQcX0T4V20gMWVo5GSsnW/lgue6ZSeDSMrV6y2PPM8/VOdnHg3DW2o0bNxBjxOHhIQDgAx/4AH7u534On/3Zn40v+7IvO3P9FxukxDga4ABsInr9Xs4IvE71C9+mHtOyFmvSyh47pTup6FZSSmqnvngT0VOyKermUeakMruJRJy0zTsBHpSTtXWqQRHQ4jEOOTZak5H7htj3nkIpo+hPDCjhHXKMR5VtSR09IU0vdSCIGt2j+3wEdTFF70RJ4EWOBbJOVyLeGxI4CZqHQxgUHfQOupzOXkFI0pdQSCsQq5tC186RlVSSoJRDiQmX+qOk91Bw4ijibn8jMSVHoYdHjuRRsOERcHd3I7tgZFAfooNHhBdgotv33QqHOBnlXhlAOHArHA8MaHoasO9W7N4hwj6tJUsu73tsOMChO+F6jUtsFb3s308CXgVIngI8AQMcOsdi4Uu0wmPDPvbdarRgIsDrAClgC9FhFV3KxaKaF7uW0J6EY4ehT78TRc1rExOz4oRdWYnQufMBkBwqeiWaNdi5iEjKsgFhcCAfEQfHA7pmpiVFNkgrJitAdieSQ0VAQPBRHlV9JzFKp5/eETsGUxbPagp9CAtJQSKMEiiXdYlI3nXiukKU6B0CouRZ4T5sYBbGRu45CJsiCxYGAjRFfxGKvCHE12uQfoeIo4pGNieUXQIIWsEILasBlCV1Pw5un48HTYq1L//yL8eLXvQivPSlL8VHP/pRfP7nfz76vscf//Ef49WvfjW+8Ru/8Uz1T/gpLpAtEWGpKZCwwEWP1X81q4GTU+QQyFli3RiwlPXOzRDIjdssYCzlPlHw4jn1fcqZoiAkAZGsPckumVwmhR8bzUkCIi6zKgnkjPQrDE6iZ/dO6GNeBVnKhD7/BmN3UcwuHuXVVRxLgOvyjNz5LJBVgKKuHo7OySGtAGtGBhFhXupXiUHp3QCSmfo6uJSd9bBbpbwgDnkRvT235oEcIX3XEF+Ovlnj0B+nxQQ1Jf31sIeehjRIq6Zk361wQGthZxhUHLpj3OWPcOhO0NOAK+4I+27FLiBa4dAdp2N6GhLYAdgd9IndYzj0xwxuhDHR9u27FXrRtQDM1ByIW+eSX6V6lHVxiOnYA7dKbqJ9t8aBtskpo5ST2ml0U+8G3N3fwL5fY98NuKs/lsR2WSu059ac0VYioy7JAolekuWtxB3Xi4vOu4C+G9D57JJjvB7gfUDXDei6/FwAzMBRF9kF5MB/u5hF2pL1OGU4luc6dPw8c/4feZ77/A6kR4zG74AF41HqsO8S/81idH5HKC0vkTRiMsHg943f7WrKenmv0eWcSckdHOIYlDQAiv6tpsmf6pumXO5LrMV0l/3z0vrKSerOzt1+67d+C1/0RV8EAPiZn/kZPPnJT8YHPvABvOENb8A//af/9Mz13xkgpQQW5eBdbi+PWYL854BEK5LHfg5htHjXCLBomSKr4yj1fUph7/IKx94nV0+0rIqKZTvHmSxdZk+CdIDBZ9aEM2NS7kxlBtdy81hBrXoxbIdsNSglC5Oieywg6Zg9iX1EOBBe3DGDAgNWSA+mCN8NKeeJMidRRLG9H7DXrXG4f4K9jpkQHdQAJIByqVtxKKzJ1rrnBxx2JzjoVjjwa/SSeVVznqh5YkbgRtjDgE03RRA2RJkVHeQP3UnSdBwY8HAc+sRmWPMI4mI5SkyNAhQACbQcupOkU9HzXvE3pCyDHA/WlwwCPA6IgZVlYO7pruHu7noCJto+LXPFH7HOBdmNM0SX2u0RUr4XRzF935OQ6D2X86jYnDOa0v/A51WfAXYjKcPlpZxluzh7LafTT+QF5RmtalSCsCuui6AusLtQI8W8ABUFLvobdgpaogEUFoQYNjfmfxSQdFuqT9HssyNtlqcMgvR9NAyMnouGzC4lF64FLdbKvk1ZY7N+zwiIyKKEk67nkvm1/Z0FAdvqUKxZbWELWEy1sTYhfRxM3T1n+beNveMd78ALX/hCPOUpTwER4S1vecvsMW9/+9vxuZ/7udjf38dnfMZn4PWvf/3pLhbA9evXU4jx2972NrzoRS+Ccw5f8AVfgA984AOnrlftYoOUJX7PWjl9EVrMSSVXQNVKNqQ1y6iF7BWApWZJRFtLxuT9GKBY0az5rAxKcusADFg6lwV8mhNFAYxJeZ8ieaKdRQKhR07kposFWgbFgBUzrufcazpj9eZzHxH7AHQhDRrQFYwl9wkAkNeEXwARMyh9N2CvX2O/X8E7Xq3YO07CpgObCmQJSMyJhrhe6lY47E5w2J3gro4HSs3wOkTCnhtwKJlkr3RHuNrdQCcswiUZxFX8ajsZFcD2xCyKDuaH/gR3dzcSQNh3K3xCdy0N3p/QXUtajiv+CHf7a7jqbuCATnBAJziKfbquAzrBE/x17LsV7vbX8YndowJceC0dBS6a/M0j4kBEq/d0j+Fufx2H7iSxMXf5owRe7u6upxT9QNbNKGty6E/gKTCzAkph0YfuBPvCLO2XDBMNvHYQsUC5I0mGJ/lTDruTnG9G2CsqwImjmDQw8khjCJR+ZwApwqvrBnjPzAq5AOfFJSjPVUoCJ89a9BFxT0DLXkA4CHlZB3mXgmVEuswiWmCBiARUEiBPkyT5k97B/L6FTsTrfV6BPK2rJa7ZNGlxTphSXkyUKzXvfufzaugCVtSNk0wE/GpVXUrNjW1N3c0NZmeRlf2ygitgE4BYRkX/la7+ch2fW2RjcHy6f9vYtWvX8KxnPQuvfe1rF5V///vfjwceeADPe97z8J73vAff8i3fgm/4hm/AW9/61tNcLj7jMz4Db3nLW/DBD34Qb33rW5MO5SMf+cipw46tXWxNiloteqe0KaDSOgbICnpbR2uBK903F3rcoksb0T4jStYyKrbdBXukYtmoehSZcQWvWSqFPVFquehME1gBRjNH1agAyAu0QTpqO8NMKfELoaxoTaKuPltG7xBymKjOcOUzycDCVL7OBCnlQdnreEE8jdAZsR4SskoUcbnP685wtE6elXeasRWEVfCS4TWK+4LPcxw6Bjk0pDBlHZRX0XOYsEbTgNI2ZWt6YRm8pKsHGMRcFmbEgwf9A+J2HoUeV/wNDvmVQT7AMdCQ38sj4N7uY1hFju7xUcHEGj2tAewLoDnGEB0uu+PEhPBjFdKP39OA64bRWUWPu/0NXA976boOfc7/otsADotmnco6hT87RF7NV4xDwAdcpoD9uMYqeFxb76VlAlbBJ4CYMuea3zOvnkwj0LIaPIZACKJfsewaEVIEUAict5Uckv6DOk6Tn1C0He/EJRQ0AmcgBhfyPfiYFhFMiw8SsoC2Q9KuJOGMhhlDgIcIw4k41J6I30sKMb1zUXVfeg5VywbifC+RGKjEmEOP7QKE5crLso6PzcNktSgAMJkjpXRXLxXYjtpQ1Gn76TKf1ZwuZck4cAtMFyM9y/Hb2P3334/7779/cfl//s//OT7t0z4NP/RDPwQA+FN/6k/hv/yX/4If/uEfxgte8IKtzg0A3/md34kXv/jF+NZv/VY8//nPx3333QeAWZXnPOc5W9dX2p0BUkpVeQlAgLb+o1aHWi30bekCV/a8Laq08nLbTkE/pzBkgHtW50GOZ0lwbuzmcSx0i71H7PNMJK9WnBO2pTwoWnUYA5V0rBfWxPjceQdG+hJLU3M9Mc8UKQMaW68m3qIoeVCiFNYDBwJ1Ck54m5f8FvxZQjMlFBXgNPcH3TqxJCl6RkCIRu7YgRAADvwq6U2AvHrwIJ2Oo5yzJLk6Yo7sOXAr+MisSBC2wVGAq/j9D90Ja1MQMYDgEfDocIB7usdSmVXcF6ZlhZWs9HhALGY9dMfoKetJLCC5HvaZ8SDHQEESuXEKfHbTnJgoIW33tbCf9DUKoDwCBgFarJE5SZoaT2scxS7lWlFT5gRABmbqCqLAyemQV1V2LmItLIADJ59z0cHBy9ICLomadd0fdft1FCRSx4P8gOPYjcCpZrHNSeF4OwEgFwAihLXLz52TZzKChbVivBQDAxTNWqvsoeY10XV+NLFb7CCi1gzoUwS5MC2hA9waWTjrwIsgkn13KIG8FPkmWCTpz/gh4Dtasgq6AKF3GCV2qy0+uEGQ0CZQAeYnYzajd2t/S3tnJ16n0bjcIfqTRx55ZPR9f38f+/v7Z673Xe96F77kS75ktO0FL3gBvuVbvuVU9X31V381vvALvxAf+tCH8KxnPStt/+Iv/mJ85Vd+5VmaCuBOASnWSnakBi50X+24llZlTlU+5ZNtLTBY8+/CgJJa6LF34/T3IyCmNCkh7rNYVjtSAGmlYz6JbEv+cWIKW2dtqjVRhoVMyCTysUAGKCnDrN4K6fhdAIKLBTji1V4xELt5XMyVeeviQVqcLsIADmFPuvR3QK+rG8usWnUOB90qzcRtRIkmAtNjmBngc6qwll0RJOvehFFoMcAuHk8546zqTjytUzK2Q38ML2AEYKChIKenNQ5pEMAREzsBAFdczoMywOUIIgSs4j6OQo89BQMUMMQ+gYs9Sd6mwOQJ/no6fx8l2216MEJywzAocbjib6TjD7BKIcyr2CVti6OAQzrBgYKV0OEYXQ5jNrHp6iZS91deKTrC0YDLOMEqOqyDl/wtDExCyABSs+OOf7OcaC9E4pByEU6H9Bzyb36y5mimGIEYJPqLYlrrCV1EWJMkaxMwoishCwiJAiYQuDzWzKqEPgJ7gDshXtlYX3cTzaO3IzhmXdwq5okBGWZSywcAJOn/ZUJBgRBDYHA0xDwxcA7wAXGQ/iMySIhOWJbOg0LPTVivqwCltI00+FMTPTsZ0++1sspME22yMjXmpAZU7LZaZm87SY2Ry5ye2NjaIs6Gk/TQpz71qaPt3/Vd34Xv/u7vPn3FYg8//DCe/OQnj7Y9+clPxiOPPIIbN26cahHAe++9F/feey9+7dd+DX/mz/wZ7O/v48/+2T975rYCdxpIqQGMFrio0YJTQKNFIyZRWvFyqtX0KKZMM/NjDaCQEybFpdnThljWOf5HObyYO6rIbIXOwohGL24WAppoHl27hAA3xPH+bsyepE4WfJx2uNFHBIesOyHwzDJkBofXJ9H7mQEKJVdP1EhMucU5igMA9vvMY+/JQnUqks2r965N5tPxtecQZAE8FNARRoOhpohXU7ZAB+Mr/iiBgFX0OJYcJjr4A8C+gJsBDsehx6E7ETdQznZ7En1y5bCbBjgQzYuyKXsKauAkqmaNo7A3elQcODpoFTks+bI7xrWwn1ibo7ifgNEBrXAUexzQCfZowLWwDydgzVNkEOMGHGCFa2EfCGB2JfQ4dMcIYNbIuwgfA+7yRyNXkLq3/r/VZQBIQuLHwj5OQoeODKtCEauQ3w91yaUswbSShQaBdfTymdkU7wK64LDShQwlTHkIkpTO8W+8HhzQiSBZ1vjRdZ7QOYQBmvxVjOQZ5+SBMUJYlfzsA+q6lAg7ec9g9F3caK5Xk8Xp39ARJ3CLmUSEfQ8jgRL6B6fX7xwgglpeYtuB4gBd74cbFzmlPiD9hpcMtWW/JBMBm/KgZXNrlk25eVp11zJ811z09vzDkD+XYMZ+d878ljffAsSNdobjAeCDH/zgSNNxHizKzbb7778f73nPe061kGDL7gyQshRcAGPEXgKYqbKtepcIZSe2zyZzq+VFET9zLPZF5yQsEaAhAM6nDjRpURKrkhkT1aSkNsmsjyIQrIsISAmuctSCYWMc76fBABIgAZ1RZx3Bacl1oTdlUgScWPdOCh0Vsaz3vFBgjNL/RMJet0ZvBjO+JXmGrd8BpP1r4w7qJGNqJy6efbdOqxJ78EB5Y+ixDh6HklV2X1YQ1vwm+zTgOPR8A4RtOCy0Hxq9s+9WuOyOJV09JUACdDgg1nt4CugxYAUPj4ij6HDgWDS7RwMGRBwSMzneZabGJoQLMqU/ir24bbrk+lGAcuBOsBo8LnvOaptCjyNSqntlWC67YwE5fJ1HsU+5Wu72NxAi4Sj2OFZ9i1tjFTr0ssaQZbI8WA90HDpZk8jjRvDYdwNU8HFtvce/G3FeGgA48GucBI8OQ9IHuRhxLMnhyDwzjmIWq8pjx+HIATHymL1aeZAPuRtxok9xckRCx5QYEgUYekLNFBtlHoGAJBC3zzw5MFDR90KSyhFB8qYwywNHoCEK2KfRQokBThYYJGFTskCeYpS2DZLQLUjlMWveuo7ZFOv6McztKEeKGeibOhX7eZtlQ6zVXOvldrWScVly3JLootvMrl69ei7C09LuvfdefPjDHx5t+/CHP4yrV6+eikWxdualFip2Z4AUi5qn3DIxjunGpLSfQPdT6/jUUuO3FhacbP6YTakmcBMtClTFr7yz+qTVzaMalcR4aFsz0BixKNrxKEuS3D8FO6JMi1eAA6hPPRrgkkI1vTleM81qFBDAkRMy+PDML6bLUTBClHOgOMfUvwKXruNwVI7i4bwZJPTNfsdhwxaUWJ3C0brHnqzA6yjisj9JQtlLoksJoJGg1iOkLKtA1lgAkPwmzEYciTukFMICIoB1R4lhWUUvIlmu6yj2Uj7iwB3L3xX24iAJ2xhkqPD1CD3+33BXAhWHdCyak8y29BiSq2WIl+AFlByFvaQ1GcK+uIuYEekFIPS0FgDicOiO8Wi4hFX0+KTuEfzf9VVcD3lF5kNa47HhQNibfI8GEHpZ3+fQnaB3a1wf9uFdwKE/wcfWlxKzAgdcEY3QjWEPjgI6N0h2XwJkNee1uIWAvBq1/r52kcUQCWtxAe13/CqtB88hyyq+FXdSTOvpyHPoImJayweJWokdQCtKCdyIkJhHGgTYdyykJWVkAr8jbqX1IDOQ8r6RCmuR378AYvJUgAjJgoXRGdzTEbDi9wUSksxp/E2fFgKzqd7JStDyL4hGxSR2s0LacsBpDUAlmJkKDND2FBVrRWhG6tSY8amIn3LbghWgz8tu92Ru9913H37xF39xtO2Xf/mXk+D1drOLDVJiRHrrS8BRY1emhFgtK4FKTX0+9XkGsLTcPaN1e4DMomj6+/K61NXTq5AWHNkjgCStUEw0YlEAA0rE5WJ94ymPAyELZPW2KwiRz6ntJvlVWlxN2RSAw4yVVdFFBDUkVEFOBJyEjXKEhnacOaMougG9DCxrWd9FxbEHfp30Cx3lhGF7bkgp2AHgsiwQ2NGQ8nis5UbYNXcClAHhbLWaYt4jswzHgTUhHhHHoU9A5nrYg0fEFX8DV9xRAiOHLmtVVrFLLh79DDDzckArnIT9xJAcuBMMIBzSMQ79MY5ij1XssCKfyhzFHo7XgWZ9BwKuuBsCYkiuiwW0T3DHOKAVrsd9XAvsBrrsjvHocAkn0QtocxLSTOnzFR9wFDhD7HHoc3izO0quKacuMGTQoqApgJcLUL0LYg7VDo4XZbyr4+UENNOvS6CW6Yh14Ky6HNnjcLk/wUngzxqOrNlqexdTqLIHRm4lgBmWgMBuUR9Bbsii2nV+gKOPQCDW16q3Qd6BiPwuqNtztF9PGfhdpAC4dUz7bD0kD3xyC4nbxw25v4hEIM8HREKe3Fhdhn4Wdw9FZZcyOJEHAtta6r/sZK0FVKYmcLUJ4xQz3nLttI67hRYi5dW1T3n8NvbYY4/hfe97X/r+/ve/H+95z3twzz334GlPexq+/du/HX/4h3+IN7zhDQCAl770pXjNa16Dv/23/za+/uu/Hr/6q7+Kn/7pn8Yv/MIvnLrNaj/+4z++oXc5q11skALMP5BzLp2a1V6yJQsNTridLBgpfb6zPmBlUYiSWHaUQRZKCVMGKJIGXxmRBDI6y5iY0GOixJSEDuMVXiNE/Io0q6RQsCUkLIoCGQDqn9ewY6XE0ZGUFSalC3xpmvbeRRGf5URcw+ASu8K3PaY8ITESBgAuOFzqOIMsi2J9yrehKe1Pgk8L2Wn0jqZvX0efAMpeChMOWEdew+bu7kYCAb1bj0KIj00+lJ7Wwq6wxiMt6meSnR3FPrl4uAzXG8AunT0acBIZdAzkcNUdAQFJ7OrA4lhHQQbxkBgYBgjMoOwJK6JaFwYaPS67Y5xEDyeAKYCBjLI/R2GPs9kaga9DXojRU4THAOcCHg2X+JopJneWLojYgyOhVtHzdzeOg92nNQIR+pi1OwoC9xVMOsCJa+IkdDgBsXsu5oUKOzIrV0cH53MOHMhnEtASokv7+JXi9X0AIAw5ZDmNe5SBNIfHE+AiQi8MyCBlAysRSHQn6gIFkFw6Cs6toD14ce0EjaIjuLW8P+JqpRhzuD6BV0iOxhUlFh2BnEMMYSx0J0mXr2v6qE+Kb/iGcNb2VYso/DktitrUmj+2ntyQzTJLUkxYu0OifVr2m7/5m3je856Xvr/iFa8AADz44IN4/etfjw996EN46KGH0v5P+7RPwy/8wi/gW7/1W/EjP/Ij+JRP+RT8y3/5LxeHH9+4cQP/7//9P3zyJ3/yaPvv/u7v4sUvfvE5XNHYLjZIaT2YJXCYi8wpbRu/qmVavK8LaEPAxvoXo/ZWZjJldlnvUorrqLoUa6o5EdZEo3gYeOjsQ6oMEaFzHI1Dll1BZkdkexLvWSZEFyHU4x3yCsZaRpsnoMLmQUmfAzFAURaFlEWxqxfnPChESKvhduLeCZFw2DMQ2fdraNKvEF0SydrspR3pGjYh5eVYR87LoUBmEL0Ep7LnEGMb0aPJzZjN4M89rXHo8qJ7lyVEeBU7PMFfT+CFwQmDiyf6xzgpGyIO/fHo5zyR3CrqKhpAuCoszFHs075V7NBjGOVmOXTH2MOAE/hURkGIMjRDdDggYVrgcC10OHAnzApFl9iey8KyDOBn4VrYT7oWjSh6gr+eIpkY/HRAZHDS0wBEDl0+lmy3yh7pAo2sWeEwaF6Q0SfG6oYIgj1lzUlHLOTF0BnBLQMoBYTr4EYz0s4FYTn4mQkh8uLAIpwF+K/vAoa1k2eZjycXMwW/5gib9CwL2EgZZkUEHkgWKjRsirKTBKToHcVh+v5RRIoIGnqTJ2VQ5pNfTjkdaB0RvIMbBgZW3iH2DGpgu7GCTcF6yC4fF1BjUeYmT83wZGB7bYoFOba/3ob9brl/SCmuW2M65Jzl+G3suc997iSQrGWTfe5zn4vf/u3f3rJlnPb+W77lW/CJn/iJCCHgJ37iJ/D5n//5AICv+7qvw2/91m9tXeecXWyQ0rJWcqA50xelBDXl4oFTETu1F3UbjUot9Jhcyi6bMsuaHAjRsitK71KuL616LNcUfNHJpvNYFgQj4JKACeXOdkRfw2xT4JKOVVpZPuvMVP6qi0c1JeRCEs46F0YC2mAASoyE3q8lLbqTNWY4BbsmYWOGIqel14X6gvmupsnZbgx92mZXJtYFA/fNasQaGqyaiCf46+wiEaZjjwaJ3uHzXKZjSVm/TvUO0m6AI3icGV28afOJhEAdEJ//etzHFXcjbQMhuY4GCilEmetfi1ulGwlr73GPJbdPulZCCkF2CDiJPrE2mqfFxy7VfyLsCUc6BQFGORmcptf3LiQgp0IMjQoavQIuoscwShLHjw+vzLyCF6DKYeGr6HCEHifrLv3O+36NwTkcr7sEUleDTwydcwE9GAyq+BqQ10qfQ0LORkvKoiCxi7oZyIyifQ8y2JdXVrsPYRXTo2ffqcCETfC5DA28P3gGBg4iqJUwaQpIKyRTYPYFPcdLU7ooPRdxOPLgEVdCbc4AlKpgFhgthjpy+7TEtDb5Za0PtW20DMqcQLbFrGw7OT0nu901KWex7/3e78W73/1uPPnJT8a73/1uPPjgg/h7f+/v4cUvfvFNEc0CFx2kxJgHY/2utsQNVJZtqcZ135TLp2YzocZJdzKVs8BG9OgLW6S/hqzRAxi3jhHEJnZFt2uHGZHpaHWCJyBiNCsWrOi2AtAg0lg022teFPmNHBA13b12riZrrNOkbEBiVbqOF/yzv4bNg7HXDehFa9JLZtNV8CkHyp4JOV7DpQUBT0KX85oQJw9bB9aU7LsbuMvniJxVyOv1XA97owyxagMcDulG+uwppoRpB7TCVXeEnta4FvaTJmSIvegyXHL7HMUePQaJoMkASqNn9jDghJBcOI8YxuORcJCyyCZdiwh6V9GncprwbRW7xGacRI8n+Ou4HvaxgkMPBldHYBDCkUEBJwJKlJ1xAoQ8RXjSUOg+Zdhljc2QzsMsECeCg7i4eloDxODsKPR4VFdoDj79BprJV62XpQo8STK+QCm9fgDhUscZg9eB1+lRbdKlfoXHTvYSGAZyvh0V0Q6DuoFyjhYVdEfPA6LqTjSMngR3x47dLzES3AAMLrKYNlASx0b7vnVIATYJtMg75tb5e3rH9FDRkyS3T5B8KFItSKJ+nBMXT5SwY8dh1Hzh3JcNYV6TohmmQwFUlmhPTpv88jTgogQvJdjZ2ZlttVolzcnnfd7n4R3veAe+8iu/Eu973/vmZQuntC2m+Leh1RB260Yp4i4R+tIbO6Van3jpNkL5UnPi6HMVhepKx7qCqbY7V87HS/p7yMqoIQlnpWxEBYQo+NB1e+x5kWlqYURCh7SOT/K16z4fEfYYjASJ5GHKOnICLF3ROB2nzIroUgTHaDSPrlwbxMe/HtwoUddBv8Z+zwwK609iSvQFsPumcwEHshKv5km5qzuBTzlTBujqvgAPhJf85sJ/+7JK8Cr65NLQAdMLa/AEf52/C5hRAayGEQ8gzi8CCEDJQOCAVrgs6e8VoBwIaLFRMilJm7hxTuDxJP8YPslfwxPcDXb3mNdZy+oih0N0WMFLrhSXRK6atTZnlM1up3v8Y8mN1QsrdBR7PBoupYy1XnQoK/NdywPAtbAngGjc1ej5GAC5lADuyf0juOKPUlr9znHbjkN27Vz2J7jaHbN7Ljpx4fG915wrnE045HV85Bk56NbyzHB+nd4P2O+ysFpXTAaArtdEKNJoTZuf3hF+fhNTKNtH75Fqvox+K4EPeSc1zDi7SM37aScVujtF42hqAWmm6c+Sm9dhPLEB8po+mm+JXNal1NYJMysn19b9AZBYFPIeVOaOKq02qdO+eW6F5SnmpFb+cbBbvXbPrbQnPelJ+J3f+Z30/Z577sEv//Iv47//9/8+2n6edrGZFGCMmGsK8dJq++bC5qYEYaV7p/ICzgGU0UtvQ46BvNR6mf5eo100sscKaGGAiLp6fO4I81o9OXFbikiQ7eM6ymsGd7CBmzliWaSDjxz2ImUk5b3jKacmxCL9HDJAUdEi31px7/iAvW6QBeR4INwTcNK7AXtuwNHQJS2LahF0ANMw1lVgrcOeZ3fQvmMQo9E5PQ3o/ZBW/QVyMjJmWVZpBWKNaFFmYojMIKQkaIgIQArn1ZDga2FfmIcw0n+oS8hmptVrULcIJ4IjXCaO7nEU0cv5DrBKLiENI1bNh675g8guHk6TH3ENHHUEIDEjexhw2Z/gKHYp/JjZEhGuyjV6YWw0sy0niYvYc8cS8cPlPVzKtXISO9b5CBuj9xIAgjzvep0Hjt1KR6HHcYoUiknkHKJLayzxdwJ6yaUi2KKjiBD7pDUCGKx44sUJB2Hj1hL903lO9KbLLAyDM2BCB3EZsCMxaBFtSVpXRx7zBECUIbFuIULSnaToIIDDoMV9o/lTXIzZlaQMiqOUj4hUXGu7mBgTKKG1YSU0bwqkrq7jSJ+4Svcsv+O0wazM53QaL+uxKEOttlfbZK9BbUqXMgVO7HG3ELDc6uieW2n/+l//a3TdGDbs7e3hjW98I17+8pfflHNefJBSApPaA3+WJEPbHL8AoKg1qTE7k/GOZzuW8SmEwLH3CL1nJkUV+YF7yNjJLIwKUa0BIgCSWK9kUxTcgAA3iE88KqUMmclx/pNg1uFhWiRyx+eQU95rTopSg+AZoABMvzvHYceqPeH7FdH5mPQFqivIq+AyKNjzHEqsAGYVOIT2kj/h1OihS2BE83N4CkBgNuXu7noaRFexw/Wwh1XoOMeH6DIA4IBOcLk7TsJWTk8/jLQZ6sbxyNermV+PxN0DQACASyzJZQzwNOBA0r2vlA2hgB4RBzSwDiN6HEmiN7WQXEgDVjIKaiI3BUrM8AzYg+hMkNcGUjugNYt1scYRuK2auZaz096QSKMBgzyLqnk5cKukY1HNyip22MMaA5yk3T+CF70LAOw7BwTgKHbpenSJgYNuhY+tL6XOe4DDWj6rrkiXTghDPwo/33PrtObPKnisBnYHqrsIAAZ5tvj+UVoPKgR+NmMA4uAYYBM426uSLD67nEmYlqguTX1NDWuSH3px9Rj3qYMAFZNALhJnmtXMtKUWRj9HT3mC0wlygjCsQ8yRPt6xaJYK1qT8bHKnyI1ByzY0K3O5omorKG9Wuhxg1LQnG66eWzfw32rh7K20T/mUT2nu+3N/7s/dlHNefJBSWktPUrPThM3V1uEpVkOeC9urrnTcEsw6twFcxsI0+TtikjBKcz9iSbQqQoruATIYsVlk1ZI7KObzhQ455NghhRmXltfkAXfymvbe5YRtERxloSsbh+DgDEDZ64SNkDwogCRog6zR06lLRxb0E5eOLlYXwMnBejfgkrhwAODGwDk+7u5u4LDL+TpCdNh3K2ErQkrWpgAk/RTCBjwaDlL0jobwWmZE2YyehrT433XJR6IuF4BdNMqS7EGFtmucgEXBV2iFIxnUjwRUaAQPtyem9X50sD+BTywHsz55ZWNtu547Rf6AcJnWuCa6FY0q0gUHV7ETQS5/5vV81iN9ja5zcEAr0bxcwyp2Eh3EwtcDN6Q8LHtYA45zmGi4srptVtGP8ql0CCn65zjogoIcreUo4sbQJ0Bz4Ne4vt5L33VxSU217+T5O1kzE+coAj5gGPgZRGT3HGStqSiAPom/A2U3quOlJ1T3nES1uk5VKgeVZeWIurWwLMj79N1L+0z3EEnW9FFWJejrG0flGIwgv/hD0ZdpmnwgJ7q0/VAo+iljzT5um0nhlGbETsy2Ycd3+pNbbkdHR/id3/kdfOQjH0Eofvu/9Jf+0pnqvjNAynmquAvAsbFtbgEtbL68ZY6UDRdPzQ+s29XV4yiJZ1Oa704Aix5ufNOsUQEoxuS3ThEEqWG574oEBNWwlDM/+awhktyxxrQCcrpuQtKYIBIvwKbnVDdQZNYjBuL096T/eF/XhZSEy7uYQAnAs9wweOx3wlAIk8I6BJcACotlHY6j48yzAhQUoKxkzRfVq6iVCwECmQVhcexx0mAASOHEqknRbYd0jBVYp3GZOKzXJ6CyBmKHe/xj2ANrPK7FPTzRXUvnW0WPgTgp3KOhx75Ex3A7xs+KimgPBMzkKCER3kY/EuFq2y/TCkcmadyhW2GVBLoBBwJmBhCuxc11gQbR8/Q04BDHuB72MRhWKWW5FYDkweLaI/TiDguiQ5HrkpBxj5gSv2lK/fT7E+EKHbELKHRYx471R+pSM+ATwOi3VdZKny128yElgWPZxfjeMmuSkww6F7A+ZjCDCBGBAyoa1yRwAGXmQ1+pAGDIAEVzouh2Kt9Lj7w2kLfsCn9m9gRpGwUjpLXJ3pxo4oj7grTml+Zd6TxoGBDjQlChQt0Frp/FQGWq77ZsyrZ9/OMEVphJOf25b2cmpWb/8T/+R7zkJS/BH//xH2/sIyIMQ+lL3M4utnBWbcnDOIX6a+HF9m8ZYlwCGPt3UXOL9lpq1aaoVsFb4dLizobY1dPl8wbvEDpC6M3ifTGzKdHTWPwKpA4xeuScDB6IIpQFkMIg0z6Xj7HszCgdt3X9qEDWIeWccF2A8wPIRXQdZ5btupCidrqUGyUmRsVR5DwpwSWQceBXnOdE8mTsORZB7rs1LnfHnEXWr9FT4Hwnbkh17bs1Dt0J7vY3Ru4cq5m4yx9JZlllDXwSkSp7osa5R1aJQekxJNGsghFNMX+ZTpIm44nuWsp/8kjcxwl8YkoGEK7HTlgF0bpESu6YA2FHTuBGbp8DYWkOaJXypvD3tRwTcLc7xqHobHoBJgpOHg09Hg17OBJRrEboqGmKfBUPX3E3kt7lgFZ4grvOSwWEPRy4k+T+eoK/nlgpvmd53SLddre/gUPP4tsrnpcRUH0KgMRm7bs1ntAxQFxHj2vrfQyRcMmvRFRLab2ffb/GpW6VwB6/djQCJl6Etqpt6roB3nM0DANrB9cHuH7Iz7UCepf/2nxDKV+aj+m9gYIXw46Uuq9RCL/jCUTQNbRI3Epk2Bm9DO0/PLOzsXMpR1PK1eTMxMe6klVAq2aEtCruj8MYeExGdJR9Zc1KN/3oJjRYlil/Sm37lsuUnNXuZOFszb7pm74JX/M1X4MPfehDCCGM/p0VoAB3CpOyxGoalTJ+v7SWW8fUs+HaKZgWfYk3mJNa4jb7WdfpsVZEJykzEiWqJxVLmWYJYY/ENVPsx9j1gwjEjkFM6JA7PvlrP4PyasZ6vKa/T/WRuHrEvQNCyuqZ2hHcaJvOcBWUBGFdBtGnAJzWPkVvCHNiB559vx6lveeBNYjQtcOhZ8DRO9Y73NNdS+yEQw73VebjuqS/V8aEk6+FtPovwGvmAMAR+pSPRMN8vaSmR+SIIWU+DmmNFVxa6+eQjrGHkMAJAJxI/V4Sza1AI8HsFcfun31ECR3O130MzzlEjIhVI3oACMAZ+Jzw2KOAI2FdQjrPONxahbSX3TH2ojItLjFCByaxnUdMGpijsIfe3RjlPdGMuwdyn9PvJ0nqlNFSxkYXPlSXjwqd8+KEA24Ml+Ei509xFNFxkns4Cujk+k/Ijxg0/dt7dv+sxc2TB4qArmd3ZDR5UsgH1o8kRoUzK9NAaYFNgNf4sZF1+hNFJ8Dfdh296nAl9T1l8EJFX59cPLo9yVAyk5JcRJ6AQLIoIfjFstGNtXDkdKLMAAN1F88iVgVoMyvKktg8VWpTwRA1EFMDPWfRI+5s1j784Q/jFa94xbmnw1e7M5iU0uaQ81QioanjK8dVX9DG8VlYVjnGhPnBe86NInVZV09a/It0lmRfUDADY8FDLH3UY/Yj61V0QUL9jiSI1ZDjBFB0phjy8Wk2Kb56Kyjk8whQUXcQkNbm4cvkgaPzIWlPvET3aKixun56x59Pgrpt8u9ybb2HG0OPY6GBlDkBgGvDPq4PvKrugVtJ2C43UgdidYEoS3LoTnDZHacMslfcDWZtdOAGh/Z62aYp4a+4oxRFo8nZUur76PCxsD+KQrpMa1xxq8SMHNAaewhJm+IpSibciCu0xmUXcMURrrgBl10QN4lLUSy9ASTKhGhdAwi9uGwcgAPJZeIQcUgD9klZF/77BHckzM86gSwAuOKOcJWOOfOuYYdsuLHep+txP2lWDtwJDmiFPRpw1R0xeNJ/EmmlWX01QulAGJ9gfi8bCg7wWkudG3DJr7Dv1rjSH8Eh4sCvcdCtpC4BJ4gpNb4NVSaK2O9YWB0CIZj1fWKk9EjrSt3pUuX5RjDAJCIlYWNmJKb3KIUNG1aS60GaDARlKw3jkicKeSIiOJz1KZJ5OrmEI9oDvIKDzmdd3AIroxNnrZwQ2jbAtM8u/to+ef5cBhNMhSjfQh9KPId/F8m++qu/Gm9/+9tvWv0Xm0kpk7mpNVLTJ5vzly5JNjS1v9rU9qMXh7BJmxIBfZdT4NswQtMBkbhzElgJEbHjtPgMMrLrR903dmbHeU5yWnybrG1EX6s4VoGJ7lPaO+rsESmpG1Km2WhS3wOgCO+zMLbvMgAJkV0AaYE4GZz1ewDhktPVibPrRgefA7/CXd1Jyk56SdwGx6HjFC0UccWLuNSwDCn0VVwPOlPnFYt520eHQ1z1RymLq7o2VFB6JGHIyijoisCXzUKCygysoscjcV8G9oArjof2jwrDkcWuAddDx5E80XNkDwjXg8NArAI5iQ6a1h8AVgYkqFuI6+mhKxYDSBEuDjkCNQDYo4BjYVV6WuPY5GtZyfZBrq13KxxgnZgXHwmPhksIkivGyf17NFwSl8+QhLYa4RTETdbTgI8Oh9iTpHAhOtzljzgahxxc5MUCHSJ6t0qROiswO3LJnSSA1LkBN4Y+PSO6arKu9XNj3WNtGDoNk2YPCL8gXlbgXq8966X6NYY1L4SYmBYfWXsVCFgR4CPiYFZJVneOvFPpLdf3z+f3GMjfEfOqxxTzscrApEgfJ5VpvqEAWQnZAAmvuhQy5xeXD4JkrBUB7TDkKKFGJtqtM4taJqWWDbwVndMyFbBNiW7LfbdQn3InZ5yt2Wte8xp8zdd8Df7zf/7PeMYznoG+70f7/+bf/Jtnqv9ig5S5B28uFO4mmKVFpxYVVNsQ0QLs6rGhxylxCK90jM4l9w6zH5SZEJlJhS73iBo2TBE5JT4wcumkjlByNKSZW2B2JXQxHcOABCM3T9ToHwVCmv7eFwAFEF2K9pPifghulHgLLqQFBAHWD/R+QG/cPVq2ozBy8wzSo2tCMIBn2SFSWvhOZ+SrwCGzGn1jrQwh1tBaPodLOVIc7Jo2UcSv41fLSbrRE3h4eSau0jEOnSZuyy4YZXcOBYQcurVoN/ieHFLEMQZcdoRHQ8QBBRylcGZe+O966NBTyEJaERb3FBKIuUxr9PKbXA9OdCckQmMOfeaFEQOcIFovepZDrESv4kYuJNXBqGvJQyKiHCSke1/ayTlZbKj2dYn80QRy6lI7kdSs1+NecnuF6FOUzoGwLqvA+pkbwx56CrgBXpBQo3gAJGCi6zYpm9I7fiIUEJPon0JwCI4wrLtifI0pn4pIRNhGEwAG9TRkdjPq66zvmwEh6rIpgUyqU4lYXdgwZIoldCTuo5LBBCJ40kKRgHXM+VJs/2J1KTZpygRYSZe8lE1pmYIO6/KZKmuP4Qa0x4LzDKrYWdXe+MY34m1vexsODg7w9re/ffRsENGZQcpWI/iP/diP4ZnPfCauXr2Kq1ev4r777sMv/dIvpf1HR0d42ctehic+8Ym466678FVf9VX48Ic/PKrjoYcewgMPPIDDw0M86UlPwrd927dhvV6Xpzq9LQElS907U+eoicLE/aMv7QYtqkI0829Dq6LZZUvfqrp8Ek0sdfjx8cqgKE080pa4HHacogwi0owNwCjaJ5pyqYPskKjpTQra3guwUJYA1+X7QKa3JmJf/3pwwkpHeJeFipf6FfYlQ+ilnkWvKnbUPBiOeJ2WPbdOWWP33RqX3IkkcVtDE7btu7XkTeEZ/b4RvVrBLCDZZCkPvgocVpLkzKayPwo55bz+VaZFI2CYLVjjKh2nbVfcCr3cWB3wgRxlcj16OAA9Ii67gD3JkwIwe/KoDB4BDE4OBJAcRY99EcF6YaPUfaMRNFcEoAyR/3Gor2Vg2O2j16+RPwc04DKt2T0l7ix1D/G1D7x6Mq1wLe7hBH60XtBR7GVtoxx6rfdVM97uGbCo95bbtMY93WO4S5iwfbfGgVtxCn3ryknsWEjAVZP67Um0V+cC7t67kcKW1yLGBiChyHlG61xE1zNgscMxR6cVngRixnEU+WaBhn4XUFK6XsmCHMpCd0DfR4uIIExLzOsAAZwSP32RyYys8RW99C3ONHxjkpQnRcm93AAitUjGzcCABZoQPUY1KaXbZgoItcKXLfi6lbatb6f27wLZd3zHd+CVr3wlPvaxj+F//+//jfe///3p3x/8wR+cuf6tmJRP+ZRPwQ/8wA/gMz/zMxFjxL/6V/8KX/7lX47f/u3fxp/+038a3/qt34pf+IVfwJvf/GbcfffdePnLX44XvehF+LVf+zUAwDAMeOCBB3Dvvffine98Jz70oQ/hJS95Cfq+x6te9aozXwyATYHrEj/nUmFVLTy5dCNBT1dhVOyS6OXy6EYwS10HdH68z7mcTVaU+1UzHVmO4KG0Hk8OIc5gQ3Mt2I5TQySZMYkjOMuuobyGCZ/Dpr4X0BPA9HfyvoW0YJsXPYpGUuwblw/Ag+46OOz7NdPuwqrY9OcHfj1iUFbRoQdSJtl9Okn5NTwFXB/2sO/WCSRkl8owyo9yQCcJoPBP5XDgVinHxxXPicyuuBtYxS6tmbMXh7TAXy8p5E+iRyCHK+4IPbEO5CS6pA9RBkSHPy9C4D0KOIlmqcEIHBJwxJNzHFDgZG2ACfEFECMGBFyWe3kIXu33OosWAOLBOwDowXPmFRj0HdIaT/QRJzHiY8FnYW30COKu0twpagMIK2QAs4ou5YBRjcqRdDPXJFstr8i8wgENEsnUywrMykJ5XA/7OHTHuOKP0nIEqlNRgHmMDvu0hnMxpdV3FBNr1rkB3dDDDT16CjgaOmTRNYtr9zvOo+NdQFx3iUUZJEpNV9wGgL4fECNSdtwgocsINF58UFmUAUgOFjLv0sDsSiovf2nIbh0FH/JwIAYgvR6EJMBN7zsiaC37RDwbOwesQ+76HIAVslmEpcBFV0aOYcSgJODhaAyIKv3cVmu4lExHi/mwbIvddjvaWSN0Lpi75+TkBH/lr/wVuJvktdiq1he+8IX4C3/hL+AzP/Mz8fSnPx3f933fh7vuugu//uu/jo997GP4yZ/8Sbz61a/G85//fHze530eXve61+Gd73wnfv3Xfx0A8La3vQ2/93u/h5/6qZ/Cs5/9bNx///34nu/5Hrz2ta/FycnJzNlnzCLv066CPGVbhhqXeVGSWYFsmTSJXGZRiLIeJfC1kbyk0TuZGbGfOSoIsYI5YEQxj/QmaQZHOUV+l2d1qbyuu1OK/bQjFsaGE70JnSwuHtv5RunwQ3CcJ0VWOCbi3BO9zzNnIp7lHvYn2HOccl2TbB12JykXRud4ZqyhpuoKyZEscRT5w6sX84B0JCnu7/HXRgJNuwYNwAPUZXeCQ3cMBw6vvcc/lnQqHlFW9e2SwFPDipkxyczKAQ04FHZjT6KNjqJn7YfN1SKMyADCZccAIbE4AC4LOPUEXBHXz0l0OIkOR5FwFB32KGCI7EI6kr+96FsOaJDPsp14H7ct4FqIWEVmUk4MgOqJRbwKJJSRUXeSzavCf8WNoi444wo6oBV0oUWNkOoxdrUduFVazNARL9iYErIh4tAf4xO6a+jlN/WGCVP3j9pdHWcb3nMDDvwKe36dllXoaEjhyQfdGkS65ALgXUzPpkadqXsyBMfLOWh4vVnHx67lEzt5h3QVcZ0Y+DjK8KzjEjObss0ZN66woInp9OP3deRmkglJelDUiAy7U/SLmt3aFZOj0srJFXJfl/4Ow6ZYdkm/abV35fZam22fX9ZvXUFnTQG7pdV0vNv+u0j24IMP4t/+23970+o/tSZlGAa8+c1vxrVr13Dffffh3e9+N1arFb7kS74klfmsz/osPO1pT8O73vUufMEXfAHe9a534RnPeMYoVOkFL3gBvvEbvxG/+7u/i+c85znVcx0fH+P4OC989sgjj2wWaomwyu9Lkgw1QuZGy5EX5WsheiVQ2cgyC2ywKWQoyrTsegoZZk2KRvUwdSvn6twoeRRI9CekUQK5bOrQKuCjzDZrTf3mSeOiAr4ujiN6XMwzS/Gbk+lnUgI3aUqMhBA5GVYvmUCjRPWQzGQ7ComOP5CZcoguTQyvJFcQJ3G75K+zK0Bm2HpR+yMXgwpCRVtCEZfdMa64GxJqrMnIXHJXcLZV1ktoLhVQDl1mVmAlWVtX8C4md8lKRp9Dt06aEa7TjbQoAQxWAOBuN2AV2fVziAHXRtENzIZcdgEfDR0OsIYX0HLFBaxinnD3BBwJWDx0sq4QAXuUE6hdCy4BJnXfHEWfRLn7NCQ9S9K7yHXrdewhIIhw+IDWArJEt0LrJCr2iCw0BkagheseknCZ751PYGWP1jhGn4TIB7RGcCeSxdalbMIhUhLLZlFsTK4kzQIMACdykzrKOijvAufjGfKgzSsjOwYmAWkNqkR/+Ii09IOX5G7ySkTFBwpACCB9EeVFyFE6Y8+pasQSQInaH/A7pOJ5LaTidNNwdgUDIBXLriqhvhqKrGwK373t2ZFtAhZsvadYwHXkJmrVu7ObasMw4Ad/8Afx1re+Fc985jM3hLOvfvWrz1T/1iDlve99L+677z4cHR3hrrvuws/93M/hsz/7s/Ge97wHe3t7eMITnjAq/+QnPxkPP/wwAODhhx/eiKXW71qmZt///d+PV77yle1GWSAypxJf4tpRl07xUiWQUQExNYFsTZti95GGFOuCggWVGdVvLPsoxuxTBhLVG739jtTDpY7NdoJAjsqRjhYBSUPihtTPFgnhxvUkoV9qr7047XgZnDmfc5/Y5GwARm4e1Zvo+jwKVlRPoJbEsm6NS36Fa8Me1sHjkj/BjWEvHQsg5dZwEr564FYj/ckVd5RWKD6gkzSIHtCKw2YlnOKysCmPhksbgto9BPRujUfDnrAMDtcljbxGwxwhIwZ1+9jPDHA8hhhEXwJcjxE9eDLci4h2iOmnYj0JGGzc49ZYyW3dE3dOWs4FzJpcNu60QyKsYsQQ2R00RHYheYIwMl50KXzso7Hj0GcKeFRcQeriuRY7OGFY9mnAtdjjijvhMOeozIqkl9drMcJjzbGyih4r6kSzIiskR8Jld4wTTW4nLrnHhgNhx1a4W/LX/H/ry9wOCrirO8awdtj//9n7/2DZsrI8HH/etfbu7tNnzp35ID8GEYVoVIgiZBAYUMpShCIW8QdWURoBDZAqHAg4QYEIKJhIogbRBB21GIlWSKyYQo0SUcZfUREVkwKMwRAthiADyHzv3DOnb/fee633+8f7Y629T/e558wdZrjAqrr3dO/ev3r33ms9632f53nR42KeITM8khbAXhKhS40bA8aQsdf2fu9cVDJ3DIx130h6J5NEBomRO1H9IFcPiz10rECFFKcPEJ+VKp1KXstKNs2Q5491F2HQiQqJo62rd0zZA2hxQX2TCIhWTDGAKCMkPSciAVVNAK0npNnKoM1UQOA0WjZql+CpXHar+/MrkPj66abuec973uMBhve+9713+/7PDFK+6Iu+CP/zf/5P3HHHHfjFX/xFPPvZz8bv/u7v3u0nVreXv/zluPHGG/39hQsX8OAHP1jeXMooyNpZb/YdQOVSIcxts46TjJBkn6wJZ0spWUFAGp2zFQqsAUJJ89jBxmFgT+vIWDsuIohx1MTDxnqapN4L3DCqYMTx7alsAwYwENxhNlrVYxKHWf9aPCYhKsGTVV0xn20wZKmgK4ZcEq6Xy0U4aNdSzVZD+41GUgyAJI1+zEM/SukslT+yoM5TDVZvRwzYojuaLnW5FQxcc4t9Te8kDuhJlDqzcFF/tuzGbgsUaXKrxNKgBFZL4WTleIjTKwMoBNTbM+GAgJZIAAeAo8xYKXiwdI3h0xkRDjO5ZBvIrtyxFFALSf+AgDsyq0cK9DsWEm2ro+GSJMVkruxrDlggq2Sa0DFjpWmfjZJ1ewTsU+/8lJpQawDQpN4tBOjtk0SejjArZFo1cGuDOP1aWi0i4yjPBQDmGeYoPBUzfbO21AiLSJY1NQhCrxGWLkfhrSjp1pRN7lLbDGAAF7t2BKz9nm0ykKiYuikYGEUSgRK1VLO3EZndTNmsi+ECYiSlSiX6UgOcOvJSz89svxEAAjhnkKJVNoZM1BMIAlDYeCZRc7zJwkR1FIS2qny2tl2qyrPa5E/7y8vhpNyTQMdzcJex/RXUfvu3f/sTuv8zg5TZbIYv+IIvAABcd911+JM/+RP82I/9GJ7xjGeg6zqcP39+FE35yEc+gmuvvRYAcO211+KP//iPR/sz9Y+ts63N53PM5/PtH9blvmtyVU0IO41Ubde+d4GgLQ9indY5KTxa81R8XQryL8ZxVKh2im2j8FTsvX5G5o3iDzfGqR2MIyzeKmXBNDLi0RKq6oToNghcySYZThT0/fLoWJyB2CZHQZbqkWJv8NDyvBmwN+s8rZOVVGuVbAFU4Xzpoe9Mc+eDgAL2Yu8KHZuxw8zJwkaItx6t2OAw7WE/bHAh7+GzYo9IySv4SkXfbsSX8Fo0lDUykHGB54jaoe9T7zJfASKMGbKneLJub5GUhVrSWxSk4+BqHABY632Sq+N3XGr0AAJWNsxiLKfGcAGSCpJbRNZdsXJGqs4/wYi4hARGJEILwvlsXBPCmhnXBNnnTN9bRKdV4vEKkEhVRUhOCszOYSNFEnXwX9BYzTetR7RQ0JIojKJWPeAScDPLSwgu955r1Ezcgou3S9a0nxWZNBAyCwlrjaRYwcGB5bVJk+U+ZaQsZO+sAIf8Q5agg/FQDOTrAyIpGSppnKwS4gFOjDWiO6GKksQSWTFQE9Sh1iYLNZAJg0ZakqaHKvAiK8uzl9uIOOTimUKkpHzlvAXeTug/TdsGTk47iazbSeTZS607TWHVfeJn2iekvfa1r8UDHvAA/ON//I9Hy2+++WZ87GMfw0tf+tLL2v9l03FzzthsNrjuuuvQti1uueUW/+x973sfbr31Vlx//fUAgOuvvx7vec978NGPftTX+c3f/E2cO3cOD3/4wy/vRKbpnl1ovG6XClduU/McO2wFIiYclDqCMk0HmaMsxcm+tdM4dqpGeiNUqSAA6onCQdNBQYmwYWLQZlGVBm6T71WQjSQbgNRCyX5lmT/gxjuBfc6jZXL+tk8GxYzQZMRGFAbFH0VC602UUDqpysJmszM3a8s+eHQ5YuAgFW5B4jqr0Zf9ZuMy4yELL6HU6Mn+13xQLEKyzi0iZXRmuEZZLN8pjSzczQ11P2yOETwBAUxSgbhzrkbt+LrmxiW8ZnUvQEKkwAchV+DnOIDoNWXTEtRuPvuDa9EUTx9BIiBOsAUwJyXIknBd5C880tIC6PT+tP1GAiKZIy1p1KOkjj6WGyfebht+Og5YUMY1YSiEXUpYhl58X/RarLUeUqkDxHrejINwETNNtZmke6ZRMnMMBor021RYsiw5eTopQNpvrEI1e+TNvFZqqXIAY6/p0Sq51mr6zNsebZv0flawHRjNYkCYJYmsGC9LEQIZUggVcNBJhBPYq2riRVFX1vH17dmy+64uHAqUCIoCJfLtJpHWSPrcxuO1wYwXt8Mef1s0uPDrQvk7rYG2y7ZhWx9cn089ydzWpqVDLrXve6B9uhFnf+qnfgpf/MVffGz53/t7fw833XTTZe//TJGUl7/85XjqU5+Kz/3cz8Xh4SHe/OY343d+53fwtre9DVdffTWe85zn4MYbb8R97nMfnDt3Di984Qtx/fXX43GPexwA4MlPfjIe/vCH45nPfCZ+6Id+CLfddhte8YpX4IYbbtgdKbmrbYqop7/8aQi0p/icd6R86r9boyo1S74q5OUAZPqgBozZ7wwQGLnad26s2qniCkbhqwDjtAyVELNHSbRjy1SBEF2XgUKGoOo1qn3aMQCEJsM4gRRKpWOzwTdiLDOhT0GizEwIOSBERpciFmq4VVezDRpZscrG9llNmATg3IQIxkaLolwTV67YEY5E67PxCAEr5kJbD5ZWRXis/AlOzBXzsR6tSn/XXAzRFihRg7Iv3TeJbHjDVmjPpuFQZU32lIwRhJeUEKkQXgFN4aCYv60YAJd0jkVDFspDqbUvBoL8NmFGJgEu5bsyVixgBywg5xqN1mTALfVnJCmm23ODg9BDx0IsKOHQigNCKjvX0RMjsR7luRvBeQ0lZHR5jpmmctbcYqZpuYBc+CoskZRNbkuKx0Fph57kt8tEkqYDYwAwiyJlD8RYpwarYSZVkSHgpVfirJRoAGbNgJ4iiBgpBeQk6jMw5MHJ+hNGSflwZOeeyJfVEKRFO5WbYtYwddqHdF/cAEgk/id1JEUjKyWqCQCynsmSOeqxTI6cWfsRO9Hyu8tOqbjPZhYLhy3RlOkk7Fibkmd3TfROG9muz8/eE+3uo+9NHkt5jO/69ldQu+222/DABz7w2PL73e9++PCHP3zZ+z8TSPnoRz+KZz3rWfjwhz+Mq6++Go94xCPwtre9DV/7tV8LAPjRH/1RhBDw9Kc/HZvNBk95ylPwEz/xE759jBG/+qu/iuc///m4/vrrsb+/j2c/+9l4zWtec9e/QV2U6lIKn/phOo3CZzobsOX1si0W/Ke2jq6BChGoiWN/FOsEiMAxjgEHZCblgIQ0bDyZcRFLLlr2A+eoAGUdz4FXr21GZqZUlgNnfU0M7ZwlnM3mMKuNU5CZJQDOhNAwalXPkAO6IaKNGW0sbp+ZyVMpAwcsYj8CKdMIyaC8AgAj6ekybtwDxYrW1SROQOrKWFTFYhMdCkclcXDpsRTES0rwlMfG5K6WxojEOMxRAZK4rdY+Ih3CSKrboihq9kNJ+cjALj4owVMwcPO2Eu0QXkj29wIuegZuzzMBCmABJgzMSDguPbOnbRZE6ByQFQ5MSxaJIQUtsk4PI9nqPQh4BKjV45sJXP19AjHmEM6KqYJs+xr8ARLhmmmKbuzwmxzAdCrd7nNQ5U9GC4nMGYBMIPS5QU9RfFWyGvGFhMAZm9Q4mBwqXoqpeOpGlr7xz8mjgtkIsB5xNNSAiuVc9sU2B5l2QVX0xAN5+t6VdHaIAWMuCxHKk16lkqr5DpSbltuAMASQeUgFApL0H4Qq2mGclC1+KVNX0Z393bSY611pdRq/XmZtKpyYpvevtNDEFdYe/OAH4w/+4A/w0Ic+dLT8D/7gD/DZn/3Zl73/M4GUN77xjSd+vlgs8IY3vAFveMMbdq7zeZ/3eXjrW996lsOe3OzmP4lQNU0BAZeWyU0rJG+pgLyrTY2NTuSomDY3RqDRWj2W8rGISgAQCVnt8L1WT5XqGfXzVMLATFRIrwZkLERsIMQiJ1VqqJ6dGUEPwFgx5AhJX5uZmyt55GPzlijVjcn9J0zNEyoirfEKgAJCLKrS6CA1cBSPi9CLkmPk6ZGc9xFAmFPCJrc4zAGfFe90C/yotXlkXeNg9Mdm82uWmf+Serd1N+CyoAEzGqQuTshOzozVdGh/ogayAn8LYhzpYLZmcpLwgSmZtHM9yqzpFwE1C8poHYUCgEQ6zFofQFWfR8zaZMCX/RngEXkvOwk26j16lEVdFEk4NdYiJOrjqSY9b2/MWIOksjEkpWTrtcoDMvv9NbeYQVJALYAjnvlvHlXx1HODgFLMsdfijzEIefYw7WGV55hr8cGNRmuWWq8pc0CmgGXosOEG8zDgwrCQ5UwObiMxhqQ8lZgw5OBpxxQyNkPjn9slD4GRhugRQpBEDzkQeAgFgASAwTB7/FHkI1pAQxQ8HoVUrG0k91G1BouA2jgc4dEbE29ZFMVSwz5IBwgXpibAWr8Uo0RNpvmGbRXbL1XT5zT95ElclW2fnRQZ2dbfH4uk79787m6fbuqe5z3veXjxi1+Mvu/x1V/91QCAW265Bd/zPd+Df/bP/tll7//Krt0zbadJ4Vxq+237Oa2RWxXWvKQnyvTBP3Yu1RSoSGLkvW2aJWZMrKHldrIPAxNcZl0UZL7FoQIoVT4cQMlzT8OWBAlpN2pYFQEm7Qwtz6SnC+WfxJFxWwErRKyGWdkdPQGg1UHC1u1ydHVPIMbRMMNeFCt0BKnJI7PhdGyQA+DGYwYM1txiGYrRGlDksWbSFkjqynQEpMpMbZXnTqoFoPsRzsxB6MREDRINSkxYqXLHyLAtZbeej8QeaXBAQwLajrKocQDhfBhGXDNphAUiVdbz71mM3VYsvib7NBS/EwLAAnIWRGgpoGex1w/MWHPAUTbpsWyzIJ34s+2DxDqfJOVTH9eKFJpBnJyDqon0fu1ZIi1rI72SRXgKspYyBFIBucstrGKyScUTAg7zwqtK228wDz1mNHgtpaSouueIVZqPDPouphaRJG2TIT4qVsPJ5e9hwJBnfu8NOWDWDGgioRsiBi1qKGq1jJQszGgqHwUb7rpcBkgOAA00rt+jnJPAJX0DwPkkHi2x51CfSfdOkTimAxwALlsmNYH0yUmG9vgBFLP8yFbvxzeWk6MYwDmWdM/EgHI68Ro5a+/ipGxTSdZt28Rx12TytO3ekjB/GgVvvvu7vxsf//jH8Z3f+Z1uyrpYLPDSl74UL3/5yy97/58aIKWOflyKTX7Sw3KGh2BriPNSkuQpDwWQkCo1JYyaWSMXPHKLZNLPUEYJzzcD6vwqMwgOGBUSlGrIJdUzAihU/QWqNBE0x84VkOESacnq3RABqBQTgKR4WDkpkFlBzoTZLDkJNhCjjeVa27JALPLjKM6fQw5YxAEz5QwEsNdaaUNCo7VohBci1vZzr7xbK3Kkyu4i9J6+ybpMwEtSEDGg5wY9NzivYKT2TTnimRA2SVI8M2Ts04AOAT0CPp7n4plCWci9SqBNCloMqEhKJmDt+TYpAGjKGDFO02uj3+EoyzEiDVjlKDJgYs8imF/KPqQmz5pJgAjUqVbTPOL6GrHmAWvIOWUACwjACAAOQkCv2x5mRlZ+SwSwDBGZGWsu5F0DKHKdWFVCBqwkBQZYWigpABsQgxQprNVOYLjMGwBmKCZ3+9Q5wXjN2csR7IcNPj5cJdJzBSvgZgRAAOEtmRPuZoh+Tw0aWVmn1h2N53HAqp/5I2FqH0sJmdInhOzusyCMVLtFG4yStlHwzaiiKvo82i0rKZkCTuzRLOR4OMAxK33H+QpuvJFEWWnQ59hBTYWUqnXl4SWJrOyqqVZZ49fRE65NLbdNGLfUOtv62RSg1ApOP88T2hR0faZ9QhsR4V//63+NV77ylfiLv/gL7O3t4e/+3b97t/FMr3yQUj8MzOPXWwmrJ6t1ju13G6IHjgOUS7RLpnyUae+k2anLrKmA9HuxhXIdZJACHCPPsgOSbLKMakbnwMSiJlVH6mQ/44Lk8rmR/XjGhYfCBLRVSocg5eoDQCEjJzHBykRujpWVOOudflUBuV4eKGMRewxZBpXEhL2YvPaKcVgWocdSCbGtKkKEBMvqVTI4Z0VqyyRcYAn93yfeCQBac0dm5TMCgnIdZkhY0IAFDbjA8uAtle9Rt8wBawSsGTgInUdwAAEh4Ow1eXoOmKvBHCADeGKJ9gDAYQ64JmQnth6EDCBjRoSk96TV8akjLfU59RDQutQoyZoJPTIWFSlYrpcqeVgqDK85Yxkies4eVVmQDfiMoOHzBRXbe7tNgIKt7Yk5CMnP7TC3XsLAzqGljIV6xKzBWNvvoFyew7wHq/kDSA0ge91zxFGeexQNDN/OgCsgyy6mVuTqEMO9vdjjYmpF0aP3ZJcbByQxZGQlzgYSU8JO1UBDDmjbJJEUVpDFGqkUoo72R5AoS1CvIU2/lkqCCkoigyKpNLlcUL+8FTAJto5FWywio7+34/N6rDYSPzOYgNwERKtEUkd1rd+sCbScd0eBoRO2bXYMClq2poG2tW02D+Odjs+zXjbln0z72nuQl/Lpku551ateha//+q/HddddBwC46qqr8OVf/uV3+3FOMVp/krfpjWw51frm3XWDGuH2pP3exaJJuwzdyv7tYQr1SuVvLp2K/bPPWEGMEelshiWvdV/agTnr395rBIXqy0bVdgSfAfqzUu/T3tN4XQASQq7ATIgJIWYELSTIKKmechnIpcazkDxSEkhq+MxiQksZ8yhVjotZmcyMm5CxDB2uiuvKUyOj0yJ/+5raMYBiElZ7XRcCDBDCrqUarHbMWvNoyzC4M+r5vEDQQXelEmMDMkHTOkfcYkbZUzcZ5PV6ArG7nwKm2inXJWqUxC591n9HWaIhrUYrrPWa6vHaORq9EdBROC9ASesAEgURE7cCGlYMbDirf0rAQYhYkLjPBJISBi0RWiLMiLBPATMl9y6I/J842sL3G/QaLvU3NMt9KwsAwMsJWJrHAMqMkoBGhUKJg6SGlKcit2V2borxjq6Ka5Gr62c9B2xyo8qwjL0o1bXr4pX2W9k9CMBlyE0VAWQGkkUQIyO2GRQZQSX3x1KlsXqtz+KIiF5HNacREdg2hNEYxtUj6ITRcgxfbdof1X2kO1tT4cQBYzBiNce2ABT7W/tEneS2fazVfexZwMSUh/jJQpDlu+HfFdD+3//7f3jqU5+Kz/mcz8Hzn/98/Lf/9t8uvwbflnblg5S61byNk5bVAOQknf2pD0v+z97Xy8fH1jvQrPBNfjwl9+oyKwLorYqCuCNsQDGESjyKingUvf6l+fh7UlMOGpS4V3ecwKhTFUJu3YFhpFSwkHYaInImmaVmzeOTzEptthAVtAw5oI3JPSsCRGo8C4O4guqAsh87nGvWuCpuPHpijrLmh5EQMFPLdZMcAzJQWiXjw7xAxxEH4SIiySBof40LIfV5RH58pBbttv8F9V7n5SD0WJL4gcyVsHmkpNqSEjGOScZB6H1Q3g8FxLRGulWOx4oJd+RyfwonRXkh+ru0ZBEN4ECLHprFvfwzXxTGNSFL2oiBFRMOcyukWhI1z34gzHX/cj6EOTWIIBxmAYC3p4RDBTBRb64jNql0QMdqta+yZbPZDyg+LQsit+ifqYcKICmwvgZaChbNyyYjYEbJoyiJCee0zlKnaTqg+OCs8gy9Eq0SxD9nHgRERmJ0uUFmwiY16PQ6N5Qxb+SYrYLmeTP4eB9DVj6VSerZQx9eU4dF+WMgnhI5aZYbHjno+yNoUc3KIwUQnlhuy3Pnz6JHPeGmjt4XxNInyHpGpNd+aZdz7DT6XFsj2Pv673T5tF0q3VOvt00AsY0LWE9CpxPQKXevXv8ebXQ3/PvkbzfffDNuu+02/Mf/+B9xcHCAF7/4xbjvfe+Lpz/96fi5n/s53H777XfLcT61QErdTro59aYfmRDdBafEKQA53azBeigDKFGqj24LZQLSy4cwngnxuIMrJ6R/LJedxx2b81SqmVoNZEZ/FZQABkoEyFAGRo6yNXGWqWSgSDxRyiWWWSlpiiblwkOxzr1PQlSUWWz28LsNJk1IaIKE8NuQMNcIiZip5aLaqWbcGQFHeYaeG5URD1iGDQ7CGufCGoBU4BUSrPyd2rfvk4Ac4Y1INGWmkuYFpRHfZKPbLBS03J5nONLB0wzehHxa+CktSW2cHpIeWuUGK47iGwMhtxrHxJrU1yEc5ojDamotkmfS725utHYrkVdYXpLU2VkQa8VkxpEOXjMSULHmjDu5x2Ee3NhNoiuExFyBE0IEIWgExQizcxIl0VJ5L5YFsX3cJwzqnms+MRnXKDBcs0RDTPLdQ8DLEc+c5Dyj5KBTvnvwv+aVAqhpn5Jvr4ob7MUee7HHQbMW4rXeh4MaBrrXDhWLfEstWrO0pBHCPQqjM2GvjkxaGdnI5qEAlboej08IJhMDAMX0zSopOzgp0Rc3ZqybAp+6P+FqEJeozGSjKmJLU8CyrXr75P3WiVndzhKZ3ka+nYKQKViZ9sHbJqmfaXdbCyHgK7/yK/FDP/RDeN/73od3vvOdeOxjH4uf+qmfwmd/9mfjiU98In7kR34EH/rQh+7yMa5sTgrz8QcTOD7g7+CmMDB+aHZ5o9Tvq/WnPJNTVQqd1sSwFkMJtU7Bj4FrZiGzRgBaIZl02WimZQBlYo9tLrKAdoraoY6iKtYJViRZ56nYujReXzpDUfPkRIgNewzblTxBQvyUApqYMW8HtGF8Hboc0UCknzZw2EATSIrCnWvWo23MP6NuraZOZOArn0U1/5LXjAX16DjiXFg7IdM8T8ROH9W6g1byBSIzOgi3IaIcy8iyJkMGgDUkDWKy5DnJIN2xcE6s+nBWVUjPwYsOCogRnog41nLFzJD0TluFsOyVgaZUHVOURAIWEhNmAV4p2STKUW+tVnkpCfI+QUCMRUOCLhNflXLzRJB/7x7s2Y0ZFSmzqYZkn3JdjBRcfxdZVwz37DcIqgCyytIIYpPfc+MAwoz5lqHDHWnPPVPWucWQAwb9vHCaxHsnJ8IsDkCSLjFEASVtSGAmbLhRZQ/8ns4pIgQB2RTkgaJATqKVFA+rgoYKgKnM3fxVTbJFeeZH43Euk5Pa2+jYpMWjOBilDohZjN50FuG7JhLn2VRFM6a1w05btwfVZG3KSbF2Gqv8bf5X9fqX4qDcm+1yUzZXSLpnW3vYwx6Ghz3sYfie7/kefOxjH8Ov/Mqv4Fd+5VcAAC95yUvu0j6vbJBSt12MbqKTWeW+2hb53CldE3fZ4Y+aRVBSrccQGa3vd9t2VZ7YPE4AxwDInu6Bl2yvZ10WEXHjt6qTGwGWilhbR1hYVyAGcstjfgpBQJNGVkgdai3FwzmAKSMlQowDmEnqpFR5ffNLGXLALCS0MXnhN7ls7JblkRib3OAq88FQcuRhWqht+lCIqs4xIXWGbdwATgiyG3QcvSpy4ujeKAZIrDqxmI9Bt1UQwXFEjDWAYhJj+7tQBZIRTFeV0met6ZCZRmDO55mnPkohQk0HqUrIeBwZIpnNpAZ4qvMRpY0Am1ZByYbNm0WAxjVBRlFzmu1Z/s0JOJycG1Dq9divZq9FpZSxpBY9EnrO6CswYhEToLjeGugBLEWlRnxUUkQr5Zks1JemhxV/FGdZU2X13GA/dOhZIioJAQdhhcO00P0nJ8+2MSGFgP/fsPTaTkOOTpxFHBByxCzI/bcaZl4deZMjmpDBjXrP5IA+BbXcN9Ci0RZ7Zq3AoIGVhGKRXz1juRG9TWApPrjNuM2dZTVCCgaCRjWdL+xREzVWTOQkdhBJba+cBUxlRTB1irniwXnzfimIfGgXUJl4p1xysrZLvbNtnV37qs/9k4WLYu3TDKRcvHgRzIzlcgkA+MAHPoC3vOUtePjDH47nPOc5eM5znnNZ+7+y0z3b8pJ125bG2SF745RGy2nKVdnhwWKgZJsTY9mWRutPl3knYd8lCLDiRv5Jh0aqxJEZt0+8cokmjerxoISDZQZWXaP6pfVD1tlFURr4Pqr9ldLyk3RP1azDtqVczRoDsQaEZNmQA/okg24TsgOW8aXLOBpmQnIMwke5/+yCVze2qsXmuyGcBiFUAvDaLwfhoqdxrgkrRMroEZGVB1ErQVqVMFuxQGtr3b8RPa2QYI8CTNZcfF1s21TxTKz2juwvSLpGVUj7Co5EBTMr1wClZo/JmxPEpK5XpdCaI440ZQRAfVqiSpqhVZalWerG9u3LIamZwzwGJB0zlkQ4oICrQ8RM73Fzp+2R0GrcpNOIixFpRVUk+1gQuSX/TEm3CyIsiTR6AweGmcUXpaUBAdmN3QDgQl7gMO+h0++3CD0O4kW3yzdnWouiBYgLbc8Ry9BpBMWs72XdhrKXVpiFhEXs3S6/LmOQckDKAW3MCCHL46peQMUnyGb39cRJ/067Kq4jGuyTCt+GymuunnPddOxOW61HzH4stiis9iMcqfQpjaaSrd+xyZCDgDrSTNv5JxVAqUmz9r6sd8Lkb1dNH3e/5fFnZ5UZfzJFWj7F2td//dfj537u5wAA58+fx2Me8xj8m3/zb/D1X//1+Mmf/MnL3v+VDVIuRZoyQtY2W+ZaYrwlxcMpnYzwR6fBW/+VbbiodZrmOBmtPm+b0ex4CLkirzk5bmKHL/QQqgqMoYSapylbrjxUKlxm0WkQOyellioXJrqu6B2zAhUAsZFibG1bwEcTE2ZN6fyzpkksrdOo5NgGiNpwCxBDrjvTAn1u3PY+gLHWejBm3jajAfuhw7Ly3ADgBQJ7bsT2njIWNHiRwAUNYq8O4WyIkiY74AgKFMRuTCNHrpqJKp8VMmhf8Rt6DlgpkOg4uNJG9sm+vyNusFZeCyA8mOl2Bko2HNFBSLpmlFbvd6Gck16jJhuGpntYDdYKMAFKCsc6hcQGkARsJDA2nB1wRAVAG85YsamnRNVjjrgtSh2go8w4ykZ6lcjJXAfB8zl72sl/K1Lpd+ixDBsv/giIkmdf+SuW8jH1T1SAMg+9qnsEvGQmbLKovpqQsKcFIaNyTzKkkKVURpb7zkjcANArQDG5fM7BfYCYCZxLzpWCFB/0lI/9LFUqdfQs1iCGq9fV517wExgVIqRqffv5a5+kaXNlTyDtJ8b9TanvI6ZuO4mxVaurv9cqn2NtF1DZJjc+rTlnva9dYOSejLbUHeZd/XcFtT/7sz/DV37lVwIAfvEXfxHXXnstPvCBD+Dnfu7n8OM//uOXvf8rO92zixS17YYkGj8gJ4GWutVpnjOQvkYusyMfgnx8vap+D3IuiqPE0sP7rEjWy0014wHGACQDuaWSvrHl2g8J8dVmWyXSYp0dQyImHNmxCPTQlIFMVcdrUZLIoMiVUVVR8DSNpHBq2bHNRmfNgFlMzsfITAgqOw4q3Y3KS4kqBx1ywCrNpIZPBtowlIgKSlE5MW/rRlWLTUp8xDP3RAHUL8RBCLtTbA8BLofcYqazeAcfoGNRlo+lAyV6hiItDoNKgeXeaSHRl/N5gX3qcBB6BP3cIjVZzycyqyRY6gaViI1FCxgLJOzT4NEdA0Ot+rGsNdW01gjQnBLmlLHhUgVZuCXizWJFAlc5YunOugW93pGTfl9GBwErAcCKC39I0kOF01K3gyBeL0eZ9ZjJ15HfT64xKGGJHqvcunlezS86zHteHRko4ExSQBtfr+foCh9AKmsniFV+7yk+ISmvhpkbC1o6rctifR9DRlCAYo6zcr+ySpFDSY0GeZh4CPoeYM4SscgkBQXt2ayUcfbMUkKJZJJcxEyquiPI8wv1RbLIiW4vExM1fYuyXhhY00C6f/2duOalhKD9jFj7U81Pmfax1u9sSfGcKB64VP95UvpnG1nW2i4gU/NV7sFIyuUKij7ZsleXaqvVCgcHBwCA3/iN38A3fdM3IYSAxz3ucfjABz5w2fu/siMpddsFTOzvLqBhocYp52QagtzxIJyKLHtsowBX99Tn6cc1gFN9DyqzHg9aZEMVuh7zCIRPbe/NrdIBjJJqXTWg02g2sBGqCAtBybMaOTEuipan5yHA0twGUJiFnzLkgGPeKDG5hNdmqykHdLlxUBLtMwUqQw5o1OitVclwb0XjdOacNNWy5lYIlSpbjSQz7IyAfepwTViJ0yzGFvr29zDPsMoSoVjlOdbc+Gw8QoizFr0Qwzh2u31L+bSUscrifFpHPzIT9qlzZZD5rNQDcR1RCFTOca0qpSOeqdtucP6LVRcOYJczG0/GztUcZqV+DiEB7qNiPi49S4rkkBtECJDYcMZhFv1MgNYE4jE/xdREViNoVqV3MoD9IJ4qUtjQoi6SNookZOKRO61GtmbIzhMSgm8epe7stfnbuERcR/+lytXbkHAQ1670cbKtAjmL3smjV7hQzBJRmcXkURTzTQkV+TtqCshSnqTqNvaIo4Un5VlyEro9k6TPXkVYH0UudRdZwYYvi0CaVZOWqhmPhQkw92h77vWGKOkeBSaSQiYh0/pAX/eFPJ541ccjOvZva9s1ITytA/hJ/e42cHKljfxXUPuCL/gC/NIv/RI++MEP4m1vexue/OQnA5CCxOfOnbvs/X9qgJSaAT5le+9Q9pz4ftf6W1JHNWn22INpnUaM45ApV7MUAEhZOSfh2IzBvA5Gv1SdA54UFiz8EYxIdZaLtjy2rFRtG6wDZCkNb0CGtqSIMoEG7XDrCEqFkGKT0Kg1vlWVNaASFIDYrNVmqlE9UswXZRYGtCF55eONKjTM/nyVZx7alwFMXs9UZhwUgHQcRQUkQ5MCmeyE2iOeCUGTIw7zTLZRyWsC4ZpwUb1SZjjiFp37sZADhU4HRhtMgxJvA0kKJ4JxQL1XCJZUTAEkVnTPokHmF2LLD/MCa93PNeGiX0dAqg6bBX/PQXky5EZpRwqwzKJfiLKSOtownMcCCB/niBvMKOOaMDgB1sBI4aPAre9bEJYhIkCAiFVTNkCzYkv7EHoUg7ekKafDXOoY2bWxKJX97aoHwKJkADTBI7WRFtS7z43JkDMHbHLrBSglLWiVk0vkrQnJzQHXqXWSdRPEN2Wv6Uc1pkRGH/T+ht7/AE8A+dgMBeqXot4pWcGKPZNV1FO2RQEYQVI8/jza5MSyAzYZsXPRVPD0NKyeD+XKINKaFNOqUshbIhBbwMlZ3bdP7G9PZeOwY9iyEMb0nO9pgMJ3w78rqL3qVa/CS17yEjzkIQ/BYx/7WFx//fUAJKryqEc96rL3f2Wne0xD66DghBzmaaXF2yIt2yoin6b5A12VRK9bZYcvlvYWiqjIYiOCrXY8tYFb1SkBEioO0Po9NN7WOkEn3FUEvSkfhQAPR9czuhFptrEelEFNdktwJkJOAYDajecAJkYDoG3SyAI/M2GvEV7JoByRzAGBxAofYTyI2AB+ENdIHLDWyrdXx5WoOZCUy9AjUsY6z9BTxpI26BG9/ouBjMQBM2CU+jmf9/Q4VoYWzlkxgu5BkEjIWlMFVrNnzdGBSB2ZSSDMqMiHLWohd4dcUDMhW3OjhfKiS6L3qcMRz9BSQsfyPcz8bAXgPqHD36SlfHeNpICATY4eoTFV0FEl2a3TVqIAgr+WbCM7VyQAOFRibKSCm9csVZkXSqQ1Qm1SNc/VQZQ9PYSDsozkSqAIucVF4pyx5oAlCWg55BJVmSFjzQ0u5AUygtfosRSeAZeeG78nlmGDD/dLrPIMwYGjELZNFdXXESaN2M3CUIi1lDFwcP+USAxoirKJCd3QgIgxmw3o+4isYQqTKLNFHRN51ARgoK+e3cgF9BunZcIhZ9JHwaIqtiqJ8MbUfqNnWJ9zz0oGAhILGX/QhU0ANVJ7iIYMDOl4ZJfkWfZ9WMu7nWWPm1hu6ZdPI0Xe1rZVrd/V7g2J8uXySq4wTso3f/M34yu+4ivw4Q9/GF/2ZV/my7/ma74G3/iN33jZ+7+yQUo9hak5JyfdmPWDUYOO07oj1tse2zftlulta9VshStr6vGx1J9EeSNAdQ9bhISg6aAqRWO2+bYul8/8dbW/ep+jM7AaPtbBepiYS2eZ1cQtMqAVj2EqHwUxTchYzMQ4q1VuSQxid1+TZgMx5jpIiNFWHtmVA8Ai9DJIhQF9ij5LthlyRMYqz3EQL6KlARlBohQ+O1ebdABHPPfBWer0lNQKSF1PYXJo8y9JWOUWPQUsaUCPoLVbir271+hBwIE64nZcqhtbFOZI+SaA8ifsO1KPNbew2kFBOSpiMNdjXZGBEzc45BJR6jlgBYmWCCk3+foG0KwUwCL0uF9YqfpIUxlgJe8m92gZl+KFRz5a0mrICjhaIiwo4jAPIh3W9Weq4AGAADmvJaQi85qzf29LR1lKyn4Pqe8zAGGNI56VawR2sCJgUOzu11lcaI2vFMDiQKvqr4YGbHKDgaNUpM7RgdsiDkqebTBw8MKYcg/IF5coYAHagKQ2oVwV1ueGjAGi28kEQBCec8T02eIGoF5CJCNwofei8cjq5uR4FKBiMmag7IM0RSN8ExaUyRj9prIilYjEZbQ6wiwXbku/a63+7Kxgot5Pva31pT5GnG23n2lna9deey2uvfba0bLHPOYxd8u+r2yQApSHaroM2DHob+GebGu7CLPV652W90DxSzkFM37UQuUCqccaAWuevObR5Eo7s7quD0pe28LHSrCjJPltI8zWzT0cDIgEdodMBFbirLyWCIp24hYlCYwYTaIpNU+GFNHYLDQU8BEoFzt8Gte0AcSErQ61H6YFDuIafW6wjBv0ucFhXmAZOuzHjUdj1irjtdTAPnUy2MOAgvBVOo7oIaqPAkLmvq4MgKXQYDIWMaAOs0l9Wsr0t+NClLVBvyawBhJeiwGUNbe+f+OdWESgp4jDtOfeIRGMC3khRRQ1ktCnRtMdUpX5gHrllRTS78fTvlxPyjivr2eUsOIGS5SKtwtKmiIKnkYy8q+Z1S1JAEnPEp2YEQkXhRkZCS2VqIxytUet54xN9T4ACmrY1z1QrsmGo1dLlvtBwMiaW1i9JQOSa17ItQq9129ahlJP5M40xzJ0aEPC0TDHRs3beg7OkZJ7UiIq69S6X4qBkRgyOi082MQkQDkFld7L+WcihDYj9TobUO8Sn2i0rAFW8pIUgD7rWuDTQImDlTowahEMnXSEHkV2XEdUgm3IHkkBQQ3mqhWnwYhjKZPd0YqTnLe3SpB3TQxPatOoy0nbT8eDuwF0naVNo1p3Zfu70t7whjfgh3/4h3Hbbbfhy77sy/Bv/+2/PREovP71r8dP/uRP4tZbb8V973tffPM3fzNe+9rXYrFYnPnYt9xyC2655RZ89KMfRZ78LjfffPOZ91e3Kx+kANtzkNN8ag1YTuKknDKlcxJh9pj8eFuaZ0qatfPNDCIpe26FBC1FU8BG6eyIGQxy4mt2DwSZmZWTsu+nb21fKDO03Iw7OZ/l6T/pOC1fxGXkYUJoJObMlqdXzxMiyevHkEW+qZ39spWBwz0r1MehDQkzr7FS+BmboUUTkqtaABQ+SmAHFFJYz4ZHja5wVp4CIXEECB7mX1DvM/FVnnvK5SCsnSB7lOa4Jq78fK2GT1RwEiDVi63o31rTAjaoGpnV0jsdAnIOznuw87DohhTWW7hM2r7rUZ4jBeHYmDfMQVjjfFoiQdIbnSqSziu3JoJxxC3O56WkvjR6UkeY1qnFQbioqaJiPhcw/g6m9skQnsnVev+YC60ZtoGlUnJUDordJj0YgRmL0CCy2OdvNIrSM7Akqf9zPme19w9Y5RJpElVW1IiRRAIsOnaU5zjMe2hpwH5Qoz7qsNBzlFIKA5Yg38e55iLmocedaY4+CUdnHgZkEAYO6FJU8JywTq2ry4x7RcToh8alyOSEcXmg2PKm/pzIxStVxlFSPPYsat0AppJlGSmBPGWrMZo6GgN57iUtRDqpEBVQjqQ8FBxrrvKp+6I6qmF1xjgfixTXjrI7UzzTyZ7x+s4CVKZ98qW2m6qBQDtXvdvbBCjepe3P2H7hF34BN954I2666SY89rGPxetf/3o85SlPwfve9z7c//73P7b+m9/8ZrzsZS/DzTffjMc//vH4y7/8S3z7t387iAive93rznTsV7/61XjNa16DRz/60XjgAx9418QkJ7RPDZCyLWJy0gMwlRufVLdnC1iZWj3XP8ruB1bXr8AJWRSoBlQjD5XJd2RyYyamUJ47Axlb7g0jzRoBz7krFj2xbfTBcqda3diUPmDI7KvRjQedHRIBTXbyP0MGcouoWMedlFCYmHDVrPNOvU8Rs3bQSsjJAYD8k2tgnhURGcvYyUw+dB5pcNMuC8XrhetY0gfr3GKNFikEV4LY4C8RlKGSLpeUyVpdaq2C8r4CnVp6XPM4AE19KEiR6ICmECrX2gxCR3BejHFWVnkuzqqQFNZRnmOdpd6QVUhOmdQfRKszs0RD7Hsd5oUDiwX16EE4ynM9t+h8nQRCnxt0WukoZPEdsXpFiSMOQu+kX6DY57c6ITcy7VEO2NR8GwIWICSwk2UBI9i26DkhEKFFxB0s192KGh5x9msmiimRIXdqvGecIKvfA8CVXGbm57+lEqMjxLhvlea+zSrNJO0TMuYsUaROlViJyaMngaUQYcoBgwLwrCkdQAD4ACDnqHQyRmZJeaY+lgHHgX+VfjBzRFPNpQJEnBBbp4WAMjlRwFGnhDgUWbLbC2wbLxjSh2hf48Id48AlLgqfJoJSknWnPJnKF6VethVQbOOfbKtgf6kBblu/Pt1uG0fxLlazv0vtXuCkvO51r8Pznvc8fMd3fAcA4KabbsKv/dqv4eabb8bLXvayY+v/4R/+IZ7whCfgW7/1WwEAD3nIQ/At3/IteOc733nmY990001405vehGc+85ln3vY07VND3TO9seubdGo5bw/Radrkxt6GEKfLiHYYIE29BbaFH+sy6QFwdU/tAKmvzQcBOP5M2GsaMMp5g3CMkAfIOpSAMNC42BngOW9h7qGAGpNuhLKygZOmTeIbwcCsGdDE7F87EnsRwV49KIItU+AwcPDKxlE5BlZrZU4DlprSWYTeB+eWBmQmrLk4tUo0w0i6WVUyew5QZpQ8XWLrmVoHgPMc7DMbsO0fUECNgQiLlgDAiqM7vfaQyr9WiHCm3yshoEN0IJGULyLkULmuGQGHaYHDtMA6t1pUr0EC4UJeOOiwwdrOfc3taL/XxBUOwkUE5Aq4VNwWBByEzlVFq9xgVcmaEwvpNlW3rtUlCii+K/I6YEktkkZSOmYccsYq93rdGmRkLKkYza3ZZMxTs7vyG7aqnjIvFANdi9C56suvmwI9Kzxo5GGpnD1gyAEbl7BL5KjPUQjbei8YYXZeWeKbT4qB4iZkLaZZ1/FRAJKCpHkAIKllvd+g7BMMAM5DCVVaqG7OObPIZp0msm7OurZjyh1d3Oi+zeyNIX2L9jfjA/KIHDv+6IRl28BBHT3ZBRi2pe7rtk22DKh6css+rfbPWfr8T6J24cKF0b/NZrN1va7r8K53vQtPetKTfFkIAU960pPwjne8Y+s2j3/84/Gud70Lf/zHfwwA+Ku/+iu89a1vxT/4B//gzOfZdR0e//jHn3m707YrG6TYDbjrxp7e1LwFIJyBYb6tLs9WZ8Wpj8D0fZ3fnUqn7a93NpPzzVWp9wTnn4y4J75vuFlUzX0kDTvXPBX/rPrrEubI4oVilV09WW7TQ1X1sLhuDkPwr9WniJQJbRTb+zZKwbY+mUNqkEiJDgiAEGgTSjqkpYSr4gbzMGDDjUdaLPVzEC9KGqYajMwnQ4i3UjE3ggs/Rasd26AHADMkdEqCNUlxAmFfeSDmn9JrBMScZ1uf0QcstbihrXOYRf0jEQB4JeMOAas8d1t+m+FnBAcOUb+TRYwAaMSgwUeHA3w8XYVeFS89Cy/HrOODRVoArFhAzVGee4rHvGMkilRUMuKa23rUKE1Gy1YVOBli/iZ1dcQA7n5xjmtCwIIIK07Y8AArQBgh6aAjzlhxQmKLehVwY1GZmttjgHHmnjbBAWfiMEqL2XWya2ipM5OmL+MGrSrExHVWrPDnocde7JyoPY+Dc6AMiFjqB/J4KBcluDEhMyHGjKZJ2iVRKQlh0UjldLER0S0tkMvkIDcYOURbRMWBCcuznFtCmgm4qAnwORblXw1+hItGZTKTy+Pr5P06IhJP4N7psq3FA239Goxcyl12vPHuz2xf20qdbNvnveWNwnfDPwAPfvCDcfXVV/u/1772tVsP97d/+7dIKeEBD3jAaPkDHvAA3HbbbVu3+dZv/Va85jWvwVd8xVegbVt8/ud/Pr7qq74K//yf//Mzf93nPve5ePOb33zm7U7bPjXSPXWbKn2Aguzr9ErdpoSu6WeTZafyBTit0mcXsVZnOlSnearoRhaWHpyjUreqk3MjtioC4mFkAygoIKdOB7lpVNbUDiAbqYGbgRUK4jjLUOIsE0JINXVGyLKQTr+NA/oc0Wq144GDfKbkzU2OGPo9kYJSwF4sxEdr69wixOxutSL+lJSQzayFsNrq15YBLrGkyQ7zHpYKVCzlAgAzZAUoCdl8VmBhfol6LPQ892lQOW+pONxrusB/XoiDrZBJs/unmPnbqopoBJQaxyaxnZGoUQzE2GDcqxX8QbzoUROLKnXGfeEIILptvPFyzARPzkOOat9vjdZl2qaEWSuPx9JgLWV8PM8xQ8YmieR6FoDDbN4lktoJRMgsFvzLENEiYoNB7fXlGq658k0B+bWy62Sk5KwSZEfvJNdiQb3W5ZljEQSYHlnaTFNARoJuKWGTW7TBom/CvxkQcDHNxCo/9jga5DeZxYQ8yO8hZRwSLnLrkZQ2ZljRTHOitZRP8kmAPmC5el6hz53LkoV4ToB7pwCEULjMxQ4/KubR/TNBJMgV6BgRNz0SWpYzkaBGZg+SEjOoVkdaCvoSEeFtkeRpKtwLt1q7nKjGWSwgRpzEu37IM7cKaNzl7QF88IMfHJmhzefzyzqtuv3O7/wOfvAHfxA/8RM/gcc+9rF4//vfjxe96EX4gR/4Abzyla88077W6zV++qd/Gm9/+9vxiEc8Am3bjj4/K8dl2q5skLINdW/jptR/rZ3km7IjurJr5gBMoixh8rcGK1tACdfrebVkdv2mgQrbvjZjo8wS8ajCt0aiHZFetwSRPK1jMzYNP7OmcmzWZgQWYgKDpcPV5V6fxAALAaRqHkCUEGZuxZBwuZmzzWIpABfUxC2zfN7niL1YigjOw4BoIXkdhFdpjhxK9ASA8zpkgOsRVI68CMUmPyNUaaLklXZtmdX/sVas8E0hJNcjoHig2AAOFOkvIMqUJQkZ0/w/WgdUjJ4GLDXtZIBFPFsa9CqjXVAnvBEdvs0911qkPOJo1GTbnhuPItlnstzAT48lbZx3Yw68vUYl9qnziBEgxnGfFRltNhAYVW4t4+ZSn4EejLnyUSIRVjn5+QX9PKnnCkhs+B28ufQZI1WP1TOS1Fbjxm3neSkeKUmiQCJFD5r+Kuk347JscgurB2U1pOTes2VSkiGrP4qtZ2mflANWvRSFjEEiqbUMmVlkx2ZyiKxVke1Zs4ehSq36IwSMeCaAPIOxR4mCaiPtV0yt53jEnnm9tgxSIFSWu5Gb9Q1E4BAEqDTRDSadQ3PShEsnZNuIs3wSOLmUQuekqMpZ/FGuULfZc+fOncqx9b73vS9ijPjIRz4yWv6Rj3zkmCzY2itf+Uo885nPxHOf+1wAwJd+6Zfi6OgI/+Sf/BN87/d+L8IZODzvfve78chHPhIA8N73vnf02d1Bor2yQQpQbsLTkK6sbZOxTXOn1qrlfMIPd+KPUUdVtql96u9hLLY6R1xxU9zATYHIKIQLjFI+HFAKjVWdo3E4CbaP8T+Z+elumEAR4JbHJFoGqFGQojsTTopIj82NE5DQuJ2aXA4N9Wtef9H0MqPNEY2qe/Zij73QISGobwqhJcZBXCPoIGthe3Gw1cJyrCZsWa7hjJJHTAB4qgMooMS8SDpEj4KY1Nh4MhGMNvQSVeCgwAP+mRm7LRVILUjTIlSUPrb+pnKTNYdaUI+eZEAOlHE+LXEh7+kgOxOlSug9kGAOqzVAOZ/2veLzYd5zqW6kjI7no5o25jVzEC4iq79MUmWQRWXMFfeAGD3gEY+1ECUUkgABAABJREFU4ucepOktkSAnlorHBgs2sDQY44ACgoKVZWjQc0bSa7IgibytuK5xxIgqp15xU/m8BMwmDM4WScjGMNO67IAv6g1vhFuggLiFAmBZL6MNVvQy4GiYSYSPMjKRcqYI8zgg6TNqBQcBAeM5ReQshHBCNTZO06TZQiAABuGCgVWVU0VDTOljEZU6BWsRkWA+SprS8bo/BCfSThsHuLfRMe7LrkF9C3HWm4GlYzyYLeTZ0xBZT0rBXyo9P5qFlf7zSoyknLbNZjNcd911uOWWW/AN3/ANAICcM2655Ra84AUv2LrNarU6BkSi1ow7q4Pwb//2b5/thM/YrnyQAuyOntTtJCVPTera1Xax1bWNIinbgIgBlWNy5Oq95YF1mStzIB2S56YzoHO1McE1wwsGeoQEJdVTavCM0zqjW7KKrGQlxrLxUSx83VinW7aJTUJQI7ecpfCaKXxiENv7VsHKJjVjEywmzEJCl6WTb4U9iE1uMQ+lBotIZ5PzSERWqlJmZKx55utGYgcjgPAy2mombnwXARi9EFlRBrNWrXajDtRW/8acZfdDVhMz8iiD+aC0mkBZUMYduUUAY0bZDdDmWiwwqRol6vdZUC+8ESXAbrKaudGAGSUh+mq95IjCKbFIg/FLEkKRKesALd9LSbUVx8U4Ksh6DdHiXFhXgzejNwddEnm1cWsWqmCaVfewgY5afrwgU/oEL1aYwGiJsM4sj4YdC0URZd4sVipghoyP5yV6lHM7ynOcz0vMKEnBQQ7oEbEmSSPVnBpLYXnUTQtV1maBRbWT0A0lClOTZA2Y2N8QMrqhETO3upE8pzyRG5OlTwd9tgOUX6YgxB5YJbTbbWyfuySZ2Z/jYCR5BSzWB0g6iEVm7IReriYyVCI4TjLlMtiPBv0A59NNosSXrHi8K5K9qxnpddvkb5dXyjZ/lHuj3QvqnhtvvBHPfvaz8ehHPxqPecxj8PrXvx5HR0eu9nnWs56FBz3oQc5redrTnobXve51eNSjHuXpnle+8pV42tOe5mDlk6Vd2SCFJ5C19kHZhbh3ofNdEZQT9ndS+ueY5LheHmPJ+dp5W+dgBb+00KDLBJlLJEfBS1amvo6no1Z3ZNBOkiE5bYuYOJiBpHps5kYByJb6IejMz0CKHiCRKhnUOyMFhJBghQVjFCWLucxGVT4YUKkBymqYAU2HWTDn1YAGCU0YS4vNbM0MYOZBIjAmNZV1BpyLax+8g8pPF+ppakAn22CmM/cZbYSHwuSchZ4jlqH3c1hAwMqSsshuOfq5JRAOaMBhpZYRUqikdtYaPTFAY8cK+h0tghPNiI5bHMSLyk1JDqrEfA74rHinH0cULIRz6u0SldsSFXQJydc4IC0u5D3MaHA+ji23a9ZBbPcFHIgz73n1uElMWFB2W/yegQUZOJFBLEJAyZIies5oFbQAQAQh6LmtWcCc8VI6DsVAzslSGT0FzDi7B4z8Hs7q9oiYfY+OI/bDBkd57kCv5yJBjvrAWFrRfuvMJXImUT05xp39XB2OEzZD44ofIkZmYEhRZPYpaBkIjaYEFFDQZCnCafb39mwFS9voBbU0UO0Ya7iC4dEX6wMAlnU9AsMV+V0BBKmHi+Z2yEzdUNbZOaDX6khOp+PZTbe/q7b3JwGU6etLAZIrNOVz2vaMZzwDH/vYx/CqV70Kt912Gx75yEfi13/9151Me+utt44iJ694xStARHjFK16BD33oQ7jf/e6Hpz3tafiX//Jf3qXjnz9/Hm984xvxF3/xFwCAhz/84XjOc56Dq6+++rK/G/GZq0Pd++3ChQu4+uqr8dX734KGZpfeoM5PArsfnNMYuU0+3+qZsosMa5EUCkDTgGat5H9jBDexnF8M4FmDtNdKnY0A5BjUoC14msdASvb3cDt8S8XnBrJe0PWaKsVjAKUBcltVZYWu25aqydwqWTaguM1GIcyGWKTHVgE2qNx23g4uM26rIoJdjpiFhL2mpFyWTY+92DuAmccBszBgGTpP6SxDp++FIOumbEH4J5EYB+GiF5szGa4RaYGS5llQ74PdghJa5BHwWOUWBxqpMZ6JSY6TEmTNHXdGxU6/Js7awN5XA2zdTAXUc3Sn3ATC+bwcmblZM3VSva/6+wWIkqkGH/Zd19ziMO3pIN4pCNuon0pEi+SeK3J9E/apsDenT0VLRpKVTIBFUTpm7FNAgpBmD4KlahgbrfOzDC0CAu7Ia9yegSUxDlWabiDFLPmNfHzEMyyodyAh+xQfGJNtW5So44g1z6S4IIoXTc8RfW6cH3PHsOeqMAC4mFpcTDMcDTMEYqxTg3UqkaeLg6qgBvFO2fSNR1yIgJQCkpJtmQk5EVjl+OgDkMgJs7ShyqhNLiZli4qIE20wLoqRYy2yQvaaYbzyMMDBSdDPbP8hsVoSMEKX9b0WGuwTwmZQfxSNYCT5S0MCLq7BfQ8kRU47gMqZhpKT+t+T2rZjTBWSW9rAHX5r9Z9wxx133C2Vebc1G5c+94f+BcLe2V1breWLa9z6Pa/4hJ7r3dn+9E//FE95ylOwt7fnDrd/8id/gosXL+I3fuM38Pf//t+/rP1f2ZGUaZuGB09K0ewKP14irTM1ettl6naqVkdTbD+1V4ot15mZIBIJ3cqkiItskCx6wiU1pOkdyjqLsihe9SxzgPMcRkEpSADFQAulqgYJAzSrOhONkpSvpLM0YvQpIBDcDp+YkInczM0JjIAXcqtbhMyCcxYrfYssXFVFODITOm7c5CwjiOwWJb1zPu3jmniEA01lLKhXpY58j54DFiFhjoSsqhIEIfUeWnViZCyIxVEWrBJhQgvWAVuIsesqHVH7qdjAa3VxDnNb1lFgUZuWgQZ0PPe0lQGWVlM9hVvRaZpDFEEL6rGkfpSyEbm1+IgsbL1qX+dora69rLwYOe/DLATRfeXgSPpGLOyNh2IW+L3ew5b+MX7KmhOW1KqNfocejLWqexYkxGJrLWWcz7PRtWsVAFpkCJRdsVM7Bx/mPQedfZ5jRoMjq01l/LdBiz5LLScDKJkJF9MMm9z4fdWpZ0rNoWo1WmgFCa3lHNC2gzyuQQpsZkvzwB4mjPkKVKV4bJFFN4M898Y18eewflYtCBJLusg4KlPy7bFmUZm0ZWCvoyu7+Cl1pFi9VE6yxz9VOynFY81SEZ/MvieTAP9d2v4Kat/1Xd+Ff/gP/yF+5md+Bk0jz88wDHjuc5+LF7/4xfi93/u9y9r/pxZIqSMlwKVzotP1pumeLWZuux680QM6nWnUfJTMgNWh0QfS1T01aMnZLe99omedmoERJp9lWWTE3GVJR5TUjEGMp81ZQYyRZVnt9y3NAwMmErZm3YcoexQ4qQSTE4mPioa2DbAklWZCoyhSDyVh4IA2JixiGZxmCmKMr7IXpfbMJhc7fKCkfDbqxHoQ1zjK82K4lufoOMrAHuCRhQX1QrBUhUeioLVyJIJyqBGH/ZDF20RTM5EY+xikwJ0ClJ6LJT5QMJ7197ad8DaAXlU9Kx1QW2RXAlkKw7gwFkkJyn1xkiY3rk4CCpE3s/w4B3Gt60X1FBFw1XODNmg9o9yqImbjKS+TQbcV98bM2+R6CxHYsGyG1dipbneIWduMCDXmrbkpK+4BFiCz0O+0YeMFwa/9US7Xo1ZNmS+ORZysiKBLtkEO1lZZyhgkENZD6zymxC1Wae6E61WeOXlb/sr5WCRvyAGzKPV7zGW2z1G8UZjQDZIiamIGR4HbMWaxyc/y3OQ+CCel5qvYRSL966kalNROogqwwKMsOUr3ERJK/R6dTBDBybSyTB7kMIz7IzuOl90AJGqSs0RQ/H0Vgd7SvzHzCORM3ba39pe7/Ezq1Pdp2y6n2frzXZ99pt1t7U//9E9HAAUAmqbB93zP9+DRj370Ze//hLzGFdAuhdR3uRuepuJxDVz0tT9wFcCpbaG3GrsBl87j7nq4qpwyVykkASQSDhb76mpTKpwTQDsyW+YAB84vkdTQmBzL0aIwenwnzeqy+i/gf1Miz8dbi0amNQ4KSFM/MthKldmEhjJmIWEeRdlT1s2q7JGBRdIBySW0QjQlJYhmr0nTccRaZ8temE9fiyQ3wyoRb0zSnBsc5uiOp5aqsdROYihZlL1Q4JIYVwfCjAiRJAVyTchY6jWIVGzzrVhfrn4wS11MTdMAFPkw5ZEy51woCicDZEL+Te5xIiZ0nS8HJNqwDBsloaZKJTXo8YJ7xADQyFFyN90Asa+XIrqklvf6O9v3wbhTaSmoVJuQIWmhBLGO71hkyAsKWLMYudm1seiJ1G/Ko5SYHE+4JQfhIg7CRefQmInbUZ5hpW6z5sRbp4lqdZi1Ulah/n1o9FkgHt3fvk0OGJLUZAJQzNwMiFjV8PpZtfQpj32QnNQaq2WskZIEly0Xvhp8AiPpXYwiq3XkRvarvDdbXlVjR+WWXfc52/pa6/um/6brbG11nzudXO443jHuYK3sPKl9BqB8Qtu5c+dw6623Hlv+wQ9+EAcHB5e9/ysbpFzOzbetXHj9D9ieAtL3W2tWVK+PARZ74GNUG2fy8Cal6jwqx1mvz8FcfA2AAiSUb2I8FABOtBuZOdXbBeWlGP+EUMgptj7DJZDcVAClAjcAiqMmyz7MbXYKVAKJ0Vtm0lk5u8OsSTttJivy486t8J3kalEIByjkqSAAI+7GMmw0YpCK8ygZiTR46mOfeswpYRkGLGjsNivqG/l+YsMurqpLIuefCFdCiupJGoRU8VOiCWtmrLjY5bcQHxDjtixDjzU3o3pAYkbXazRlEG8W5JLmATm51rxCZshu6pYgkYdQ8WRM2hxRjpM54DDvjazxLYKRWYzVDnOLxIQ1B7Qk37GtbpdWv2MCkJhxlEVWHMhACXs0JXGRYfeaGpNrVBRZAYxrQufyZymQ2GgNHitGGdyYT8oDBAdmVkBxplJka/MgAG0ZBez1HBHAWMbOgco8DNhvNlqNO2MWkxBoleTc56iePiIxbtUK38pBmLrNhTHuOGsXl0UtFxSc2HKTKNuzBEixT5TJhpstUvXatrdoTD0JqZ57Uw9Zo20pHlLgQlUqx967uVt5xo71bWHcF9b934lApfaxqvvjS5Fm6/V2gZVpBOgeaj6Hu6v/7vEzvrz2jGc8A895znPwC7/wC/jgBz+ID37wg/hP/+k/4bnPfS6+5Vu+5bL3f+Wne05TuEpBBYCT3Q8vpcGvIijTiMlo/5dqZpPfTGYOOQMq0UQj3BRKWYoJmlQwMThKKNiIs06Utb6lUu0IaGEBM1Wtn7ojk/FRejgnyurMjzKBG9bqx5DO1ABIPTOsZpg5B8SYfZYZg4bCw/i6mpGWmWY1kEFhrlWQrZ1rLgIoyhwjPtpgfBDF60OGpSADFPVugOb8DTIb+zSKXPSq1phBTctonLKRdWTdFcu1aiGGXwFSd8Yu5pLg0YUFwQGKFSJc68his/XM4pXiNXKcF5KQqQy+xkvpOOKI57r/Yj7XKc/E3h/xbCSRtXRSVPlIoOyKJ9suKEgAgGUYPFokniySzokg7KukeNrse3csx5hTUHVPwDoXyXYAVyAOqO8Kuy4dB+ft7NOAWRirewx4AmNJtb2PxFiEcg/MKOFC3sMm1SUGBNCsIcCuT8I/mYcBmxzRUB6ZvFlqqA0JORK6FP0aS3aF0Q8Rw1CqdFJgIKqyZ8tkwBoNyvkKKE6z1XPqkuJqMkKpmozUz7RNJCp+igMVi8jkMvHZGjGhMokqvLnsAIZiKGCmihSf2P+dVXU5bSfxVT6ZoiX3ggT53mw/8iM/AiLCs571LAyD3Lxt2+L5z38+/tW/+leXvf8rH6SckkA1enimOvsaxe+SIW97r+0kgLKNULZjJ3oe7O6xQn4tRFoL89pMiCKEswIcj3IY2LCwcMVr8f1kAENJ7Yz60Pp1qHvTagWdOVpBZnPYNPmxfC0u3BR9z0xIHJA5e/XjWZC0QgZhkxs1cAtIDmSsyF1wd1SLohzmPeyHjUpoi3dIx6JuWaAv5mcVkRWQQbGuxWMk2dqAzbgRARIlMRdUqwZ8EISLsWIbyGWgXrEMti1lLMBOQgWswq8AgaMsvAmw+J+44ghC7l1nYaJMXWYt6uHLIPJhq79jkRfxYmFP71jkxIoUghvsU+/AC5DzllpHPZIOznYLtRRwlAc9PtSPxD4DDtQCv0eSNA8X6/t+Mq2PNrjqTdzr9TZH3rqUQFYu0Frt7o/yXCXZUsk5cQGq4IzErZRPIEaGGQImj6K0lBzgRGR0PEPKhIupRacE2iZkrIe28FSUA9Sl4iVhcmS7vz3oaAiiStm4T4oVHaw4KX4VlAjr3inVZMJJsYxS8ZjsvYKY2nRt2iURZMJjEVdCASYjEn0dKTFkpDuvHbUvAVB2+qecxXzTz53GIOoz7V5vfd/jqU99Km666Sa89rWvxf/9v/8XAPD5n//5WC6Xd8sxrmyQYt4dl2qnATLTsGMdfUnpRIAyBSKjZfUshSb7mIY362rHOYM4ykOunRvYsjMaQdG8MmXZtQF4J9sB4IYKH0VnYwT5PFcpH1cDBe0Ik0ZQAks0pbL4hkWAtWaP1eshYjRNBjO81P2QAubtgD5F/6ksgtKniBjU0SMkbFLjnBSrRGv1VQIxNtx4FKTPjYMVqGrFBl8ZrHQgZ1Y7fZH3okoXHVBRCM2RsFHwYYN/UOlwAKtyxqSxEnFYEGEN9nSPiCV4NGBbGudQVSKm8gFkPNinAZnk+60BV/aYw2rUKEBNFDXZsBTZE0BiqY21kmMFYAVNiTVe0dn8UNZ5z0Fbq86uAUVRI7cIq3JJUjMblteBs3x3BWXXhIg1pxEpNiMLUHHCs6SKkMVZ1o7TMx0DW5ZmW2UhUd+eF7quXRPhnkiUpJdEmAJNAOi4wYwGdyPOTJDChAWYbHKDDRpP9/Qc0XDCxbSQVBkEMHcpui/KemgK+K7uZQcwg6jQGDqWkqZ87JJqdFKeQUEfZA+sIRvBV1KCIgKV8Gmk7NkWRTH8RzXwIYws8AE4sDHzNlMJStTVQrBVv1X3UzGKFHkHUDnWTpoEnqaNIjmnBCh3xZvl7myTKNld2v4KaW3b4t3vfjcAYLlc4ku/9Evv9mNc+ZwUy2tOEftp3WPrNjF0Y+YCULZU2qxZ7PaXYhgtG7XardFe16TY2unRt7FcZcVLUeBS81Tqjom4hHbrZQZeHMxElKqrk2bbU9JZnwEfS/uoVIgzIRuvJAUkJQ9arp5VBWERhD5FbAZJ18SQ3TBLLguj0Z44KMH0YppJKJ4j5jrwbKrw/jJsRHqsUYJ1lho4MwUuQnoNvs9AGR0EjKy4QQ8xEDtkmXEfBOGptBBX2fvFjM+KjIOQsCAe8TE+nghHOegADjUmK6+t/FLPUK7FgAWJ4mehwS2L4GQmHOaFRkJKpKPnxv1TPiseYaky614r/FolZ0AAzbmwxjXhovAvlCRrxFEpsjd4TSOgEHazDsSHucUqy3Wx1MtCv3NL4jibIUTYBRGWFJHAWFBEJMKaMzZa6XjDAzILSdbiDlcHqWEk9Xei/7M6QId5ho0uk/OiShYtv10HKYu4pHHpekuJzfR6HKkMeamk44iMVZ6J306UkgurNMOdSdJnA0uKx0jdtszuY1OoWVQwhixcFGK/52WuUSKKHmGJkwmVur9yQOGjADAjtxHQmHQJ7mNXgw5L8doxCEqQRQEgVV8wep0nnDdzvt4WBbF+KzM45RFA2Rkx3mblUPfdo/3z+O90+a7zupR1xD3Z+G74dwW1b/u2b8Mb3/jGT9j+r+xICjBmelvbRcbate0um+Vtn1fNoiXHUjq7LPB9vyWlM3ooq/LoIALH49tztGkQtoZyrQOqOyzKcD6KrQdUnZXOwlDtkpuq82EIIFHpsXeETfbd5UTIgRA4a1RWTiZMQEjNS5F6PQHnZmvsxb7IcnPj3AhrVuUYOssFtGpunou0uTYdq9xZ6+rAUaMpJtHtVH89VdZYymOtKaZeQYfxTdwRFUXNYj9VhihgImRQj0QIrJ4q+pUWQfxF5JwYi5Bwewp+jh7JseiJyqNblhRYRnASrEUXlmGjYEQ+myHhiGfw6semImKpdGwDunioDALwLFJBgxZUZPSUsc6Eg2BW/6zXmBBArtQRHg8hkBBp7cawashLithwRqeAZUmMRchYsUVULAXWa6qucSJsqVCtUTSNoli156Acm8O8p464vRNrayWYkbN7vb8sSrfKY0NIi+QMefy8e6FMJr87g4KSLglIyVlIx2AxdgMk4sjO+8IoLeuDkqZflDrlf4GS4jGgwRbtHEo0xZaNBjmd4BjRPvR1JMcisuJAazw3JOwGCBNL/KkXy9bUzjawsGPSdyIwmUZTpm3qPvvJ7KPyKdaGYcDNN9+Mt7/97bjuuuuwv78/+vzTuwqytV3gov68/mzKO6nByCl5KNbswdzJPZna49uDDo3+7IoAGds+jOXHAHx25MtZeh7pNFiKCk5PRTss1s6OW3jOu1YaeMRFX+cID1N72kfTPAaUcibhpehMEpBliQjIASFUKgiNlthsc9l04jIL8lD6xdSioYy92En1Y5v1c0Abeo8stDSu7ltb3huxckZSfM5SGxK5ECXPDBlzSlipcsR4Kma2JjN4ACTRkV4v00LTH4Wjod8ZY36KRFIKGDEr+Y4LwEkgdFkLLZLY+691IJZzaf37CflWqjgHzlioYmXqwHqYFy6vteuRIBWihatSagXJ4L6oXHgHB20tZXfVXTFp0T9J22RmBCL0aoOfWdJgo+/GYvS2oAarbEBRxkEhIvPWUK5weMSzRaJdtrzUJTrSatfmrmtpK6BxgzZJa7VVzabgpOtAGVeHAXemBVpORXlGEieyKNIsqE+KvmdVoiWNHg4KRJpGTN5yVg5VBPIQNOqoX6COjDDG6RqNhjhdx9EFxtYB0AlHlQZyXopFWCztU4OVVHZLBnRytc6IvTzpl2IEhuqAp227iLK7AMRpPE9qJc+lSLSnETF8AhrVv+1d3P5Kau9973vdVfYv//IvR599pgqytV0Pg93IJ4X/dn12CSReGxXtNHLb9TrQ8YfMpH92KipNZlX7sIEWAMp11JyyRDCmkRJT8JgaaNRZ1gEbUw1oJ5nVNr+Otuh4YtMvICvYoWLcJsAFXlxQvoKkBkg79UEVQJmkorGZu5knCQBschzJcTMTEgUfcHs1cSnmbgNmNCAgF28TBE2VFOXKmlv1EJGBY6E8jI4LWAHgtWNsoD6fRXJsqQ4DHQGi1LkmSG0aQOvS2ACuBKIMibLYz2ODNCARlxULedfSGqABwfxfVJmSVbnjdWuUp2JRlSOeYR8d9qkDCDjSQosGUNYQB167XmZ/nywypWmU2mPFSKtmS19zVToFH/ade7BETACsmLAgxtUhCmEWjMPcOX8lEuEwC3hbVRLv2kDO3H3F+VcgyjTaZb4xxs35eLpqdB/Jb9l4FCUie2HCnqMorXKLlbrMeoqG2KXQTcjCScnBOVabJJWyM1O597WJcFClt4m8QjinIIaH1ggSmYzSB3gQkKvn0SIpASV6YqkgtcVnvZnqSMuIZGu2+LpvByKZ/XM/JZtsEUltn5MUJhO3WfnupS/cGVGZRqwvRZ6tP6+BxzbJcS2VrpdNz/meaHU0665ufwW1T3QV5Cubk1K3OkJiuc5d5K+6naVictU4pZ2fnarVRFubudT/7DiErTMCGqrZkXUUAQXI+IooaSDbpwOharU07uTcEE47N45ctqlPx5m49rXYIyqshNcmSgXkQNLx1yqV1TDDnf0ch/1cO3/hQgzqh2Hci+Jl0TtgOTZoq7dJRHa1D6CDIAecz0uRp+qgaxEI58twwFL3e0BFfgwIv8QM2tYc0MMAkQ7UzDjijMOccMQZG7Z0kXqL2H4ATx+ZrNlMyxYaKTjMi5F01ozoACBDCiMe8cwBxZpbHPHMl82QfIDuICThdW6P1QFyErI63Z7PezjMCwcNBtiKwRr8+wBwECKfyb6XxKNIi/3SysnGYVYHWb+uco1M6mxAZWOybSUNL5SPZC1AgOQRz9CpIV0kRjGmKyZ19h7AMWWYNYvW9TmizxFdjuhSxDoVRVZm8irIKZMvM8DCTO6VImRyVF4pivazqnsqHop7FgWUyUb9nNnMnMtzKf9KCG9qQyCEWNuW3XNJnGSB0OfiKFs1KUiqgEPr9zigkC9cVs7FD2WbkdvWqHSdir/U+rsAzFauzA7Asy1a/Zl2t7Zbb711p/R8m8nbWduVDVKm5mvWTgIZ2wzbdhW7utSxdwIf2s1HGaVo2F97h1DPFKwZB6WKkLgrrK1iOevp+/reoe3Latb/GIBoB6r/WM2oEFmUPWZCVUVpvCZPJk/z2IwzKkDps8xOZRBoMGgFWgDo1J/ioFkr0VMGpk1unJ9QUhkD1tx6GqOrTMmO8hyHeU/9U8pvUQMkAzrGhREVjw6a1WXo9HMDFTPKzs3YMHA+Z6wYuCNH3JGj81SuCRo5YR45s2aIu6q8Z7ej7yAF8o6yRD0ygpqzlfTPtLUKYgBJ56zy3AfuoqBplEQ7uK1+bfJmUmQjp7aU/br0HHAQEuYaFTLSrLXDLCmeXtOY14TGybRWTNDWNxWUgSurqNyDHBRZxWhzmu019WbN3XND8YjJ6p9i6TK7LmuWIoGrPMOaW2xye8xp1l5bimiTo4MRQEo79FmqHF/sW/QaUZk1pUK3jv2jyEo2iTJr2Qgm4XMZ+NAJAGWABk0l2X8kqVWbiDgRHpMUTuaS1rHlZOkcggrkJK1rPkv2rNbdm02SMkZ9kn3GWQmy20DNlIu3q9/zY+Xj/fM2ufOutk19ZG3Sp462uScly3w3/LuC2kMf+lB87GMfO7b84x//OB760Ide9v4/NdI90zYtJDjlnGwDF/VnJ6WGqv1TjDvWOx4CPbHV/JI6wrLlZjXOiJm5bYvKOmm2zoVPO7F6XUin6MUD7VmvlQc28k46OCsBTwSQduxZeSisaZ4YksiNibFBCa0DGL0eFKBscgQwQxtKimceBmQQDtMCi9B7RMVa4oAFdf7eZtKmfunRIFRGbkfcYkEDDkIh7Jo3iqlZxDlW1TcoYGVfv6dY5MulUeaI03vMmiahpHcCgFaJtAvKiCQS3FUWx9lYpT/q7yVSZklpLUIvaQqeI3GpcyNHF65Fi4wjnnuxQvMRWajBnV8TVfwsKkg2Q8Iqy7VZc4NAPRZUavJ0yj1pIUoeAy6BChnYCgpueECr1ZDBJYIk30u/K+lyjci1yK64ku8k4OWIZ5ghYRnEO2aV5yPpskmrjafScVSZ9MyvT9braVGUniM2uXBYAom/z5qD35dEjHml9ulVxWPNoigiVin9Cqnix6TIdRSFI4tqjqmAkKyPaTBQweCWgF43V3QrlYzLZMOW2zNufcMoespVtNUec5IblFIuERYb5Kcpc2vTGmT+XS2iU7lsT/vTbeZtNlm8lDx5ykep20n8k89wUu6RtouPeeedd2KxuOvVoK1d2SAlT8IHu9a5VNRk27rb2g51z652UkHCY8jeJHkoKRvvUGx6VQENd560zsk+yuJr4ttX6xZzt2ofOsmzdA7raBQGQm5LlISyFBtEADDQKK1EUfJEnANCkzSCoh01E1ImiEGleI8kJpd5BmLJ9ysYsro9bUhobIbLItHtucUyCBDZ5BYxyKBdR1Bmuo3Jc4OqZaKSRGvfkBQ2WHAaKYnmVEBFJGCp73odDMX8TT5vaVyzJoEwpyK3tSrAhu9kkC4gCADWeg0kIkDukppAHlEx3kVG8KiROOjCPwfgapcIxrmwRqdnYkZnIEwG9UIyNvBjyqhWAYGkylhN7OQ6zEhASQThgIAliU+KPVlLEkZJIHL/lKTXa8MFiLTIXrBxGQYk9aWJKKZuCxpG33HaLHJSp8QMrPTc4BxdREcdem5wR1piw40aCZYfQSodZ1xMovJZqE+PpQP7FNUWPzjnpddISQwZ0IKDwkfRZ0+JXj6u2gSf9PmsU6s+USgpV9KoS71OsP2QpXbIwYcNjCGVZ1a2YfFbqbshQgEkcrJj4uyE80E2ibIHo1YuTqIrJ/Z507ZL3TPll+yKmthfi5RMl30mzfMJbTfeeCMA+c1f+cpXjszbUkp45zvfiUc+8pGXfZwrG6So9BHAyYj8LGZu0zYBLmcpRz7K007DoCw+A5RyKT+uy8VcKYwiHpSVAJtlFkYEAQaeZ9GQcJSOqkRmpENikBPxrNO0GZdxT2yst4mfSCFt9gQ/TkntABRF6UMAmjZ5DRNmqa5KJKZtzBlEUTgp6i1hCokmZKnjo+F+Aw0DR+xR51bk80rSsMoziYpkmRkfhDXWPEPPxkdJzlO5YNV/MTh5si6qZ66mUcmcraZ9IhhLAm53Y7nss3JT9ESNICyJRKLMZTA3K/newEoVoYnQ9XQgPKxksGbOZscysCKRHVn/IFxEr3yTI57hIKy98OD5tKwG7IQlbdCTrLuv0aYIdrfVmUarYg3i0CDqNP58DphRxmFuFUwwMtgrH8v5zLDmAYd5QIK4zi6oQeYePQpwk+smYYP9oN+GEiKA2xU8ZBCWJJGzIy5pPgC4XatcG9hKVcTlSKNsBlDMtO+OtI+1FhgMeuNnjug5Yi+KXHnIYtrW62/d5SiFL7nY5RMEUFoUSMA3g1TKnDOJoVsKyKkKZRgz3VrdZUyiKUacDUP1INqzWjYZW+djHF2xZVUAyNcJg1Y3ThVnZchVFKUa6Ou+bQfxdDpR8/5wmw3ELkv8aWqm/luDlenrXfuYvr+neSnT3/uubH8FtP/xP/4HAPnN3/Oe92A2K33YbDbDl33Zl+ElL3nJZR/nygYp24ixl2KMW5uuN42i1OHI6v22cuSyux2eKWf5DvW51f/qTkANOYh11mbkVmCU3rGUjxDqqHR405aB0AN5podieC4bmVxBxBB3TIm+1B0ApKOtQt3JPCMUgDQxOUZrY2WJXpm5LWKP/abzqsMNk9fvkXo9xlMQk7ZlUCWKDmC98hgsdL8InVcItgG7Jo7OKLnvRtCUTlG1yG+QIeqbAPYZtMlwra1Z5MgzEu7JioEjd77NSJRxnygeIaka1KXwYFQflpJumDEEVCAiUueS45qXYoRa81QJlN0PZqakYbsOfRVdseYRF70uy7AZ2eVfEy66k6pZ5Xca7ZCo1Lh2z5oTAgIW1KCnLB4xiFjzUK0Dl2DPCZhrCm3D8n7NKsfWSMqm8m2xtN5KwZp9nwTCInRaCVuUTplDAXUc0KFxAu6Gg5dc6JUHVaocZ6nVEwUwX+gX/lkbk3umWEFM8/sZUhCwncl9URhAaDI4k9bswQSY6B+CKoAI1JXlIvVHAS/19voccxDAsY3DwKQAxfdnf7evLxtVA3kNBHQy5XV7bNJSRVPqPm9rJKX2Rdk2GTxJbrxLrTONnEyXbXt/T7Vd1/gs218BzVQ93/Ed34Ef+7Efw7lz5z4hx7myQcq0ncTw3pZeqdtJha+2PFi7wMr20+IyE7KHnBhoaMycB+SzJlgCvSyOAdzUnYfMwDjCjZxyqELH1YzKjKL8q+cCRgjVNiptJEifREH/MgDQWOEDFHBCjBCzO2ymZGoHiaTEoJVjibEZGiQmLFsZJDMTrmo3nuYpl6Fc84SAi0nr2yiAWqCH28dTwrxSYJiPhqR+cpHWcsB+2OAoz0t9HErueLqghCNuEDB4esfPQUmeK464mhJ6jZa0KC605qNipNOFVk42EzMDKEF/2oXyY1pkdFW8fRF67GOD83kp0RIOXiTRvF9s4BU5bdYUF3uKpGdRAAFynQ2Qye0gKZ4ZkkYdoqfEWkqYUxJDOyoBNCsyuCDCnII6zgIrTlgzI6NHy0aSZWwUoBxyRgtgP5Tv3lLAhrMTaY3Xsk+DWtiTfz/7TvK++Lsc8QzBCc0l5ZMQ0OXoESegAJq6BRJgu8ozL8EwcMAsDAhMWMRezQYlwpI4oI3ihcI5gAAMTA5eQmAZh5NEEGU9C2lgnE5heErWyLTc6PK+ABOOVdQTKHb2dVQlTEAIqtcELZ2hEw1V3WmI0yPRbBHfVPoiMiByrD+Uz0d9mr3f1gdOuScn8QLP2s7ih3IPEmc/3TgpP/uzP/sJ3f+VD1Jqzf20TWcG27aztquwYB22nLacwVWNn3LYE2r57PJOsZlMnByHLdcsfhmwyAjg6h4n0zIkkq5FWCkp0NCcNKN0aBaEySpbtFo9ekhfpz4POWfIXVODHkCcNrMRCHkEUOSrqmrG/CaGBlfNNmLsBsbF1Iqpmw48begwcMTAEfskICYiOyfFIiCAKjyYnIuyynNEYrfGlyq3su5h3pO0BzboIFV+rwkdOg5Y64C/nqSdxK8jYUFAD3NeLWofs71v9WIckNBlW6hDrSK/TqMzCyLsB6l9k7ikfKKmmaxdE1Y4ynOPihyEi8XfRNtBWDvQsqiJqaIOaO3+MEc8QwAcmFiryccuzUbxSqlbz+q6yxkttWhVxTNXIJbAaClgjgYZGS1FtFyRmyHutKs8LjC45hLBekDscd6UT5pa6RGQqu8mPipppHiqCbM9zxGQPepQX5er4sZl7X5fqjngXuxxMbW4mMYFBTepcZ+UvoqoAEISHwap+s0MhJiRhiiejQwdsewp0T8RAhiGKkIaALJin2Agk6R0bLJh4GQytpNGVdjAhv5mHEj5aePfkBWgEOdqH7pOIEWM03SMzlhCHpFjpxGTE/1RpirMk4CKp8i3rLNt2a5oS/3+Chv4r6T2mte85sTPX/WqV13W/q98kHISIo9x9+enscu/lLTZiK6XCnfihEjLdLlFWZR9bwQ4ZBn0udkCYjLAWzCUgwxN+3j6hyafQzpICuIo61EYBT1C1GMNE7Aw+EwNBJk1IgepO6Y+EXZy3RAxaxJmzeA5/UCMeRyK/wRIiYkRbUgym9VeJWi0xWSiH+/3MQ8DrqaMqBpNG3Qt5O8GbrnFMmxG6Q1zKj2fl2iRsB87V+3IOgVURfVKyRAuRYKke6zq73LLlGdJpfDeUWaNtmTXzyQGOrB7jQAQIzkGOgRPZxmp9Zq4Ei6FDqRWydgs5P28FeDU0YcIxjnaoFNFT+2qe15TIufTEtfEFfbVadYGZ/Et0TQLl2KP8hnjTvSYI428UkzRIxGY4GqfliT1k5lxe5boUotxgMGqRUtaSMDJWlMyVv4AkKjaDGpSx61KrSW61NLgqR4rlyCE6+j3Q1TfFwO0A4ubbGJyE8EAxiIOLocfKCOTOs3q9TFCuCh6tGZVCqgt8zmroVstPa7/Af6se1Qiwx9Mj5JkVE60ZQJioMSefyG9E6A2/CqYUg8kPY7x+CKBEwt/TdMqNaDhoKZuviD7pGpbH7eVl2JtmkY/bbsUQJm+36X82fbZJ7J9mqR7rL3lLW8Zve/7Hn/913+Npmnw+Z//+Z8BKVvZ3dZO80CcFG05g39K/WBuBSS1bK+uIDqdBfhnmCxHIbFqJCUMjNyWkC4Hq3hM45AhV50cQ2dytm87v/Ffhs26bDtFLayzLe18OQVwZoS2HDClgPm8RxNKgbVuaNDEhCZkLJreVT0IMittQnagItyAcfpHCIvyug1aFbiqimumZMvQ6UCdNR3SeBXknhssQod1ngnB1c43rjyK4CkYymOAwla3R7ZZM2EB4aDYZdsAAJvVO2M/EA6zKltQ5Mim8jlSQuoyMBaccbuSZxfUi3cKWOXPHs8fSbbrZjb+NjAfhN4JwMiNpoHEj8UATAJ5xWQvBUDFaXatnBlbf0HikZIJmFNwb5SexYG2h4Ga3t13N5wROLkzrbUVl2vZ6jGOcolgRWKAM1YwXsrgaSxAeDUBWVNwc6xzizVaJwMDogBraXD+Us+SOjNuUhMyNllASROSFLNU4u6QA9apRUMJsygVutdDA/MBamPCoKDEuh4DeJ7mYfNIgfC7rFAnSbpnmzCRMpUCg3IbebQEwHG/FKBMKOpFlnKwCQcAcBVZsTRuZlDO4+KlqNI9Wwb3EQipuHqX5OJdikB7/EDj/vEsgoh7ML1zrF1muudKAylGoK3bhQsX8O3f/u34xm/8xsve/5UPUqzVN/QuhG0y39O4z55U/+eSp7Kl4KD9dfZ8LjnfaRiEBAjQkFXeS2AQAtR7xNI9lVR57K/CyGH83tQ9tY8CoLM16ARskKgGGgaXvr6kiwYBP5Y/R2NRHzkJZmDoI6LWMXFzKyqFBfsckPuZc1SSDnQL9JiFhHkYxKY9N5iF446h/1+7Un+L1jkai1gGr7p5DRqY0kXMzJKqPxahw37YYIaMZTD7/FgRaJNW401oCVgAWulX+BmRyCMrws8oA+9hDtgPGQdaTLDHiGIkFYQje8poA+B+sfOBGhAgA8At+wHgkNuRjf+MskckehZZ737Mfqw1S5mBOcSbZa3pHEvlzMPaI0Y9xLNlzdFBziE3OKABB0FG1zVnRJUfR4roOSMreLkjJyyItIhgsco3gNIxuylcUj7PUQ5YUPaijh0H9AhYZYmstJRxmGcuGweAfepcuQUAOfQ4ynOvgF2nqmS7Br2aAfY5Kkk4jlI9F9MMAwd0uRHCN2VshgZDEGIsALQho88BQ4pIWcAKEaNpEjabBiFk5CxEWWYCxSzE2WkNH30OESApH9aPCajJZEacNc7ZFKAUYCFEWQEmrKTZOiqikVJV/YUul/Ry3YKi8Sr9wxPVz4lREm3HIi0n9bdnARonTSiny09SAn2mfcLbuXPn8OpXvxpPe9rT8MxnPvOy9nVlgxQLXQInI+6zfHYSB2VbO0tZ8KmMjzOQkoRaicAhSO53W2PpvCxKwsCIF1JSOOTLQypkWtXnlPVtZpZF2eOdoRL5a+5KyYtzSQEpl4WVh8LEQNSUlMkxEbTysUg0Z+biqeqIWUggi6hUGCNzEP6wpjwGRASSCEmAWNxbFdtWQc2SOvfKME+Nnhut2dKMatYAcLlugji9Bk2h1FGGlUYXbEBtVU5sUZHOoiZkKqDCU7kmSIqnVP0llyAfZfEe6VD40QESnTGfkGkV6AUJV2ahclegmMrZ9q2akQFm3R9G6hxL2wQwlmFwpdKaSQ3p2BU8gACu/Ur2nSEqnMyMRAOWwTgpAbkaCGZUiixaVMl54/o9zbHXzrcQaC3dZO7CKsXWmkZGCDY+ypGCEkvDSMXn1qNsABygAEqYzVGMA0PCxdSKyaAu69T/JETGXtOjM+Ksqnq6ITr4zkqgJWLEKIRxaxS4KHvqVnNVfKag22SIvYBhjyofRpqqpUq149tUxFn7vFYEbVUJodpmqDh9PrjzuL/SCdY0rbMterJT3WOv5eId225rq7fdBVq2fXZMCXS6w90tbct1PvP2nwLtjjvuwB133HHZ+7myQcpUorarTVH1SdvsiqDs4qlM3tsswh/giTuj7IvhRQbr73Hah1A7I/NCEY8FdrLcSHLMCkR0im9eChRKHptSyV/brA0JOnKIqodDAYQmkYQS/FhTP1lnnA2JmVvO5OkjS1NY12yRFAAuQ84gbHKDNiQlMc6K4iI3aGPCnWmOeRjcddbkx2KJLjPkNias1QJ9qYXoWhpU1QIHMibddQt4lOhLbcXec0BEwiEX+XHPIp01s7akF9wATW2Dv6CAoL/fYU66fgE0cn7y3grr9QqeZpwRA6uJWnZn1s+KjI7VHI2qOkBMOFQEKlJZAQrmpgsA9wmSogGK/b8Z0dk9t6oiSAY46vPs9bssNKqygah4EkuESUCPfM9DjZZEjaK0kHM6zAF35DnuE9eIYOel9JqeCsQ4zDN0ECVSrOTYptaKSsiIxFjl6CRjQMBJRuGkJBaVmNxzZouf3SunQ+O1o9adrZeRWfx4DMAAEinJyj/JlcIHzBi4AicG7omkcjHT8VSApVgtqGoBSvtnY3QdjbGBMFfPdB08JiCYbX4VHanN3wT7bZm07Yo8TAzcTgNYThQknIZzclI7jcTY9nlPpn8+zUDKj//4j4/eMzM+/OEP4+d//ufx1Kc+9bL3f2WDlLqdhi0+fX2adtJDsyWKUvulyDo77jglyDJX8Q1mkQE2JttR0GEfV6CEjQSXaJQt8nHGNyqdmMmVnVpS//oVaCkllHWfCQBKhIajRlTMBpM1ZK05d1P6sDJ+rZw9KWm2MTM3DsovYXRZLNhDELVPn0s9nyGL6VbPEVfFjZNLAXMl7UdF4HqO7qgKABfyHhICrolHKv8NOMIMLQTM7McOC0o41BovB2HtrqidRm06Fv6I1OKRwdrIsZGkTo9V942kkRO9tD0YLctfMXuLKknOCiCg0Ra5J1YszrwtZWxYFEgGmlrKWIBxmGVss+hNS8CS0qjm0FEuwGRJ7Lb0ay7AAUDxhgHcxK7Vc6uBlEWZliTgY6Hf8TCXaMuC7HeRgXmdi8dM3TZsqqnBVVWW5rHjrLLVLZLfM1I+VsPIOCjGQTJwusniTrzKM/Q5Yhk7UZGhVX8UASwGii1yNYuDft6ioYwuxxGHJTOhH8RhllnM24DJfCKweKQkAgYqYc4IBSq6XoY9zGPVDqFMMqyxpmzYoifsVgKm7BPvlArYEEpxQXWiJQu7ZahpJOFYj2gpHkvzWDHVLX3ZSQofjw4DOJX8eFol+VI2+XKQcq6n5RV+pt2t7Ud/9EdH70MIuN/97odnP/vZePnLX37Z+//UASl39Wbc9dCcJoWzJYpSt2k05dhsY8RL4XrD8cPHNSgwDMHe+Tn1I0P5I6zqmzqNo+ZvDMmFVx2Zp3R05gZUGCewV1fdbuRWfx1RM4gsE2hb6dyk0we6IWKvlQHNJJ1mqFXzT6ZtTz0rbFZs5EpLCSQKWGchzQYSJ9X9sEHHjdZuyZoGCEgQr5Te7NF1Zi7E0UELzknapNeB1GbaCYS5gjcBO5LGyCzS3CUJZ8WkxokZawUQIHkv3Bat6EwlNRJBOGKTNzOO3GSseHwkEJZk+y7maBKBMfVR8WupQZABmaifzyi73HlJxcNFUl3y49ZVmu0zu+NNnVR/R4szBE2LrfX7CjCT6EINWExN03PAIdrK5yVhVcmyrYhiBGMNdm6KcVKMVxKpFKTsKxO3eRhGhFqLlojSTM5ho1yU+n5cDcVBM4aMIRtRljGPGd0QvQREzoS+awoPawIwZGLAILWz9+dNibX2PIq6jkrI0dQ9uXpmbZICHgEUzoQcGYFFLVyiOVT6hYDCO1HSrJwju0+KV2WXiws3TKpr98iFHE3IRnV7ALdnYOaTgccuFeWutlNmzNs/v4fbp5tPyl//9V9/Qvd/ejboldx2VUM+zbo7HpgpIDlRgjedgdTVQiu/AjFasGiJPfDwsupkYCWQp3Q4lg4MGOesuZrEHZ8ujcPDBmgEjLCHkmVHKHeK54X0nxEtSGaPeRAUxdqhy9dlDCkiBpWWDo3XPmElKRbAIge6mFr0HNCoZNT2YwqgniNWaY5VnslsWWfPougokZZe69JYRMFkyFZ0r6Ux6VZUPVnN2yqlj3IjVkw41ErHsfrNp3dJYsYyRESCG6D1AFaV7jti/ADW7xckypV9GrCvkuoIxj4F91kxcuxBICWzqoKICygRQq0LwjwCMicBQ16ckIXYat9pRqSFDWW9ViMoC5JoSasRk6jr2bElWpRH32vNEVJFidQQrjwPR1wKTloERSpCS/TkQl5A6jAJ9ygoWBSZ9oBl2GBGovaSmjzyrw0COANlr5hs6cCLqXUOixUP7NTR2M5lYOGebFLj956p1WJgpCypHuNfARIpNH8UTqFMLBggJq/ZY7yUmn9yDNRYMNPAiaZkOUCI6wEl0llHVxyYQCcgthK5l4o1s8cftZpQWxFmR6ukPCbS1uBj4j3FHoWZRKR39bO7LPO3EX2n63yGKHuvtf/+3/87vu3bvg2Pf/zj8aEPfQgA8PM///P4/d///cve96cuSNkik9uq3tlmh29/t70GjrHXLyW9O1YN2d0cq5nHtgerVu8wpN7GpGQ60zjd4+OtAhhP41BJFXkeu4qisK7vxlCaSyBT9BiIAYCKIChOcwJQrI5PCBlNmwrBUHP3KZMrJRIXT46BRe7paguQ1PHhgE2O2KRmNIAYqFjGzehyCY8hSw0aylhQh2viChHCPbFUgRcYBGmNl1LnxfxRTG1ig/iao9aRkQq9t6cW5zN8ILcBPkBAQCRCZsZ9QoMF6fmSDNLG81hQwIpZZcoCavYpYEnk/A3higgQWRKc22JRGbsrEzMWFDAjwkEQN9jEwKGmTBIXvjNg3BAFOJDoitjZl/1bdMVAUQ9JFclvWsiwM/2+MyKtjszuJttzJY+uRmIDIyZ/niFrWkd+IzOdOxfW/psf5gXOpyV6brDKc5xP+8gQG3wrvNhWgA4QGfJagatFVfZiL8AzJPflWcQeAVU6EqKCMr7UZmiKkVsKGHJA3zduhz/0ETmHAtT1GZTnQ4G/PzOFX2J4xFpI1TPvQKM8y/IhQEPl+KogJSQUT5TqWBZ5pVxNfmyVAO3f6pCoRlVyLpOoSb8zanXfuo2zt006vKsS/bRVIOtYdNnOtT7vXet9pn3C2n/5L/8FT3nKU7C3t4c/+7M/w2Yj/fIdd9yBH/zBH7zs/X9qgJTT1IOoc6LTGzelSWpF/+0ANtvysPZvV9v5WZoAlImMzjuVCS7wvDbziFx3LI9ddVwGNmAghMf7ZOtUj5385NiRj0dmNGwdoljky1eRFFMgSQH1Kt10PwkWMOJ1UVgUFkfDzNex0HyXG5kBI2AeBuEn5OKTApgiJLsc1dQ9HUdJ95gkuRr8xEqd0CF67RjhjMhfAyer3HraZ07Co5EIg7QNw83Loh5nrR18AmPDGRGEJZGDg6PKMr5Vq/nDyi4+AO7VYpWINxqlMECTWOoAJQBHWh9I6gIJvySprNhAyZpNXSTA5EgHnhZASyIxzoACEgNojPPZrPwlWnKk57lmxpIi5hQc4Mg1KACqBeMgJOyH7FEe4/rUv5/8hiVNA8AjKmtucZj3HGhaCqjjKNWbUWoWGVg1gLLJDVZ55qaAluJJGik5GuYiPUZJ95hXypADNkNJBdWtbQcvBeGtXicrQsiQKAqhRFZUemybuEpHn+3aoM3lx0agTcI9GaeBJn2Sje01UVZbid5MIhDV+uPBfwwepm7ao0Kq03ZS6mYKbrb146cBGTWQ2bXsngQrfDf8u4Lav/gX/wI33XQTfuZnfgZtW9KqT3jCE/Bnf/Znl73/Tw1OyqVY4lNSlSFtopM9Vbbsd6ujbBWN2UUku6TRkZ3PoOHRJmoqJYNykI9Z1mNWQq1K62plDzEh1dEQ76z0EBmlTojOvjgASHpZokzzLG3kJeGVcOf1e/xaQs5Tj5UTIQfN3WtF5D5F78jbqlPvc0AMGZvUIHEu5EXlp3Q5Yr/p3OBt1gxuZ544aBE+mYG3mppZaV2eHhGrPHeFz4wSAjLWeeYmZgvqcSEvsKAe+9SNB0sEJ+G2lDxlkDTSssmENUuhPeN29BBpsRXhW4aIliLASTxD9PdqgwCSrOyPpdrLrznh6hCxykKAjQQcUHCPEeGIlAgHIKABqNRCNFJzY0kDIkmqRtJN0Um4Nh4Zp2QBQhukUOBhlvIDPTPOZ5EzL9xLRjZU/iXWan2/UN+UOu3k1aL1OBYdshTeQej9mqZcHHMBdbjlwj05CBdxlOcOZGaU0KuLbCRGZgGdmQpQaSlhpbwkuU6WBgxes8eaqXcGjtgM0jUyk0f9zPtHKh4H56OEwMhJx3Ji8UWx6KeGTKjXshOBPRLpfkXW5SQ6bn1vt2QFZKwuD1ie3zDISpaJdQmxbcoAJZ3wTDHD6FhjgMI5yyQKGKsUK46dH6Pu304rOa6j2Lv665P6zV1k2TqiUn+ve6h9unFS3ve+9+GJT3ziseVXX301zp8/f9n7/9SIpExbTQbbFjUBxjd3jeCnUuAtFvi2f38wdxga1W17XQsqn9VkNQVR3qloB1O80arOQb+OREFK6ickFqZ/qmZpwCgCouKbLSZRZd8EBTO9zgh7ApJ0usjyehpVSYPyUZhElgmg71UGWnFVIrGre/pK3umF3bKoKOYOWhps1PPCAMQqzfRSFtv4rGmEpRJkD/MezqelpgqiphhaHOW5e6sIebbBYV6ogZvyU3TQtJSFGaG1lLHKVtiONX1hg7DW6WFGQEBLEYEILSLm1IgbKwLmFDQlkj3SAt0eED+RIy1OaGNXr5GTw5yQYBWJC4fEAIzwRErayrgoV4eEORVlkAAGTf+YMqe6v2S7jAMalIRLWGg0aEESGeqZlShbzmmmKSvoemP/YGlWs8jIybIs4rzWVypeKYOXNoCec8+Nk2HNF2edW0/jrdIcd6YFMkpJhVZ9US6mmarJpDyD3G8BA0c3c5N7qlwHI8wSsVT1VuO2lEKlZIM8CxZpnDwXYaPPTRXN9CNwFUmxbkUjJqPnVxv5Z+zcM+emJB57olR9B1AFe7YFObbxUaapnkqCXFeBt2Vy8uFkgOL7ytsByrZ0jhzgeB897een0uR7g0j7aRJFAYBrr70W73//+48t//3f/338nb/zdy57/586IGUbINmFxqd8lW2clfrzer3RbviSnx0Lh4Ydf5lBlvqpQQgzKCXpeHKZCbmkkNmlibIfzU1bGHg0E9NtigVIsdxmOGChRAgmndQHR2ZjVPZpnXAmkVkSi+Qya/VXXdHkyAZWTI5sJNj10KqfR0YbbYYf0FTSZABaiZaUICtAJVLGPAxOmvXic3VIH0LGNSJtTbxcUI/9sFGPlWK/7qqhCpAYp+KIG6/1M6eEHuQqGONgrFiWJTDuzBvckTtk5ay0FMWUDEmjDhKNafWvVBcm5XLIjiWVBD0/ia4chOigZl/BTkbhxlhtnLa69UXxQ9iwRGkMyFgarOeMFgbQgiqNaBQN6ZhHIEbWJTe6W2kK6DCzmNMxcGgpJVJHWb1+Zihn6qo1N7BqzlmVWNbqatAmM5bUTvCq18I7KoUWawL1kANWaYbB6gFpqrHmngSwA5auksATVN2TIoYUkHNwgJISSUFBJoRY1a2yKAoTiKmo+i1aYs8k9LljjVpWkc8RMDEQY/OvGgRRtcwWqURZOGvj/o8yIwxZ+pVpintbq7bfVVxw+rm3kwDKafrskwDGtpTOLr7Kvaz4+VRuz3ve8/CiF70I73znO0FE+Ju/+Rv8h//wH/CSl7wEz3/+8y97/58a6Z4tuVVv9Q08ReB1O6lw1bb16nDmCXLlkex4CkyAMkupH67pOdtrhow0Bhqy2t0HTc9E2dZTPcBIXlznuuU4GBH0EEoI2qzwKcNTOaSn4ASHGqzYwUIGRQNoQE4yfDaNBPiJGH0SB0+TdS6awSd1mQldjlhSRhMy1oPMlve0lk/mgJZ6HWgCAqUyKAFYW60XCCmy1bhEQkCLMigbwfIoz13lc0DittpBBlJT9iQQOg7Y6HHMyRUohfhM6rvOAQcha3qDkVGqI9+RO8yVe9IiIkMIsxbBAAtQWFKLHHovVDijwkmxeEKv3JHEMmtPymMx8ipIuCBSuVhcb3su5F5LxUgaqHBrMjJ6q2iswAnQdUPEghp8LAkxzr6jRFWCBNY0LTUn+UIZwPncIDJjSQkzyjjixomzh5WzbEsdzucFjnjmwMn8USS910jKjos0OHHAYV74eyvM2AMOZgA4mO2zvU9O0rYmwDbqfcWuLBNFWdAoCmvNHikBYY8AZ6hc1y5k9Ywb+TzoRCFTkQ7b9vocGTgJlm7VXXIEqJcI6UiOjDLpMDt857DAwEoVWeFKYpyxG5gkzV/x8X5wG1A5lRX+tGL93QkctqV27i1gcrkRkSssmvKyl70MOWd8zdd8DVarFZ74xCdiPp/jJS95CV74whde9v6vbJDCNvLS9pBf3aY38TYgYO2slTov4aniD2/aUtPHOoL64WXtuux71R1ehv9qLhmGhn8TkLXby42GrNO4A/QcOQMwPopdRnsPHA8PE8QzRQFMCSXrOSWSMLeiIum4BUCFwMJDViJtUo+UlMURdT00aELGnTwHEWMWEgaOQJaBQ6zwyQcf5BbnmovOR7FBbJVmmKv0NJNKUbU6boTwFmzwk5KDmt4AIbBYrkMHuaQpnLUOoiA5/xkXh9IMUgM1QseEmQMXuV4mA076my4UoKzYSg0KHwUovI6WAv42d0qahXuoJOWitCDnvgDG/ZDXZq5mHBZASLmdHtuIvHVaSCIjJeJSQIlY3Rv/JENIuxvuMCOpR5QUoEQl3JbiiZ6lxJoD9mkQZZSmdUxSfT7PsOZGUmpsvjcZB2HtFYutjEFGgBWJzErG7lSVVUeD7hiWOIhrtDFhU627qdI4piqakfCh9mKvZG0Bz+bhs0mNR13kfXCOinGuoKRv0nufVYLvF40hPJRBIyhcqeWskKBNOqruy0pU2PPnaiADN3XUpHpv/BPo35GRGxd1IPVJPFKqPscqJSNlkRmnqh+sOCmXtMK/1ASv7q8vB0hs6++nqZ57oX26cVKICN/7vd+L7/7u78b73/9+3HnnnXj4wx+Oq6666m7Z/5UNUraxuYHtN2mdH90WtTip7bJwHp1K7TKbd64ny6wTY5g9PucMShlECWyOs/X3SAxqxH2WBgaaKgoiFd6kQ1JeipFpj5Fn69PZ9tVHHSWX7SqOIVn/ktQSXxUMiCxyZF3OTMAQRkGsGLLUQVGpZ2YGdJbKGlWxtM/M0j+assmcMQ/mlVEKv2Uml5MacAGAeRDlzkEonIVEhEXoRZYaS50aADifl1LjhxIWGMQqXQe0FuL+2qG4v0qaQs7BbN+TRg/EOl5kyKVyci4gQIGHubW2VFIuAZLCAQRwZOV5dMw44mKQth8kxbJQ0GDViIGKmzKKhpCfG44RcMX7ZM1J9uNgrjQDQ0aA7bhEjeyYAuwKQOkhxIvMhLWueVg5yR6EDpkJR1VFSwOeZtpmKRsz3luGjRKky29nSp5l3DjwCeCKw2QKMiHMDigk6NrdOIaMGYBNEsmxEWYtjZO4lH8gkochKykkJ011GjBJQb1RZN9eewdw3xOfPGhq1Yt5okRLRqof67YMmBCJSZxGReoKyhZB8ctk+wHGfUvNhxulS6Z93vbKzXVq+ySF46jdFXCyDdScFD3/TLtH22w2w8Mf/vC7fb9XNkjRUPexto3QWpNp63VOg+ZPUWNiK0DZuT8FJzVYqWXQvo6cJ+Us0owmgCKP7PE9ksIKCsg6JwU/IPc+qVtdrweW4tEOkxvrTLUsYUXwIw3scNRjVNtLaXr9qwBHOviAWLnP2vLMBOQAJsa8YbRRiudtUoO9RlI6XWrQhIT9VtQmR2mGRnvKQMI/yYgQcTLj6ngRtw/7iEqgbSnhfFqKXwplZASslDDbcwPorL7XATToYHYBcyzQI3NAR8AcSaTHylvYQKoLr7hxEASUga8laCRCtC5zkoiD1cyZh4B1zm6e1jPj6jBDj6SRAQEdtueFckkksgIs9B5YVPfuMpi6hmAVlI3Iaj8TUCIplhbaJ/KUj1UsNjm0RVkkQmPnIkAlEuOOHLGkpBlAVpmzlBCYkUSdLE1mKR4DKHY/HKmHTavVqWtwkhEEXOrfnqMruFpUqT5KQLBjRNyp65r8fZMbBMpy78QBd3QLLAAglIKNXY4OWAyUGCE2ZQHFBEjxTI2YSKpVHJU94mmmbYA/OMciI6iaRkecW8Y4RnqvwYGDENb0D9vrkt7xB9bSPZUyh4xExTyOljCLutD8USgUocGOVpcB8dR2jCOBwY4Nx/30pfrNbeBn2m+ftJ97Mrqiv+FlbX+FtVtuuQW33HILPvrRjyJPfoObb775svZ9ZYOUS7WTzIF2tal87qypn3o/o8NOU0ql0/C27UFiVt5LtZ0CAruZPbKhxFfEasam8mJEBTOa8uFAozCxc1cAIfIZMNH0jmwn4IMsvWORHDu3REAAwiz57DNEif6kIag6osKHABqVJCcmhMoq3ySgZrB1MbWYxwGzMPis+Kq4kXA8AvocMQ8DVnmGWDmTWghonVupyIzsfJSOIwIEuCyo17RQlRKqrNmzLyO3bre0wSo3YvHOYlxmBNq69QxVHAmQ6Dk72TSSmKGtuEdESZ0A8CgJAK+ZIwBBL3mV9snMbhy31gjNGnBOjKWANhpJCQTMKWCVk6ZsqqKKFRfFUjcZZpjH2Nf79nzOvrzLpBEldnKxpVt6Dkgqfw5KSAaAI/VA6blBV6XuAHEHXqW5R06s3ae5Ex034/o8KhVf5xYZhHVuR8UEew6i6FEC9CwmzOOAo2GGLhWPFAG30UF0ZnLztkjs1viWwkwplOgJ2aRJoidUuTFTphG4QJiAECvoOVSRS65SQh4dqQCPPvMeUd02jtuAacxrnQhR7TTLFWk/Z5EeM5dIyqSwoGzCLgiooyejSMq2SeIuxc+2dUdfpJpUTpdN92Pnfy9FVD7d0j2vfvWr8ZrXvAaPfvSj8cAHPvDSdhtnbGdS97z2ta/Fl3/5l+Pg4AD3v//98Q3f8A143/veN1pnvV7jhhtuwGd91mfhqquuwtOf/nR85CMfGa1z66234uu+7uuwXC5x//vfH9/93d+NYdhdu+XEdrkX5CQ+ymkKYW1zWNwFUMIlztVmLTYL0VmOW1mb9Lm+ieuOI0DzziVU7Mw8wEEFG8FPO8cwjBVBti/bhqt/4pkiqZ5RLqA6BnvnDASrcMyi8qFqmUSry/n3lXlWlyO6FKWiLIB1arBJDQYFI+eatYIRGTispo/JUje5dUkyAFWNSJpIiLLJpcih+iK2vhesU0krAJcmW0TA7PKNXGsqFaD4lwRoegYiFb49Jdye4UZoUSManb7vwThkwmGWs7KfoWN24NJDChuKU21xixWDtSQpGwiYEZWRVie274ixlLn3z6WisZi6FUCzZsJKHXkTS8rIji/nVhQ7Pco9nvWa2GcF/MnyCzzXa6W29SiFBE0i3nHE+bR0AGMpHgObVgohcfASCas0wyaLUujONMeQ46hm0EYVY3I/mWxdyy3k6LyUWi6fddkwRAfbQomj0oVwSe3Ic8P+WnZCBTRUERKr3wOU545S9ZxziZDURHh7f0xxVx+zanZsmlrhT9sud9mq/5pKj13heFJhQN9/Lv1kXdOn3nYXz2+XBLk+B1vvXuamfLq0m266CW9605vwzne+E7/0S7+Et7zlLaN/l9vOBFJ+93d/FzfccAP+6I/+CL/5m7+Jvu/x5Cc/GUdHR77Od33Xd+G//tf/iv/8n/8zfvd3fxd/8zd/g2/6pm/yz1NK+Lqv+zp0XYc//MM/xL//9/8eb3rTm/CqV73q7Ge/TVp8T92YJxTM2u2+aCHkiTstC/JnDbfSkMpMwIqAZYCG7B4pZKmuUDrFsj8UubGWaw+DLZdloWe30QY86zHal8/W1C8l1B2nrkc2i6zqknAfhECIkt4JauxWLl928qyZZBnHhCHhdVNiDDlgEcsJzkOPC8MCqzTDnanMsL24nLqQWn2fdW6xyjMHKuvcumeKnw/yiJ+SEHDEM00LRZcjWyThiFscuWw5+6zb5bwQUGOFCm3fh26tL0DhKDPWnMVKH8UobUHiRmuRlp4FMFiExqz1D3MQ9YxubzJgi3x0eiyLwKy4+KJE/Sf8EQE5AHBHtmrNBXj1KgEHCmgxlc41QUDahiPO55mqdlq/ZhLlyB59ElffZhSpAoB1nmGdZziqoiaRGCu1ve+qCsgiNW6wVvJs/Vuu8gxHae4lDuzcBWCRVz3ucsTAEevUqhtywFE3w6pv0SWJplgUxTxSmIFhCEhDUBt8IHUBnALYXGTdrI1K6D9XICEDobcoCY14KmNgYhFMoFb++GRiRwQlN1T4K4zKCl+kx6OUsrWTJnv1elM587aUi2+3I7JsfWdKl54IjvpJHv+bnvu079/FWfxEN74b/t2F9oY3vAEPechDsFgs8NjHPhZ//Md/fOL658+fxw033IAHPvCBmM/n+MIv/EK89a1vPfNxu67D4x//+Lt20qdoZ0r3/Pqv//ro/Zve9Cbc//73x7ve9S488YlPxB133IE3vvGNePOb34yv/uqvBgD87M/+LB72sIfhj/7oj/C4xz0Ov/Ebv4H/9b/+F97+9rfjAQ94AB75yEfiB37gB/DSl74U3//934/ZbLbt0NtbzUk5CxF2VztLemfqlli95h2zgK3us8ZP2SZFZgaikvSUX0J9kshKpokVtlQGzo2MXjXbH8zIlt6x0HLJhPiI5YQ9DSmbJNlMp0g7WzflMGlyUjdNS0NV8U4CYMZuUowNMGzMxAiaDvBrBKDVQm6W+mmCKGmCRjDO90vM44CLvdwre7FDizSSnBpgAYBWzeAkPbDR9JAMbovQO1SfqZw5qa/Kftig0/jDSn1YsqqA9qn3815XA2SLjBVHBBYreLGfJ+ViZJcuZ1jxPvJqyoC8D0q+NZM2S+l0XO6r3iM0jIg6TSNAJuj6M8pajFB+n1UOTqStI0hWKXmN7MRcAyULSmiJnQg7w4BISa3tgUOm0fVJSqDNLAZ5FqkAgK7iBCXKDkgCsnugWDvKcyxCL1EvTQO1NGDNM5jrrAGdGqTMw4AhR/XHIU/zRJJUj6WBrKUspReIGHttj/XQYNASDnYNLKJChFHxTP/bB013VvdyYKl9NUwGdot0JhoRXcEqIzZwMuG1+GuNgtZk2qLksagLRuCicFWOD/LkdXqs31FOyrRvql1nsSW1k6v97CoYWL8/DS/wpP58ut0nC2H2MoCGb3/G9gu/8Au48cYbcdNNN+Gxj30sXv/61+MpT3kK3ve+9+H+97//sfW7rsPXfu3X4v73vz9+8Rd/EQ960IPwgQ98ANdcc82Zj/3c5z4Xb37zm/HKV77y7Cd+inZZnJQ77rgDAHCf+9wHAPCud70Lfd/jSU96kq/zxV/8xfjcz/1cvOMd78DjHvc4vOMd78CXfumX4gEPeICv85SnPAXPf/7z8ed//ud41KMedew4m83GixYBwIULF46fzLaoyrblZ2nb0HoNZKby42l05SRp8uSBH0mREdUDRfcTgxy/1opqWue4URMAKiofCw97LhvyPpvfCgoAYcC3MZ6LS4rtVGnyz5ZZ51l30pmK/0kOqj6Ch8utZSas+wazRsFEkgjELCSkHNAlJT2GjAYJGSRVkjX1s8kNsqphmpAlnB+AnAjL2PlxjHRZbPQ1lZRbB14yUIpPR4+oXBWx3A8AFjSgU5UJGFgjugx2aSoijRwkzlhzcOmzcTIALRzo5FZgncVef2HLWMixSSMpC4iXiEVS1ixsGud25AI0kgI6QG6XdaUAsrTUPo3Tq63ekr0eW75rdu4L9L4BBPyIk2tWbxhxdZUCgS2QBSR2KABimmI74hlaJK9KnStTtkXoS3QkA4d5MXIQNkBiURoASoou9XWakNDlBlH9Tuw+83uuSj0B8OvFTGhVgWY3eErGQxnPH+yaEAkgkRCVgpmBxJm54PWS0rHCRhW3ZHqNpxHLaatVPsciCLkAlZCyFiNUoGEhtgqssKl2agI/4H2UT64qfLHdQVv7v7P2uaftu7fttwYq217fi9yUe7K97nWvw/Oe9zx8x3d8BwBJwfzar/0abr75ZrzsZS87tv7NN9+M22+/HX/4h3/o9XYe8pCH3KVjr9dr/PRP/zTe/va34xGPeMSofo+d2+W0uwxScs548YtfjCc84Qn4ki/5EgDAbbfdhtlsdgyNPeABD8Btt93m69QAxT63z7a11772tXj1q199/INdN9+2m/w0URIDHfXfbW0KVHasU5PKtlZC9uPq65SBxmYtNuqX70QAMGRQE8Cg8fYaxRCQQW72FpLKkLOQ91zFo2iF4yTy4qgC4BbObbFiZyVyhQJMIGFrTpDzJkhnHbnCdhmsJlY5R7SthHMSF7vxnAO6oUEMMtXcpAZEjJYTFiH5YHMxtT6omCttVrJsn6Iaeokte+KAjc7OpQpujyUNohAB+Uw+I6CnpiLQBliF5INwET1kABWHWjUGg5iNhWrau9HUUJ9brClinwYskJGQsR8yjnTWHsAuLzYoG8E4UjM4AyjGF7HAVUuS9plRPuYme5gLKG6JcRCMX1P2UUupjacRUVRENoZ9PM+xoIQFkjj9IhTScBCwEcE4zNGJxDUAidUgtqDeFTsAsAwbnE9LrHmGlgbshw0O856e9+DRC4tUCbjJRa0zUQgZGTaDcGHYQxNUwh4G3NHvjSTGTRCQK3yn6OUYDLRYUUFzRo7EQMzo+wZdJw8K6/3PWkCQTWasxFdL+ViqhjXaaM0fGyOw2284jJ+/KQGzRGBQgI2tYxMFW561rIbyT7y2V86SPk7ZB/ERaZa59EeVL0qdot5GmPU2tXo4i1DBv+gZoifTZXXffy9xUu4u4ux0Mj6fzzGfz4+t33Ud3vWud+HlL3+5Lwsh4ElPehLe8Y53bD3Gr/zKr+D666/HDTfcgF/+5V/G/e53P3zrt34rXvrSlyLGuHWbXe3d7343HvnIRwIA3vve946/y90AEO8ySLnhhhvw3ve+F7//+79/2Sdxqfbyl78cN954o7+/cOECHvzgB4/TPcDJN+VZVDontV1y5C3W+iO32Us1q+OTMigaY8COyYU9pMx8CuqZQgxuyMmwrt4hM2zTHkxnbbnBsZmZpX9AcBXQlOMClA6USaSXyCJRRm3u5pb48pqJxehKfVCsc845IMbsNVAAzTqFcVVZIzEOqlq50C+wiL3bmG+Ut7KnnioX0wyIHfYqWbDZo/vsnCTa0IJVpjw432FfixF2DDcSA6Q6b4DM+lMIXrDOzxMBh7mkKvepR4egYERULwFwoLLQi9nC+CKybEbZianW1vpjLInFIt9m+CzW+Q5ySJRGcz3vCDgxV9bPfgzL2AlvRmJKtu8IrozpSrXiDmKQt2ZJW/UIJdWl5OFAGYkjOo56HaOTXi/kBaIRkykjI2NBPQ7znq/fU4PE5LLjReiQUIAmGCOOUM9x5IfShIQhRyfrijpMFDvr1CBkATpNyOhUQdbGhF45KADQp+DgpY1J0zua5tFrbSlMv3HVsA2AS/PrNA5UqszQlA5QCLMGMFCimOY4W7hlcMAjGN4eSmiaqE71cDkuqY2AnwuDQ3BX6BLqZO3HGNtcZq2dqj+ryau72mllxydFU04DgnbxVT6RrQaLd3V7QMa4qn3f930fvv/7v//Y6n/7t3+LlNLWyf///t//e+sh/uqv/gq/9Vu/hX/0j/4R3vrWt+L9738/vvM7vxN93+P7vu/7znS6v/3bv32m9c/a7hJIecELXoBf/dVfxe/93u/hcz7nc3z5tddei67rcP78+VE05SMf+QiuvfZaX2dK6DH1j60zbbsQ5Na2DUHXoT9bZ1e7VIRkV9vCT5mWNN/ZYiwdAxuhTCqzcI1qbWaULXdT9hmSymTrZziMr4V0bNaRah+WZZlVRh6lhhKORVjGqSBdMEA6aZ1BAhJFYQCco8zcGpJoCpPyVBjJZqshu1TVjLHaZvBlzFJnpRuiqnl0Rg1CYHbLfADOixiyyLZrxU9tmW4pHABI3OKasFLdT9aiewFHuVFzt8H5GzOd1VuqqOPogMOa7TtzQE/BOSMZhfjaaxTJpL4ZkhqbW+SpGv+ExBrRYsBaU0amtEnKTRG3V5FwR2JJ/UCItJ0ew0BIC3Yvk1K3R45h7837JYFwmFssaQBIJM3WegV+C7LqyMUBtg29A7kjnrmDLFgAyoJ6tbTfc+Bi0uNVnjvJuTZtK4UD55iHAcu4wZxEdr7JDe5Mc+zFHvPQo09zHA1zdSDOGBDRaBkGAE50ziBcHFrhpWiUJAb2dbuh0SADOZ4gYrDeq2RRw4mqJ6hFwEh6bJOBXKVrJumeOlJSAxTjg/m+Kk4KoBOHGozoudg+5fhZ92F9XHVgryGWx8sm1Y6tnWjatisaXYOKbf3srtTN9PPpZycRZk9a9olqdxNI+eAHP4hz58754lOPgadoOWfc//73x0//9E8jxojrrrsOH/rQh/DDP/zDpwIp7373u/ElX/IlCJdwW7f253/+5/iiL/oiNM3ZIceZ1D3MjBe84AV4y1vegt/6rd/CQx/60NHn1113Hdq2xS233OLL3ve+9+HWW2/F9ddfDwC4/vrr8Z73vAcf/ehHfZ3f/M3fxLlz5+6aW92UCLaNDW7NpGunvLBb2y7Zcf1aH7xdD/coBWTW+ABc5ZOymCoBY0IbkUiMrez6JFfsBLpB/yZ1na3TApUVt83oYMGPusNUrEEZiB2NZJIAilSS606wWidDAEuGS5KzEQ5JCg+mRPITAR49sTD7emjQpViW5/E175JZnme0IWnBuOhqjos6cNURlLo4nS8D45q4UgnrHOfTUvgPtHE/lTW36BFdIrvm1gm0WaMLxsdICB6FsMHbZLupioJYO8ytg4Mjblz1MyPSSIeqUphwqNEec8GNJFGaNQO3q1fLgR7Trpapgg5zcIACSLpIivpFHOYWh0pE7VkGa0stRQc4Ep2YQQClpYw6RK0eLWTWfeq8SOOaW3QcsU+dpNFQCLBu2OYgqZEUkFYyls9kvTvTYvQbLmOHDMIdw3LEQ5Hq15IWMnKsRFiK/47cS5LqkevERXLMpCqfsk/jTzWNqNGI2Dkqo3HPUD/g/igcAVawXj8z8oZGap3QY/Re0qvVfgjH0ghSJVn7AC6V0n29EfiZ9Bspj/vGVPmjyMUcgxg77UtFJOqo8lnSOCEc/3zar9vn9YTzUoqebWqgK6SdO3du9G8XSLnvfe+LGOMxq486ODBtD3zgA/GFX/iFo9TOwx72MNx2223oum7rNnV71KMehY9//OOn/i7XX389br311lOvX7czwZobbrgBb37zm/HLv/zLODg4cA7J1Vdfjb29PVx99dV4znOegxtvvBH3uc99cO7cObzwhS/E9ddfj8c97nEAgCc/+cl4+MMfjmc+85n4oR/6Idx22214xStegRtuuOHykOKuGjrTyErNPK9bjf5tf7uOc0ITwuo45WPL67/++gR5n+WLJZVDZeTJWTocYrGiz7BSMOBI2lkVXgpA2tGVMLRzTHwmhhJWVoKg1/LxpDncPIoSeY8phFxR+FBWt1lLH1H5CULg0QSDSEiJTczqLispICEpZoQoCh+z0k9MaJpBwvZ67C432CQhTc7CMPLDyCwE2yZkLELvgKXF4FGVeei9BgwgKZPjduwZS9rAbNmDummZgihQRpcFyByEtfA9wJ52Mb+QjgmH3Cp/w2r3DJhRdjKq3V2JpYowAE0BqQcLZSx1v51yRUAJLTISExaBkZmx4gJIDAQtMDgRtoVWTAY70dbM6QC4QgkQoBKINXJDHiWy738hL7AIPTr3Ugl+rVokBytys8DJsgDc+de5QUzKd9F0E6IbtFlqJ4MwpwExZDdySxC/kzvTXMCKhh7M9n6ogIoBm01qRgCFNXJnMnM9Xa12LLJjEKTaNybjXpo8u1Wqp+aMGICoIx4j/kI9WaiiLaR2ASP+Ccavmch/M6vZ47V60pYJm/FR/Jz5eH+0zS/lNO0kl+5tEe1tgGYKRHalf6b9ex1tuRe4KXcXJ+W0bTab4brrrsMtt9yCb/iGbwAgkZJbbrkFL3jBC7Zu84QnPAFvfvObkXP2aMhf/uVf4oEPfOCpFLbMjFe+8pVYLpenOsfTAJ9d7Uwg5Sd/8icBAF/1VV81Wv6zP/uz+PZv/3YAwI/+6I8ihICnP/3p2Gw2eMpTnoKf+Imf8HVjjPjVX/1VPP/5z8f111+P/f19PPvZz8ZrXvOau/wldkZHzkKs3ZXmOQm0TMix5bC6TEHPJfO4tczPejAjt9kMo0rVlBkFJHyrygImgEIh9gFlRsUMnUUpWLHO0J7nKgLiX4fKOpbi4aa8L4UPdb8mSWYrTa9gRev5ZOi5QqIqTZMcuNQz0xAyYhAQZv4VpGmfmq9i0tZNbtCGwh8RRUcSK/TI2KMec53dL0LvXAaTr87UFMwKEZ5PS6nfoyZjiQPOs0RY9sPGIyktDWrNblGIku7IALLa6Ft6xsiZrapyREEUsMkRc13HOB5rFnBxEHqV+jIOQu+k0lUluT3MAnyWlFyZMyc9B9ZogR57zRItWTpvZXuPaFEVKT1QcgxepA+k1Y97IToDXhNpzS2ssvR6VJOnwVGeIZJcx3oZAHcPbmkQ0z0FlUCpaGyk6EQFJLX6W5vSS34zkR7vRSmvYEUDAYnKmQePfQYo/ympp4pG9EQ2T8hJeBy5n/Qz7jaLQo51oFFSPRIthDyvFnG09A1XUc1RpIRHwMaXD0VtxwTEwdZjGGdNT76AjGk0YRqlENINRtJjbSe6yk4/38U12SY9rttJ/JJLEWlP2te9EUGpAOZd3v6M7cYbb8Szn/1sPPrRj8ZjHvMYvP71r8fR0ZGrfZ71rGfhQQ96EF772tcCAJ7//Ofj3/27f4cXvehFeOELX4j/83/+D37wB38Q//Sf/tNTHe+JT3ziMSPXk9r111+Pvb29s38xnBGknKZ41GKxwBve8Aa84Q1v2LnO533e590l05hTt9PkK09C58DulNAk4jK9JiOy7BYS7TEy7TYXWtaCg3Wosgp10pABDKDYqvRR89HBwBEhDIxU75tKBKUcR2FPNbOr0zgCaMZRFsdKCkoEUxFyw75PmdxbRIVHYMZeEWyiwxiGMuDKNSKNSJeBBWyVaKVicqPeJ42qeqwlJszVOt+4KpYqyCCs8swHt6UWtwMBB/EiOp3R9xzRUYM2Dpgp6dbaBZXDigpFZtYRjAX1MmArURMAjrhxA7MDGtQZlrHhiJWmbhaUNG1TjiFk26AyZvLo0EoH4JWCon0aKlAT3QUXKEDIvFLAQs419VGrBFpT7WQml0cf5hnA6jXDwHle4BxtvLhi0mjHEc/cVyZX52+vey7ptkXoESn7d7mQFqgrG0dkzELW6994WYMIFtmL/m6b3DhQ6VX+fTG1I0l5G5KkASk7aLE2cFH69Cl6Ki1VlcZN0WNpHbutAQHZnPTGT6REcZR/g7nKUomYGFbvccwzxQa0uvggkwIUlGUGVsIgRowAitlbufBe6mKchs3FFh8oJFm/KAk8DKVP3NInnTTRmk7IeBtQqSMclmLYtU456LaDHV+2i4f4adSe8Yxn4GMf+xhe9apX4bbbbsMjH/lI/Pqv/7qTaW+99dYRf+TBD34w3va2t+G7vuu78IhHPAIPetCD8KIXvQgvfelLT3W83/md3/lEfI2t7cqv3XOSFn5KmK2XnZYlvss1cVt6SZdtAy713+PH4BJN4SypG0mES2oHGv2oVT7KOSHNVwMS4g2DKHhY+Ssy02MEFN8UlxWrKickcdJ0+3373CMj0PC1RjIiSqeszWaR4gHB4KjH7oOkgZRYy0ywqsc5E+wLmSNtzoSB5Bq2jUmJJdVgM/bMpOBErmeXhZ+SKlMPASrk66/yTKzUg5i+mUlYz42SJmUG30ulGwQ1h8sURiBICKAs3IgqBQQFDokDFkEAS1YvEwMOpg2xlIlxVwxoXBM6cYjVdFGvxFuwgImaz5I1yjFDseSXY+hfJvVwESpw3Uw+7NtoGsRAlUiKBwcJ14S1q2XWHDFDxloJyTU4se8vt6f8XYQOfdrDhbQYqaUi6b0OAZHLsFE+UXTPlTvTolo/Y27FIKt1NlmcazcsRQQvptYdZTdZyitkjap0SQBVLV9fW40oggMVq3xcuCc8UqGZ9BiJMCJ8OSiwH6l8JIBC1nXw4MjHgzFOnK1TOy49zsUbiSZpmJH6h+HRFEpaXT0lOZ+Kd0IGVmoeyinbsb6s6gu3ApWaT7Kt7zxNWmbX59PI+L0MUO7pdI+1F7zgBTvTO9tAxfXXX48/+qM/umsHuwfblQ9Szhom3AVqpvnabazzbe6JJ6SDjtW3qJYBGM9Yam8CU+WkJKCnrUhl02PmoGBhMgNhiNW9epVkQqnZMwEh3s8qRyUk2a0tdxCUZPBDAnKU6WFdawSQHpLVIwV2WolkPCI5eOaAdj6MIs45BxBl/5qJCaSDRCRWRZDMepnE0ty4BY2Cl0FD+J0qXOQrsc+mzzWiTek5InL2qIolJNbc+uAXkZEpoGMgIrg6xQzFArLO5OX1mhuPFKxzKzwaBI1gAEcktvBmtQ+IpLfjAKsx5LwUje7I7VBeL6qUUEsJPQSoBLBa6AessnyH+nYw+3rjlvQccD7P/P1aTe4W1KsseKgM7whHKgc2nsgaLSLEIbY2Vltn8T0RN1kBLEeq1LHrGxyYlaKAgcT6vqWElhJW+XhOPHFAJi0CiCxmfpb+ISFNb3J0EvWUKFsDVpDcF6R8p26IsNIMdbOuImdygOJNI4YwJU5Vy0opS6NaWJ6OJYzSPBaxNMmxgAyWffTj/dTEdue2MFdqoLI//bKyzpDHqp5pmscs6mtlzw7AclI0/Vjqe1fqZxcv0D7bNoHcNuG8VL9/b4GV+je4q9t/pnm7skHKrpvxJG399P1JYGXbOlNQMp0VTFJB0zDpaNklingxy+AshcS4OM8CmlJR4luI0r8Em21VkRmIvLiWEnOUjjSk8V/htMCt88lASEWwlRPTzjD521IpeYDcVaweKjrDAwg8BLEKD6KQkP5aem8KPLqMBKjrZ5b0PZNb6BOxS0YRpVMPxGrsFoEkXhstZTQhYS8kJ9HOw4BlEBKXDYb/X3OkjqadSmp7JYoGdGgQOY8G6cTkgyoAjQKIakVAxBzIIlc+gnBUAjIO88KLFgLADIV4CwAfy0s/xgF1WrWZsVKQkZXQua9RjqzfERCAsuZGCvQx4yCsJX2jaNQJsblFLb+urfY7RCe4rnOLfS0hMC3UKDLuUj7AJMQtDRpZkvcz/a6WEgKA83np1zciY0bDSNHjNve5KbJlqGutRuuMKDtUvKIAkaIPai53lGZYD7pPCEk2QNxnBwWDds9MAUr2KAqQjZui3JPcx/Is1OPlQEooh9vdlx0CwUCMRSANdGwBLECJjHhTAENQT5RclsMiJzUHpZqAEBHIAEhNJpWc6ljVIxcAdTstMOGTwMe07SLBTtepP9sGUOooyqdZiufTpV3ZIMWImXU7LXCpW43474pEeVd9ii2pn23tRNO3OtyQMtBYPlf+oxzAWbkeGcgxyAwKJXIyNXajUNxnRyoCTQ+ZZ0pJ8+ipxNKRugeLxalRoipMABJE8RNQoipBHWgJopCIcp6m6LDQes4BUQsQMhMudi1mmvoJClA6AHtNj4bE/vyqZoNAGYFkADIXWiNOmj9Kn8RjZRF6ZFWL9BzRhs4Nw1Y882jCXLkU+2GDxOSGY52mKHpuVIFSIgOARBuMz2KeIRmlErM1i9xY67nxSEtmwopbT714IUOQKIcIrhwKxCLx1R/T0jnjon4CGK6hi04etpSOya0BqZkzU46Hecz0PPN9L8MGa61QbODMRk2pr6O3K+R6HRrw0O/Zc4MWQo41Vc8mt9hAI1m5QRsGLAFslNci+5OUzqBRNHOSncfBibAXkyiB1kOLQf1yLNLW5bGTZp/Ei2jWJFzsWr+VRR4f/PY2EM1J/zIAdZ8VsEGS3mS4kVvd7JkxvshImWPNoih1qkfXsYhJnWIt0U9CGHJZP5D7ofixpyDEj6nRWvuHsg8DKqMqxxP/p2OFUutIjfWjl3Lv3ta28UumKXprJ0VN7q2IymciKXdru7JBSn3j7mJ07yLF1g/NNt7JXVl2F9tWgGJ1NEIAYtAifvH4DCQxqJHig0g8MqqVcYNBATAJM7OlbcRDxWdcJi+u/rpqgctr82zwAoIJxaUWur6mdph4DIT6AFZggihpIVZPfs7kvhDDEMAN0ETr9AiZM7pUFfIzpQZHDDlgnRrMLP2jEZTMwf0y9mLvKYA709xTOgkCaja5WO0DAAJwZ1rgKhDamNwS/3xa6sdZohaa5pj6n2RIFWV9I8UKFRTsUyd+K7pOTba1WkG9R2X+/+29f9BuV1Uf/tn7PM/7vvlhElIkMRqEWqbWQaASSGPr2JbUSCkD1s4gQyVSRkZLHG1aLXE0EdtOqFqLKCPWttJ2anHst9ApVdo0/CpjDBDIiL+obUNhlBtEJrk39+Z9n3POXt8/1lp7r7Ofvc85z/veX+/Ns2bee5/n/Njnx3PO3p/9WZ+1Virct3QBpwW05HZGwIR3ATsI8Vofk3NQgGETzel3vo4QwdCeScSm17I0upb9sIwC4jNB9SGsG2H30G4MLVZjV1iIIbL2/h8Ew7Z4xN9DrTViVy0eaDPJHvTpOB1xrScNOVax9ZPtEp2AXwfWo6z6Joa3EzjUeG1yD34VHQzbopoUYS8Ts+KgGZedeR7IdFOuMwDEDkae1yVXjWwfXwEXo30UzACArXDMrh2KbbhAQGcmXxoxCAiQIePy0YlE7vISpqQQBLDmzi6xzPb/1CiqlutLattMRUza7XRmdJ7sqEc7f2d6PGxDyuAitU2Rsp015GYLBNaWlaofW9OEbyM6FWBIoxYZFyto03OQiqXaOTnJmWLFcClilBKtXLpFeiqUgEn0p5sZHanbx84QyCR4ozQDHFDgvZMqsGnWieBAnUPoWK/Rdx6h55lr2zYMUIJE+EhzKpBtfAIqmudiJSHK+/2S67EIYDnT7UTditZyUc1KIwnJONy1H9R+0b9WKucGSW52EJY41e/hVH9ZFIxyZlSXolDAxfB0wNVBekcEuvHxkIgk74JhTZQtSOneVczLSeIcToUdcTvxudq09ApoktvExXZ1f010tk/LWN1ZI59OhctwmnYQyEc3VCvuI4DzlQTJyXsqXBZFsD1SmPDpsCsFHBc4E3ZxorsaJ7qrcSrwfdN8NDG6hhYxz8l+WOKJfhen+j2c6XnZmX4nMjn6DDzWXiY5UzgXiv62rQhn9f62fYODboE2pPo8C8/Lu+Cx3y0iK9dJHpSua+QZbFJdHnk/Qifuyt7FnCguOLhWvgv41xfGvm5Oalatabx0EqBuH2FM4nIDZnzPonjfUUoLECTSJ7KeOnFIwMOFxKpEUKJ9SQicODKrI0b9er81MKnxM2BSTJtVs9GKliHWdUCdMbGm0UH5ZNT+be282Vha/F/4hV84cvuXBkhRyx/8XIA1B6XPsbGsitYKAKYU6TOkTHVmxJ0HdT3Q8cw5llqXbJGpMwpp9qQl2zvTvopbQwIxDER0J8TO0mmpeAEuvuUIUKs4V6mDRiwMfOm9g18l8OKCAywm1E6eElDRPxKBopMcKZpC3zlCp5VoHUUKf+n7OLtVoBLIx2yiXfA43e3gVLuH0/1OjFA5CEs82e+gE12LzvJ1sNRwZRVyqntGgYZ3FGv9BGKXiOb6UHePApXTYZfTwhO7f7hWzR4CeZwKe9inJS737Kqy7iIvyeL2XIerHBc11HWnaYnTtMBjYU/acnEdADwWLsMX+ivxmACPVP05nTMwzH67FG3ISTmn02EXj4XLI8DpyadkbXARXHE7HqdDCkfeD0spF9DGY50JO1ED1BJH5JwJO2iDnpOLqe1batCGJv7WXfBRKKvJ2QJ5HPRc5TgIM6RsVCBmTBqp76SgaGWyyfbBYdU1OGgXWHWNZENG1KEE4mdRATMcuDZP7/h/BQq9Y/DQupQbRQhEqzXRd0bfQwX1XtgTL+8dYFw5+s6STjZoGNZs2iOn7QpTQqm/cEScwTpopE9IEx/NjVLIi7LmzgEOzxxrvqcakJjSBg5V9vXtcisBonNtdBb+jpF967d+K37wB38QbZv6hC9+8Yt4+ctfXqzAvKkdb5CSg45aGPIYOJnzkKsp6Ci5ejZ8EfLMsym3ihvObCRVvnYyuTLf9QTX9tz5EFKnpM07nX2RASi0lvnS9Uw3+57S7E6bCImmtgDFzvZckENqux2zKPzSSace5H/znXoTuiiJ3lSX0nXMkqiwcdVxmnznCGfaJc60O3iyWzKjEkNN2RXQhQY7Taqc7MERP0/2OzLgMdDp4YUh2eMIEpLidiKuBCDsA7MAp/q9OOA+1l8+qKJ8OuyiQcBK2Ao1Tfb2WLgcf9Jficf6K3jwJ620rKwJu17+RMBBdBeB6+LsQGreiL5EwcBj4TKcCnsROCwd53dh4LOQfClJf6LiWM7v0sUqxfu0xBnajaxMTz7W2LGmbWgNHiAVY1y6Lopd1fTaDgKnuD8TdnCq35OSBgyOlr7Hlc0BGnDae97PSTSSRG0JoOnEjRfAYmjNdaJRPN4ld5e6fRaicdpZ9KkWkNbrIWdC4D3X6QkOoWf2RBlAaE4i4j91vZDkBXIhCWYZgDh4fQf0GbefBVyoJCmCEZ0gdIkNBRBLXERxvC5X3Uogdvmq+6YniezJ+g3pS6JgtpICn99Fl/VPfk2bMgs0lMDNlNg1Bxi17UtA5AIyKiny6vB/x8k+8IEP4N3vfjde9KIX4Xd/93fxX//rf8Vzn/tcnDx5Eg8//PCR2z/+mpQ5n+0LOobgp3ydY7MIu99I1lrXNHHW4sCAZC3ixw/PSaN84nE0Z0rjBKgArg2ghcxWZKaVa/iigFZdO4rtpLOE1ggJxOn1dYYG/tNoZ6s/1IiGoAUKHbfviZfF8arnhkiACHrHELkxgEp27yW5W7PoOT+F0OdL0Q/sNP3ADaSsy0G/wBXLFTryWIjIVBmXhe+juPKypo2sijVmU5YDVuJM2BGAE7gir3eSnZZBwJf6K7Dnuhjho2G2tiDhflhi5TjKR8GLgoAd1+NU4EyMl7uD6EpSYNA6Zld20GMlrpXGtK3gw2pNFCTErLiuG+R6gTBGzDS4mHRNbZ+WuMrv42TYY5Aj4tg9vxJhbBfBlV7HUpgeZaMUXOjfrm+xK2yR6n9Ud6Ii2KVn1upMz3WXtJikbgMAB/0iprnXOk87vuNQanHzQZ6NnnwEIsqmcG4UZk70+QkCip0869AuQ8AKdLxTkK2FAgkyqpjIHoAj3PR2WxBvmBWrOWHXqrIlGM6mCabeVhq9nFQ85jZMXR7nJF+S5EZR5jXuKC+bimVLZTm03UIKhWiBo+42CjLYpJ5PiVmZ6sd1m20I8nm1b/zGb8TDDz+M7/me78E3fMM3IISAf/SP/hF+6Id+qB4QsoEdb5BSsjmx81Pb5NE+uc2hPCtiW6KsUimkM8g6iBgLHDx3JotFegnV1WSuxVEq0a7jje8I1BNowQmswoJnYw7CmICzwhIBYcG5TDwEhCgz4s1ERsCIIwCdzCABprh7Ai0N3pDOmIIklYOMAEsgVvBRTYt3IDgQKxS5Zo/mPonsCYBGatZIhI4DDzpLH9BKYhfVH4RMAMiZajm7a9fvyOzb44rFAc+4HTMrB2HB+Tici0nBrlyciZEwbVhEENCIEEHdKV/WPClhyAwUToU9XC5hvBwBkwbylbAn6krZkeUtmihSPUO72ANnsV05YEdvm4m62acl9iA0q2M3UQNCi5RcrUXSaiiIUVdUCy4AqK6gxoV4fGVHglwPlwVQ8NIYNxh/vso9GYssDnJugEXIqvtRs4nfWmLtSEtNjN7ZcR2HCoNSGnswO6LAYxUW8JLt+Ey3w6HGAj7avkEbWLtCYOZk1bEmRRO1eYk4U6BLSKdOCkqcYy2KRsR5fnajm5PAkWudvouIiduADKCExJLY9ySCGQUqSBMEklmCD2mWnTRp/J4PBjYioBOtmk2Lb9ZTifk1gMU1fm1ZXOdMsrZSrqhaH2td5IcNPBhLH1ECKls7L/a//tf/wsc//nF81Vd9Ff7oj/4In/70p3HmzBlcccUVR277eLt7cqsh57EHu7TfUSN2LMjJc6igNjOhIYtiKyP3AbRqgT7Adf36rELadm0fmRZLB0NpZJ19ZR1innjK90DTGlFefjsUc3SIGhbfO/gV1jNl2gm8zD7ZDeSYRfEE6hyo9VGYyB4urpeixd1CcDhol2gl8VYfPA66Bfa7RZwlA+wKAFSjssB+v8R+v4i6hbidzMZPtXvoqIlVk1VUq4xAgMOZfjf+6QCvLqBrmydw7YL/ABnkJWX+fmCXjNYEUqEtAAn77WM6fU0Pr2LVFk1aJ+BihSaKUwN83EarMytY+pNwRWRszhBrYh4Ll0cNzGnZH0B09SjjoQzQybDHQAqiRREB7mPhcrkOHzU0e55B0mP95fhSfwWLjMMeHu8ux+P9ZVEMq2YrU6v2R90zu76LQEYjs7TSNVdnFvbD6JOe6HZxptuJywI5HMhzodmKg4S3d8FH1oRfGzd4xhzk2eu8sCYOaH1kUWzStmGYrwCUIMv1ZZL3Ln/nSLI2R1bFGfE5sFahXHOjRGG8fbfIghaCX/UsmC2JUYmGLApV+jrpj2qV3IfbhtTfNU25j82rx9t97PKSsHYTdsTmgbmQRkf4O2b2lre8Bbfccgv+2l/7a/jt3/5tfPSjH8UnP/lJPO95z8MDDzxw5PYvDSalpjkpIWpdNsWWbGoFMLIW/WNmD2T8unml5DWXDyBgpedwZE3uJnlTyGkHqlVNhf5YSHxxdF5zh6dsSFiAj9PzTNLZRHIqthVMEztWs05dOzEBHLlYOZmWlCooi9NchYW0IOngZYooYIXvAc9gAwDfAA4kfY5DCEDnOKV+r+GkDji92sGy6SNY6Z3HsumhocudiDA5Spu3CXBcVVnElRolcrK7DLu+Y9aFAs7QDnrzGx7QIrp/AM6cCiAO7hqqrOvPiGZFM+DuuB4rIIb9qqtH99d6QCCg0QyzYZfXU8OaEWFDov6DgFPhMvTOm/NK4lWuchxwqr8MPbwUSfTSpjyawpTsuD5G8XgEcd2EeI4BPhYSZDdTFyN2VBB7IKBGI6dauednwg6XDPAtHu8uw0HgOkwNQsx/cpB7SpF0REvjsuPfNGCvabHfL9mtIyHogRwza/osE5dU6OT3bomLBnp55rqOa0VRSGnw4STHj0lx77SYIBntSZcYlege9YBrXQTwFqCsu3kSwFh381AUuEeAYtw8UZOmLKq8/64Lpp9zGIYaGy2KFhPcxGyfVmJDKoVYRyd+eSZctVxrWGJKtO28r9dtLwBgOaqu5LhpUn7mZ34G73nPe/DSl74UAPDc5z4XH/3oR/HDP/zD+Mt/+S/j4ODgSO1fGiBlkwexJPaqWe3lKi2vtZdVQiYiuKYZ6FAGoln7v47+xL0fiTofznH2WW4QTkGPdFpc2E/bkPZEj0KGGXEaciwvlc74yCNmpY0gxCWamt1Gadanwj24tJ0jAAJYnEd0D0UmxTlgITV+AJ6tLgInzGrkkht2RfUdR/w0DVPynQwmnvSeDkWQly3bFObaN5yVVgYt75JepQtNLELHGgi+MRrKqsCncSEmfdMCdwDwZc0+nuj3Ymp2rY4MaL6TTgSpO7jCHUQgEogr7cJ5YVp6nCYW3V7bPIEzxMCG3TXq1tmNDA3XCuKkaVqQ71TYw7XNE1iCQQZMLpUleuxLhNGKFjjVsxvqCr/Cnl9hP3CqfgU4PZy4oxToBKlezOfSSI4YzhS7E4W/nFQuYAlg12vtIvkdJHFe6KVQZGBNSx98LBjoXcDpbhfeEXZ9B7iUnK0jDlVf+B777S72+/WuyzIpAHDQLmI17b5bxPE4GFdM0OgdAS7UeWb3COsFBAVkw1MKQ1aAQunZL82IY14Ty36YbS2rydtR1KJEV1FvthcdiuZOQQDrUA5EYB/ZHgYmTiICESN78hN0g2U1HcqoBmXMzTNmueC1tq50LDvRzBmXrcvnvNinPvUpPP3pTx8sWy6X+Mmf/En8jb/xN47c/qUBUoDyA5mLYkvbjS0fAx41swDGvEiUvdzVaJ6BkC0Mlw+SuwVO7ha41+LJt+fkTQthaXpO2OZ6Er+2OrkBTdvhgiR1I5nRkUudLQA4YqZGOkIHRJeOFebG9rSzliRvkAkbBAxFcNOY/qdXlMOfyQGu4QyfQZK9OSJ4T+g6B+c8vA/oncPSB062JQBl0fScpMtxIi+tr8LAhKJewZvpis7QV2GBhbgWvEsVdJ/odnFZ00Yxp+6rhQqZ7eCwYh2cTxEnO9sVVwhnce1wst+L+o+WgD3fSj4XZkGUKdE6Qpq1lV1GWn2ZWYyWOPPtfliugYz9sCOMCQt59/wqfg7OSzTSDv6kv5ITucl5qij3dEiurRA85ziRxG17bhUjk3py0Q2mQGTpOzzR7+EJARzWWuLMfwsRQT/ZL7FyCyzE5aOVq0+LbghItXcCOMILSDlP+F0idMTuP0DGcXIRoLSyj7bnHCdu854rHQetdkwQHYpLLpuY3ESeZ024pu+RJ/jWJbYk7xr0eRdw4bsEZlRzomHIHEJMqa3eROHFyJ9s0CbAScZZJ2LZmJpA+zD9vw+cziAUssvmBQuV4c2ZkzxfVKmI4KaWu3LGQEu87oxR0W112RpYOY+ApQBSN97/GFkOUKx98zd/85Hbv3RAylGt9sDr5zGzL2vtpTXLratnLZGSMiwldw/JOQVKmWWJYk0fJ52Kb3sE10hiNoqZXF0nYMkBJBEJcVbmuWOOSdt0BtdkrIleFoG9Soq9BIjEjeRctQ30KXW4Vl+OL+MiSP4JnsUmdWC6dDRA3zNFH/shAJ1kDeVJoovJ3pywIIEcFovAockuYL9j98XCB1yxWMUw1pp1wp4chGUU2R7QAq1LUTZqOlAfiKsjgM9JmRdNINeACxVqdA6HKPPN0/PXujecjt+jp51YG0ejhLRooKawV9GuRt6cDHvSFtfC2XMtp+tHG48LQOoQccQOsz/MjlzhDuJ3ICXBC85LIrwOHh5f1uwzaOobvjd9ynOipvqRg7DEwveDDLYehI4auVfCbIkAdtd3wlA5nOl2sBMT8/mkPzJh7L3olTRPSis5T/RMNB+KcxS1KBSUkROwrAyKPOP8jsh3RzGJIYcd6/r0HLjgUs4hwuBZj8kSCdGNAwH1DFycuG/Mg0UYuIUiU+n5RWQ2pzKyWRdJ14G6bqhFGdT5ksPZtnKXjhchv7IYJVb5KKLYOQBlThu5q+g82lPN3fPjP/7jo+vvvvvuI7V/vEEKmbd/DHHXxFQ5CCn5MEsRP3ZbYPgCT72YI3TpWlI3G/EjHQl1XQIxWnAwKKpw7Jd2gUHHwgO9hBP30oaAEt8CYenErWNnggRPLmafBYCGSMKOjQtJXTsU++5025QY6Tl6KIEd7oCVWVEwBM1FoeHJOqvteWbrHKfQD9QA6CNQARLOYbe7w0G7wM6iTynPyaEJDS7zAauwQBc89poOZ0RYueN7YUdYu9L7Hk/2DEgax5loe8lw2gUfwURDAcE5PN5fxrlYHUURqSaBU8HrGak4zLV+GsABAR775Aesyan+Muz6FnuO84T05Aa5Vxq3lGOFqAWxETb8/yK6Xc6EXXyZ30+FD8MyFkbc8y325Qfe9S32iYWnKqLVIoyNo+ge0aKA7GJqYmZcdtf4mIRtKfcUlMBJS00s8NiFBqHwvjbiBuqIAUgInPdmJXlwtGaT1nTqg8du0+EgLPhRopS6ftU1/MpIqHHbc5QYVzR2/NoIuNG6PNSb51ufQ33ILKhWgKLuGANSNFeKettSvhOYScHwf91uIK6V9dy+6RcEkPg2MKAR1iTmSEECOqpFcX0YFhLs+3XXsvnsQtYXAWUNX96XlSZrtk+d0gyW+uexSMwsYmlt362dF3v3u989+N62LR555BEsFgt8zdd8zVMcpFibw3pMvRBjNie+f0q/Yl7qPAeBJkwq+oLjTIdnkdSEmE0yCeQaRH1KhwRkHOdMCA1rVpqeONzYu5gPBTCMCwE+pFDRNQGtAgu5BdE1FOnv9NkBQBCgIv0ieR4gqCGerYruJWpTGp7Jksxi/TKYPk46YXKDOiu2ko0KVK1bh4jzcDQuYK/pYoKvLnC468L32PE9ax+EAViFVElZ7Um3E90S2nYQbUlPiDqLL2v2eb2JKOLU83vYcx1H6tACO67DFf4AV4CFZSf1MYKHdy2geUbAUTc9OfRo0JhChQ0CTobLsOM6nAm7UQB7OuwMKi5rNlgNgW4oxDT53hH23AqPBw4XbEB4ot/Drm+HICSknCwMvlgvsutbnOl3U26X0MSSA7aY4mVNi13f4cl+Ge+//l5P9ssYotyGRpgS/l/bBIAgyfoCuLDgKnBBgl62U6BC5NAT4v4AYtgxR47x/06eJ9LnyTFgiXjclH6IoEUkPw7yrAfZThnGvGsRUB8j36wuxWZjdojRd+rqie0Hiv+zF8rBt9IPOAfXdtAU+M5GxtgJmHXrWAYlT38A1PsioM6U1Gr1lKJ07LqjsB32Omufgc0FwkexwjOw8f7HyD75yU+uLTt58iS+67u+C9/2bd925PaPP0gphabZB3RKiDUluJp6CfLzmDzd4ctvU1DndX3W/ds6Kncsxm0861KA9BIqqAgB6D0DhE4iMxZe9CIE10jn1us1s7iPLNAwjIn6w4ncMB13wx2thlTG5eoiMuJcDbVUABLFiHZCFpEQAOLZrVuwHsV5ERATn1PbNmiaACIvAVvijiDxMAUP+BDT3y+bPmYeVeuIQ00XPmDHd9iXwRKBtSrqClr4Hj406J2GyTYsVpS2Uj2aBmfCDjMRxC4mza3ShgWCk6gXCtgRYe2KOPTZakNaWmDH9Tht6tFokrdGwoYV6MR6OB7YQ4sAxNDbfdrBjusiIxNT+xNFoONFbKvMT6vnHdmZJi63VZsbxwX+9PoCHBqHWHunQYhZfTW8WMEHCHii28FlTRvDspmtamK4OEddDes1aRVr5wi7TRe1Lb383s4RgoCTENStKrmAQmJaVG+i2WXjhN8WD9THRHOiqJtFWRDDCEY3jrp4DCiJYAOpzRihY7fT6B1KxwEwKFWh73rMOgtIqntmUtTlG1mUrucMs32P6OLJdSim4nEtgZtzDhDBf7RSROOY5X3knD6zNPnMme4xfcomxzpL9lRz95Tsqquuwpvf/Ga8/OUvx3d+53ceqa3jDVJKD94mIMP6LXPBlT2GnZnYNseOCxRZlLx2T56Rbw2oDHQtxGLa4EFdD9f0DHIUqMQoHnGHBEUL2qBM9hr1pRMaYVDCkqsWu5jvQUS3ytpG3zzP9MLSpWifvPKy6lAG90eplsTAxPDkwFVjSWerDcU33TUcEeIc4GFCRMkBInxcLNJ0tPEBIXguERQ42qNHAhNaMXfpk04EHtjvlsCCv+/3S+z4Loo1+bNHIB6UdfbfUYMd36HrmyiQ1Uynp/o9NC5gVxKStWEpQGUJ3wRJ6MYgg48jrhu535oMTnUiykgEclhS4o60hhDA4csrKQTYGwZHTaOPlq6PEToKQHYaTo9/hnYjWIngISyw5zlfi7Irj3eXm4J+CagpINFjH3RahZrdRweB09g/2S+xCguswiLeX03Spu43TYMPIEV0CXvl5Xrbvonao673sDWfVKuyWPSRQSFCDDXWdwIEdvmo29FqTrROD4QtgUTqaFfSgVPf27aCGajEZaOuUUcptFj3GYQXW3AiYllrGtGjYniu0xUKA3dI/RZJ0cA+rAMV27ZMmFzjo+tojg2SuwHrfasuG2O7S8Aj/14DOaU+eqz9rZ0Xe/zxx/H4448fuZ3jDVKsTT34pc8lxmXsYS4xKGPLJyxnVaphyWv6lACEHtS23KGQ5gpwSacCwHUB3vUIu/wzu8CuHgYFAC0slc2dZRwXYucJ9EsMWR2XWBQQd9o2GEJnlfq/l4gdBHAUEAEIjvdbiMspEJzMYAmA1kkhKUZIcAgyWmiRAEcNmkUvyblI7qfePxK5jgMFj65v0JPD3oIH+DakarzeEYJj0W0qTphcQQBXWw4Nh8AuZD8FK1yVV1iHntkdXo8Ytnx5s8Ku6FWUlTgIS7SuiUCB13cxR4l3hCW6QVhtrDtjQAiLdTni59rmdHTh7LgOe+CChg0INpndmbCLfS04CMKp/rK4Tq9Fi/apIFgLAur1KiDRBGvKBh1gEdPZ8zlLJl/ySXMS8514BFpi4YJUsObPq5D0LQAzXQeduOB8gHOEg24RWZY+cP2dtpeU9xIZxxKMlBSQQ/mRQIqmvidw4UByw4Rt8rxDcbOAcHVxes2HEhmStJ1drrqUmPPE1PiJglmkfChRLKtuHuL3Oa6X9823ksTR9mlaZFAqpkcWxYplx6ywvur60cjFqSifqT6yBkz44PZE6u3n25a+nw8zQPPQ+x8je9vb3jb4TkT4/Oc/j3/37/5dzJ1yFDveIIV0dJ1B5U25aOb4RmsAxf5fEpMNNnfrDMngEJV1eYK3vmcdSteL/sSDGs8dlHOIBdE0NXbgTlB1Ieg5oifOxgKEhUG6pY4QmmGVYyCxKhbceALCEjHMeCA2bHRW6ThVf2RRZHbqKYUzi3+fIPkqnPiKegcKDVwjYMwTNGYjDUgJoLSSTr/r0zKbS+VAKikDwAEWWIhQVF1DO5qCPyzQBWVOPDwRTne7COTwZct9dDKYamKwXd8mHYb02wokltTjjLhGNLy4DcsIAK6M2Wh5R5t3ZSlhyACi+FXBTdS9hAVOuT1c7leR1QA4Z0tLC9aOhB1AUvxrDSK4gDP9ZYMKzpqw7kDOz4M4gZskjLvcr/BEv4tOgI6GZ/M9ZHfRkz2LcU/3O9jxXRIiC0Nl3Tlnwk6sbr3fcy2hzuhMDrpFfAT1t1z1DVrRmfQikA1Gg8L5URqEoOhZXRnyfsQHGrG2FAywsBa1KUhshy4jb8KIFchAt8EgB1H+3ti8KHnitsF55O4XSdrm2j65eeyfZnRVwayt3zNoNrVrCwkOJlA1rZ3+P+bqKTHWNRsDKnbZ2HFLepTzDVSeYiDln//zfz747r3Hl3/5l+P222/HXXfddeT2jzdIUQZk7IFV20RDMvZyjD3wY8nfANiiXNWZSW6l7LPRl9yzr6XrgeVw5uGIQHBwMSRZrkEZWblGBhXO6EiSmBZIM0PeB4BjtsX3FCM0FJhotlkvpxUFtZrITZgXB3CiN92mdRz902hjQMxI2yHd+xYMXgA4n+r7hOAjpQ8w0HDgwUjZDidsRCuMiuoa+uCB4HFqtWuKEYa47bJhoexC0rKvQhMjgp7slwi+58RjQEwI92THAltd7h3hTL/DafZFm8EhyjxQa0TQQViiDQtcu3gCp8JerHezdD3gOf9JQxQ1L70wGjGBnLhinpBkbZomn88hYF+ZEBkZ94XJubLhCCBlRFSYehB20AZmgi5rOFPsGQEeZ9xOZFwOegUl4k7yPU53e7JuEV1pmkRvJSyVVivWooCaqK+XnCcaldOIjkjr8Kz6xIJZ6yMISa4edfNELVPvUi4UAcjQ70B0dyI4+JX6aJCKB8oA5CW6h1wCKPF90UyygqfjPp1mXR6eNxcETO9Zcv0IoxIoVjJnQCWalF5Ail50FMeGpEXpOv7T7LI2qzTR6KTJOQfqNU9/JpYdy58yxlBb101NgFtyC+XtzNHAXCAXz1NNk/LII4+c0/aPN0hRGysVXgMjU6xKvm6OCKtUiyI/v8NYjZ4NxC4fgBO8OceVkAEGINKJakgyLTynwA8AFjK7U3dSz/01NaoH5NT0nLdEjucdggpewYDGa6FB56ILSAsVkk/Ahulqdo2QrXzs0syUHLHQVtcL+6IDRVwW+yuN0gggauAca1LsZosmxGq5ROzS0TTpJGAE4ArLHhRdK4E4y2lDAZ1oRTryseLuwgUsZdA9LXVpdnyHTsJwF00/qKYM52Mo7tL1ONnvDRLDxaKFLuDx/vKBaFXFtFo1WEWtp/q9yGDs+VYqFbN2RL/3cNHVo64aSFr9axencSbsJJ2KtKuhxHr8hQ8xIkcz7up9U2CilacBie4RgLjwPfYWbTzvlQA1dufsRHeOMldPdkt0pkKxsikapQX5ffrg0QeHEFiX4lWLpDV4VJsi/6uLh3rR/ZAyJ4lhsW6eBBicPqbRzRPBh8MAdPgOQ10JsWeWQQ2tMZLx0QiISdxUZMsCWAUsCaDEqB9ZLg82/68AJRDQsTt4rdIx38yBWLZmRDQujq31a1OsdYy4Ed1MYwRstuZQVDOPjNpzGHDnivd9a8fDLg2QAqSHdSzaR20uw7IJVZlb7QWeWdq8OMvJxW55unytjBy0h3Rw3vP6VtpY+BSJ4N3w3XWWgiYBLS7qTAAhOoQhiTNLXS7fmcXAILySHDh0UxkS3dC2rVEWugkEsJB0MuL2cTssXAm94yglIOa+2D9YYne3jcftek5BH/oGO0Zg6xzF4oTeERqtgCzsRoAbCEJXAhq8uCyWvkdLHru+RxvY5aIDs9ab0bwiLbHWRZmKP2mvwK7vsBvdNwxyLverWN14z7doQyNgZQ9f1uxHgNOA2RQNEw/kcEaOpyHBqjHh1PSI6fc1vb/qSvbDEvDi4iHeX88hkMNBWGDXs3C3DRx100v4MICYpVevbel79OSwUgYHxOAtNDjT7XDVYiSNjSZeU/OgyIgd9Bq5xq1rFWMiN8g2y96NBE6c4+chaFSQ5j/Rhz2Y76JHccqsOPO8afp71ZAE3k43GQAUZU90EqDYOrpv0rZWt6LgRYW1MO+N3V8Bir7frmV3TsyJkrt6FJxYsSz/qOYdGLIqNYsRPzXmowRkNonc0eRwdllpW+3f9TrnunPmAJmzaRUwutH+F7ndeeeds7f96Z/+6SMd63iDlDFNyibLag95LUpoykov8QibUhLQ2nWp3RxQeX7BkZgDB8QU9VHo4Ro49MJS+Ag0bBhkzJsi/0cU0jNICoD43jkCqAeimydG/sh55MJZ7vCRXl5lfDV0WQYLF0QTvISELnPUD4AB+0KdA7wDBfHFL/Q2y0y+XcTv+vM2TUAvQKbx6T72waFZsOBSly5lVq/Mirp9AMSIFI36OZBcH11w6IgjVeB431OtsiUhRrEAkiBNRKRL10emogs+poVX0KGZWb/UXcF5T7CMtYSWrkeLJC5tQ4PHu8tw9QIIUhdI2RGAwZAmalMgcrlfRbCiCdyAxGDs+hTizIJfj7b3UVQcXURynewe4vwmes0atbMv16hMltIWgRyelCggrWzN992j8Ryd1fV+kAtFjQtPpqieXpiRvueK2n5BKUmbfVEsQDGgxLVuwJBI8Be/Jsp0kAElCiq0XX0XZFlMyKauoT6dh2aWtenv47qeRbF8XMKgynHbw8l7HwGK5k2yllc4LkT02ARufD8nRkjr5tm0/lnOjOR96ZgotnRe+bK8ttAhAxqOavb3Ouz+F7uVcqOUrKa93MSON0gpPeR2+ZyXYU6W2MM+NNp2XiW0kNStlp+gCli8S52QzkTkj0MCE6vkNPLHBabFl547pQ78BHgH3wst7nQ2KAndPAMJL6hEfexRbwLpXHuK0UKDBFUANFeKzj4pML6yid50NsnnC5lNOCmWKMdsvbiEHKfSbxCjM6h3oIbT5vdEcF6Rkf4UDDYWMjh2EoocdSk+RP2KztJ3G83rwZWnVWirKdr3wSnevSOsetajeBBWwsYoE3PF4gBXNCtJr9/EBHEL3yM4j4PAn1tq2O0mLg09XhuY0QgyoHCq+CayQEsksLB0fWJVlCVyIephlI2JjyOcVCD2MdJIXTu6XRcatOQHide60MCHBZai1VkIEHuyX4pmxMfIHa1UzM+zi/dcwYsu52vl3+zJ1VIYrqQ1CrJvejeArlNXQWJRYqp7cqlMDTmO3nFIIcaBw+6TkNUIxzuXQDbSsvhsutSs1qVCZFvMBAD6LKfQ5ciQxOOaY2j+E/veS4QPiJJYNu5DSRRLKS8KWbeJFiod1AUTwFIIQy5ZOcmkASu1fnQs6ifvn6d0gvnn/Jxyltr28+czmdtTwD7wgQ/g//7f/4tnPetZ8DO9A4e14w1SgPWHl4Yv+KEU3jX9yZi+pRiRMyKiLTZTByhV05evDyAXOPolJKcFC9Tk9Due0jknjAoQgQp3tpJm2yGJZx2i79vDcYhyYDbFRvEoOxO1KJrkKk/yZiJ9ImUOaUfrBMXIC0KsQrvgfdwCIEnqFqssEwSUyOmQUPcIg4HLe07RTqJhcECs+9P1DUgSvzUCWDQ1u5pqJWzq965PA20QCmvhOYR2r+li9toVEDPYemkbfIayLzMVT4pbiAsV8jqtYRPzkPRpeQMGIBoR5B1Jdll5LIh1Lg0C9prEuPRgEPFkv8SBZI5VzQmHHjP4eFJcUUtJ1qZuG+8ICzADtHD8w2tYt4ZxqwBZGRR1q/XBc6ZYA1j03LkoZMDOohdvhUOnYcZaCFCs7/jHdw5SNsFHnRLAyxyAQXI2ZU806iu4xJ70vFzdjwPWw461AqgjiDHA3QtQIcfAPz2UaZtY4di6ghzPoH0nGhS7L8AApCf4Vc/vMbCmxdP091EsOzYwbzBoryVwmwIj5UbWwchh3TBz+/Lz6eKxpr/3UfY/Bvac5zwHn//85/GMZzwDAPCqV70Kb3vb23Dddded1eOcWwh0ri0HJFOq8tymEKBVoqsafVPUOPFC5cndhod3g79qlA+AmAehF1W/VeHbmVkXuKPT0u2RRmZ6OQr4xJVi65NAog10+0HSKqQZ34BJIQABKU9EAKcUb4XS1tmpLtdgBYm6UDrdqV6gZ0YFSuF3DqHjmbP+abhpkAq3Wq8lBDcoRudlgDzoGnQ96x16EWL25HCmZVagDx4H/SIOwo3TzLYcjXLQL2II7H6/jKyBMgU9uShEjccGxWy2OxLt04quBUB0sRyEBU51e3iy38GTYSeCipPdZXiy30EPH9Px8yPhRMya/t+XMOI2sFuHGZ2FFCNkluSgX+B0vxtdOnoOC8caE91G1+n1dcHjiXYXp7ud+N2bXlbvE8n/ei8VbGhYeBt81KBo5liAWS119ag7p+8EPApaIPls3yDqGahS51LxSgU4BNY2tZwTxXdah0eAi3XjRGaEn8UojpX3wneAb5FAEEHcOBRZRX3uvQhgB8ngdHutfmxYFt42SIgx4CVpm0bmRSZEAYpE81Av77zNjxIzUhf6Eb1nRPHPLpttVhdjlwHl/rfkTs/3L62fY/mE9TwCFu0Tj/J3HCx/Nn7t134Np0+fPuvHOf5MirX8Qax9j2r4EfSfo/4xP+xUGHSp8JYNSy7kUrEWRbSapMm5Yaej1nUg5+Fcz9E+gYTRIKGHTeTLshHRHThKSGv49DwzBQkXs0AUz6rYj5O+pVo/StiwDoWP6Z0bhCFrNMSaTrkHz24XFIW3kYr3CRRxQiuZIXu5nk7vIQuAY9bQJoDIwavusnesSZEqyr3WBmr4fgRx93D7qmNZsM7BEYLj8NeliQZSt5FmQVXWRaN0OhGiduDl6g7pVJQr7qAn++Wg4m8rIGEJ4IA8tEqzDvwBDo3oVjh9vIvRNi0Sk7GklPMEhFjcL0ibC9fjSYlK8nLOXBuIGYkvrS7n6BwX0EndHnUfaQj2fr+IUVCrlkO4NddJF3x06ShToqHEAGJb6t5RAa1G7WjosD7dXSd1dzQPShD3jzwyoW0Q3TAKVFX4qs+oYVSswJWfQ2FYgIFO2wt4cZEhGb4HEXSDwUrMd6Jg3aXtAG2P30k3OBcB+AWGg6PoAtxBK9sIY1IQzFIIQBBwkmtQtEipFeOjDESGxU6zPqtmc3WAwwMNJ5U1lqXmEiqBljzY4RhoPLZWt0sDpFhBlv3/MPsC5X2PEkZshWN5+xWAMub6GXQya51QzzR316fQPr22QJyRFgC6wFWSHWJtH/JSeLAnqcnjBHxIUcIGRlybOl5H4NT4VrPWSzXlJo0XQS89jizyWUYGB2ZKksg2Ue+AzEyJZ8lOFzUkWgMHWsgGPbsBQufgF0IwUQPf9AwkJCOshrSq64fvPycEC2DhLTmHheRKCRIlFAdal9wuGvWjVX95oG6iZkVNixtqe63kC2lAWCIVOAzKJEhBPWVddpuOC/BRcvsshH7SRGbKxgCI9XkASMSRpLx3bhC9k9xOUufIcbQTfColsJJz02tU0+sAUmZYZ1xaaprzRNkVTmXfiMvHoRXxrBOxcmRbYmFAfZxTgUAA0dWnz5UyK4P8J718tuInSiye7g7RS7lWwEkQpgRDgAIkIBK1JlL8Tx/XxDbysxtdPOaVXcsui7Q/Mz4pH0qM7uGbgBhuHEKqz9N1UUw/yItS0J/Ec8iE+7x91t/VAEppuephDuvKsaBlymrb1XK3nA/T3+4o+x8Ds8n/7LKzbZcGSMmR85zt7cswpTqfstKLmr+keR2fil6l2FlkQttSQcLhPpQEu94AFfnfEQFtzxHLy4Y735ZAO00EAhAGRmebXnKc8HjH18ZiWyA0LqbGj9llhRYhn2bDAGKoskY/RL9+A45KcohRPypGVMAUb1Onol6p+ePkfCUqiZrEBvUrJ8Ao3e9earz0PaRAIbfdNGEw0HtI9I/n6KalDzi9WsZQ5pYcLlsKqyGMwmU+YK9pEYh1F3Gd81FgCwA7olfRSJaFD5GtWPh+wF4oYwIAPjSAR2RWDvoFfEMx3XxPDru+xxP9LnYlpwsoxJDhIO6dLuxIyn/gdL+DK5oVOkncptFICiQUnKzCAntNGyN31NWlSdoCuZimXl1gqRYPX2vXeywMSFx1zSDtvxXHas6TRHx6eQcosQQOzJ4BKb1974Dg48PmVNekLkT76hHS8ynuG47CcRFoDJ7TyPQZtkSBiEN0D3lhUawuJYYTQ8EOJXASL9K4etogxQNDGvj5xpmBXPbtOpACFAtGCsxJKYFbTNxmE7RZy5O32eVqc7PQ2u1zQLNp31vrs/NQ6PM48B/VZXOc3D3f9V3fhd1dTjewv7+P7/me78EVV1wx2O4//af/dKTjHG+QUkLcOVCpIfqSoHYuNWnNvrDWJdQ0QwYlj/Ap7ZODmEwDUwIla2BFQ5KDlG+X9kgZlMBvkAOAVqKCFh6h8TGBFEdAyDG9i2HCOlMMEp6swldnInfieek+woyEhbx8HdaLD0rnzjoTKq6K7Is3E2LISq0H1Pn4hrPbJ/UW1Dv01MS8KvZOqoCzaRDDjUPwIKkRo/V/OHSWuA6QEXuqm0I1LvtYRnEoFy1cYK/pYE2jgZi5QKz+u9cwpT+IEGLsJbVtFhE0AJLFVa6mF5EqAOx6QGsEaci0sjZdpo9ZuJSsLQji2/GdhFs3EZAoCNFK0n3w0R20cCFqTZQJ6Y0WhcjFnDUaLtxJBtlA7K5TvRAPoogARYWxmvZe0wCRsG+cQRYptX3Mg+Ik/wmSGNsAD+tuyUifciZZ+R0YEJtlwFoSt/w4+u7oNr4j8z/FiB9uS4oGBoI74HDjQRFBTdrWh6hBY4CSsx8M5EquHcuepDxMFbaklGW2Jp6dC1BsG5uw3vn2c/rxrbvnnNjtt98++P63//bfPifHOd4gBSgDh7mMSs6gTG1bOl7thZwquGW3mxDk1jqZwTrvEEMNle5VENdIQUHv5HjKHjUS8ePgGgAd96K0UD2Lg+sC61w0fXi8HNHJeO6AuUAaNHLYdNicnl+rJTuPFJUjnXyMEpJz9C0QluzKiUWcQ2LpYzSFCRMlpfx11iz7uIXS/4BWOVSgwrefAYhvQhLVNhr1I4NsgIAVztvBgyQDljZ4tCb3R6BlTE5m3TxPdrz8oF9gt+kYjAAYRvc47GMpTIzDjknFb90sFqCoHfSLKFztXGJw2o5nORqt1PWLqBmx7QYRYmhxP+8ElIRFzNILcA4ZZT2c40inPngEJ1lgRSdi3UHMRhGWix4uAhGS/DLDCCo+DoMRIrAGxaXvQ3UskkaFxJ2jwDQIQAm8LhX0M2H28ZmTZzZAcgGpkFb2ofQZJNoTEc8CBoDE74YpMf8n8EJxP6hoNn4HhxkTC2Vd36dIH2VcjAYlAhS+WVizTJeSM7Glquxrn3PgMea+3tTmuIVKLqCp/dbYmSOc46aWP6eH2f8Y2C/90i+dl+Mcb5CiFKt+BjAqYq1Z6WG3D/lctsWewyY+2ZIwzftBh7J+qIK7Z5ALoY86QmDB7UknjK4HFg0nhJJje4DFs41nYOFcpMJ9T+gFuADSYTtJ+iazXhbTpo5eWQ8GRsKsALFN6HZmGZHMXiEDiFLtMqigEZykFybHpU5AFgSsdA5Y8I7UigtgIT+Pc3G2T72HX8rnwOG9MUyZHKCFCwE4Ym1KrA/UNyDiBHGckobQtov4+fJli8uWnBxNXR8usOgUAPYl8kYBgzITClbUhdLRsFrzXtNG9kSjh1ZG1Kp1fDTsWZcvXIiuGGsWSHEk0CKem64jchFkKbBZ+p4z4vZNdFldtmzhhSFZdZzht+29pK53sMX/gohjGwGHmjHYOUIwtXkoiN9bfi917aQfBvx763cbYhwYyA62VWZDmnAhsYBeszKLGyeyg2S26TTCx7wLAlDisysA35oex4Kh+B6R1OKxrErbs24sYAhMiFJkXh/KLp5CDpQpcWzNdVzUq6SV6byiRq7AthxWn2KPU7IciJT6aTu5PE/2VHH3nC873iAFGD6c9kWZu2/JauyMrsv3tUg/z7A4dpzcMv3JnFTVw/1leyOWo05cPs4xMAkCRjpx9TQe6CTqx7EQxLXEbSw9xDHEHWixk4WkzxdXkClOyD5+jTByUaCoxQZVw5jS4gPq/+fvoi8xwEdDRQf0fEPpPANAO4n1YbBE0Y1FIqolOI4GEj0MSBLCUUDfLzj5GzVomhArHPOtJRF8+qifaBxFF4YXvUUbPJ5Y7UZXkBbHCz5g1S2jADaKQ+V/zS3CafibCGT452QAEFzSqKSsrgmonOl2oo5l4QL2Fm1sZ9UvsN/5GNGzEiFwFxrsi16mJ29ATzqWskXeEXrN82LAzKpvJEkxMyfOEbNNvUbr6CsSQKQhxQwMY8K2wL8Leg4tjwn/HPg3DYhAJE1Q0vMS3Yl9eh7U1aNMnNN9BAirtCMKaC0jEgwQsTNkStsoyxeLbDq3JpBN7ZGwMSYfCvH7FZkVLR4Y+E9uNKJQligVDyylvq9YKWrQ/l/NfF1zVfNO6wBgE8HqYcHLmAvfHjsu2zIpx9WOP0ip2aZsSkm8NQZW8vUxgqZyzBIzcxSmZW2bypOt1G4n07xFM+w4AsG5AOrE1b6DBHLaANc49N5zX+SdmY1KzREBFeq20Lo35AGnA4bORAEGCgbgaMREZEX0EqVwIUcIUexigjyxEeA0BN8J4yTMDQ8Ueo+BQUwp6UIAII7kgJlox5DYePvQNCRp9T06ArynCFyIHDplK5o+RgK1fTNIpw8gumt6ye6qOo7GBywc61+UxdEwZY2GiftLSDGAqBN5srssun6WTR8BSiAHeODkai+6l1ZBsu5SyvjK4KWJ4EQ1JDBuK72fffCcWJVcdO1oQrZeonoY3AD7K9bHLMR11oGvLWWJ5X07YUdC8PGBIA0d5p8JoRcxkv6W4hZidbOLIcSc5I8iE+c0y6wbgg4FFF7cP/qsxBwp1lurrh1lVgIG7pvcVETrsmgerWas+7ueYu4UfdhcoHIkT84IEIG6HjHcWK3QD1gwYkWzY+U4Unum7U37VKDMthzGrGu+1M7UuW11KcfWLg2QUnv4R6JoAEzrRqb0KiUB16bbbqKENzZJ1QYCiz9kFukbOAlJXqOje2FSOmlr4dP6ngcdIgYIkYGAi2CFrOvHpQ7eURK9Ni3F2aaGJgMyDopGRbUBAKImICwAv5JQZo+YEReEwYwZQUKYHYADJ1WfaZBWP+bMUE2KV3eagACJ9NHoEl1OBLRtIzN+7ezT/deQ5hA8R4IGh0XDjMOqWyAQsLeUjLEKgroFdhouTugc51Pp+1Rsrw+eAU7gvCaNCzGXyI7vcbrbGbAdHfwgNNjmI9Gsr2pWY6KZX/cWLbM7fYMVcViwAh+uWOwjtFNBrJ6nskgA19jRMG57Lr1UKOb7VLjHsi0R/5ah86wdcpRcPEZjEsFmML+v5jNxEiLfpVD1QZJBQlZDx/wPA6gpPYdACkXWmjvWbII2AMPkhQpIOpKoNpJ9BKBo8rZOonkOejgLStS9o9+1wnEwbp7MtVNKZVBjUtZq3uSRPFNAZUorkq+3yxK9hqLl4tpaP2tFu6U++ygA6RC2ddmcPTveIOWwD95R9SpTlr8k9sU9zGykEOGzpsy3ls+mtLaP/m/dPvpy94Hb6nteLgAEzmk0cfTDsxCWc6cAqZMn6fwDUugyGhddLTE9f7CsFTiSSNkWK5CFYVdIO3uHoMUGs8tUHQI8pcifvO8jxxoGT6AeIGKWCJ6BGwE8QHq5HngE4lpArFXkk2skzJlBi7IWAauO3S5t32Bn0UHFt6uOGRMLQlby/z5x9eSeHHaByJ4ciNbDOXE9edYo7FPK3qpj7YG4bVTw2/gQaxCpy2lpWJ5YRNERB8YQF+QL4HP34ppqXIjXpxFMFqDFSJ3ew4u+Jope5YdsRW/SS96d+JiGpFOh3oF6z2UdIPMDzW0izx/Ma+PE5cO5exw0tDg+dzT8i+wImRBkoc9iKLEss1E7KfmaYUUK7XIbFL9zIU4M8qDE+ju9WWeqIPuQRLQM/MMgadsg7X3fJ4E8/5BFFsX2F7NYk7weT75+yuZMwEp9lq2EbPc/CntjgdD5ZFKOerwt6zOw4w1SgHEa8SgJ2DY5fn4eJcFtzfJzrLl2TKdRTexmzTIlgTiPApBmaFK7hzTSp+HOwMFzDhUAtPAMHDTxG1jkRw1isTvu3Lm39yBJ3MZT0X5pQExIzIiceSpISEhROnL7rAZFBxO9ag/Hyc7M7DeaMC6aYdQFSfImYdOseZHvon1g1wYnFINoKSggRgGR6mm8uCQQQMTMis3jsVi4yLZoQUMNsdW0+1qThoEEsw4ewEoAwEHfYGkYEgUBgRwQOAU+51SRKBmkqs2BHHpxM2n9oYVPDIwmnAO42rB1I7UmSZ0eW7exy/V+rLomVpPebxdR8MrrEfUmMedJl9Lau4YT6NnIHFLdiLAkFFwSxNrHWxGsul9WXlgI2c4BWk7BmzHcRvlEl4+yfi6BFAtCfC/tIrEpuo3VrWgpCZvUbfi/sC+k4MUAEQcgSDmKnuL772z+EAUoNu29um9zHZrP8sCULO9j8ozYc1wnc4IDStqUXEM4xpDU9i+Z9+tAZzvYXxI2InI4BmYfwkwBP6oNmfPwTqWAzpXkOWovKc2Bdep07NiaO6UEULQzM23HdaUskwpUtPhYTiGrAC8Ent21qZCZ0tU2IyZHM0gn3HGHHKlt7U9MinA+TvrMAwtT4CovGOSd0FMzEUC+FcqdmDWJ+gGzL7fr4nrVJbjOpYFNQUfnU2p9/fk6j6B/K3aF6Iy/axuOAuoadG0T0+yz8NKhXS3Qdcok8D49MTujETxdzJ7q4k+jqeDb3ieAIHlGVn0T3SaaT8RaFzyebJfxGPvtAn1g1kOXqcak7RucbnfQBh/b7cw59qIz0XW63X6XErMdtAs8uVoy4AiS+0QieOJx2ga9LO+7Bn3X8D0BJEqeM/cSOYTWI3R2YOHfIFYsJkhyNr7HrnUpYsdULU55eVzSkCg47hx879LzZZ8xZ1wz+rz0EmbcYbCPBSeARPr0NMw6q8+isi/CmLhewL7+gISY+8QFgn+yjVoU1/fyLmb9WdclHYq+07lNRPSkk8/cO4M2cl+WjaiqAJM5fWrJ5T3WH9t1tT5W11uAcoHBSeyHjvB3GHv729+OZz3rWdjb28PNN9+Mj370o7P2e9e73gXnHF75ylce7sDn2I43SCnZ1IO6qVg1F6yV2slfGDs7sG6e2jGmrLTvlIjWpsQG0uc+SEeXHTt2hlJYsAvRR26vzRlKmjvlVIiQ1yPR2XHmmIEJ46vXjt3LwJIPEN7MfIHEuHAoqIsARAeaqCMgYOA3UutcHMxshAcCxL0AGRR5dh86LVwIU8BQziWYjKiZxkLdQgmwDKNkNCX8QbvAqmvi8lW3iEUN++DTdwEQgaTwYZcywmpRRNt+Hxz2W07CtuqbyLwQENvm/Xx0C+n+be9jO16YnAhKDGBq+4bdW3kBR+LijhDwwzV3+Pcgueehc+gPGg4r1j8CqBWBrOpP1PS3GSRqQyxKGaN4AqJAW80WCrRRQfa5suHF3oITIOlJZHuu0WPAh4B43dbpe6HCWdGhxOWBYi4UZyYEru/ThEGBSQhc4qI3dXmsm0etIpwvpjDI+w6bp2mqn9pEh7d+MuU2au3oujEgY5mZmkB37Bjnwugs/G1ov/Irv4I777wT99xzDz7xiU/g+c9/Pm677TZ84QtfGN3vM5/5DP7BP/gH+KZv+qbND3qe7HiDlDnIuaQP0f9r6+z3KcGWvgD5dvn22gHlVuow7GfDqtTqJNiOaNApKVCxYEWqolLXAW2XQIgVnCmI0YrJMVmVdLDSyWqkAreLSG8zfZ5mmBxuORwUBj59rQCLBFR00LAz1why+jQ4pVmwABYThcFtJEYFJDNvA1AY5AijEmfvjrdZcV4Oaj1C2/AMP5iqy4BUWmamIAQenFW70vceq1WDg4MFsw3kInthbgEIMEABaE1VZq4A7PDkaokn9nex6hKw2RdGIxBwIAwKPwMMgIKAEGVR2gzMpMeDcOZgB13fYNUtBuDpydUyVonmZ44SwGnluoU1aVtmmIKCMgkjDp2LQIV6U6m6F5dOr58FoOjv0/Nv41YJjLoA+IPElviOqxj7lUvgQTVOnoFLBMh99tcNvyfmwwDo3jy75hn2PcVnTwGMV1E3AL/itPbq3nH2/VBWMhD8quOcKAPdWurX1M1DbZvyoZSKi2ZWDB/OrdCnDJJL2izYQFqeMyvW/TPWF5cmbLVJYL6+1v+OgZcLpUm5APbTP/3T+O7v/m687nWvw9d93dfhHe94By6//HL863/9r6v79H2P17zmNXjzm9+MP/2n//R5PNvN7HhrUih1CgObQvtjgGNqu7GXRlPhl6jUOUh+ysW0dirDF29WcSdVhAaKMzKHBeAcp87n9KoAXJzZeQC0bBCWkrdiqcJZ6ZucQ/Tr+9TZxhwpnrfnYoNuGGoMJICS96eiS/EOUXzrHKBZ3YcalwRsdLaOhvfRKstysZx0yzlOTts50FIGD3LAgePj6sjWO74mPVWjkyCjR7EPYif1aOBSqLL3hLZt4mfyxNE8TbroA5MzxIpsO9GYaFp4dQn15OAN0Oj6JhIFBHExCRuiRfxidWEBG+qCUlYkjiNNH11GIXg4H9CbZHBdx+BELdgigMT3xpn8J5bBoC6P+0aK0nJILJcBluSQshM7ARhhWJ07j9oZuBczEJJ/1kRsgxB7E3qsbUR9SQQyFKOBUrvmvZTPNtQ4RvIETX+PddO09wpQTNI2kuOPgZRoY24dZH3IVE2ekpi25sKxmpVct1eKwJnqk/MJ49TEVCOGatE+59AGk7FD7g8AJ0+eHCzf3d2NdXKsrVYrPPTQQ7jrrrviMu89br31VjzwwAPV4/z4j/84nvGMZ+D1r389/uf//J+HP+FzbMcbpJSsRPeV/JqHtRpw0ZdPrRb6XBPGjnUQ8n3UxxxPxQhpreI/zs4CEDyAHtQKnQxwxA+QhLRAzD6ryd6cA6jx3LE2knbcgwV/QGIhwOCRGjeItiAZ92EGCXKQWj4GcDonAx1inhWb6t7rYQRwaDgz6fFj3om0nRNNAklkEDmwvoEAaFZaAUO0IDMAUAI6AKzDOLTs0nCeL0wHZQpAr0Btp5PfxYCD3iMEErYlAQPOvurkFjhAM9CGpEWx7iSuXSPnIm1rtIz3kua/CTxeS6hwxAQdJ6pTYSxRqjbcNAG9FIbk352jpbquidegx0/Xhih+dV7uhbBKTsLAqWcGyoaNp6J/iT2xgDL9iPzbKVumvydgWQ5O/MauINlPGBRtrwRQbBTOICutuBtVYKvFAqPbSMGC7qMFBBUgGfCtkTwAOMy4FfeOgJ5owmxaoWyeUbZYAT2zmAMlBxYFUexaVtm8j5rq10o2xoyUAgzG+uUa4z0FVs5H4ETJdJJ0lP0B3HjjjYPF99xzD37sx35sbfMvfvGL6Pse11133WD5ddddh9///d8vHuIjH/kI/tW/+ld4+OGHj3Ci58eOP0jJtSFjD659GXJh61xkX2ozt7F6PlNsyRFfLOsCGsy2CtloQSokkWULwJEwKua6tGqyE7bFEUC9hKJ2iE5D8i7W+AkLl2a+4p9PUTk8AIYl4sChnT3pwBQcZ6UNcpourY9jSZMGDGpkTAuQRinNxj0PVJxnxTED5ClJVoiXkxYfJAey90uPHwBJ9ALqAK0TRCT3wMm5moE5SIZbJ8xLH5RN4ak4hZS5Vm85sykNFot+EKbLv2sCOwDrS5jNHuYe8R4x5TyQKjwruIj6EQCLxfCZU9GrRi+F3ksOGWZVvGd0yeDHrXXInIhNWBUBIqQuHdWb6H1vLfUv/wtbNQAhpiaTFU1HfVBkM/R8EoiJbEuBTeHfGyDHVb0jBjXbcbs02EfNSybmKNSVbb3JJuv7kHKh9CHqT9YStqlwHUiRPKJHmcooW2VVa3o2G80j+ZOqAtopEJFbjZEeAxul44y526dYlbX+ff7pXyz2uc99DldddVX8XmJRDmOnTp3Cd37nd+IXf/EX8fSnP/2stHku7fiDlCldCVB/kGuuoKkQPLt+U5dQpU7P2mfbiRzC1lw/llXRGj/esSAPHW/f+PIsR9tse/jGISwbOO94ZqjsBYGpaAlT9hCGgxhsKCDgTl9ClduU1C0sgJiiHOCiiB2iewhO3DyL1A7kGBqlEdPpE/g8euJIHsN+xLBTLUYHxMEvuny0cqyyKrZoIZC+67LI4OjBmU1xnpkNZSKi60PcNfFXF3dM6DmJmuZlWa0WZgxLoEH/58eaN9CsrXGSLetDcAJQEuvRS7XomMdE3FAR5CiQ0SCS3gExeBmx3aCRUQrKlE1RhkQFyGSiqMAgA4ApCgkGeRqtI8vSZ3kGlDkS5iQyI/rD6rmYgtNRWxKQkrGJVio0Zn8LWBWcyDFiBBoSK6Ki8GiEyMb4nuvwODJAmW8+i2Q1o6y6VtX9Cmmj7YaRPIOaXOaQhjUt1dhZW1bp1yLjUrNNAMoYoNmUxS65ikrtjE0yL4Ae5SgROro/AFx11VUDkFKzpz/96WiaBo8++uhg+aOPPorrr79+bfv/83/+Dz7zmc/g5S9/eVwW5PdfLBb49Kc/ja/5mq85/AWcZTv+IGUTG9Oq2O+lF9YCjyngUJsNqI25fUr0bG7ZNlUtSs6i2M8KWnyQxLQ9nPfMpjjHadB76UQ9X5MLIeZQCTuSEE6o9TiTJcEs3klafEpt2JmoB+dcITDroqDDhBzneqPoKsoACSC32bIqXoCKzOapkQHDm4EJaWAjCKARgGOPR55i5EhsJ07r5UR96plYKiVCUbttcHCLwH1q7xC8ixPkHo4rROvlG3cKAxgH5wILdQPFHCNOfuPQS90lc+tsJtiUxl/AEnGdIe8pupgANziusjOuYVCkDffRv4bovqFGQAkZgGLDu5VBs6LYOPALqJM/ckjuHL0YwcMxRLg3v50wZXmIsGpUIjBFElrzcdOmnLJ+uO/gf8gxTKj9eht8gxkc83MSQ/eJWISuof1ad0H9oPrjaKhxIZKnlBOpBlRGU9wfZtDehE2pTdJKyzcFQNamXETq3j/fotmjAqMN993Z2cELX/hC3H///TGMOISA+++/H3fcccfa9l/7tV+LT33qU4NlP/IjP4JTp07hZ37mZ9bcTBfaLh2QYh/GmkvnqO0fdr8aO7KBUHYwKyqkyJ+VeXbMQg9agYHIglhMC7DrB55BhvdMW6OHdw5h4eHbwGRJcJyxNQBYuDirdKQTSRfDNMNCZ7LSuYpAMVZK9gZ86ODquF1pStYhDiJxZixghTwDFa3M7HoBXjqQyb7qkqLeRbfVMFGci7+9Ah4uaCgHbgANjaWuiexMFHjq+fdeztVzJnctbjjor0PUrDhPUSAbQUPLwlgEH8GNoyb1x2BgotP+EIQxkRwsAAtsCexuUoDSt+wr857BEKnORi6flPnQEGwgsUkBSWOibJIKavXxk7w18bdTAGKeWcV6ADNsVqIU3Sid/GaqfdH6Th0iW5G7eBCS7jkCGNGBRKChz4zDMMOsMiGC5bV9jejh7xTZFwUoUZOi7hwFKKtu3cWj/xNxqHEnkT59GaDUgMrAauL9eLML/eOYzQUouTs9368krK3tW7L8unKt4RiDfR7tbDEpm9idd96J22+/HTfddBNe/OIX461vfStOnz6N173udQCA1772tfjKr/xK3Hvvvdjb28Nzn/vcwf7XXHMNAKwtvxjs0gEp9sEf81HOEWjlLp+SfqX2UlhFeeLey8fLszvmYKZUEbmSDK4KVPL1pdwpAAMaHd1zwV0IceBzXsSQqw6emuj6oZ5SfgopVqigxXc8INvy9HzbeGTyxCn2dRDRmW/8X/UmPW/vCNyeRO+QlwFG3QZIjwGfB3igafV8xL3UGDDSICV765GuN/IqCXjEQVYUvORkRBXRJg+Y/PuTHcxFx5GYBchgTxEM9Ei5WPoBguE/ZlTS9gDFgC0+2wQmmAUBYpp6EZZScEDjWLzbMXiKYErZJAUmwoYMXikNH7bfCcNCf/ZRlHXKsAzEpYZBUxCo4MLgrQg07boYlWPAhh7Ph2H7gzBkAR9eQ9N1fdwn5T/R9jSSh91GAlAoy4GiIfrqBvKAazkUeVA0kAVK8abGNACtSbbISJaBinfTwMTaYdzVpdo9NZf23Iy0dtuSu6bEtIy5ikqTutrkNF9+BNBwHOxVr3oV/viP/xh33303Tpw4gRe84AV43/veF8W0n/3sZ+E3jB69WOzSASljNhdh5y6f/GW0AKXW3lFC3krCtakOYS3FtYOGKfIp5sp9WgcnPkiHGEAuwEHieJeLApDhTpi8h6MOnojZlmXDQKXhzjqABaZoXCxAyMDDxVBMFtJCRK16jg6NYqIsssd14KKBEQ0hsi86aFFaBdUD+M4ADB7XZfCSNhZJFEkNBpVzSUEEuRh6SjG8SLZ3AAQoMdAhGZQx7Bz15LwUzeuZleEsrNKGakcklwi7hyiGO1OvmVtJXExIiebkvtjaOtpeWGlFR0oSCPIcUt1o+yQaErCrxgnKM66V4b0VxkNACzU0cI1Bb7EO8p0Bop7vOzpJYS9J9mxkDAMFw7ZYliOktu3v7zsMwpUHYMZ4WGzKfFsc0OZJUf2JguUYpQOkpG26PuZDMZmZA9i1I5ll1yZQum0IHGpsAUqlsnFJe7Kx5cX4bB8yRwuXp6sfAx9zUzDYCeYawKD17/m+uY1tf65Nn+Oj7H8Iu+OOO4ruHQD44Ac/OLrvO9/5zsMd9DzYpQNSagxHiVpUm0tjjvlQay6lqk5kAnTkljMa2oZtL54KpXBgoCyqszoV/ayjpPPs9iEJS/Y+thEjfrqe6/4Q8YAMDaMEwoJn5eQAj8BARRkFBQh2kAN38sGEKitdHhYuuUwUtFhmRZJphQUDHTsgBU2RDqRZt3WtmNl5dBmZZZFVARg4NDpI8+inwltapOJwCogcpfNGizSQ6s8U5GD6MwnAgAcDBt1Wj+GEyzGiW0C29erCUvcMh/2qPiZVGHZpP4myiWO/AqrgOGLJ0hdBTl5YIL4fdpDFAJBEQEFpPbs9XPoNKaWyj2LY3vxWNnmfZpA198oyI/FZ69OxgARKB24gdcO49fZsrhXVpthoHgcYjUkCKL4NKdMs6X1SMAJO1Kap75VFUS2KyYPC71U3DDfOcqBYdw8wE6yM9TUloFHbdqydo+pUSm2NAYwp8FHa3tmO59zbhXD3XMp26YAUoAwg5r4UNVYkt03EWyWzKa3HOoaau0g7jLH01YV1o7kV1mZtBEInA0eIOVQQhDUJxKI+78AxKibpW6ODp0tApQvsEoo5WAxSIXb3kIhzAR40mlZZCw4ZjoM8JTGkZVhi4jYCmgP+TF7+mrSvBTwxMqhnNwgDLR6IbPsqqOR16RxtZAl5i4HSMxFdQXJ8wCUAZBkXDWkGIkOCpZzsIKGcAUx6L52E+BoBL6304sQa4vY7Z0Zlc0wHxBBrew42gkcErgqiIkAhASFkjiNtRC2Kup9COhx6x6dvfpvkYkmsi/4eup4sZgtpG72lifVIl0/OwZuKxCkxW/pttA6PZUnStct3+d+GPysASS4hgjvoYx4UboPSu6cMioYZi1g29g2VJG21MOMqWJkzGZpy5cyZVM2d7Om29rilXCz5ZK+kV8n/tyxOyT2/tWNrxx+kTImtcnFWaf+pF3XsJSytG2NbciuxJDHra8EmfM2zss4C6x2hdfsAMTQZJJqMBYb5FOzsJPA/rgUkMxuAgBgdQqYvQeq3HYFzpYgOITS2M0ISwJrZcwQPIoqNM2PDfMT9kVgOFWZGiGTcETzzkeylJisoIH2cjtviSojC3s5EDQFpcA5IIMAJsFFg5LR9Z8ATxQE7ulHy36g14MEkQYs6GN23k1DhgKyzN/8TkhtHB1snbWu7stx1bsBCOQVZZECKtOk7pIRuAjCHNZ3c4DeMuWsobcP/K/ixy9JnbxiwJHLFANTwfWXgsZYnhVQga9x38RgJgMT0+g2STkXS0g9ClylF9GjNK3TpPXW5W4VIGBOpx7Nq1wFKRfSuDMqhgMkUmLBatE1TINSYEj1mTSi7SftT5z7m9jmfQCV36x1m/61FO/4gRa2GuO2DW3ID1WwqF4q1mstn7DzVSqxIKSx5puUCWtuZDQBMocT7sKHU0ZLZ14EHHwCRuuZZMWTAJGDZAAsFOzQADnCADwRaiAC3Axw4Zb6HQ2hIBiEWgeqgEQWoEiQTdQMtg4SwlPMT8auKaQcABgAWZv8gIAmAFqZzxOcQo4t0/JdB1fmh50iFoCQUvzI0KfrFVOpdmN/GIVbzTaneU0I5AAmYWEZEAYTRysTP6rpS0BFYyEqLkBgUARekA7ECgiZhLT4PWWeBhZ6KZVOE5XHiQnJI7ElkQCj9XhYwpqRqqXFvsv9GDYplTRQQten3ifWbOt2Or0QZMHv7nOyr2WOt+NaLSygC1UHkjgEopKAlhRe7PsREbcyqmMGx6+NnpwnbrAaFQtSSlVw91nK3z5qNMR9atkPPa2pyVT+J9X4sX1dqc5N+VLefWmYndxfJ4L5195xdO/4gpYbgSy/EJi8MUH/ha77TEvipLS+Jy+KqGf7mQq6U0j557oT1gmJmn7xz1LARLYMuU39HXkKTkXKp8AruiBXcLBvZH0nroYM+Ba4FpJSH6B+aoCCIorCWBy0SdxHiQGVnzE7cNeQBQqoP5Dq5DAUt3eBSeGBTDYhTkkE1NJRAjrk1KroknSA6AC7leuHfGtGd5DrZv0vnTw0LTNnN5CLAiBlTHaIIVTPlxm0BcSMJ6BAWaE2kK4AiZnXVbXoHLFNeGKW3nNwA1yu4MQCX+Hw0sRqZFSm8WD6HdChvBnkAMbOvRlANsrjG0g0YsmcwAMcwLLGuDunAkEBF1AWp60ePodjBY5C11obM83maZ8uAEpjPEbSYNPeuJ5B106oGRQXjNpNsZxKzrOUvckU2ZZIprdXcydeNtbNpAskpNju3Oe6huW76uRErFypF/taObMcfpADrbEQJKABl8FBrr2Ylf2m+PD9uieEpmddwz5kvu7luu0/OnuS+ayKCU5CRJ3qzLp+4npLrJwRgseAx24nOxDlg0USAwi6aECN/+C8NCPHcugBaJs2D5p4gD4SFjD4qtPSIs9ggSEdnzuSRwkkXiIAmhrcCsQ2bLC66kmzfbZiYsCPLNBmcjryU2osuHWUXgMiQBBU56sAcixMSUvY7vc9goGZcRZoK3sU08S6CqQhs9DvSusjG6KPZyu+/kGtQrYgCI2FOVCOjbpgYLgwFfFxxWPU7lgnS2jkRK1EStGpeE72/McW9np8CDmMR3OkttiDFvmo5CDIAxmXLBu2HIdhM29EQ4OWghBBBOALBdxxezEJYSiyKhhVb6r/PAIp18VRASakvWKuzU6vNs4kQdq6gv8Qab8Ik5/vV+sUpUWzJhVQ7p/NtWT93qP23Fu3SAClTVmI+plw+Y+BjzrFK+85hR4BhpzPVcVgf8sxZxaiIVoGKWkqJChCBfMOZaTtwlA88D2hdz8cXgELeDyu8OtFKODM4kmgGfApLTlE8BGpcAi7OQUrGwCtT4dxAsxJdMg4I0MgbU0VXZtjRvaJJ5AyzEnUrAex6kHa91d9EcCFMh7IeWbHDGMpM4MEdup8AgYIGh8GTsBULAzgio4TIZtiQ2hgA5Ai+9aKdoegeUk1IZHgUiBigQHCDvCNexLzx/kWWwQ337dNvEBmz2IYzTIn+tqk9ywBZ3UlyBaXfJbZPaV/f0aC9gWBW71cElhTvhe6nTUdhrLaZ6U80vDgljROAYiJ4ACRwoqet33ORrLp4covhywJwM4Z0DbSUhK5jgKPWR5TCkGv9Yw0I1AIR5kzkajYlnB1bbts+jzlCtu6es2uXBkgpZSGc63MdmxlsMiuo7XdYRF+LLKqdU+UlLGlSNFR5LMU2d5J2eiujeeCMpW5nyeezlBGbTOeqM3wQHJk2lg0PSgFw2XQhhcQ6GQyc1ExxEk4q1+4MA+JJBlsZCCX7K/kkgwmN1BMCIpPiApILCogDZxzoF4iDWczXYqJHonh3weyFU2aDryS6geL2OmgbkaiCGAZO3Ogg94cTlwi0si8SE5NpfPQz3xM+J0/gSCsBJ85eDzG4ivlpGPklQECONR9k2tYfSs4vilSBgWsEALvUSC6SkuA4/uJkWBZkAEeBBpnjGGATfytYDQmiziUyZCZNfgwTjvulgUTBq+9ocL1RDBsjgrQNkvtHzKCYrLID0B+Xicsnr8VTsjwnEQqgZMrmJnGzZvuVsbxQpe1LIGKMYd70euboVErLLxJ9ytaObpcGSKlZTR9SMueGPtwcmes2QHrZc73JJvRjaXZQ6ijmnPsMywFJnnNhsE2uU7GzOx8QU+gvOJOZU7ePnmMfuC3neHAPPVKdHi9Aw0kRNs6OGhoXQ4DT9JoHz+TiYXbFDmrkgGZFCAsgqEYjMDBQsOACRa2K78Dp801iMNXKxDHfRp1IhPXg3unPpan89Wd0YDgkA766RuI6wwJENqbje2ywhrSvvwXY+2ZynSgQizsAwpJQdO3EfRVUAVGXY1kJdcWQuJ+sdmPgnjOMhF2Xu1E4b4mLkTFWM6L3Tn9em98kRt/oudlIHbnWZiU5dUjZDXOuITuOsiux+rBdx7+PZWKsm8cFSlllbdgwYeji6QJS5I4BK9IPOK1gbAGKunis7qTi6pltpTDekh2mXylN9PL+aoyVLjEbNaAxdQ61vvViMxVAH2X/rUU7/iAlf+BLL9GUv1P3KQnLSi/UWNTPFFtjFfZj++T7zxG6jVCa+YysHh0wAlQG63pQF8cUuEVjxJQuJn2DkwFY/PY+NCKoRRr4PI/rTLUD5AjkvLAtLjYJB3hQnIiGhYu6Ap0Nk7AtSbuh2wIAr48uDeOyiHlObIQMkHKyyABvk8apLmaQqC4AaFxat3RpgIsshGg3VJiqwEpxrjPbBiDmIVHVsUNyFwHR5aTMjLJNsWCebQ9grQmQgIQAtShFMoxODkYiqLGPo4ZVm/pHcZWyFgGpVpFLETVRL2IBjwE0KWmgHp9giwTmWpSUHZYGot0UsWOuhVLCNgTAt+yCiRWMAWYcBZy4PjCDojV4QkjgBGBdigUrHYMSaluTxE1AKyV309igNJkL5WzY2GSuNEkbc5GP9Ynal+aTO7t/vs8Yk7KpFuZ8mQX3h91/a9GOP0gB6g/uXKqw1gGMMSR5u3POca7lL+8hX8CxKKFq55eHJufrvOPRJjDFQMHBdSKqpUaAkkn65hHvXUz8FgCIYJYWXqh0z+4JBSu9ZkwVYNGShMWKqJY44RsAnl3rgNsIUKGEgaL+hFirQl5cSjG4QoAQyUAqIt/oXiCIBieBHjtI2wgU1XtoszF3iN5mA4JiVM6CoBEx0MdNt4uuMyaxyOg9ImtCAjBsn01IkUvirrLgigbnIccg8xcZDRcHdsveKKCzWWRV46P3e8DGULo/3iRTIw1ltucCiKsl3efoHoogJJ2nZULiPbQAxQIgXR9YQxSrGndBfmeCrWDM11sWyOo6nSTEFPe96k4IpJ/V5L3KGagx2ygnypgeZa4LvH4i9e3nuLjzvnns2LV+vNT2Bnq882EO6Tk+7P5bS3b8QcoUa2KXT/kyS77VUpulc9h0dmGtVCMo33dOgrnsZT307KuWp8E7xBT6ClS6jkFBkFT6DQHew/WBj0+eGZV4joALPYtrF14GLqUgfBzguJoy4sDEA6SLIMBRYk1EkosAF0NlWSDrQNoGuE0vWhbVLsidEmbCifBXFqvLKCAChZj4S9kJIIlKHSIYGIAYRUvOHFO25SJ0Ahh9rK2cBmHLIhiAZAe5KBzWisxAEqn2SNqXxtSh0RBguV/R9SP3Jeo19PgKogQoKXBTMbM9j4E41rAkGgJury/muwn6bFC6VgKa1gAqpHNnjRJiuO+aYFZvg7pzrDZLs8oaliTqW0SvlbYl+LZPAllgrT+wLqHo3mlbDHQquXtnwsWTpwuYzahM5UGp9XE1K2k95uxT2jbvTw/jwi4x52Ni4IsIvGztcHb8QYra2Ms7tW6ubiW3TcL2xvQmtTTU9jzmuohmvph5xMCgDHyeUMp+1xo/ClR6QP0EzCYsAPSSSr/hfB6aS8UbcEEiPHSBk7/FpHKeI3JA3HbjQKDElMDBd4E1MCqKDWlgtbVxeHbt4rqg+hGdlWuEC2S5ZiAV8GPFmgAPsGGBJHAFUgZaOQevg7wAEpvxlpAG/dxtpHoWB0Qxq+ZSiVWbRUzr9MAGDfG5piR00FBfZSgUlNhHIzIYiemJrIkbzgg1X0yMoAppeT5rVHGyghp9OBTI6W/pVftjkrDl7p/IzsAAJ0r3ztvU9Wa9utgG4tpMd+IG10wpckf3F3Gsa3sGKOqy0bD7TrLGesdRPKuWv6sWxQpkC6LYwT2rgBK7fs2OWlNnDiui3/PJ4Bwba38OWCox2HMnXhcSoNQmxJvsv7VoG/+KH/7wh/Hyl78cN9xwA5xzeM973jNYT0S4++678RVf8RW47LLLcOutt+IP/uAPBtt86Utfwmte8xpcddVVuOaaa/D6178eTzzxxJEuZM02fQHzB8sur7WXgwm7fWnfOQ9fKX+BmrY59fKFMGjHVcBOKTFU7AxV/BWyFNyxZK/dKVHcpCm+bSimTQseAmL68J5nrezjDzx4dJIMjmS229EgssJ3FF0Trgd8yyJH36WZcZwhU9pnUG9FB0KkAW9dqyDtGYGt64FmlQZZFscCvk1tKKgZ5t5Ix7QDo7YZ/zq9JgZevnWxSJ5vwYxI7+CCRuu4eHw+FxcTsTmpOBzZBQuKkP5X95Fevz0Pp7lnjCtI84tYoBD30/Ut76PHjxlhe7mO+D/fXy9RNINzyNuMTMcQbKgAVkGjbUsjdmIIcY/Bs8AsSeBCgV3SnIAIftXD73fmObXPM6XCgAC0UCApg6Ihxtl7VLWJDLPRbN9gdWiVgqNFN4l956f6kbw/PJu6j5qQVo+TA5na8Wv98yZpHM6yxT7lCH9bS7YxSDl9+jSe//zn4+1vf3tx/U/8xE/gbW97G97xjnfgwQcfxBVXXIHbbrsN+/v7cZvXvOY1+J3f+R3cd999eO9734sPf/jDeMMb3rD52c95acZQ+piLpra8hpKnKNGSZmaO5W4gca1MnquZZZEFCyNmE7/Z/SK7AnDnm3e8Clz6HmhXoLblznrVwrUMQFzXA20nYEZATJDoiAC4VgaFlqfNsRaKDjgKRtoA3zPw8G3SD/DM3A50NAAFCWQk4WX869KfDqL617QEv6KU5E1n8jY6CIishNVbOHscE13CF4ihxiMMj8ugJLUbtzHnmtgl+cnbtE7bXdtP3SoKiFayn3WXwIKDBDJ8Z66vG55vbLdP2/nW3E8FN/lxBHz4DhGQaRtJp0IRqPhWgQ3WRNOa36Q5kM8dInjVCsUuPjuBdSYdRWACyHO46mP0Tgwx9mbglD/ONNsBqxa0WhlwHobarlyMXqibNavmVqlcRjYh2bjC+rmyUn9XAkV5fzrWxwLTfXbJvbR1+Rxr29jd89KXvhQvfelLi+uICG9961vxIz/yI3jFK14BAPi3//bf4rrrrsN73vMefMd3fAd+7/d+D+973/vwsY99DDfddBMA4Gd/9mfx1//6X8dP/dRP4YYbbph/MkRxBgVgOLsYAw1zwY3dPn8J8jBku11JBV9Ss9t2DmtjM4ix2YQkXFPbWL9iO9qcWQkkiaskQ20jocoaAdRouny+H+wR8XAiwPBaMTmIi6iX0OOeQCKadRKODLBriBRENCysZREsi28juxFnvmS8JS6KLWMqmCXirJ1PiAc7Djfm6/bitlIxL9+H9H/UiRigFCCDtE0oJ+utawhA0rggbTdmcfA3DI49p7i/jciiBLqiq0PvY0mXou3YV0P3Ne14s308BwI4FFxCiI0Lx4KtwUyShgzK4DcBt+fNvdd2lTVJ+6XfXfdzgdYjd/owAMnxne8DP+8WoHR9StBmxbFrFcULlrGTYyUthvsZF8am/cbZZEHmWn5NJZf1WL9TEuPmfe3U8edsd7ZNntUj7b+1aGcVYj7yyCM4ceIEbr311rjs6quvxs0334wHHngAAPDAAw/gmmuuiQAFAG699VZ47/Hggw8W2z04OMDJkycHfxvZ3BfUsis12lFtri94zKdayxY5Zbpdieod07wcckYxcAHFNgmjVLYClbYFHay4E9cQzZwJ6tkFxMLJAL/fwR90KeQz8Mw36gTEFePbAL/iom6+DZHWj6yLzpo7nUGnwVwH2+QekIHLZdsAyc0hM3OvborOuhAK7IK6TJTp6RK74FvDFlhXiGFVlHmwbhZvBvfIbFh3DBn2QxkHPZZle3RSrwwLZdcrbImeoz2WdccMAEs8LgML6yrS0GFmQ0w7lokB0m8ov5tqVwaRP/H3stctYEZYE9fL79SFyLp5FcsGcSMKgHF9iOyJCmrjBETdlOLucV2fWEELUOaAE/Mu1YqAlraNNlWINLepfq+mc8n3z9mNGpM8xYKUjpsz2rbfLV3HFCN9GBf7WTZHdOS/rSU7q8LZEydOAACuu+66wfLrrrsurjtx4gSe8YxnDE9iscC1114bt8nt3nvvxZvf/ObNT6gkiM39oBaI6Hb2/1p7c9eNMTg1QDGVnKmUwnpqZlVjVXKBWSFCKHaotmLymI/ddtgdUwdEAkSWS7jgQYtmeFwinuF3wprAA6GHa1iYSI2H845DREESuSOhxCr87AK0IB9HhDiJNpIwWgoIC8kp4rgScBLcAlHUKbtGU4aDwMJTGeTDAqaarimAaHcNWTsAsxnC+lgmgxrAyQCuES3OuJg08sYEJXGzykaoNiNkjIRsQ+Y6YhVhvX4TJZvyuci52LBq056eQ8ziq/sJ0IjtC4ADUjvq/tKyBwA4VNtcW8xRQ4kxsZFEvqXUvghfrWi2xJxYlw2DFXHphFTBGM4lkaxx7UTAsmpTcrZA6XnX/wvuHG7KXGtlIKpF8cwqOlqzMZe3tRroGWMjSn1VjYXOj1sDOyVBbWmyWNpf+6/tQH/J2LGI7rnrrrtw5513xu8nT57EjTfeuL5haRYA1F+SfF3tZc6PYTPSnm3bpA6PdTdtCngsbZwtH0T6DNojFLNkliIX8nDlHiB0EpkiBQq95FPR2Zcmf2sD4ImTogXJaLugCFYYTMio65zkLxHXji7WAVbABABEtwmfhAzYZkbbCOjoxWUUOMPpYBavIbHggZ48AC/7alisHCOCFqM9GYAYQnI10fB/m0ckbgMe7BWEWa1KBDX5YG5ZEwNG1E0Vt7eh15l+RgFNio9O66JrxaVtYsi2npPRosQQbkpMVNL6KKhI5+L7tEwLSQ7cOfI9RnMBA+G070PSoxAim+c6A0qI1t078TcyACVWMOYcKOj79C6ULAPyh03MNgpQ9P3VSuXlBsrLc9ahFr5c6iMju1rod3IQUmKXS67wsc+1fXMbA1rny/T5P8r+W4t2VkHK9ddfDwB49NFH8RVf8RVx+aOPPooXvOAFcZsvfOELg/26rsOXvvSluH9uu7u72N3dnXcSNXdL/sAf1V9ZiugZO3Z+3JLNYVGAIWtSaqsmpDMApLi9rOdmJzreGkDR7y7rLCgA5GIH74gkTFlWq8/fm1EspKm/6wKwbHixJILjHCtIA4xz4u7RwVAy1/YCYpwDFhT1HgxqKB1fB1tizQkANApU1PSUAK4QAINDZGDWrUOjoEXuqbgkYjI5jfMFD7BBBzEgZYul4TGVEVFwFNkDZYIUCCggG/wGw2U2Gsl16TjW9cO5TmgIrsxvpiyJlgeIydwIA7CTQMcQRMTzsqBIttHrjdsoQAnDc3IGhKirSV2CypjoPXY91ZOyAcmt00tocQwnpgRQcnHsWjbmso29d9VtavvZfsJrzHulbxljUmqM8Vg/VQMOc/u7qXOxAuWp48+Z0Ll1hvNc2lFdNlt3z9DOKkh59rOfjeuvvx73339/BCUnT57Egw8+iO/93u8FANxyyy147LHH8NBDD+GFL3whAOD9738/Qgi4+eabj3YCzq0zHVMv7aYIe05kjT2f2rKaqyYHJ6XrKLEiU24feZGr+Rbi4WgAZOzsr/S5Wnsk99F7J7M9Zk4oiHtHcqi4RlxAhq51OjvUTqsF0PfwgYW4YdlIPhD5PVVgKwMWuVRdma+F2ZY086cBkwABPVzTBaBGihuC1rKtcnVllvvGS9W+VQZ+dVd4Svszu0PCCujMP50POceXHORzj6FyTECLddEM9CQJ18UBnuRc4hOkTIVci7Y/rC9EAtYM8JH7FJkO1Z5A7pdeY6alsUBssFzON4IWZMfXS1axstbTIUJMY4/UlkaD2aRw0OvQXCcKTmLjlPL4WNdOMKHHqj2xtXdyG2NU9FJzJmXEzTpsOwMz+fueDjDOQljTPrDUH+n+dju7nz3f3LVjrcaglNqc0pDU7k8p4qm0fDvwH1vbGKQ88cQT+N//+3/H74888ggefvhhXHvttXjmM5+JH/iBH8A//sf/GM95znPw7Gc/Gz/6oz+KG264Aa985SsBAH/uz/05fOu3fiu++7u/G+94xzvQti3uuOMOfMd3fMdmkT256cNvGQZF43Now01nFGMgaM76TcIGax3Jpm3lL3Dlxa/5zotAJQcotc5al9uU+gcBcB6u8QAtmO1YLgRkZnQwdHBykQ5tugBaNly0UNw3TjUOWj/HzObJO/gVj8w8S+Y2ORkXb0SB12lyN2iyOAEe8V5IozYKJp6ugARySNEnym4I7lIwlO6PDP7qhnL8WXUkzFpQBAz6XcFSPC/JumuBiEYi6XZcv4jSNXXDa1ChqgKSmEwOCYDkeWBsYrwB+xEU1WEQMszrrVvGnKMBRqwl0jBhvbf8TmrOlAhIlFExLItXYKLaEzluuu9m0HUuApQITkJIocV2+wlAMmYb6Uv0HbWhxrlgfkrEP+dYmyaFi4znIe5DCczk55gvm+v+Ltn5BijyTB9p/61F2xikfPzjH8df+St/JX5Xrcjtt9+Od77znfihH/ohnD59Gm94wxvw2GOP4S/9pb+E973vfdjb24v7/Pt//+9xxx134CUveQm89/j2b/92vO1tbzsLl4MhOJjjzinNBOZQnXNcO5bZOaxpG8DR2hkT4ZZmI5VOYU2rkjMoNX3KoJGQ0r07DyJhTlSr4hyzKmrxeKwadTKgkFSh9aGJwlrNFkuiVfGdYUE8szcazgzIYD/oFTTUGVCEELPBqubEVC12pCn4gX7ppEAieGD2aWCOxIF2YIqdDfhxZhCPLhsBLJq6nk86sRLkKOlTkDEQBkD5fJCX2zmoPBzbRhTMkpxjBHvZY66uFxLQoCwIORfz1iS3DMV9Bvt6ZYwo3odY/E9uniZgS5WNSQBIxppAGJZOhNqRHTFgJNt+wKC0XQQEsTBgHmIPlN06RiA7miixZmNReCW2tda3lFwvpT6t1s8dtc/K29JzzvvjMbfOFGiZOuY24+wlY47OaXnNc2MnT57E1Vdfjb96+Xdg4XbKG21KJ9p9xrY/DKtyWJuiMu0sq7TfVEcj29VScI/lb1hLpV9y+dT0Kvpdt3GeRbNNA+f5fxXR6h/pd1sHyNDV1DhQ0wALzwNr4wEvOVIaF10Gtt4L51XRPCpujS0JS2cGa5cxKTAuEHYLhYVhfOKGeq5pW2VKSBgV3WYgXrX7OLt/AhH63VY8HlxDZGGGYMRGNFkGxYpbfZdAigUVA01OT8PjkdnO/B/BitlGwaONBOLzl3uomhIZnPJoHWbNQvqsQERYk1hnB0jvp80aC8T3I4bF58LYVm5qLZrN6FBqbtJ0CrQ+cObpB+yy0vcRN+7ou75J/zQFUOYMFyW301xWx2psdPvSsrkmx+9ohfefeRcef/xxXHXVVfP338B0XPrmb/xRLBZ70ztUrOv28aHf+Efn9FyPkx2L6J5DWU13UnuBSgjf7pu/aFNal5qVXC2ljsi6rnJfdGm5bad23MpMLe9ggfVO1i4v4tqRmWX5fLL2ZQAh3/Ag37sEVjT5W/5bKTgCkCos833lEGHPP4cCJOei2wUOIPDg4wNACxl/4wCvlZddHATlQOn89fLELaRVmWOiOG8GeacAQJgXccvkobzpRht8Y7az4cjxGD4tj2G7MACE0jnktXds+5a5AMx5D5ibdC0gDEKDkzZEr8kwJ6aNpHlxUY8SXU1xH2U/zHdlXWwiNohLR1i1yJro8xL1UAagmGPECJ0QuKRDHlacmwUsEyLZtXfFvHfOuZRMUd9pdePU3uPSujn7jVneZ43p3UpW6ivt5zl9Ym5TLMgmrMrxm4NvLbNLD6TUHuCaG6imV9F1Y8v1BczbmEuj1jocpUbHhGIWrOQ21dFMRRWEYTbaOVZjX4pgp6RbURdQII6GcY5nxb0HmgC3XDA7siZc9ojZQCUayPUMVnzgzpvZFKEPgmSoXTBgiboUErYlXjaxroXAWt/I2CACFR20GRwQFzDUayaC5mzha9aBVlgXifwZ5B/pMHDRkI0qEvYlhvVmbIUz22llYhd4e+um8fYxVWBgs7qa5fn2rJkZfo8uloxFsR60mGE2bpNAgrIxCnZidI7qZ1SL0wYGSyADNhigaLQOM0MG3AfZNqtePAgp7nsGx6WkbApIrBsne7XW9FtmgB2UoyhMDDYCHGPApfQ5F8HWBuua7i6fENVsTr+3idWY4do11FxkY66zc21bd89ZteMNUkIAmpH1c3ydJZt60UptlGYRuc2Z6Uy9XJu6dsZmQnMV88aqIZKFtgZJ4LSzt6GatrKyLouldlmnQgpWALjgI3gb5FfR9Pqavtx7oJPlC7vOi3vHScgtxc7PBXC+lehGkZT3cko6OEYw09DQFQSu80OegZLm/dABP+U14UHYk1sHBQa0OACkM3VlQCzbQoiuo2Ha+sQwqJuHzPNohawOQwBjywAk/YrZr7B9TNqmbhqiCPyG4dbrbfHvQlELo/tbkOI6vu+avh4AfNsnxkS0J0l0a1iOMAQngAAUrV7cdbJdgT3JWJI5XvEBO2JtSiMxFt03ZpswqLZPyvcrTiQqbc6d4Nl2c1Z7Th9s+8Gx/ngKhFwAbcogW/Uh999asuMNUqzCvOS2sVZ70Evov/Si2eNM2SYzitq2Nd+0Wmmmk7dVSthW8oNP2ZwXPZs9VsW1Yy4ga8qs2JBldf0sORIIzoEQhr+LjviyPensv5d/mgaucaCF6GCiG2l4eCcsDQlFwKHNYPCj2hEHAC5G5EQRra3xo4N4cFF/4oMBILJpHh0UI4xcIlHyKBqrCYluICACjMj0qMvE3KIYPUNp+/QOrYOSXLuS7pGci0RVWRAVtwlDQIMIpEw5AiDqfpwAzySI1XZY3KqROvH4QbQpCobJ/EGOq0A6DymOx1gfGXIBbB5CXNNyFW3uZCCfVBxmklHaZm4EoFrJJa6W91dT7vKSq3bsGmrhw2MTuLFrCqH4+27teNjxBilq1tdcehFyEDPlL7XrcxdPybVj1+efp8xuW2qr1mHNZWWmOseamM/O/kYifXIrddxVYS0wXFZjWawbqA/szlksOBkcGVEtH9AMPJSyisZr6tkt1BMcehbV6rEkmy2DHwfnKbEUGtnjHOtKZD+XXZNmQ9XoHGUznCMGTBZT6jYuRQulXCMUXU/OuYEeI8DFvC4DTYgCjfRjCEthAJEBG9BwZR10CSBQrO0TjyGCVd9jLUW+C8SCYQEhmp02pcWXexSSq8T3QXLAyDVGtxTBtcKcBEriV/0dY4K3IQiJwKQfLo+C2BhW3DM4Aca1J/L8sbuMn9tSttgxUDKVNHGwbExTous1Z1DNHWJZo7z/ybfP+5m83yyBkLH99fPY5K5mY31RqS+fA1DORjTkUWzr7jmrdmmAFPtwlwCK2tSMIDf7EufLay906UW1A/5YZzL1Mk+p7mt+Zf1s/wfqAKVkI7M22yEXBYNjNge8ADwaa34VYuDh+h7wHm7Byd1I/k/XIaNsbLPn+j9CIUQ3QcM0vYpt2b0go/mAmWJWgogTuIWlXD8hDmykg5qyGPYUyIAaF/+BDzxo0wIMoPS+qsYCyjRgEJqbKjmn5mLIrwODqQheEI8bWRbjGuLjcXZdzVmiBf4GYcZWW6LgpUtsiW8DgyC9D7qdsCHRfeMgwELARJ/OwUbpRLcOMJyMKNjQ39sCMRut0/ec60QBST9z4DKsj2VUxtiUuQBmrS8oieh1/yYKnYbr57Awm1qNIaktH2OMpwBP6f7UGJTS+rF+za7XdVTuu86J2fftsPtvLdrxBilhhvNvyvVSojVrHUzJt1oCJbWZTC5m28RKnY/tlDbxK9dmdKV2C22MgpBMdDsZnmwHgZorSLftC3qVpmGw0jSsN2h4VspgoXBvAv/jZLkjAsHDkYhtOx/ZFecNy+K4LhCzE1zk0NHwnhMhJZNzwjDomONdzJCq3we5QBwNNSdAHNgBcC0hwxAM8pAAg/BlTfYWi/cRkvtIGI+Up0WAUmey4Ap7YpPFOXFzUZOAVmRhAlJyOBIXlwIL67aJYCcxLcqaQN0+6sYh04Y+a1nRP/s5imGVSbPMSc2t4zxi+Qab5h6og+fMNkrKltscUJFPDrQPGmNlFLSNTdCsjbHJm7ita59r7efsdt4XH9ZKouMNu9utXTx2vEFKbjWUPuWeKbVTekmmZgVTgCi3Uie1SRtTHdxUJFBNz1Lq5AanuD6LzI+Za1LWqilnia9qCbB0fxbEDtuEill9wwOYdzHPimuGbqqBSyhetlwHIEBF6IBWxi7vWbuis+eGXUsk+e9jHg9AdCo0yMFiKw0DhnExFpOwgcdJBg5Inaqs9ytKxJBLrEpsJ7IrHDoNRym3iwAT0km5ybQLcWlFwBTSOYEohgQDzIhQ4GtWwKVMjQp2VYNSen9sMjfXB2jBvxiVY4DJwKUz0DQNmchBhtjYRgZOBsJsMc1+rEUwK1ZlBjcVZI6kABhcl1rN5Ts8uXX3bG3SUurT7Dtec9XUXEFT/WfNSq6m2jY1y5moMU3LYQTJR7Rt7Z6za5cGSKm5etRKvs7a9od5QKZARa0DKL04Y/TpwPUwk9qdK8KrHbfWoXo/OYu0YGYMtOSuouIsNtep6ODSm3UBKRqoF5GttMeRF1hnw/jAKceG95EdYP+P6fS9h1tI7hR17zQc4uwk0+zAm6iiW9HAKCvjAqVHQv8XYBIjinSxvQ0tHzM0LoKHjMwZLHdmZ1sSgF02BCyQ3ENkwIbob/RcrTEII8RU9fH3AQY1hmRgs8nWBmxJ30ewGIs/6q4KOgAMIlPs79WJf0hAChf+M2KZyLoUAMrggrL3ISsUaN08ky6cmpWEq5u4ZuYyLlNWAhul73ZZyZ1t2Y9NXUxT7qHSueY2FvloJ2YXIvwYGN7Dw+6/tWiXBkgZoz7z5dbmMCal2cYmFKi+6DVmYm6bJReSpXZ1m6loAGs1EZ5t3+5n7mfstCudQd6hl5iSustoZHmpZkqsBwSAJO+Fc0DXp4ggjQ7yXgSrLnUmMvDKQfg/HWQ7Xs9J0KR6roKrhWd2QcOaHUw7AAmCcOJbcTAFEI0LJOZDaVxyFyGxFGo6wCO4JIDVqCMVe0rEj5NsuzEtvrpjIGCkT0DJFtyLdW+UgTECXoQEvGw6+ngcATp8X2VGqboSK2A24CS6a/QEcs2Irg8G5HX9ut5Ei/+VnpGSqzEPh9flhe3zHEK8bWX2nrOTTbO+XY3ZnGNz3NGbunbsvrX97XFL96PUVmn5XJuz7dRvYM5tO+4fXzvWIIW0MwXqvtn8QS4Bjnz2UJltj34v2WBanQGIGruyieUApba+6kYx59I05c4znwGK0QYzqFkuHaDMotjvUR+SUfQu+4212jICYl0g54BO2BDvkTLQehBMtWV14Qy0IcywkKx3FBhsrIIc3icmQPdzHOac0unLzFyEsQowAPAArjN2AT2D64UwHPJbezLsg63oG10l+g8i4AiNg1e3CgDXEcLSD9oFMGB79P+42rkETsxzp5E4PgIUWhe9mntZfPZtmLA5vt3HtfI89z2o6zEQwubVicf0JAqu+oCc4RueaqGNTSJHau/kpjbGuObvYQ4k5kx+5uxT6x9zcFPbfo7N2TZnhsfWx1M5zwiFYFzKh9x/a9GONUgB0gPoSszJFEDJP5dmETWX0CYvn9pc987cDqYEQMZ829ZsZysuksmkU3Mta6MWAVHeN9OvlAabnKIf0xZouv0IOn3SrnjPYcyS4ZZsXaBgBgFewOeUMSxKHzggikxZ0OvgOgYzcEaACyR9jYYVy+DuZMDlEGYXxb1y4yJj4iTDrnOOM+eaY0frzDPkgSZ3zQCsK6FhWvpBuvw+APYR03etN0BCgUj2Tq0t0/3zfCYZozUAQLpchLADl05e+G8iXX0JIEfg3PjiPqMunpxxzLUhamOgZGwCUTpOaaJUEuPn/dthJ0Ely9srHXPMSiDHtj22zxRAWTvViX7kHNlWk3J27diDlIGNCdOA4cthB9KS37VkJfCSdxwlF0lJHFuzTQBQbTY1x2rbl8KlS9vY/49gefiyXTYppLWfc+2BD4lhUZEtf+FjeQcsFgxuhZJ3nUsFDKNLR3ZTwYk9rv5WwsowYSKui45SZluPAeAg7/m7RyyEiHgYw2QoeASfvuo60ANaPNFJDTyI3iYmgtM2JGR6mB/FuGW07TjIE5KrJ6TzUDajN4NFgJQOoPjd2fdi4BaQ741fq6Pj8qgdorSNgJKiENa2ba3gtll7lrxLTMocUFMC7zVAX3t3agP8JnaU/UuTn9IkrXbc2rpNBlX7fJTOf+w4pbDtCeByQern2nfrsPtvLdrxBimBymnxx9TfpdnH3AdqDngZc5fYNnK6NN9mbgdUo3uP4kKy/9vjlJbPsLGOIncFjWXzHESbD5iBWuep4gc//A6kBHGLRXIxOAeExoAwx8nFnGFDbIxwnOGBQULXpyy2YDdQPJb9XYzQxPneLDdgpcl+P12vCc6aIdMSAYh366nwNZ+JghxdblkLIGlFrBuwRP5ZNyJVZsZ6LAtWss/x+CqCFVBCevy+H2pUbJhwRT8Sl5W0S3afEWCSg+aNzAKXCGIrepU57+jYZKdmpd/iMFY65lGBVX4uNTeRNbusBExyFjiE4W93HlmUrZ19u0Dy57NoVZFlBlDsi6JU/1FoUB001mZoE7c0f2Hz/cfOqfQClzqRuW3M6bz0XtnvJbN6lrFtzP8a9TOeTnzoKpq1bT4YUcBaHgyAl3Vd/KO25Uq4bcvLVi3QdlGk6boeru1YuGmvdzDzD+m4KvDUQV3ZiBBEPMsJy1xPIogN8c+1ffqTInpu1cH1Pf/td/x30MKtOvgD+dvnv+bJlj+v5G9f/1r4My38fsttHpi/todfdXw8OS8uDtjzucqxBwCkEyCh19mbvwycJPdQ4HvY93KP+Y8OViB779WlY8GmcZUNfsr8uTBAJRX6o+hOzJmSSWBSY0xKlkWEFa00ObJMUn6cHOCUwF/+N+e4Y+ywfb43mTjl12TbKK0bs5rIuCKSpb5f+70Hz9D5sNrvscnfIeztb387nvWsZ2Fvbw8333wzPvrRj1a3/cVf/EV80zd9E572tKfhaU97Gm699dbR7S+kHW+QYij+yQEMGNd/jO1jB/4pEFA918LDdxSQtEmnWTt+boUono0iETZ0/9jfLA/1HHQ0Ynkos2432LYWsaGfA2EgtFXw0sugqWClbUFdD1q1oNVqCFjajsGK/nUCYKzbAkj3rs/+8gFdAYCCkp7iHwIYwMif64m3CUGAg1nXJXCDLgM6vSzr5Fha/0aPLcApMo36v/72nbha9PwtUOkVtJlOVsGW3Ben9+2gTfdyxaCQBKCgXTFA6ft1YFkaaDLGxAINylijWmHM0Zw/+htusjwH9XPenyl2ocSUHsWm+p2pSY7tS3SyZ/vH0j5j7Y1Zfj91GZCYk9I2FsTmwvpzbeEs/G1ov/Irv4I777wT99xzDz7xiU/g+c9/Pm677TZ84QtfKG7/wQ9+EK9+9avxgQ98AA888ABuvPFGfMu3fAv+8A//cPODn2NzdEGcdkezkydP4uqrr8ZfWXw7Fn43rfBpoJslmp0j+joskMhnHoediZwPG0uMNNXBjolt7fKa+8hYLeX45EAS25xYXwIuKrjVAc8KcCV0OTE0DTTnSlofUj4WTc+vlZizSBXSiKKcvrbPRtQZFcBwAbitbadtWUGqtqsDd85ElJ5/K1as3HcFZVYUvCZ8tXVz7HHUlWOAyCBPTn6dI1bKucOXMG9Z1eYKNUvuh/WTLLMWuZC0tL19x/IophozMtWnjfVP9tzycyodZ8ydY02voeZinyvaL/Ul1sVTeG66cIAPdP8fHn/8cVx11VXTxziE6bj0V7/+H2LR7E7vULGuP8D7P/VPNzrXm2++GS960Yvwcz/3cwCAEAJuvPFGfN/3fR/e9KY3Te7f9z2e9rSn4ed+7ufw2te+9tDnfi7seGtSAMTIjtxyP6XtvOdY3smX1pdMj5m/xIdlX+bsV6Nv59gUZX1YywVupaiHDODUZrv286ioNh+87bLcTVCbXVnAotvGiBIObSZtr+u4HQUjnedst4AUKxyeo8uzvVk2woIbgGdTlAEE60bQZyMAgLnGgUjY3AcFDNr2QFdSmcmT5ieRxQMdUHLnuDY7vmFTBnlM4r6GHSnZBhqC/JmZiiQbRnxkILr2LtSA+1QI7Jz30gI6u03pd88Bo3XD2HY2tRpILS2z2+bHnJxEGK1Obnl/YZdP6eHivhfHfPtsRfecPHlysHx3dxe7u+vgZ7Va4aGHHsJdd90Vl3nvceutt+KBBx6YdcwzZ86gbVtce+21hz7vc2XHH6QAa0Al1YI54kC7yWwhDhph+D2ftZRmWqX8KTmwsZ1R7VzGqNbSvjPzDBSv026/aZTPWKeTDQC5qHYw8NTqq5Rm4vlnH9bXKTgpMTJ51JCCAU8x3JcanxLIUTO8N/p7+mwgMgONI7cu7C+xIvZZ0IHLuZTjxQ5eAcP9Kp2nsxoSBfXStrPt6r2wg4pxswBIoC7/DmAtVDxk+xrLGZDJ4pVZuQW7X3n7Qzz/Y+3MGVBrZt9P7Q+m3u0c1MwdGEugtLQ8335uH3iYAXoOQCz0nQOWtdQfnE8tSjqpw90Duz+AG2+8cbD4nnvuwY/92I+tbf7FL34Rfd/juuuuGyy/7rrr8Pu///uzDvkP/+E/xA033IBbb731cOd8Du1YgxTKo3vMrHltxqSZH/PBfmQWWfSz5tvnIKBkpVmRNbusNhsZcx3VAFCtMxo7lxqIqm0/1Z4ut2xKAdgMBhM/BJz5NnnugzV3UEVUOZa4a/2cMyADGFbCLLfse8cAhoJjMWjcL3MT8ckMxdsKVIKhwy3AsR3f2m9fGTDstvkMWNuPQEIGxcazXiRnvcy+a24b274KFfOIKgWAfZ8GE+smykBozbVXAxwa/TXHjTO5TY31s+trNudd1nU1ViJnzHT5mGvIHi+/vtpEotRWaV0+WRu7f5sOzmNu4NJ55+vjYUvvwAUAKGfRPve5zw3cPSUW5WzYW97yFrzrXe/CBz/4Qezt7Z2TYxzFjjVIGZiNAJAOMPm4ff2FtC/dmA9W15cGjDHkbI9bm5GUQFCpwymxK7aNnI2xdlSgYc8hb3furLHSIW0yINXo/KmU+4PihsA4AxPPl9aX2X1L6waVmocD9OCIcQDww8G6U8bFQ0ZdaaPimhgbsMaeFSKkqoSUwEaniyjTrRTAb23WagFeScw8g5av/fZrKeorgLa4fc3mak/y7SsD5saWT5pqEyS10iRl7DrteZbAUd4H2mfF7mfPKV83dZ9rx5hjY+61wXbrbdIIU3fO7CwxKVddddUsTcrTn/50NE2DRx99dLD80UcfxfXXXz+670/91E/hLW95C/7H//gfeN7znnf4cz6HdkmAFNIsnPyF/w8+AZW8E8pfwikac2xdjYnJ15UYGOeS+NLub9ePUbFjrEvekfV9fSZWsxrzkSdVmmMTA0GJRVGbXfOnsH6wbwmg5JlKS2n4S8nCNLyx5nIa5PQIQ8YlnpwHMik/afv6bOSukmj9+vnn51ixVHdJn1MLhiaufXCyJVYwu5eF86hVxI6i1r6vPiM1xs1em247aTW91PCg6X2ZYgrt902tNHCPMaGb9F35+z7VTm2/2j6bABS7vQUfNeauxKbI+moulAvNoJwlkDLXdnZ28MIXvhD3338/XvnKVwIAQgi4//77cccdd1T3+4mf+An8k3/yT/Df/tt/w0033XT48z3HduxBSkTKFqjYTKO1zroGJkrr9bvdtsbA1DqWWnt5h1ab5cwFFvn55/tv0s5RNT1j7Waak9p6IBvUKlZyE6wlh8tCUovZRseSxJVASQ3A1NrUbYAh+zfQaXgANIx+ySOScssF5CPgiUrJ7dbcM36wz+A6NAoqX1bNWVRhtQpuuWJkHubPhHOh9fA8Km6PGuCecn3W2K0Sa6nvoG2zxMLWmNmSe3dsglTaL7fapGus/VKbU8zKnN9uw6ie6vNwoQHKBbI777wTt99+O2666Sa8+MUvxlvf+lacPn0ar3vd6wAAr33ta/GVX/mVuPfeewEA//Sf/lPcfffd+OVf/mU861nPwokTJwAAV155Ja688soLdh0lO/YgxVoEKjUhLZA6kxKaBzZz/8xZNzZTyTuEGkWfbzPnHEqd2tT2h3UJlc5r6lzldxgt4jZozgKDMv1rc62s7ZNZKU36rOiAOVqWzNaYg3z7XPuSgwL7uaSLsftZ8GBZmLHw7DHQUzqX2vELWpMiMDSfJyO28tMyoHOWODY3G1U2fqDyjD7fr8QAjLWfb3MYFtf2FaV2avuPMSr5NmOs8VS/WNp3Sl9SW54zsCGs/9aFaLHBpPV8WwBwlEMfAme96lWvwh//8R/j7rvvxokTJ/CCF7wA73vf+6KY9rOf/Sy8ua8///M/j9Vqhb/1t/7WoJ2aOPdC2iUFUoD6w1kWT2ZPQ95h1ABLPqPQDi33/dq2au2Pfc73rW0/ZqX2ah1afg21bfNOuMQyDdwsG+hW8vOoUelHnG3ng2XeRjEt/8hAUGonB00AuF5Mk517zkbYzLk1V1Juup8FFyXWp9RxH3b2WWu7AuTGAEo8lRm/4Wz3X/6s5O5L/ayWsxw1m3qeS+vyd2Rs/zmTiql9LUucv49j+845xqaujDnvs7WRdAXVXCgZQLH/n++MsxeqwOAdd9xRde988IMfHHz/zGc+c6hjXAjbMHb0IrWxB1C1A7qpAgrrjwYQQ/7GXmpdbulY/T8PNdV1JZeSpX5Lxyh9zvct7TM2U5o6VqnzLAG6MVrW3p+5s6RaJ5Wfi37fINx5Ku35YJl5Rmri3HwwHEulXgI0o6GzwFDPMWdf+2yPCXzXGguSJbayXpmVGhOTvVPx/DIdio3WKemEisxX5ZkoZRou58opaJ8G15E9WzlDNydyZwxg5K6akjun9l7b7fN+YwqI5VZ6tzcd/DZ1MdesBDpKfXC+T21/YPg8Zv3/BWNQ1OxveNi/rUU73iBlEHGQIWlFzwW6eq3Dr70og2NVwAYwzRLkHZMO5GPhwmNt2X0sQCmdm3Vz5fvXzu8wVmOR7LHzz/l+c49RAjozmJo5M/ZaKGxJgFua0a/lcim0XxPuVmns2rJawbwxQWvNdH2uNdk0ImdNj5NmvzWgOMx1UX4XBwDGCifnPk8zn5HZQHgOgwkMgXtpm3y7KZdMbVnpnGqTl/z4tf3GbFMmqPTu2nbmhnXHZYYlKZ3ChQYqWztrdrxBitpUbQZbB2SO7qD28lZnwn6zQb4EeHL2ZYrVGZut2fbmnL9dXhokbPu2s8/BQr7dmOUCRNtOaUCptVcTKw6oYV6W1/4p1QsiM+vOAYcVfM5hCEZNn8VNxab5/iP7jB639F3BfZ56faLdMcBXG3xKGqO1ZSUAUhO+joGQNf1RZcDMrZZbpPT+5kxqDkzs93wCUTrfTdy5pWPYZaXPY+2U2NrS/rXJR60PsP1GzmLnli0rRvJkAIUCDf4umNl3+7B/W4t2/EGKjZAYLM5f1mx9Ti3X/MhjzMMUbTtmdv8pvctYG/n/Yx2RrstZj6lBtbbNmO99yvIOqzajmjug2O1L/mysgw4LNCKAmThOFbgUtqm5g0YH9aNaqZPLZpWjuo9DdJAD8CfM0Ji2Z9LG3AC5gLU2M5+yOeLWGqty1HchXz5n8mD3Hetj5uhKSkCm1O9MTdJyl0xu+T3OJyY1kFP5XQbPzqYak/NZZHDr7jmrdvxBCjB07WRuH6qgbsA89KXOaO5MJjW2/rnURm2WkrMnuv+cjm4uXavt57OeqRnslOXnXVo/9h2I939A+ZcGoBLrUsrbMgVqTDvFlPtmXW3fGugp3b/J6JVMxzHqNtoUSJRYmRHgUjtuCWytAbbMrWrByqzoK/s7znkWlcGrrbNWEsuO6bbGAMqUG6f0eRNwMgZAptw4NeZozrHn2iZuWvtbjgHQeGojz/3cZz/v75+iocmXgl1y0T0AUC06OGYWqJT0I3abfFDPmYkarTvFiowN9HPOXfe3/+fXo+cxx19tz622/2Fo4pFrHOgMSnTw3DDpfPt46PVw1alQ1jWAMdNNUBqUawO0Jn/Nw3FzEDAnrJqI4EwMZM5qEFE8Hp+vM8lny+yP3X/K1q7NTUTwKHgouQByl2LOrNUYEbv/YdaXzq+0rPa867sw9l7PYUWsjU1wStvb8yzta9/hMcCzCaDJ+8uSTdzvyRQChUieqe3Orx2VDdkyKdYuTZAyZfbBbszLWWJU7MOm2pN82ZjNfcnnULQ14FELGz4M4Mk715LNpZSnjj8Wiliikqdm1nPFsxU6fwwAjDIhFbdADfColQbv/Bj5NlPf0zmVmZjBMps5N2tvk8ytYyLjcUaoADriuRWAgf52tXdOn7m5boS5ocQl3dNckDzGlpQsBy4lkDM3VFrPNd9O2xxjf8+lzQGGajNCjYuWJ0g8n1ZjwjbZf2vRLl2QMpNNoT4MozXmqvutjbleaq6YuS6a0vqpWU+tw5k6Rt4ZzmVZNp0FlgaT3Db5Hea6eKzN2LYkrD3ycbfYP1cAACH0SURBVI2N6WPy86gN9mN6lypro+2H4TFLOhLX+Ah4xhinUg6UqpWAQ0lnkoPYeN5hHcSWWI0poFwTdtb2KzF6ury0z1y3T748f6fy7ceONwdk5NuN9Rc5awzU3WBzB+cJgDKHQRlv4BBM+tYuWjv+v+TYSyEPddSm5NoVG/UD83LYmdLcl37uCzqHgQDW3UpqJZCjzMcmNlcQWLu2HMw4lypNl45RAj9TGoCSL7t27rp9af/cxhgc831SGzIVoTBmMzQXNU3H2Palz2PtTm6vjEx2X8baLGlziu6y0u9f0o2MDY5TA+chAWTxGPY9GNO26Pex93Lstyz9FlOTmhLLa9fNOXaNXbHtlN7pTZ/90jmh8FweNsrlQrIoAA4VzbON7qna8WZSiACH9ZnAuXxI51DXarVOY+5sowYatPOzs89N6VorOBzzTddo5lqbY9/zdkrakzlRFzXzflrLkc+G54gl59phWDg558MIV6cYi0HUUmH/seOs7TNHiLyJGLIGVHJwUnMDzX3WDmObAIujHDfvH6YmAjWzIHLsHGsMypTNAYIlxqVmY3qiOeUXMOHqudAi2aNmuL3Q53+R2fFnUoAC7Sl03wTlN4j8MWzKYEDLc0aU7LAdVd5h1ABN7qM+mzbVEdbcV6MM1kTnN8aKHGagz9rJxbezwl712JmtMRjZNtFFmJ1DyWpMSNWNNLZNZVmt/dn3oLLPKJMzJwJnk23mCJPzCLC57FrJakCMaBrE5kzjGKNaYlbmAKIxpnbD33XWsXOrRePk5zTFWha+jz6XlUjNQwGUo9ynrV1QO95Milrx5bVUrJdF2Uw0r5p8WKsNtHNejBLbssYMTQhV5zAitVmULrfra6xMLjjN27PnO2Zjs+fS97F9M8tdE0X9gi4v6SEKIGksVJ3ygSxnasw+YyHKVb0DJjrykTbWwNWYuyTbJ97HQnTU5DnZ/UZ+S+fcIHmenkNRm1L6fQ4LaMfOOz+PqWez9G7W2IoxtnOTyYeN2Bnbd2pSY9/93Mael5JrqcaCTgDPTQTawARAuVhsrut/bP+tRbs0QMqUjSR8c6WKrT6LoBjrDEsv+dyO03YyucYkn53pcmsln3gNoNh1pc5pjFHJj6/XtwmzU9u2JgKsWQ106DpddliXUWZxEK3NJsdsyt1VG2xthy/L11xWU0JTVEBRDUAYIOWy37lWjHHymucASnsuMxilteWbApUpl6Ztd84gn7dRew/teebrNmVHrWunNqCV+g67ff49t9o9tdegtqn2Z0LfxCuC/DcxYFuR7MXiJgkEHCWM+DgAsfNol4a7R20KgdZ8hfnyTMC0FrYatyt0rLnNEdfV1k2ZdgxzAcYYMzP13e571JkgsBlbMod5yZfVRLhT5zLW2c4BOLVzH2NPNjlmAcCMnp9ulwM3u7wEbrKBRMHapOvoMGBOzbrOSla7X2P7zJ2V1vQWY23ad2GMTam1P2ZzXDx23VT7tbbGJj651daV2s2fA33/pI3R52gKbOSJO+doQM62m3zM7G932L+tRbu0QApwRJqt/qAP6O+pGbt9IZqm3pGlxtPsaq6PutZGjV7O3RJ5u1MgaU4HN3dbe8/msChjg3mJWZnLlpQGVNuZ5oP62LnZfWvHys85dyno58J1RhYjByg1VmnMMoZm1vnObbcGlKZcXLltAi7GrnsThu4oNkfPla8bG5Cm+oK8vxg7/1JbhxkMp94tfT4n3HFrETz61/cXDxuytYvGLk13z5Sv1m4qbImrlf4OHsMy9TNeog2Ov7bfHEal1jGNbZvPUsc61bmsTq5jOexsZYwa1+/2/5KrIzddn2tUjmI1BmPMxvQZU8yAATGuaVLnPsYklAaIGpuCoQtnkiGZsinNS2n7EgMWwvizNAc8zrHc/VlbX/tuzzc3qxuZA+bH3LRj51Rabp/5mjYudykDZ+c90XZLzDOm3TvDRaXrn2JZsvug53Q+jVC+55vsv7Volx6TYm3shaiqwMfZlGoopTU7QOYdQU4RW92AdmqWWalQ8pUTnJ69jZ13acY1l9k5bEdwmP3GBqk5wG1s+xoQqB0rB0wld08NRNh2aoO1MddMPAMlVmXK/RRC8blOCd8ylqd0vjVavwQ2a6xKfu9Kz+qUTiLfr2RzXKNz9tXtpwb2sedv0+Pn2425gg5zrFo/M9cdOtJefMZK+UDmsCebhPVOua7PtW3dPWfVLm2QAkwM2sbvXivvreHJWeG0UWofqLMWJdACDDuInMYNoRwKXeusLMgp2RhtOxfk6HmWjl0CYWNAa0zAN2WHYTfGBjvrUgHK51X67Sszx+L6EvAsCRJ1FkysCaG2mx7gx44/5XaR/zeuxpyDNXteU/ejBGL4JKrnV7SSPmsOuC+9i7XvdvkYUJg6jv1+WAFtzYUDDJ+x2vF1O/u/XT4HmOTb5e+7rF8rxTA45/JveuQonu1Af8nYpenuOQs2iPxRBXmgwUvGhdoKA+Sm0QYlyzv42kC+ycs45r/OfdybtmnbyD/nxywNXiUB3xiVbtu0M+2xa7Pb2mPo51KnPUHvO+eYna1pQ+YAWdtm7Z5NWcmlM/c5tEAvBFDpWnKQVTtmvl9p2Sbumjkao/x6lZEsvZv5c3m2XAFTIDE/B92n9LtPMZMlcGL7h9ozXjvnGvs3BlQO4xqywCNLXX9Ok7M5d35dKCEAOMI5nw232yVkTw2QMjZbysKTJxF8ILimkCBs04FB98m3HwMSNV/r+Zo1TAGSUuefd7gllqKmC5iahevnqQFibOCf2yGUOm6Ao15KwOCwHU3pecgH6hFA4JxjkDHFOtTcMfb/EktVWlb6DeaAj/xcNrXaYF5ysZXAwGGOp21MPatjINzqNkoaivxz6TzySUtpGVDvX6aOUXqO7XmWwH32TEzmPlGgQgHOu3PHnlwod89R9t9atEvf3ZPblE5lRpXNORlCq5YP2Pm6yReb5nc0+X5jy6bEfSXqvERZ51YDKPk6HYzPho3pSg5rJWAwpS8pDRhztC66zG5fu6bsnq4ls8v3qQGeMTawtH3t/5KVrnsumBlbn78v1qWlA8VRANC5shrIsDYFpua8u7X9Nh1E7TOY39PcdZnf71I9GsuMTOVDGWNRSvdsjkh5a8fKnnogZc4LXQIqFeHWLMCSMwbV4xo/d60DywFKjXHZJEpozMZYiMNoFzY9hzlAI+8wz4eavzT4jc1aazZHDBrCEMSVNC2lc7KDtm0zb2fsuKXzGttnqr0aYJg7C64NsPl5nYtnINdaHWbGvAm4mOt+zdsssbGHdeOWgHh+rzPAeaQcKHNt7vNyviN79ByO+re1aE89kKK2gfCN8llAplAfpTVr4ECXbwoUcpuic2sP/pyOPjuGs1WOx0S5+THGdDc1dkbNdo5j9/mwM+Ypm9tpTG0zptHQz3qfc9H0mG2qWyn9Xzqvw+hhbJtWq1K7DmU8FFiW7mG+vDZw50Cs5v5QGwNTuc0d+ErPSu2dmsNC5sccE/La45dYtymdC1B+13KwUnNH5+6dkfBhW4OnGrBgti/a3Pt2IQb8bRXks2pPLZCindaGVKkbU6TLuiJQsZ2kBSX2HKyew/7FY40wKvn2c90uNYAyMShO+phrgGnMxZUPLkdx+4zN6HVZbdCo7VMaHGwb9rwPO+hnx63e5znszdTyUpv5/2Niy7ngpdRODZDm2gy7fgro5kzfGJM5db72HEvnki+f43atsY9zhLIlRrW23VFNf6vadeffK9qrlMvHjQ62k0LZTcKNtwzEJW1PDeGsWu2FmwAq9oVaAywm4icmxtIX3s7mptw8JRFqDmTya8iXzZkt6YCp5zQm1J0SbObb5tvkIst8Xc10n6O4l0oDbUl/MeeeldoufS6ZvY5ckJqfS63N0u+wCROQt70haJrVRu350f/z6y3N2vPfojRgjjF4pfubv3+bsAm188ht6hkqPcd2nzks0hQzNMfyvqPU1lEHehsBaYHK3FwomxR7LV3DUe/RWTCiADqCW+so+16K9tQCKTUrzuK0k6u8NHa9KUwYzQKVTc7D/p9biZmx60pt5ets5w2MaxDmihvtNvb8SoNzfsxSG/lnO7BNDTZTA3xpm6ntrc0FNWPM1BhQmLt9adAvMRVzBvWp+zMGNmttltqbAp2bTiKmQHZ+HAvMa4zLYTQMY/vMaTO/ztr7v0lfUtKulVyxamPPUa19U8+JFzmQjfUNVAcnOSCxdXiOu9ERXTZbRmhgW5ACTHQyQ7Ayyqogo+r7HnlF2fpxJh7MGh1ec0fYAb0GWPJBpTabq22fH8ueS2lAywe60ky7ZHMZlRqwGTM7qx1jKHJwB5QH0dq16DFmuNVma0LyQWeT+2Q1I7VztduXjjvFpowNeKVnbwyMlGwuUC1tv4nOpLY+ZznsNc1hbkrgJGdRpyy/x7V7Ppdpm7Nd5Rip6vYMN4+EHsfPU1Zjk0vbXWgjAo6SmOViuIaLyJ5ampSj2Jw0+pnwaVATZWpgym2sU8w7MSsWrAGNEpjJgcyYNka3z600eOvyGmgo6SDy7Wqd5aYz3RLoGBMblzr9ObN0u31tO3ttpfsx1UbNfVWbFddsyv22ietiynIgUgNHtWOebQ3GHLfKYc+r9nuM7X/Y66tNfvK+YJN7NQfcjbGrOTgZDR8O87az57DVnTwlbcukAPP91XN8piY7LWFEr6LHq1G9teW1WZb3TL/mVPac2emY9qW0f87UlI5Vmo3Pvc+b2iYunTFtxNS++bEOcx01LcoUzV477xxojIHD2rHGGLLSOU6xaTXLn80pJifXbsxlx0qWM31jz3lNG7MpwBk7j5KdSz1F7bmbYxlLt552wfYlM38by6TMtYtAbzLLQgDcEdxWl4LL6yzaFqTkdhi/9NhDlaXS52VhXa9SO+5Up6bt2e+1/WyHPIc6zS0fBHOaOt+uZvnAM2eAnHtuc23K5VE637Ftp65Jj1n6vMn5j812Dwu4au6cucefeh5qy0qWA+amGZ+9nw3LgUoOjkoTBDuz32TwLIGf/Dzy7e3yUt9RshyU5ABvE5eQ7DMKTgp2qNDiS8G27p6zalt3T8lGfZ5mRjH3JdS8AERI1UALg4PtGKf851Mdfml9DZyU3DtjdHbutmia9HlsgCsdZ+7AugnLAEy/6DWXx2FmmsD0NU2ZvYbavdxUmzHneLV1tcFsrqiytGxtkCucQ83VqDa3A5/jtpl633L3Qu1zfpw572fpXEv7burKGzMLzO356rraPmvLCGvsSSFsuKTZm21n81nf2rG2LZNi7RAINgcqg6KEvIA/B0uvFwY060uunUut89qY+Sm4d+y6Wsedd2hzBuNNWZaaO2COGHWurkLbm+NyyWex59rs8zCl2zgM41SzvC37vXR/57SRLzsbYCpntGoMTYkNGWMkS+eZ73MolnVmG3OFofacx7bL3wXVA9XaqumdvOf6VCXL+zhgPLBgEwZljAW+yI1CAB3B3bMNQR7alkmp2ZhIayTZ0FoWxdJ2JkvtgFmxlr+MpU5pDl2cr8u3qWlgamZnYrV7ZEV7c6jw0oy1dE9q21sgM4fG1n3nsDhTM9uzbbb9MVHknHY2YR3mzKRrQHHK5sz+7TMz1X5JDzXVdun7JmzknOe8Ju7M37Ext04NxGzqyrSfx4B2fi9yhndNlzT/vh+6YOBo33sMXCH2uTjs39aibUHKHKsOxuMdxxpYseCmlE4/j4TIz8GeS+4/z2ditU5V99l0hljrxGqz2Nr3MYp/rI0xm3JJbCoa3KSTGAuxPYzV7vOmx59yOSgwmTMojg1aFoSeDVZn7JxLTNnY9nabOcyatU0HydI7WNsvBzO51sX+r5ZHnM1ldea46/L+QrZZq8GTgw7nsbEAdizwoAbwtoP2U9q27p4pm6KMS7H+WRQQBRpSn/Gl5lo4rjSITw20R6E/85lf3t7ZoFanhJOl+6qU+6bHnZrt5+tyV0DNStvk323Ei91+zG1Tanvqnm/q2pl6bnOQWzpuSbdTO898ELZWiwrKTferuYjGmJxN3TtjVrtflhmcq7spvU85a5gDE43Um7IS8JljlnHM7ikRwQVTh2cD8etkqvvKftHyaziu4CQQ4I5w7sf1us+RbUHKHJtFrxuwUpgtrAEVIAlqBxPEkUGKNyh3cnOZk7FOtLQ+P05tu1rHbQFRbVs9h7kaF7vf2bDaLHZu+2Oai02uqTRI63drORAqmR1MpwZue1zbbgloTTEXei155NAUaJsCI9bmtDHHNgGqChxKLpuSm6f2LuT7lNaPud5q4u4CE1Jtw1rhXhYByoh7G0BZJDsnWVutLzjORgTgCKzicb/+s2xbd88mNkebMEOvsra9WbYW/TOHhp4CGSW2xFqNFs8p6XyfGvCZstosfG47uf9/LiVcE9Ye9jqslfYdG0zGjr+mAxhxV21yzmMuhPy4uXthTtSWfp4jsAUOf79rjErNVTDmehnbH1gH0KV3pfSb1sBMfq/GQFL+/m/qQiz1ITnIqQIiPf/6YGv7smK/VvoMDO/NGGje2tawZVI2s7nswVQzyqrk2WrVssrKzg4SYzONWiebr8tp6xJNPncAmeMastEhY2HXtcGjxAqUrrF23LNxDWNt1u5VKVpmjFEqLZubwGzus1hySZXOuQaU5hx3KvKm5HYbG+in2JvSOdXOdey8pxi0uW7CUptzmJb8PC0bNReglJgSXZ6/h0Z7kvbPmday5qQISubqUy5xEEKBQEdw90xWm3+K2QVlUt7+9rfjWc96Fvb29nDzzTfjox/96IU8nbNrm4hq1ZQyLYlqD30ehc5xDITks7ypznSKhdiExq+1MwZe7Pd8u7kajqlrGHTihQEjZ3Zq+42df+1+25nwJuBU29zE5roMpo4xxuwdJkJoLlO2ieVAcOpZKu2XW86EjQGTKfbTntsmNpZnJwOgGlo8cDHbFAoVVnhUe7JpFePY6Ias6MVs9t4d9u8Qtul4+qu/+qv42q/9Wuzt7eHrv/7r8Wu/9muHOu65tgsGUn7lV34Fd955J+655x584hOfwPOf/3zcdttt+MIXvnChTulwNvZyzX34dJ19+TMX0ORAVXIN5YNezpZYsJJfh/W/j1ne2W4KFuaCoNox57IfY8vGWJrSfrXrGXMHTiWa25S5ssesnfth3GhqNTfD2CBi180ZhGtMUs2m1k2trwGROe9NLble6d7XQt7nuJ3mtJdvn1vp+cxYk/UUCOZvE3c1ryh/XttuQ5ANHEvWxaahOOzfprbpePobv/EbePWrX43Xv/71+OQnP4lXvvKVeOUrX4nf/u3fPurln3VzdIG4pZtvvhkvetGL8HM/93MAgBACbrzxRnzf930f3vSmN43ue/LkSVx99dX4y3gFFm55Pk53vk29VNlMYzQro24r27iSv3/KNTM2cM5hQUp0fKnDn3PMseVTwGOMtakNdHPdH1Pb6rHnul7yc7xQHe0m92Hu9nPcM6Xlc8HolDvtMNdSe3ZyNiWPYhpzu03dqxoAmXrXNrHcDVw7rm6LBE54WWFSNde9k++nNuXy2eQaD3NPKtZRiw/iP+Pxxx/HVVdddVbazC2OS+7bjjQuddTig/Tujc510/H0Va96FU6fPo33vve9cdlf+At/AS94wQvwjne849Dnfi7sgmhSVqsVHnroIdx1111xmfcet956Kx544IG17Q8ODnBwcBC/P/744wCADi2OUiLh3FjWWax1YhnIyCINh6HK4Je+B+Adt5xHJipVXX2ZKz5+Il43Ovu056HuDPkcaP24saPMjjd5LmaZ/Y6J5bpOz6nU7tQAGffJWKa1bYAYhlVlxGoD4/jh1/a1yxQcHSYHSbz/c64f679xfh5xW8vAoX6/Bu0bEAIU2Au5xlLkbelZKa0rXlvtfIDBfSkeF/X1to05jJ+2VwMT9h0D1t+tkrn4Tz1UWJsfhOlbliukyRCFwTVXWRMLRHJQMnYamwKOs9i3d2jlFM79gNHRwThQm9pfzvXkyZOD5bu7u9jd3V3bftPxFAAeeOAB3HnnnYNlt912G97znvcc+rzPlV0QkPLFL34Rfd/juuuuGyy/7rrr8Pu///tr2997771485vfvLb8I7gIfWhrnejE99yOELm2ta1tbWvn3Kjy+RjYqVOncPXVV5+Ttnd2dnD99dfjIyeOPi5deeWVuPHGGwfL7rnnHvzYj/3Y2rabjqcAcOLEieL2J06cONqJnwM7FtE9d9111wD1PfbYY/jqr/5qfPaznz1nD9ylbCdPnsSNN96Iz33uc+eM+rxUbXvvjmbb+3d42967wxsR4dSpU7jhhhvO2TH29vbwyCOPYLVaHbktIkKeM6vEojwV7IKAlKc//elomgaPPvroYPmjjz6K66+/fm37Gs119dVXb1/WI9hVV121vX+HtO29O5pt79/hbXvvDmfnY0K7t7eHvb29c34ca5uOpwBw/fXXb7T9hbQLEt2zs7ODF77whbj//vvjshAC7r//ftxyyy0X4pS2trWtbW1rWzt2dpjx9JZbbhlsDwD33XffRTn+XjB3z5133onbb78dN910E1784hfjrW99K06fPo3Xve51F+qUtra1rW1ta1s7djY1nr72ta/FV37lV+Lee+8FAHz/938/vvmbvxn/7J/9M7zsZS/Du971Lnz84x/Hv/gX/+JCXkbRLhhIedWrXoU//uM/xt13340TJ07gBS94Ad73vvetiXlKtru7i3vuuecp66M7qm3v3+Fte++OZtv7d3jb3rut1WxqPP3sZz8Lb9JXfOM3fiN++Zd/GT/yIz+CH/7hH8ZznvMcvOc978Fzn/vcC3UJVbtgeVK2trWtbW1rW9va1sZsW2Bwa1vb2ta2trWtXZS2BSlb29rWtra1rW3torQtSNna1ra2ta1tbWsXpW1Byta2trWtbW1rW7so7ViClE1LUj8V7MMf/jBe/vKX44YbboBzbq0GAxHh7rvvxld8xVfgsssuw6233oo/+IM/GGzzpS99Ca95zWtw1VVX4ZprrsHrX/96PPHEE+fxKi6M3XvvvXjRi16EL/uyL8MznvEMvPKVr8SnP/3pwTb7+/t44xvfiD/1p/4UrrzySnz7t3/7WjKkz372s3jZy16Gyy+/HM94xjPwgz/4g+i67nxeygWxn//5n8fznve8mGTslltuwa//+q/H9dt7N8/e8pa3wDmHH/iBH4jLtvdua091O3YgZdOS1E8VO336NJ7//Ofj7W9/e3H9T/zET+Btb3sb3vGOd+DBBx/EFVdcgdtuuw37+/txm9e85jX4nd/5Hdx3331473vfiw9/+MN4wxvecL4u4YLZhz70IbzxjW/Eb/7mb+K+++5D27b4lm/5Fpw+fTpu8/f+3t/Df/kv/wW/+qu/ig996EP4oz/6I/zNv/k34/q+7/Gyl70Mq9UKv/Ebv4F/82/+Dd75znfi7rvvvhCXdF7tq77qq/CWt7wFDz30ED7+8Y/jr/7Vv4pXvOIV+J3f+R0A23s3xz72sY/hF37hF/C85z1vsHx777b2lDc6ZvbiF7+Y3vjGN8bvfd/TDTfcQPfee+8FPKuLywDQu9/97vg9hEDXX389/eRP/mRc9thjj9Hu7i79h//wH4iI6Hd/93cJAH3sYx+L2/z6r/86OefoD//wD8/buV8M9oUvfIEA0Ic+9CEi4nu1XC7pV3/1V+M2v/d7v0cA6IEHHiAiol/7tV8j7z2dOHEibvPzP//zdNVVV9HBwcH5vYCLwJ72tKfRv/yX/3J772bYqVOn6DnPeQ7dd9999M3f/M30/d///US0fe62tjUiomPFpGhJ6ltvvTUumypJvTXgkUcewYkTJwb37eqrr8bNN98c79sDDzyAa665BjfddFPc5tZbb4X3Hg8++OB5P+cLaY8//jgA4NprrwUAPPTQQ2jbdnD/vvZrvxbPfOYzB/fv67/+6wfJCG+77TacPHkyMgpPBev7Hu9617tw+vRp3HLLLdt7N8Pe+MY34mUve9ngHgHb525rWwOOSRVktcOUpN4aYvntsdLcJ06cwDOe8YzB+sVigWuvvfaiLN99riyEgB/4gR/AX/yLfzFmXzxx4gR2dnZwzTXXDLbN71/p/uq6S90+9alP4ZZbbsH+/j6uvPJKvPvd78bXfd3X4eGHH97euxF717vehU984hP42Mc+trZu+9xtbWvHDKRsbWvn2t74xjfit3/7t/GRj3zkQp/KsbI/+2f/LB5++GE8/vjj+I//8T/i9ttvx4c+9KELfVoXtX3uc5/D93//9+O+++4775Vzt7a142LHyt1zmJLUW0O8N2P37frrr18TH3ddhy996UtPmXt7xx134L3vfS8+8IEP4Ku+6qvi8uuvvx6r1QqPPfbYYPv8/pXur6671G1nZwd/5s/8GbzwhS/Evffei+c///n4mZ/5me29G7GHHnoIX/jCF/AN3/ANWCwWWCwW+NCHPoS3ve1tWCwWuO6667b3bmtPeTtWIOUwJam3Bjz72c/G9ddfP7hvJ0+exIMPPhjv2y233ILHHnsMDz30UNzm/e9/P0IIuPnmm8/7OZ9PIyLccccdePe73433v//9ePaznz1Y/8IXvhDL5XJw/z796U/js5/97OD+fepTnxoAvfvuuw9XXXUVvu7rvu78XMhFZCEEHBwcbO/diL3kJS/Bpz71KTz88MPx76abbsJrXvOa+Hl777b2lLcLrdzd1N71rnfR7u4uvfOd76Tf/d3fpTe84Q10zTXXDNTtT0U7deoUffKTn6RPfvKTBIB++qd/mj75yU/S//t//4+IiN7ylrfQNddcQ//5P/9n+q3f+i16xSteQc9+9rPpySefjG1867d+K/35P//n6cEHH6SPfOQj9JznPIde/epXX6hLOm/2vd/7vXT11VfTBz/4Qfr85z8f/86cORO3+Z7v+R565jOfSe9///vp4x//ON1yyy10yy23xPVd19Fzn/tc+pZv+RZ6+OGH6X3vex99+Zd/Od11110X4pLOq73pTW+iD33oQ/TII4/Qb/3Wb9Gb3vQmcs7Rf//v/52ItvduE7PRPUTbe7e1rR07kEJE9LM/+7P0zGc+k3Z2dujFL34x/eZv/uaFPqULbh/4wAcIwNrf7bffTkQchvyjP/qjdN1119Hu7i695CUvoU9/+tODNv7kT/6EXv3qV9OVV15JV111Fb3uda+jU6dOXYCrOb9Wum8A6Jd+6ZfiNk8++ST93b/7d+lpT3saXX755fRt3/Zt9PnPf37Qzmc+8xl66UtfSpdddhk9/elPp7//9/8+tW17nq/m/Nvf+Tt/h776q7+adnZ26Mu//MvpJS95SQQoRNt7t4nlIGV777b2VDdHRHRhOJytbW1rW9va1ra2tbodK03K1ra2ta1tbWtbe+rYFqRsbWtb29rWtra1i9K2IGVrW9va1ra2ta1dlLYFKVvb2ta2trWtbe2itC1I2drWtra1rW1taxelbUHK1ra2ta1tbWtbuyhtC1K2trWtbW1rW9vaRWlbkLK1rW1ta1vb2tYuStuClK1tbWtb29rWtnZR2hakbG1rW9va1ra2tYvStiBla1vb2ta2trWtXZS2BSlb29rWtra1rW3torT/H4uurOXDMMorAAAAAElFTkSuQmCC", + "text/plain": [ + "
          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "flux = flux.values.reshape(500,500)\n", + "fig, ax = plt.subplots()\n", + "pos = ax.imshow(flux*power/heating/volume, aspect='auto', origin='lower')\n", + "cbar = plt.colorbar(pos,ax=ax,label=r'Flux [neutrons/cm$^2$-s]')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "bdbb596d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fission = fission.values.reshape(500,500)\n", + "fig, ax = plt.subplots()\n", + "pos = ax.imshow(fission*power/heating/volume, aspect='auto', origin='lower')\n", + "cbar = plt.colorbar(pos,ax=ax,label=r'Fission [reactions/cm$^2$-s]')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "2ece2684", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "absorption = absorption.values.reshape(500,500)\n", + "fig, ax = plt.subplots()\n", + "pos = ax.imshow(absorption*power/heating/volume, aspect='auto', origin='lower')\n", + "cbar = plt.colorbar(pos,ax=ax,label=r'Absorption [reactions/cm$^2$-s]')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f35270b2-0a23-49c9-905d-70f6ddc1376f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openmc_notebooks/msre_iso-temperature_feedback.ipynb b/openmc_notebooks/msre_iso-temperature_feedback.ipynb deleted file mode 100644 index 7e8bc53..0000000 --- a/openmc_notebooks/msre_iso-temperature_feedback.ipynb +++ /dev/null @@ -1,1303 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1df9294b", - "metadata": {}, - "source": [ - "# MSRE isothermal temperature coefficient\n", - "\n", - "In this example we will calculate the isothermal temperature reactivity feedback coefficient of the MSRE. To do that we will parametrize the fuel salt temperature and run 3 different cases (in reality you would want to run many more). We will then fit the data with a linear function and approximate the average coefficient to the slope of the curve. We will verify that the coefficient is negative and is approximately equal to $-10\\sim15 pcm/K$, as reported here: [ORNL, pag.39](http://moltensalt.org/references/static/downloads/pdf/ORNL-4233.pdf) " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "49285ef9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "rm: cannot remove '*.xml': No such file or directory\n", - "rm: cannot remove '*.h5': No such file or directory\n" - ] - }, - { - "data": { - "text/plain": [ - "256" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib inline\n", - "\n", - "import openmc\n", - "import numpy as np\n", - "from math import log10\n", - "import matplotlib.pyplot as plt\n", - "import os \n", - "\n", - "os.system('rm *.xml *.h5')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6d36f317", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the same temperature to fuel salt, moderator and inor vessel\n", - "def build_materials(temp):\n", - " # Fuel salt\n", - " salt_density = 2.3275 * (1 -1.18e-4 * ((temp*9/5+32) - 1181))\n", - " salt = openmc.Material(name=\"salt\", temperature = temp)\n", - " salt.add_nuclide('Li6',1.31480070E-05)\n", - " salt.add_nuclide('Li7', 0.262960140146177)\n", - " salt.add_nuclide('Be9',1.1863E-01)\n", - " salt.add_nuclide('Zr90',1.0543E-02)\n", - " salt.add_nuclide('Zr91',2.2991E-03)\n", - " salt.add_nuclide('Zr92',3.5142E-03)\n", - " salt.add_nuclide('Zr94',3.5613E-03)\n", - " salt.add_nuclide('Zr96',5.7375E-04)\n", - " salt.add_nuclide('Hf174',8.3786E-10)\n", - " salt.add_nuclide('Hf176',2.7545E-08)\n", - " salt.add_nuclide('Hf177',9.7401E-08)\n", - " salt.add_nuclide('Hf178',1.4285E-07)\n", - " salt.add_nuclide('Hf179',7.1323E-08)\n", - " salt.add_nuclide('Hf180',1.8370E-07)\n", - " salt.add_nuclide('U234',1.034276246E-05)\n", - " salt.add_nuclide('U235',1.009695816E-03)\n", - " salt.add_nuclide('U236',4.227809892E-06)\n", - " salt.add_nuclide('U238',2.168267822E-03)\n", - " salt.add_nuclide('Fe54',2.8551E-06)\n", - " salt.add_nuclide('Fe56',4.4818E-05)\n", - " salt.add_nuclide('Fe57',1.0350E-06)\n", - " salt.add_nuclide('Fe58',1.3775E-07)\n", - " salt.add_nuclide('Cr50',2.1224E-06)\n", - " salt.add_nuclide('Cr52',4.0928E-05)\n", - " salt.add_nuclide('Cr53',4.6409E-06)\n", - " salt.add_nuclide('Cr54',1.1552E-06)\n", - " salt.add_nuclide('Ni58',5.8597E-06)\n", - " salt.add_nuclide('Ni60',2.2571E-06)\n", - " salt.add_nuclide('Ni61',9.8117E-08)\n", - " salt.add_nuclide('Ni62',3.1284E-07)\n", - " salt.add_nuclide('Ni64',7.9671E-08)\n", - " salt.add_nuclide('O16',5.1437E-04)\n", - " salt.add_nuclide('O17',1.8927E-07)\n", - " salt.add_nuclide('O18',9.6440E-07)\n", - " salt.add_nuclide('F19',5.9409E-01)\n", - " salt.set_density('g/cm3', salt_density)\n", - "\n", - " #Moderator graphite block\n", - " graphite = openmc.Material(name='graphite', temperature = temp)\n", - " graphite.set_density('g/cm3',1.86)\n", - " graphite.add_nuclide('C12',1)\n", - " graphite.add_s_alpha_beta('c_Graphite')\n", - "\n", - " #inor-8\n", - " inor = openmc.Material(name='inor-8', temperature = temp)\n", - " inor.set_density('g/cm3',8.7745)\n", - " inor.add_element('Ni',68.5,'wo')\n", - " inor.add_element('Mo',16.5,'wo')\n", - " inor.add_element('Cr',7,'wo')\n", - " inor.add_element('Fe',5,'wo')\n", - " inor.add_element('C',0.06,'wo')\n", - " inor.add_element('Al',0.25,'wo')\n", - " inor.add_element('Ti',0.25,'wo')\n", - " inor.add_element('S',0.02,'wo')\n", - " inor.add_element('Mn',1.0,'wo')\n", - " inor.add_element('Si',1.0,'wo')\n", - " inor.add_element('Cu',0.35,'wo')\n", - " inor.add_element('B',0.010,'wo')\n", - " inor.add_element('W',0.5,'wo')\n", - " inor.add_element('P',0.015,'wo')\n", - " inor.add_element('Co',0.2,'wo')\n", - "\n", - " #helium\n", - " helium = openmc.Material(name='helium')\n", - " helium.add_element('He',1.0)\n", - " helium.set_density('g/cm3',1.03*(10**-4))\n", - "\n", - " #Control rods inconel clad\n", - " trace = 0.01\n", - " inconel = openmc.Material(name='inconel', temperature = 65.6 + 273.15)\n", - " inconel.add_element('Ni',78.5,percent_type='wo')\n", - " inconel.add_element('Cr',14.0,percent_type='wo')\n", - " inconel.add_element('Fe',6.5,percent_type='wo')\n", - " inconel.add_element('Mn',0.25,percent_type='wo')\n", - " inconel.add_element('Si',0.25,percent_type='wo')\n", - " inconel.add_element('Cu',0.2,percent_type='wo')\n", - " inconel.add_element('Co',0.2,percent_type='wo')\n", - " inconel.add_element('Al',0.2,percent_type='wo')\n", - " inconel.add_element('Ti',0.2,percent_type='wo')\n", - " inconel.add_element('Ta',0.5,percent_type='wo')\n", - " inconel.add_element('W',0.5,percent_type='wo')\n", - " inconel.add_element('Zn',0.2,percent_type='wo')\n", - " inconel.add_element('Zr',0.1,percent_type='wo')\n", - " inconel.add_element('C',trace,percent_type='wo')\n", - " inconel.add_element('Mo',trace,percent_type='wo')\n", - " inconel.add_element('Ag',trace,percent_type='wo')\n", - " inconel.add_element('B',trace,percent_type='wo')\n", - " inconel.add_element('Ba',trace,percent_type='wo')\n", - " inconel.add_element('Be',trace,percent_type='wo')\n", - " inconel.add_element('Ca',trace,percent_type='wo')\n", - " inconel.add_element('Cd',trace,percent_type='wo')\n", - " inconel.add_element('V',trace,percent_type='wo')\n", - " inconel.add_element('Sn',trace,percent_type='wo')\n", - " inconel.add_element('Mg',trace,percent_type='wo')\n", - " inconel.set_density('g/cm3',8.5)\n", - "\n", - " # SS316 control rod flexible hose\n", - " ss316 = openmc.Material(name='ss316', temperature = 65.6 + 273.15)\n", - " ss316.add_element('C',0.026,'wo')\n", - " ss316.add_element('Si',0.37,'wo')\n", - " ss316.add_element('Mn',0.16,'wo')\n", - " ss316.add_element('Cr',16.55,'wo')\n", - " ss316.add_element('Cu',0.16,'wo')\n", - " ss316.add_element('Ni',10,'wo')\n", - " ss316.add_element('P',0.029,'wo')\n", - " ss316.add_element('S',0.027,'wo')\n", - " ss316.add_element('Mo',2.02,'wo')\n", - " ss316.add_element('N',0.036,'wo')\n", - " ss316.add_element('Fe',70.622,'wo')\n", - " ss316.set_density('g/cm3',7.99)\n", - "\n", - " #Control rods bushing posion material\n", - " Gd2O3 = openmc.Material()\n", - " Gd2O3.add_element('Gd',2)\n", - " Gd2O3.add_element('O',3)\n", - " Gd2O3.set_density('g/cm3',7.41)\n", - " Al2O3 = openmc.Material()\n", - " Al2O3.add_element('Al',2)\n", - " Al2O3.add_element('O',3)\n", - " Al2O3.set_density('g/cm3',3.95)\n", - " bush = openmc.Material.mix_materials([Gd2O3,Al2O3],[0.7,0.3],'wo')\n", - " bush.name='bush'\n", - " bush.temperature = 65.6 +273.15\n", - "\n", - " #Concrete block\n", - " concrete = openmc.Material(name='concrete')\n", - " concrete.add_element('H',0.005,'wo')\n", - " concrete.add_element('O',0.496,'wo')\n", - " concrete.add_element('Si',0.314,'wo')\n", - " concrete.add_element('Ca',0.083,'wo')\n", - " concrete.add_element('Na',0.017,'wo')\n", - " concrete.add_element('Mn',0.002,'wo')\n", - " concrete.add_element('Al',0.046,'wo')\n", - " concrete.add_element('S',0.001,'wo')\n", - " concrete.add_element('K',0.019,'wo')\n", - " concrete.add_element('Fe',0.012,'wo')\n", - " concrete.set_density('g/cm3',2.35)\n", - "\n", - " #Thermal shielding as material mix of water and SS304 50-50 vo\n", - " water = openmc.Material()\n", - " water.add_element('H',2)\n", - " water.add_element('O',1)\n", - " water.set_density('g/cm3',0.997)\n", - "\n", - " #stainless steel 304\n", - " ss304 = openmc.Material()\n", - " ss304.add_element('C',0.08,'wo')\n", - " ss304.add_element('Mn',2,'wo')\n", - " ss304.add_element('P',0.045,'wo')\n", - " ss304.add_element('S',0.03,'wo')\n", - " ss304.add_element('Si',0.75,'wo')\n", - " ss304.add_element('Cr',19,'wo')\n", - " ss304.add_element('Ni',10,'wo')\n", - " ss304.add_element('N',0.1,'wo')\n", - " ss304.add_element('Fe',67.995, 'wo')\n", - " ss304.set_density('g/cm3',7.93)\n", - " shield = openmc.Material.mix_materials([water,ss304],[0.5,0.5],'vo')\n", - " shield.temperature = 32.2 + 273.15\n", - " shield.name='steelwater'\n", - "\n", - " # \"Careytemp 1600\" by Philip Carey Manufacturing Compamy (Cincinnati) \n", - " insulation=openmc.Material(name='insulation')\n", - " insulation.add_element('Si',1)\n", - " insulation.add_element('O',2)\n", - " insulation.set_density('g/cm3',0.16) \n", - "\n", - " # Sand water (not sure about this material)\n", - " sandwater=openmc.Material(name='sandwater')\n", - " sandwater.add_element('Fe',3)\n", - " sandwater.add_element('O',4)\n", - " sandwater.set_density('g/cm3',6)\n", - "\n", - " #Vessel anular steel\n", - " steel = openmc.Material(name='steel')\n", - " steel.add_element('Fe',1)\n", - " steel.set_density('g/cm3',7.85)\n", - "\n", - " return openmc.Materials([salt,graphite,inor,helium,inconel,shield,concrete,steel,ss316,sandwater,insulation,bush])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "5fa45be2", - "metadata": {}, - "outputs": [], - "source": [ - "# Import h5m files\n", - "core_h5m = '../h5m/msre_reactor_1e-2.h5m'\n", - "cr_h5m = '../h5m/msre_control_rod_1e-2.h5m'\n", - "\n", - "# Create DAGMC universes out of h5m files\n", - "core = openmc.DAGMCUniverse(filename=core_h5m, auto_geom_ids=True, universe_id=1)\n", - "cr = openmc.DAGMCUniverse(filename=cr_h5m, auto_geom_ids=True, universe_id=2)\n", - "\n", - "# Create regions\n", - "core_region = core.bounding_region()\n", - "cr1_region = cr.bounding_region(boundary_type='transmission', starting_id=20000)\n", - "\n", - "# Extend control rod region, to include upwards translations\n", - "cr1_region = cr1_region | cr1_region.translate([0,0,150])\n", - "\n", - "# Create control rod region 2 and 3 as translated region of control rod 1\n", - "offset = 10.163255\n", - "cr2_region = cr1_region.translate([-offset,0,0])\n", - "cr3_region = cr1_region.translate([-offset,offset,0])\n", - "\n", - "# Create openmc Cells \n", - "core_cell = openmc.Cell(region=~(cr1_region | cr2_region | cr3_region) & core_region , fill=core)\n", - "cr1_cell = openmc.Cell(name='CR1', region=cr1_region, fill=cr)\n", - "cr2_cell = openmc.Cell(name='CR2', region=cr2_region, fill=cr)\n", - "cr3_cell = openmc.Cell(name='CR3', region=cr3_region, fill=cr)\n", - " \n", - "#translate control rods at top position (fully withdrawn)\n", - "inch_to_cm = 2.54\n", - "start_pos = 19.2 #inches\n", - "top_pos = 51 #inches\n", - "setattr(cr1_cell, 'translation', [0, 0, start_pos + top_pos*inch_to_cm])\n", - "setattr(cr2_cell, 'translation', [-offset, 0, start_pos + top_pos*2.54])\n", - "setattr(cr3_cell, 'translation', [-offset, offset, start_pos + top_pos*2.54])\n", - "\n", - "# Create openmc Geometry object\n", - "geometry = openmc.Geometry([core_cell,cr1_cell,cr2_cell,cr3_cell])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7bb7b060", - "metadata": {}, - "outputs": [], - "source": [ - "settings = openmc.Settings()\n", - "settings.temperature = {'method':'interpolation','range':(293.15,923.15)}\n", - "settings.batches = 50\n", - "settings.inactive = 20\n", - "settings.particles = 30000\n", - "settings.photon_transport = False\n", - "source_area = openmc.stats.Box([-100., -100., 0.],[ 100., 100., 200.],only_fissionable = True)\n", - "settings.source = openmc.Source(space=source_area)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3b6184d5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.2\n", - " Git SHA1 | 030f73a8690ed19e91806e46c8caf338d252e74a\n", - " Date/Time | 2023-01-17 13:43:17\n", - " MPI Processes | 1\n", - " OpenMP Threads | 56\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_reactor_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_control_rod_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - " Reading Li6 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li6.h5\n", - " Reading Li7 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li7.h5\n", - " Reading Be9 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Be9.h5\n", - " Reading Zr90 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr96.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n", - " Reading Hf174 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf174.h5\n", - " Reading Hf176 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf176.h5\n", - " Reading Hf177 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf177.h5\n", - " Reading Hf178 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf178.h5\n", - " Reading Hf179 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf179.h5\n", - " Reading Hf180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf180.h5\n", - " Reading U234 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U234.h5\n", - " Reading U235 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U235.h5\n", - " Reading U236 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U236.h5\n", - " Reading U238 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U238.h5\n", - " Reading Fe54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe58.h5\n", - " Reading Cr50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr50.h5\n", - " Reading Cr52 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr52.h5\n", - " Reading Cr53 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr53.h5\n", - " Reading Cr54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr54.h5\n", - " Reading Ni58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni58.h5\n", - " Reading Ni60 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni60.h5\n", - " Reading Ni61 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni61.h5\n", - " Reading Ni62 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni62.h5\n", - " Reading Ni64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni64.h5\n", - " Reading O16 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O16.h5\n", - " Reading O17 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O17.h5\n", - " Reading O18 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O18.h5\n", - " Reading F19 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/F19.h5\n", - " Reading C12 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C12.h5\n", - " Reading Mo100 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo100.h5\n", - " Reading Mo92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo92.h5\n", - " Reading Mo94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo94.h5\n", - " Reading Mo95 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo95.h5\n", - " Reading Mo96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo96.h5\n", - " Reading Mo97 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo97.h5\n", - " Reading Mo98 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo98.h5\n", - " Reading C13 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C13.h5\n", - " Reading Al27 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Al27.h5\n", - " Reading Ti46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti46.h5\n", - " Reading Ti47 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti47.h5\n", - " Reading Ti48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti48.h5\n", - " Reading Ti49 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti49.h5\n", - " Reading Ti50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti50.h5\n", - " Reading S32 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S32.h5\n", - " Reading S33 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S33.h5\n", - " Reading S34 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S34.h5\n", - " Reading S36 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S36.h5\n", - " Reading Mn55 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mn55.h5\n", - " Reading Si28 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si28.h5\n", - " Reading Si29 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si29.h5\n", - " Reading Si30 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si30.h5\n", - " Reading Cu63 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu63.h5\n", - " Reading Cu65 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu65.h5\n", - " Reading B10 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B10.h5\n", - " Reading B11 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B11.h5\n", - " Reading W180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W180.h5\n", - " Reading W182 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W182.h5\n", - " Reading W183 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W183.h5\n", - " Reading W184 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W184.h5\n", - " Reading W186 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W186.h5\n", - " Reading P31 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/P31.h5\n", - " Reading Co59 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Co59.h5\n", - " Reading He3 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He3.h5\n", - " Reading He4 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He4.h5\n", - " Reading Ta180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta180.h5\n", - " Reading Ta181 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta181.h5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Zn64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn64.h5\n", - " Reading Zn66 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn66.h5\n", - " Reading Zn67 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn67.h5\n", - " Reading Zn68 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn68.h5\n", - " Reading Zn70 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn70.h5\n", - " Reading Ag107 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag107.h5\n", - " Reading Ag109 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag109.h5\n", - " Reading Ba130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba130.h5\n", - " Reading Ba132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba132.h5\n", - " Reading Ba134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba134.h5\n", - " Reading Ba135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba135.h5\n", - " Reading Ba136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba136.h5\n", - " Reading Ba137 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba137.h5\n", - " Reading Ba138 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba138.h5\n", - " Reading Ca40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca40.h5\n", - " Reading Ca42 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca42.h5\n", - " Reading Ca43 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca43.h5\n", - " Reading Ca44 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca44.h5\n", - " Reading Ca46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca46.h5\n", - " Reading Ca48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca48.h5\n", - " Reading Cd106 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd106.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n", - " 1200K\n", - " Reading Cd108 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd108.h5\n", - " Reading Cd110 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd110.h5\n", - " Reading Cd111 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd111.h5\n", - " Reading Cd112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd112.h5\n", - " Reading Cd113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd113.h5\n", - " Reading Cd114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd114.h5\n", - " Reading Cd116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd116.h5\n", - " Reading V50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V50.h5\n", - " Reading V51 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V51.h5\n", - " Reading Sn112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn112.h5\n", - " Reading Sn114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn114.h5\n", - " Reading Sn115 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn115.h5\n", - " Reading Sn116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn116.h5\n", - " Reading Sn117 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn117.h5\n", - " Reading Sn118 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn118.h5\n", - " Reading Sn119 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn119.h5\n", - " Reading Sn120 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn120.h5\n", - " Reading Sn122 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn122.h5\n", - " Reading Sn124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn124.h5\n", - " Reading Mg24 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg24.h5\n", - " Reading Mg25 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg25.h5\n", - " Reading Mg26 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg26.h5\n", - " Reading N14 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N14.h5\n", - " Reading N15 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N15.h5\n", - " Reading Gd152 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd152.h5\n", - " Reading Gd154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd154.h5\n", - " Reading Gd155 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd155.h5\n", - " Reading Gd156 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd156.h5\n", - " Reading Gd157 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd157.h5\n", - " Reading Gd158 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd158.h5\n", - " Reading Gd160 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd160.h5\n", - " Reading H1 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H1.h5\n", - " Reading H2 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H2.h5\n", - " Reading Na23 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Na23.h5\n", - " Reading K39 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K39.h5\n", - " Reading K40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K40.h5\n", - " Reading K41 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K41.h5\n", - " Reading c_Graphite from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/c_Graphite.h5\n", - " Minimum neutron data temperature: 250 K\n", - " Maximum neutron data temperature: 1200 K\n", - " Preparing distributed cell instances...\n", - " Reading plot XML file...\n", - " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000 eV for Li6\n", - " Initializing source particles...\n", - "\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - "\n", - " Bat./Gen. k Average k\n", - " ========= ======== ====================\n", - " 1/1 0.59413\n", - " 2/1 0.87144\n", - " 3/1 0.95932\n", - " 4/1 0.98230\n", - " 5/1 0.98710\n", - " 6/1 1.00177\n", - " 7/1 1.00787\n", - " 8/1 0.99209\n", - " 9/1 0.99546\n", - " 10/1 0.98464\n", - " 11/1 1.01001\n", - " 12/1 1.01107\n", - " WARNING: No intersection found with DAGMC cell 48, material 3\n", - " 13/1 1.00053\n", - " 14/1 0.99278\n", - " 15/1 0.99531\n", - " 16/1 1.00262\n", - " 17/1 0.98915\n", - " 18/1 0.99553\n", - " 19/1 0.98836\n", - " 20/1 0.99625\n", - " 21/1 0.99275\n", - " 22/1 0.99348 0.99312 +/- 0.00037\n", - " 23/1 0.98095 0.98906 +/- 0.00406\n", - " 24/1 1.00824 0.99386 +/- 0.00559\n", - " 25/1 1.00497 0.99608 +/- 0.00487\n", - " 26/1 0.99537 0.99596 +/- 0.00397\n", - " 27/1 0.99525 0.99586 +/- 0.00336\n", - " 28/1 0.99927 0.99629 +/- 0.00294\n", - " 29/1 0.99275 0.99589 +/- 0.00262\n", - " 30/1 0.99491 0.99579 +/- 0.00235\n", - " 31/1 1.00042 0.99622 +/- 0.00217\n", - " 32/1 0.99247 0.99590 +/- 0.00200\n", - " 33/1 0.99000 0.99545 +/- 0.00190\n", - " 34/1 1.00013 0.99578 +/- 0.00179\n", - " 35/1 1.00463 0.99637 +/- 0.00177\n", - " 36/1 0.99759 0.99645 +/- 0.00165\n", - " 37/1 1.00124 0.99673 +/- 0.00158\n", - " 38/1 1.00066 0.99695 +/- 0.00150\n", - " 39/1 0.98342 0.99624 +/- 0.00159\n", - " 40/1 0.98485 0.99567 +/- 0.00161\n", - " 41/1 1.00215 0.99598 +/- 0.00156\n", - " 42/1 0.99562 0.99596 +/- 0.00149\n", - " 43/1 1.00358 0.99629 +/- 0.00146\n", - " 44/1 0.99845 0.99638 +/- 0.00140\n", - " 45/1 0.98804 0.99605 +/- 0.00139\n", - " 46/1 0.99894 0.99616 +/- 0.00134\n", - " 47/1 0.99234 0.99602 +/- 0.00130\n", - " 48/1 0.98830 0.99574 +/- 0.00128\n", - " 49/1 1.00189 0.99595 +/- 0.00125\n", - " 50/1 0.99920 0.99606 +/- 0.00121\n", - " Creating state point statepoint.50.h5...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 1.1035e+02 seconds\n", - " Reading cross sections = 1.5148e+01 seconds\n", - " Total time in simulation = 9.9007e+01 seconds\n", - " Time in transport only = 9.8742e+01 seconds\n", - " Time in inactive batches = 3.9581e+01 seconds\n", - " Time in active batches = 5.9426e+01 seconds\n", - " Time synchronizing fission bank = 1.3993e-01 seconds\n", - " Sampling source sites = 1.2650e-01 seconds\n", - " SEND/RECV source sites = 1.3055e-02 seconds\n", - " Time accumulating tallies = 2.7440e-05 seconds\n", - " Time writing statepoints = 1.3125e-02 seconds\n", - " Total time for finalization = 3.5460e-06 seconds\n", - " Total time elapsed = 2.0983e+02 seconds\n", - " Calculation Rate (inactive) = 15158.7 particles/second\n", - " Calculation Rate (active) = 15144.8 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 0.99553 +/- 0.00123\n", - " k-effective (Track-length) = 0.99606 +/- 0.00121\n", - " k-effective (Absorption) = 0.99435 +/- 0.00114\n", - " Combined k-effective = 0.99510 +/- 0.00102\n", - " Leakage Fraction = 0.00001 +/- 0.00000\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.2\n", - " Git SHA1 | 030f73a8690ed19e91806e46c8caf338d252e74a\n", - " Date/Time | 2023-01-17 13:46:56\n", - " MPI Processes | 1\n", - " OpenMP Threads | 56\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_reactor_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_control_rod_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - " Reading Li6 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li6.h5\n", - " Reading Li7 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li7.h5\n", - " Reading Be9 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Be9.h5\n", - " Reading Zr90 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr96.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n", - " Reading Hf174 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf174.h5\n", - " Reading Hf176 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf176.h5\n", - " Reading Hf177 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf177.h5\n", - " Reading Hf178 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf178.h5\n", - " Reading Hf179 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf179.h5\n", - " Reading Hf180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf180.h5\n", - " Reading U234 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U234.h5\n", - " Reading U235 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U235.h5\n", - " Reading U236 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U236.h5\n", - " Reading U238 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U238.h5\n", - " Reading Fe54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe58.h5\n", - " Reading Cr50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr50.h5\n", - " Reading Cr52 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr52.h5\n", - " Reading Cr53 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr53.h5\n", - " Reading Cr54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr54.h5\n", - " Reading Ni58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni58.h5\n", - " Reading Ni60 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni60.h5\n", - " Reading Ni61 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni61.h5\n", - " Reading Ni62 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni62.h5\n", - " Reading Ni64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni64.h5\n", - " Reading O16 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O16.h5\n", - " Reading O17 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O17.h5\n", - " Reading O18 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O18.h5\n", - " Reading F19 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/F19.h5\n", - " Reading C12 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C12.h5\n", - " Reading Mo100 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo100.h5\n", - " Reading Mo92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo92.h5\n", - " Reading Mo94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo94.h5\n", - " Reading Mo95 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo95.h5\n", - " Reading Mo96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo96.h5\n", - " Reading Mo97 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo97.h5\n", - " Reading Mo98 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo98.h5\n", - " Reading C13 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C13.h5\n", - " Reading Al27 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Al27.h5\n", - " Reading Ti46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti46.h5\n", - " Reading Ti47 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti47.h5\n", - " Reading Ti48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti48.h5\n", - " Reading Ti49 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti49.h5\n", - " Reading Ti50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti50.h5\n", - " Reading S32 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S32.h5\n", - " Reading S33 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S33.h5\n", - " Reading S34 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S34.h5\n", - " Reading S36 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S36.h5\n", - " Reading Mn55 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mn55.h5\n", - " Reading Si28 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si28.h5\n", - " Reading Si29 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si29.h5\n", - " Reading Si30 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si30.h5\n", - " Reading Cu63 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu63.h5\n", - " Reading Cu65 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu65.h5\n", - " Reading B10 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B10.h5\n", - " Reading B11 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B11.h5\n", - " Reading W180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W180.h5\n", - " Reading W182 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W182.h5\n", - " Reading W183 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W183.h5\n", - " Reading W184 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W184.h5\n", - " Reading W186 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W186.h5\n", - " Reading P31 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/P31.h5\n", - " Reading Co59 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Co59.h5\n", - " Reading He3 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He3.h5\n", - " Reading He4 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He4.h5\n", - " Reading Ta180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta180.h5\n", - " Reading Ta181 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta181.h5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Zn64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn64.h5\n", - " Reading Zn66 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn66.h5\n", - " Reading Zn67 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn67.h5\n", - " Reading Zn68 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn68.h5\n", - " Reading Zn70 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn70.h5\n", - " Reading Ag107 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag107.h5\n", - " Reading Ag109 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag109.h5\n", - " Reading Ba130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba130.h5\n", - " Reading Ba132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba132.h5\n", - " Reading Ba134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba134.h5\n", - " Reading Ba135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba135.h5\n", - " Reading Ba136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba136.h5\n", - " Reading Ba137 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba137.h5\n", - " Reading Ba138 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba138.h5\n", - " Reading Ca40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca40.h5\n", - " Reading Ca42 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca42.h5\n", - " Reading Ca43 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca43.h5\n", - " Reading Ca44 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca44.h5\n", - " Reading Ca46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca46.h5\n", - " Reading Ca48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca48.h5\n", - " Reading Cd106 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd106.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n", - " 1200K\n", - " Reading Cd108 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd108.h5\n", - " Reading Cd110 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd110.h5\n", - " Reading Cd111 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd111.h5\n", - " Reading Cd112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd112.h5\n", - " Reading Cd113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd113.h5\n", - " Reading Cd114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd114.h5\n", - " Reading Cd116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd116.h5\n", - " Reading V50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V50.h5\n", - " Reading V51 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V51.h5\n", - " Reading Sn112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn112.h5\n", - " Reading Sn114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn114.h5\n", - " Reading Sn115 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn115.h5\n", - " Reading Sn116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn116.h5\n", - " Reading Sn117 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn117.h5\n", - " Reading Sn118 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn118.h5\n", - " Reading Sn119 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn119.h5\n", - " Reading Sn120 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn120.h5\n", - " Reading Sn122 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn122.h5\n", - " Reading Sn124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn124.h5\n", - " Reading Mg24 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg24.h5\n", - " Reading Mg25 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg25.h5\n", - " Reading Mg26 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg26.h5\n", - " Reading N14 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N14.h5\n", - " Reading N15 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N15.h5\n", - " Reading Gd152 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd152.h5\n", - " Reading Gd154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd154.h5\n", - " Reading Gd155 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd155.h5\n", - " Reading Gd156 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd156.h5\n", - " Reading Gd157 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd157.h5\n", - " Reading Gd158 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd158.h5\n", - " Reading Gd160 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd160.h5\n", - " Reading H1 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H1.h5\n", - " Reading H2 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H2.h5\n", - " Reading Na23 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Na23.h5\n", - " Reading K39 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K39.h5\n", - " Reading K40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K40.h5\n", - " Reading K41 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K41.h5\n", - " Reading c_Graphite from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/c_Graphite.h5\n", - " Minimum neutron data temperature: 250 K\n", - " Maximum neutron data temperature: 1200 K\n", - " Preparing distributed cell instances...\n", - " Reading plot XML file...\n", - " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000 eV for Li6\n", - " Initializing source particles...\n", - "\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - "\n", - " Bat./Gen. k Average k\n", - " ========= ======== ====================\n", - " 1/1 0.59985\n", - " 2/1 0.86992\n", - " 3/1 0.93662\n", - " 4/1 0.97317\n", - " 5/1 0.98089\n", - " 6/1 0.99006\n", - " 7/1 0.97465\n", - " 8/1 0.98408\n", - " 9/1 0.97688\n", - " 10/1 0.98133\n", - " 11/1 0.99735\n", - " 12/1 0.97989\n", - " 13/1 0.98175\n", - " 14/1 0.99775\n", - " 15/1 0.99462\n", - " 16/1 0.98558\n", - " 17/1 0.98992\n", - " 18/1 0.97169\n", - " 19/1 0.98839\n", - " 20/1 0.97831\n", - " 21/1 0.99006\n", - " 22/1 0.99916 0.99461 +/- 0.00455\n", - " 23/1 0.99631 0.99518 +/- 0.00269\n", - " 24/1 0.98196 0.99187 +/- 0.00381\n", - " 25/1 0.99825 0.99315 +/- 0.00322\n", - " 26/1 0.97554 0.99021 +/- 0.00394\n", - " 27/1 0.97223 0.98764 +/- 0.00420\n", - " 28/1 0.97858 0.98651 +/- 0.00381\n", - " 29/1 0.98642 0.98650 +/- 0.00336\n", - " 30/1 0.98457 0.98631 +/- 0.00301\n", - " 31/1 0.97973 0.98571 +/- 0.00279\n", - " 32/1 0.98574 0.98571 +/- 0.00255\n", - " 33/1 0.98439 0.98561 +/- 0.00235\n", - " 34/1 0.98536 0.98559 +/- 0.00217\n", - " 35/1 0.98966 0.98587 +/- 0.00204\n", - " 36/1 0.99909 0.98669 +/- 0.00208\n", - " 37/1 0.99175 0.98699 +/- 0.00198\n", - " 38/1 0.97021 0.98606 +/- 0.00208\n", - " 39/1 0.98010 0.98574 +/- 0.00200\n", - " 40/1 0.99120 0.98602 +/- 0.00191\n", - " 41/1 0.97703 0.98559 +/- 0.00187\n", - " 42/1 0.98874 0.98573 +/- 0.00179\n", - " 43/1 1.00000 0.98635 +/- 0.00182\n", - " 44/1 0.99398 0.98667 +/- 0.00177\n", - " 45/1 0.99704 0.98709 +/- 0.00175\n", - " 46/1 0.99588 0.98742 +/- 0.00171\n", - " 47/1 0.99577 0.98773 +/- 0.00168\n", - " 48/1 0.98891 0.98777 +/- 0.00162\n", - " 49/1 0.99467 0.98801 +/- 0.00158\n", - " 50/1 0.99553 0.98826 +/- 0.00154\n", - " Creating state point statepoint.50.h5...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 1.0659e+02 seconds\n", - " Reading cross sections = 1.5197e+01 seconds\n", - " Total time in simulation = 1.0105e+02 seconds\n", - " Time in transport only = 1.0081e+02 seconds\n", - " Time in inactive batches = 3.9431e+01 seconds\n", - " Time in active batches = 6.1616e+01 seconds\n", - " Time synchronizing fission bank = 1.2863e-01 seconds\n", - " Sampling source sites = 1.1769e-01 seconds\n", - " SEND/RECV source sites = 1.0570e-02 seconds\n", - " Time accumulating tallies = 2.3695e-05 seconds\n", - " Time writing statepoints = 1.3315e-02 seconds\n", - " Total time for finalization = 3.4400e-06 seconds\n", - " Total time elapsed = 2.0811e+02 seconds\n", - " Calculation Rate (inactive) = 15216.6 particles/second\n", - " Calculation Rate (active) = 14606.6 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 0.98752 +/- 0.00155\n", - " k-effective (Track-length) = 0.98826 +/- 0.00154\n", - " k-effective (Absorption) = 0.98970 +/- 0.00114\n", - " Combined k-effective = 0.98972 +/- 0.00125\n", - " Leakage Fraction = 0.00001 +/- 0.00000\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.2\n", - " Git SHA1 | 030f73a8690ed19e91806e46c8caf338d252e74a\n", - " Date/Time | 2023-01-17 13:50:33\n", - " MPI Processes | 1\n", - " OpenMP Threads | 56\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_reactor_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - "Using the DOUBLE-DOWN interface to Embree.\n", - "Loading file h5m/msre_control_rod_1e-2.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.01\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - " Reading Li6 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li6.h5\n", - " Reading Li7 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li7.h5\n", - " Reading Be9 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Be9.h5\n", - " Reading Zr90 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr96.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n", - " Reading Hf174 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf174.h5\n", - " Reading Hf176 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf176.h5\n", - " Reading Hf177 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf177.h5\n", - " Reading Hf178 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf178.h5\n", - " Reading Hf179 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf179.h5\n", - " Reading Hf180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf180.h5\n", - " Reading U234 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U234.h5\n", - " Reading U235 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U235.h5\n", - " Reading U236 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U236.h5\n", - " Reading U238 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U238.h5\n", - " Reading Fe54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe58.h5\n", - " Reading Cr50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr50.h5\n", - " Reading Cr52 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr52.h5\n", - " Reading Cr53 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr53.h5\n", - " Reading Cr54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr54.h5\n", - " Reading Ni58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni58.h5\n", - " Reading Ni60 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni60.h5\n", - " Reading Ni61 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni61.h5\n", - " Reading Ni62 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni62.h5\n", - " Reading Ni64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni64.h5\n", - " Reading O16 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O16.h5\n", - " Reading O17 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O17.h5\n", - " Reading O18 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O18.h5\n", - " Reading F19 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/F19.h5\n", - " Reading C12 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C12.h5\n", - " Reading Mo100 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo100.h5\n", - " Reading Mo92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo92.h5\n", - " Reading Mo94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo94.h5\n", - " Reading Mo95 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo95.h5\n", - " Reading Mo96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo96.h5\n", - " Reading Mo97 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo97.h5\n", - " Reading Mo98 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo98.h5\n", - " Reading C13 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C13.h5\n", - " Reading Al27 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Al27.h5\n", - " Reading Ti46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti46.h5\n", - " Reading Ti47 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti47.h5\n", - " Reading Ti48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti48.h5\n", - " Reading Ti49 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti49.h5\n", - " Reading Ti50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti50.h5\n", - " Reading S32 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S32.h5\n", - " Reading S33 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S33.h5\n", - " Reading S34 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S34.h5\n", - " Reading S36 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S36.h5\n", - " Reading Mn55 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mn55.h5\n", - " Reading Si28 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si28.h5\n", - " Reading Si29 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si29.h5\n", - " Reading Si30 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si30.h5\n", - " Reading Cu63 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu63.h5\n", - " Reading Cu65 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu65.h5\n", - " Reading B10 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B10.h5\n", - " Reading B11 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B11.h5\n", - " Reading W180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W180.h5\n", - " Reading W182 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W182.h5\n", - " Reading W183 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W183.h5\n", - " Reading W184 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W184.h5\n", - " Reading W186 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W186.h5\n", - " Reading P31 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/P31.h5\n", - " Reading Co59 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Co59.h5\n", - " Reading He3 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He3.h5\n", - " Reading He4 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He4.h5\n", - " Reading Ta180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta180.h5\n", - " Reading Ta181 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta181.h5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Reading Zn64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn64.h5\n", - " Reading Zn66 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn66.h5\n", - " Reading Zn67 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn67.h5\n", - " Reading Zn68 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn68.h5\n", - " Reading Zn70 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn70.h5\n", - " Reading Ag107 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag107.h5\n", - " Reading Ag109 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag109.h5\n", - " Reading Ba130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba130.h5\n", - " Reading Ba132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba132.h5\n", - " Reading Ba134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba134.h5\n", - " Reading Ba135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba135.h5\n", - " Reading Ba136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba136.h5\n", - " Reading Ba137 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba137.h5\n", - " Reading Ba138 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba138.h5\n", - " Reading Ca40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca40.h5\n", - " Reading Ca42 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca42.h5\n", - " Reading Ca43 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca43.h5\n", - " Reading Ca44 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca44.h5\n", - " Reading Ca46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca46.h5\n", - " Reading Ca48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca48.h5\n", - " Reading Cd106 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd106.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n", - " 1200K\n", - " Reading Cd108 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd108.h5\n", - " Reading Cd110 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd110.h5\n", - " Reading Cd111 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd111.h5\n", - " Reading Cd112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd112.h5\n", - " Reading Cd113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd113.h5\n", - " Reading Cd114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd114.h5\n", - " Reading Cd116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd116.h5\n", - " Reading V50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V50.h5\n", - " Reading V51 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V51.h5\n", - " Reading Sn112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn112.h5\n", - " Reading Sn114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn114.h5\n", - " Reading Sn115 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn115.h5\n", - " Reading Sn116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn116.h5\n", - " Reading Sn117 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn117.h5\n", - " Reading Sn118 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn118.h5\n", - " Reading Sn119 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn119.h5\n", - " Reading Sn120 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn120.h5\n", - " Reading Sn122 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn122.h5\n", - " Reading Sn124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn124.h5\n", - " Reading Mg24 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg24.h5\n", - " Reading Mg25 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg25.h5\n", - " Reading Mg26 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg26.h5\n", - " Reading N14 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N14.h5\n", - " Reading N15 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N15.h5\n", - " Reading Gd152 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd152.h5\n", - " Reading Gd154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd154.h5\n", - " Reading Gd155 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd155.h5\n", - " Reading Gd156 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd156.h5\n", - " Reading Gd157 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd157.h5\n", - " Reading Gd158 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd158.h5\n", - " Reading Gd160 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd160.h5\n", - " Reading H1 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H1.h5\n", - " Reading H2 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H2.h5\n", - " Reading Na23 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Na23.h5\n", - " Reading K39 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K39.h5\n", - " Reading K40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K40.h5\n", - " Reading K41 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K41.h5\n", - " Reading c_Graphite from\n", - " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/c_Graphite.h5\n", - " Minimum neutron data temperature: 250 K\n", - " Maximum neutron data temperature: 1200 K\n", - " Preparing distributed cell instances...\n", - " Reading plot XML file...\n", - " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000 eV for Li6\n", - " Initializing source particles...\n", - "\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - "\n", - " Bat./Gen. k Average k\n", - " ========= ======== ====================\n", - " 1/1 0.58567\n", - " 2/1 0.86522\n", - " 3/1 0.95393\n", - " 4/1 0.97631\n", - " 5/1 0.96262\n", - " 6/1 0.97669\n", - " 7/1 0.96797\n", - " 8/1 0.96948\n", - " 9/1 0.97837\n", - " 10/1 0.96869\n", - " 11/1 0.96878\n", - " 12/1 0.97342\n", - " 13/1 0.98926\n", - " 14/1 0.97921\n", - " 15/1 0.97947\n", - " 16/1 0.99110\n", - " 17/1 0.99039\n", - " 18/1 0.99421\n", - " 19/1 0.97364\n", - " 20/1 0.99937\n", - " 21/1 0.99256\n", - " 22/1 0.98223 0.98739 +/- 0.00516\n", - " 23/1 0.98421 0.98633 +/- 0.00316\n", - " 24/1 0.99580 0.98870 +/- 0.00326\n", - " 25/1 0.97299 0.98556 +/- 0.00403\n", - " 26/1 0.97458 0.98373 +/- 0.00376\n", - " 27/1 0.97297 0.98219 +/- 0.00353\n", - " 28/1 0.98161 0.98212 +/- 0.00306\n", - " 29/1 0.97695 0.98154 +/- 0.00276\n", - " 30/1 0.97210 0.98060 +/- 0.00264\n", - " 31/1 0.99224 0.98166 +/- 0.00261\n", - " 32/1 0.98297 0.98177 +/- 0.00239\n", - " 33/1 0.97624 0.98134 +/- 0.00224\n", - " 34/1 0.98156 0.98136 +/- 0.00207\n", - " 35/1 0.98727 0.98175 +/- 0.00197\n", - " 36/1 0.98541 0.98198 +/- 0.00186\n", - " 37/1 0.98024 0.98188 +/- 0.00175\n", - " 38/1 0.97897 0.98172 +/- 0.00165\n", - " 39/1 0.98925 0.98211 +/- 0.00161\n", - " 40/1 0.98232 0.98212 +/- 0.00153\n", - " 41/1 0.97919 0.98198 +/- 0.00146\n", - " 42/1 0.99294 0.98248 +/- 0.00148\n", - " 43/1 0.98525 0.98260 +/- 0.00142\n", - " 44/1 0.97696 0.98237 +/- 0.00138\n", - " 45/1 0.99247 0.98277 +/- 0.00138\n", - " 46/1 0.97144 0.98234 +/- 0.00140\n", - " 47/1 0.97344 0.98201 +/- 0.00139\n", - " 48/1 0.99184 0.98236 +/- 0.00138\n", - " 49/1 0.98350 0.98240 +/- 0.00133\n", - " 50/1 0.97664 0.98220 +/- 0.00130\n", - " Creating state point statepoint.50.h5...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 1.0648e+02 seconds\n", - " Reading cross sections = 1.4451e+01 seconds\n", - " Total time in simulation = 1.0340e+02 seconds\n", - " Time in transport only = 1.0315e+02 seconds\n", - " Time in inactive batches = 4.1904e+01 seconds\n", - " Time in active batches = 6.1495e+01 seconds\n", - " Time synchronizing fission bank = 1.3368e-01 seconds\n", - " Sampling source sites = 1.2047e-01 seconds\n", - " SEND/RECV source sites = 1.2851e-02 seconds\n", - " Time accumulating tallies = 2.2970e-05 seconds\n", - " Time writing statepoints = 1.2931e-02 seconds\n", - " Total time for finalization = 4.8340e-06 seconds\n", - " Total time elapsed = 2.1040e+02 seconds\n", - " Calculation Rate (inactive) = 14318.3 particles/second\n", - " Calculation Rate (active) = 14635.4 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 0.98212 +/- 0.00133\n", - " k-effective (Track-length) = 0.98220 +/- 0.00130\n", - " k-effective (Absorption) = 0.98054 +/- 0.00135\n", - " Combined k-effective = 0.98143 +/- 0.00120\n", - " Leakage Fraction = 0.00001 +/- 0.00000\n", - "\n" - ] - } - ], - "source": [ - "# Define temperature to simulate in Celsius\n", - "temperatures = [598.9,648.9,698.9]\n", - "keff = []\n", - "\n", - "for temp in temperatures:\n", - " model = openmc.model.Model(geometry, build_materials(temp+273.15), settings)\n", - " res = model.run()\n", - " with openmc.StatePoint(res) as sp:\n", - " keff.append((sp.keff.n, sp.keff.s))" - ] - }, - { - "cell_type": "markdown", - "id": "f3b687f4", - "metadata": {}, - "source": [ - "We will now fit the data with a linear function and approximate the slope of the curve to the average isothermal reactivity coefficient. To do that we will use the `scipy` library functionalities:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "658554f6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$\\\\rho\\\\,\\\\pm\\\\sigma$')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Calculate \n", - "import numpy as np\n", - "from scipy.optimize import curve_fit\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Define linear function\n", - "def func (x,a,b):\n", - " return a*x +b\n", - "\n", - "x = np.array(temperatures)\n", - "# Convert keff into reactivity\n", - "y = np.array([(x[0]-1)/x[0] for x in keff])\n", - "yerr = np.array([x[1] for x in keff])\n", - "\n", - "#Fit parameters of the function to the data\n", - "popt, pcov = curve_fit(func, x, y)\n", - "\n", - "# Calculate average isothermal coefficient in pcm/C \n", - "alpha_iso = popt[0]*1e5\n", - "\n", - "plt.figure()\n", - "plt.errorbar(x, y, yerr=yerr, label='Simulation data')\n", - "plt.plot(x, func(x, *popt), '--', label='{:.3f} [pcm/C]'.format(alpha_iso))\n", - "plt.legend()\n", - "plt.xlabel('Temperature, C')\n", - "plt.ylabel(r'$\\rho\\,\\pm\\sigma$')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "da6d7a55", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/step_files/.gitattributes b/step_files/.gitattributes deleted file mode 100644 index eebab4b..0000000 --- a/step_files/.gitattributes +++ /dev/null @@ -1 +0,0 @@ -*.step filter=lfs diff=lfs merge=lfs -text diff --git a/step_files/msre_control_rod.step b/step_files/msre_control_rod.step deleted file mode 100644 index b30f172..0000000 --- a/step_files/msre_control_rod.step +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:50fc634b0c64e76a5ba5b082db932700a85946e2ebf3d734d885535928619fdf -size 24940308 diff --git a/step_files/msre_reactor.step b/step_files/msre_reactor.step deleted file mode 100644 index 1e55649..0000000 --- a/step_files/msre_reactor.step +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1928af9b205bc3e3b32bc9a518ea7201ade5403511b1d7c630a72bdef4eac542 -size 75859745 From aa6630546f2d93eb45fe7eb8929dd16805a10ce4 Mon Sep 17 00:00:00 2001 From: church89 Date: Fri, 19 Apr 2024 14:16:41 +0200 Subject: [PATCH 25/32] typo --- openmc_notebooks/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmc_notebooks/README.md b/openmc_notebooks/README.md index d298c88..c17780c 100644 --- a/openmc_notebooks/README.md +++ b/openmc_notebooks/README.md @@ -1,7 +1,7 @@ # OpenMC MSRE notebooks [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) - + `OpenMC` simulation examples of the Molten Salt Reactor Experiment (MSRE), operated at ORNL in the 1960s. All scripts are set up using the [h5m meshed files](https://github.com/openmsr/msre/tree/master/h5m) obtained with the open source meshing tool [CAD-to-OpenMC](https://github.com/openmsr/CAD_to_OpenMC) from a CAD version of the the MSRE, designed with the CAE tool `OnShape` and available for export here: [onshape msre model](https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/). From 29468c39fce4dadd20fee1de5a5f5f93ee8e5f33 Mon Sep 17 00:00:00 2001 From: church89 Date: Fri, 19 Apr 2024 14:18:22 +0200 Subject: [PATCH 26/32] typo --- openmc_notebooks/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmc_notebooks/README.md b/openmc_notebooks/README.md index c17780c..1378e78 100644 --- a/openmc_notebooks/README.md +++ b/openmc_notebooks/README.md @@ -1,7 +1,7 @@ # OpenMC MSRE notebooks [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) - + `OpenMC` simulation examples of the Molten Salt Reactor Experiment (MSRE), operated at ORNL in the 1960s. All scripts are set up using the [h5m meshed files](https://github.com/openmsr/msre/tree/master/h5m) obtained with the open source meshing tool [CAD-to-OpenMC](https://github.com/openmsr/CAD_to_OpenMC) from a CAD version of the the MSRE, designed with the CAE tool `OnShape` and available for export here: [onshape msre model](https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/). From 1d5bb6b1384608a2f0b6441e5b81cf803505ee0e Mon Sep 17 00:00:00 2001 From: church89 Date: Sat, 20 Apr 2024 20:20:18 +0200 Subject: [PATCH 27/32] add transfer rates example --- .../msre_depletion_transfer_rates.ipynb | 2453 +++++++++++++++++ 1 file changed, 2453 insertions(+) create mode 100644 openmc_notebooks/msre_depletion_transfer_rates.ipynb diff --git a/openmc_notebooks/msre_depletion_transfer_rates.ipynb b/openmc_notebooks/msre_depletion_transfer_rates.ipynb new file mode 100644 index 0000000..3834f53 --- /dev/null +++ b/openmc_notebooks/msre_depletion_transfer_rates.ipynb @@ -0,0 +1,2453 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fafca9f7", + "metadata": {}, + "source": [ + "# MSRE U235 Power Run\n", + "In the 1966 the MSRE started power operations with U235 as main fissile material. \n", + "In this notebook we will run a fully coupled transport-depletion simulation using a CAD design version developed by [Copenhagen Atomics](https://www.copenhaagenatomics.com) using the CAE tool OnShape and made available for export here: [onshape msre model]((https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/)).\n", + "\n", + "To effectively run OpenMC, the MSRE CAD geometry is meshed and converted into an h5m format readable by OpenMC, using the open-source meshing tool [CAD-to-OpenMC](https://github.com/openmsr/CAD_to_OpenMC). \n", + "\n", + "Furthermore, to model fission products removal we will use OpenMC [Transfer rates theory](https://docs.openmc.org/en/latest/methods/depletion.html#transfer-rates) capability available through the depletion solver and integrated into the main code from version 0.14.0. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "079fc782", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import openmc\n", + "import openmc.deplete\n", + "import numpy as np\n", + "from math import log10\n", + "import matplotlib.pyplot as plt\n", + "import os \n", + "\n", + "os.system('rm *.xml *.h5')" + ] + }, + { + "cell_type": "markdown", + "id": "bfca4d07", + "metadata": {}, + "source": [ + "For materials nuclear properties we will be using the same definitions used in the first criticality test derived from the [MSRE benchmark evaluation project](https://www.osti.gov/servlets/purl/1617123) and available in the International Reactor Physics Experiment Evaulation Project (IRPhEP) handbook. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4bc6b094", + "metadata": {}, + "outputs": [], + "source": [ + "# Define materials \n", + "salt_temp = 638.3 # in C\n", + "salt_density = 2.32556 # g/cm3\n", + "salt = openmc.Material(name=\"salt\", temperature = salt_temp + 273.15)\n", + "salt.add_nuclide('Li6', 0.0000131541127279649)\n", + "salt.add_nuclide('Li7', 0.263082254559299)\n", + "salt.add_nuclide('Be9', 0.118688703357854)\n", + "salt.add_nuclide('Zr90', 0.0105474637491834)\n", + "salt.add_nuclide('Zr91', 0.00230014661352455)\n", + "salt.add_nuclide('Zr92', 0.00351582124972781)\n", + "salt.add_nuclide('Zr94', 0.00356297220526352)\n", + "salt.add_nuclide('Zr96', 0.000574011632608622)\n", + "salt.add_nuclide('Hf174', 0.000000000838247756705461)\n", + "salt.add_nuclide('Hf176', 0.000000027557395001692)\n", + "salt.add_nuclide('Hf177', 0.0000000974463017170099)\n", + "salt.add_nuclide('Hf178', 0.000000142921242518281)\n", + "salt.add_nuclide('Hf179', 0.0000000713558402895524)\n", + "salt.add_nuclide('Hf180', 0.000000183785820657672)\n", + "salt.add_nuclide('U234', 0.000010347565467384)\n", + "salt.add_nuclide('U235', 0.00101016470221956)\n", + "salt.add_nuclide('U236', 0.00000422977321829143)\n", + "salt.add_nuclide('U238', 0.00216927473057667)\n", + "salt.add_nuclide('Fe54', 0.00000285638012142825)\n", + "salt.add_nuclide('Fe56', 0.0000448390593090724)\n", + "salt.add_nuclide('Fe57', 0.00000103552942297801)\n", + "salt.add_nuclide('Fe58', 0.000000137809956243416)\n", + "salt.add_nuclide('Cr50', 0.00000212334843928242)\n", + "salt.add_nuclide('Cr52', 0.000040946661076878)\n", + "salt.add_nuclide('Cr53', 0.00000464302267471169)\n", + "salt.add_nuclide('Cr54', 0.00000115574661884993)\n", + "salt.add_nuclide('Ni58', 0.00000586242358522768)\n", + "salt.add_nuclide('Ni60', 0.00000225819506936691)\n", + "salt.add_nuclide('Ni61', 0.0000000981621760803009)\n", + "salt.add_nuclide('Ni62', 0.00000031298397136929)\n", + "salt.add_nuclide('Ni64', 0.0000000797077903148755)\n", + "salt.add_nuclide('O16', 0.0000514608160260434)\n", + "salt.add_nuclide('O17', 0.0000000189357310970321)\n", + "salt.add_nuclide('O18', 0.0000000964845153990466)\n", + "salt.add_nuclide('F19', 0.594363006576997)\n", + "salt.set_density('g/cm3',salt_density)\n", + "\n", + "#moderator blocks170\n", + "graphite = openmc.Material(name='graphite',temperature= salt_temp + 273.15)\n", + "graphite.set_density('g/cm3',1.8492)\n", + "graphite.add_element('C',0.999992212250888) #endfb71 does not have C12 cross sections\n", + "graphite.add_nuclide('B10', 1.76873036539477E-07)\n", + "graphite.add_nuclide('B11', 7.1193229306592E-07)\n", + "graphite.add_nuclide('V51', 2.12200224802724E-06)\n", + "graphite.add_nuclide('S32', 1.77900760271203E-06)\n", + "graphite.add_nuclide('S33', 1.40462754188233E-08)\n", + "graphite.add_nuclide('S34', 7.95955607066653E-08)\n", + "graphite.add_nuclide('S36', 1.87283672250977E-10)\n", + "graphite.add_nuclide('O16', 1.85674385782835E-06)\n", + "graphite.add_nuclide('O17', 6.81675170610618E-10)\n", + "graphite.add_nuclide('Si28', 5.31911333979174E-07)\n", + "graphite.add_nuclide('Si29', 2.70215087309286E-08)\n", + "graphite.add_nuclide('Si30', 1.78336189959512E-08)\n", + "graphite.add_nuclide('Al27', 4.0117589478321E-07)\n", + "graphite.add_nuclide('Fe54', 2.31047953307142E-09)\n", + "graphite.add_nuclide('Fe56', 3.62695875239409E-08)\n", + "graphite.add_nuclide('Fe57', 8.37622947917593E-10)\n", + "graphite.add_nuclide('Fe58', 1.11472237523719E-10)\n", + "graphite.add_nuclide('Ti46', 1.12809907219147E-09)\n", + "graphite.add_nuclide('Ti47', 1.01734025419449E-09)\n", + "graphite.add_nuclide('Ti48', 1.008041983054E-08)\n", + "graphite.add_nuclide('Ti49', 7.39759512794646E-10)\n", + "graphite.add_nuclide('Ti50', 7.08309478054763E-10)\n", + "graphite.add_nuclide('Mg24', 8.53578076978748E-09)\n", + "graphite.add_nuclide('Mg25', 1.0806153652092E-09)\n", + "graphite.add_nuclide('Mg26', 1.18975751709533E-09)\n", + "graphite.add_nuclide('Ca40', 4.58305905846326E-09)\n", + "graphite.add_nuclide('Ca42', 3.05880815220158E-11)\n", + "graphite.add_nuclide('Ca43', 6.38236631448551E-12)\n", + "graphite.add_nuclide('Ca44', 9.86193787556798E-11)\n", + "graphite.add_nuclide('Ca48', 8.8407592652503E-12)\n", + "graphite.add_s_alpha_beta('c_Graphite')\n", + "\n", + "#inor-8\n", + "inor = openmc.Material(name='inor-8',temperature= salt_temp + 273.15)\n", + "inor.set_density('g/cm3',8.7745)\n", + "inor.add_element('Ni',(66+71)/2,'wo')\n", + "inor.add_element('Mo',(15+18)/2,'wo')\n", + "inor.add_element('Cr',(6+8)/2,'wo')\n", + "inor.add_element('Fe',5,'wo')\n", + "inor.add_element('C',(0.04+0.08)/2,'wo')\n", + "inor.add_element('Al',0.25,'wo')\n", + "inor.add_element('Ti',0.25,'wo')\n", + "inor.add_element('S',0.02,'wo')\n", + "inor.add_element('Mn',1.0,'wo')\n", + "inor.add_element('Si',1.0,'wo')\n", + "inor.add_element('Cu',0.35,'wo')\n", + "inor.add_element('B',0.010,'wo')\n", + "inor.add_element('W',0.5,'wo')\n", + "inor.add_element('P',0.015,'wo')\n", + "inor.add_element('Co',0.2,'wo')\n", + "\n", + "#helium\n", + "helium = openmc.Material(name='helium')\n", + "helium.add_element('He',1.0)\n", + "helium.set_density('g/cm3',1.03*(10**-4))\n", + "\n", + "#Control rods inconel clad\n", + "trace = 0.01\n", + "inconel = openmc.Material(name='inconel-600', temperature = 65.6 + 273.15)\n", + "inconel.add_element('Ni',78.5,percent_type='wo')\n", + "inconel.add_element('Cr',14.0,percent_type='wo')\n", + "inconel.add_element('Fe',6.5,percent_type='wo')\n", + "inconel.add_element('Mn',0.25,percent_type='wo')\n", + "inconel.add_element('Si',0.25,percent_type='wo')\n", + "inconel.add_element('Cu',0.2,percent_type='wo')\n", + "inconel.add_element('Co',0.2,percent_type='wo')\n", + "inconel.add_element('Al',0.2,percent_type='wo')\n", + "inconel.add_element('Ti',0.2,percent_type='wo')\n", + "inconel.add_element('Ta',0.5,percent_type='wo')\n", + "inconel.add_element('W',0.5,percent_type='wo')\n", + "inconel.add_element('Zn',0.2,percent_type='wo')\n", + "inconel.add_element('Zr',0.1,percent_type='wo')\n", + "inconel.add_element('C',trace,percent_type='wo')\n", + "inconel.add_element('Mo',trace,percent_type='wo')\n", + "inconel.add_element('Ag',trace,percent_type='wo')\n", + "inconel.add_element('B',trace,percent_type='wo')\n", + "inconel.add_element('Ba',trace,percent_type='wo')\n", + "inconel.add_element('Be',trace,percent_type='wo')\n", + "inconel.add_element('Ca',trace,percent_type='wo')\n", + "inconel.add_element('Cd',trace,percent_type='wo')\n", + "inconel.add_element('V',trace,percent_type='wo')\n", + "inconel.add_element('Sn',trace,percent_type='wo')\n", + "inconel.add_element('Mg',trace,percent_type='wo')\n", + "inconel.set_density('g/cm3',8.5)\n", + "\n", + "#Control rods bushing posion material\n", + "Gd2O3 = openmc.Material()\n", + "Gd2O3.add_element('Gd',2)\n", + "Gd2O3.add_element('O',3)\n", + "Gd2O3.set_density('g/cm3',7.41)\n", + "Al2O3 = openmc.Material()\n", + "Al2O3.add_element('Al',2)\n", + "Al2O3.add_element('O',3)\n", + "Al2O3.set_density('g/cm3',3.95)\n", + "bush = openmc.Material.mix_materials([Gd2O3,Al2O3],[0.7,0.3],'wo')\n", + "bush.name='gd2o3-al2o3'\n", + "bush.temperature = 65.6 +273.15\n", + "\n", + "#Concrete block\n", + "concrete = openmc.Material(name='concrete')\n", + "concrete.add_element('H',0.005,'wo')\n", + "concrete.add_element('O',0.496,'wo')\n", + "concrete.add_element('Si',0.314,'wo')\n", + "concrete.add_element('Ca',0.083,'wo')\n", + "concrete.add_element('Na',0.017,'wo')\n", + "concrete.add_element('Mn',0.002,'wo')\n", + "concrete.add_element('Al',0.046,'wo')\n", + "concrete.add_element('S',0.001,'wo')\n", + "concrete.add_element('K',0.019,'wo')\n", + "concrete.add_element('Fe',0.012,'wo')\n", + "concrete.set_density('g/cm3',2.35)\n", + "\n", + "#Thermal shielding as water and SS305 (50-50)\n", + "water = openmc.Material()\n", + "water.add_element('H',2)\n", + "water.add_element('O',1)\n", + "water.set_density('g/cm3',0.997)\n", + "\n", + "#stainless steel 304\n", + "ss304 = openmc.Material()\n", + "ss304.add_element('C',0.08,'wo')\n", + "ss304.add_element('Mn',2,'wo')\n", + "ss304.add_element('P',0.045,'wo')\n", + "ss304.add_element('S',0.03,'wo')\n", + "ss304.add_element('Si',0.75,'wo')\n", + "ss304.add_element('Cr',19,'wo')\n", + "ss304.add_element('Ni',10,'wo')\n", + "ss304.add_element('N',0.1,'wo')\n", + "ss304.add_element('Fe',67.995, 'wo')\n", + "ss304.set_density('g/cm3',7.93)\n", + "shield = openmc.Material.mix_materials([water,ss304],[0.5,0.5],'vo')\n", + "shield.temperature = 32.2 + 273.15\n", + "shield.name='steelwater'\n", + "\n", + "# \"Careytemp 1600\" by Philip Carey Manufacturing Compamy (Cincinnati)\n", + "# http://moltensalt.org/references/static/downloads/pdf/ORNL-TM-0728.pdf\n", + "insulation=openmc.Material(name='insulation')\n", + "insulation.add_element('Si',1)\n", + "insulation.add_element('O',2)\n", + "insulation.set_density('g/cm3',0.16) #https://www.osti.gov/servlets/purl/1411211\n", + "\n", + "# sand water, not sure about this material\n", + "sandwater=openmc.Material(name='watersand')\n", + "sandwater.add_element('Fe',3)\n", + "sandwater.add_element('O',4)\n", + "sandwater.set_density('g/cm3',6)\n", + "\n", + "#Vessel anular steel\n", + "steel = openmc.Material(name='steel')\n", + "steel.add_element('Fe',1)\n", + "steel.set_density('g/cm3',7.85)\n", + "\n", + "mats = openmc.Materials([salt, graphite, inor, helium, inconel, shield, concrete,\n", + " steel, sandwater, insulation, bush])" + ] + }, + { + "cell_type": "markdown", + "id": "7a0355f1-a513-4fc5-8812-7dffb5138c11", + "metadata": {}, + "source": [ + "The MSRE geometry was produced with the CAE tool OnShape ([onshape cad model](https://cad.onshape.com/documents/4f04f63bfd4138a61a54b3f8/v/b8c29a0cedda86dfc6948111/)) as step files and converted into OpenMC readable h5m files using the open source mesh tool [CAD_to_OpenMC](https://pypi.org/project/CAD-to-OpenMC/). " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "edfa5bd1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10000.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10001.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10002.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10003.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10004.\n", + " warn(msg, IDWarning)\n", + "/home/lorenzo/openmc/openmc/openmc/mixin.py:70: IDWarning: Another Surface instance already exists with id=10005.\n", + " warn(msg, IDWarning)\n" + ] + } + ], + "source": [ + "# CAD h5m files\n", + "core_h5m = 'msre_full.h5m'\n", + "control_rod_h5m = 'msre_control_rod.h5m'\n", + "\n", + "#Geometry\n", + "core = openmc.DAGMCUniverse(filename = core_h5m, auto_geom_ids = True,\n", + " universe_id = 1)\n", + "control_rod = openmc.DAGMCUniverse(filename = control_rod_h5m,\n", + " auto_geom_ids = True, universe_id=2)\n", + "core_region = core.bounding_region()\n", + "cr_region = control_rod.bounding_region(boundary_type = 'transmission')\n", + "\n", + "_offset_xy = 10.163255 #cm, _offset_xy between cr1, cr2 and cr3 (from Onshape model)\n", + "_lower_rod = 61.728 # distance to lower limit (from OnShape model)\n", + "_upper_rod = 51 * 2.54 # escursion to upper rod limit (from ORNL)\n", + "start_rod = 56 * 2.54 # rod iniitial position (user input)\n", + "\n", + "# Create control rod regions w\n", + "cr1_region = cr_region.translate([_offset_xy/2, _offset_xy/2, _lower_rod + _upper_rod])\n", + "cr2_region = cr_region.translate([_offset_xy/2, -_offset_xy/2, _lower_rod + _upper_rod])\n", + "cr3_region = cr_region.translate([-_offset_xy/2, -_offset_xy/2, _lower_rod + _upper_rod])\n", + "\n", + "# Extend control rod region1 to include downloads translations\n", + "cr1_region = cr1_region | cr1_region.translate([0, 0, -_upper_rod])\n", + "\n", + "#Define cells\n", + "core_cell = openmc.Cell(region=~(cr1_region | cr2_region | cr3_region) & core_region ,\n", + " fill=core)\n", + "\n", + "cr1_cell = openmc.Cell(name='CR1', region=cr1_region, fill=control_rod)\n", + "cr2_cell = openmc.Cell(name='CR2', region=cr2_region, fill=control_rod)\n", + "cr3_cell = openmc.Cell(name='CR3', region=cr3_region, fill=control_rod)\n", + "\n", + "#translate cells to regions\n", + "setattr(cr1_cell, 'translation', [_offset_xy/2, _offset_xy/2 , _lower_rod + start_rod])\n", + "setattr(cr2_cell, 'translation', [_offset_xy/2, -_offset_xy/2, _lower_rod + start_rod])\n", + "setattr(cr3_cell, 'translation', [-_offset_xy/2, -_offset_xy/2, _lower_rod + start_rod])\n", + "geometry = openmc.Geometry([core_cell,cr1_cell,cr2_cell,cr3_cell])" + ] + }, + { + "cell_type": "markdown", + "id": "0abbe529-1d22-438a-aa58-b083f8faac87", + "metadata": {}, + "source": [ + "Let's set some generic settings " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ba99fb4b", + "metadata": {}, + "outputs": [], + "source": [ + "settings = openmc.Settings()\n", + "settings.temperature = {'method':'interpolation','range':(293.15,923.15)}\n", + "settings.batches = 50\n", + "settings.inactive = 20\n", + "settings.particles = 30000\n", + "settings.photon_transport = False\n", + "source_area = openmc.stats.Box([-100., -100., 0.],[ 100., 100., 200.],only_fissionable = True)\n", + "settings.source = openmc.IndependentSource(space=source_area)" + ] + }, + { + "cell_type": "markdown", + "id": "dd3cd25c", + "metadata": {}, + "source": [ + "# Transfer Rates\n", + "The circulation of molten salt along the main fuel circuit of the MSRE made it possible to have a continuos separation of fission products. \n", + "In particular it is reported that volatile noble gasses would bubble out in the off-gas system removal and noble metals plate out on the surface of the heat-exchangers. \n", + "\n", + "In this particular and simplified case, we can set removal rates as negative transfer rates from the main fuel salt with removal rate coefficients that are function of the time characteristic and efficiency of the removal methods for the two set of materials. \n", + "\n", + "This depletion capability has been added to OpenMC main branch from version 0.14.0.\n", + "\n", + "We will use the same removal rates as the one derived in this [ornl paper](https://info.ornl.gov/sites/publications/Files/Pub173113.pdf).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b9f8eada", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2024 MIT, UChicago Argonne LLC, and contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.14.1-dev\n", + " Git SHA1 | d93ac83697e7fdb4fd4cc0f2705795398bf804bd\n", + " Date/Time | 2024-04-19 17:18:01\n", + " MPI Processes | 1\n", + " OpenMP Threads | 56\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + "Using the DOUBLE-DOWN interface to Embree.\n", + "Loading file msre_full.h5m\n", + "Initializing the GeomQueryTool...\n", + "Using faceting tolerance: 0.01\n", + "Building acceleration data structures...\n", + "Using the DOUBLE-DOWN interface to Embree.\n", + "Loading file msre_control_rod.h5m\n", + "Initializing the GeomQueryTool...\n", + "Using faceting tolerance: 0\n", + "Building acceleration data structures...\n", + " Reading Li6 from /home/lorenzo/nuclear_data/endfb80_hdf5/Li6.h5\n", + " Reading Li7 from /home/lorenzo/nuclear_data/endfb80_hdf5/Li7.h5\n", + " Reading Be9 from /home/lorenzo/nuclear_data/endfb80_hdf5/Be9.h5\n", + " Reading O16 from /home/lorenzo/nuclear_data/endfb80_hdf5/O16.h5\n", + " Reading O17 from /home/lorenzo/nuclear_data/endfb80_hdf5/O17.h5\n", + " Reading O18 from /home/lorenzo/nuclear_data/endfb80_hdf5/O18.h5\n", + " Reading F19 from /home/lorenzo/nuclear_data/endfb80_hdf5/F19.h5\n", + " Reading Cr50 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr50.h5\n", + " Reading Cr52 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr52.h5\n", + " Reading Cr53 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr53.h5\n", + " Reading Cr54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr54.h5\n", + " Reading Fe54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe54.h5\n", + " Reading Fe56 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe56.h5\n", + " Reading Fe57 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe57.h5\n", + " Reading Fe58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe58.h5\n", + " Reading Ni58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni58.h5\n", + " Reading Ni60 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni60.h5\n", + " Reading Ni61 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni61.h5\n", + " Reading Ni62 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni62.h5\n", + " Reading Ni64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni64.h5\n", + " Reading Zr90 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr90.h5\n", + " Reading Zr91 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr91.h5\n", + " Reading Zr92 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr92.h5\n", + " Reading Zr94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr94.h5\n", + " Reading Zr96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr96.h5\n", + " Reading Hf174 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf174.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Hf176 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf176.h5\n", + " Reading Hf177 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf177.h5\n", + " Reading Hf178 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf178.h5\n", + " Reading Hf179 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf179.h5\n", + " Reading Hf180 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf180.h5\n", + " Reading U234 from /home/lorenzo/nuclear_data/endfb80_hdf5/U234.h5\n", + " Reading U235 from /home/lorenzo/nuclear_data/endfb80_hdf5/U235.h5\n", + " Reading U236 from /home/lorenzo/nuclear_data/endfb80_hdf5/U236.h5\n", + " Reading U238 from /home/lorenzo/nuclear_data/endfb80_hdf5/U238.h5\n", + " Reading B10 from /home/lorenzo/nuclear_data/endfb80_hdf5/B10.h5\n", + " Reading B11 from /home/lorenzo/nuclear_data/endfb80_hdf5/B11.h5\n", + " Reading C12 from /home/lorenzo/nuclear_data/endfb80_hdf5/C12.h5\n", + " Reading C13 from /home/lorenzo/nuclear_data/endfb80_hdf5/C13.h5\n", + " Reading Mg24 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg24.h5\n", + " Reading Mg25 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg25.h5\n", + " Reading Mg26 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mg26.h5\n", + " Reading Al27 from /home/lorenzo/nuclear_data/endfb80_hdf5/Al27.h5\n", + " Reading Si28 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si28.h5\n", + " Reading Si29 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si29.h5\n", + " Reading Si30 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si30.h5\n", + " Reading S32 from /home/lorenzo/nuclear_data/endfb80_hdf5/S32.h5\n", + " Reading S33 from /home/lorenzo/nuclear_data/endfb80_hdf5/S33.h5\n", + " Reading S34 from /home/lorenzo/nuclear_data/endfb80_hdf5/S34.h5\n", + " Reading S36 from /home/lorenzo/nuclear_data/endfb80_hdf5/S36.h5\n", + " Reading Ca40 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca40.h5\n", + " Reading Ca42 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca42.h5\n", + " Reading Ca43 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca43.h5\n", + " Reading Ca44 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca44.h5\n", + " Reading Ca48 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca48.h5\n", + " Reading Ti46 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti46.h5\n", + " Reading Ti47 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti47.h5\n", + " Reading Ti48 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti48.h5\n", + " Reading Ti49 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti49.h5\n", + " Reading Ti50 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ti50.h5\n", + " Reading V51 from /home/lorenzo/nuclear_data/endfb80_hdf5/V51.h5\n", + " Reading P31 from /home/lorenzo/nuclear_data/endfb80_hdf5/P31.h5\n", + " Reading Mn55 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mn55.h5\n", + " Reading Co59 from /home/lorenzo/nuclear_data/endfb80_hdf5/Co59.h5\n", + " Reading Cu63 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu63.h5\n", + " Reading Cu65 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu65.h5\n", + " Reading Mo92 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo92.h5\n", + " Reading Mo94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo94.h5\n", + " Reading Mo95 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo95.h5\n", + " Reading Mo96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo96.h5\n", + " Reading Mo97 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo97.h5\n", + " Reading Mo98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo98.h5\n", + " Reading Mo100 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo100.h5\n", + " Reading W180 from /home/lorenzo/nuclear_data/endfb80_hdf5/W180.h5\n", + " Reading W182 from /home/lorenzo/nuclear_data/endfb80_hdf5/W182.h5\n", + " Reading W183 from /home/lorenzo/nuclear_data/endfb80_hdf5/W183.h5\n", + " Reading W184 from /home/lorenzo/nuclear_data/endfb80_hdf5/W184.h5\n", + " Reading W186 from /home/lorenzo/nuclear_data/endfb80_hdf5/W186.h5\n", + " Reading He3 from /home/lorenzo/nuclear_data/endfb80_hdf5/He3.h5\n", + " Reading He4 from /home/lorenzo/nuclear_data/endfb80_hdf5/He4.h5\n", + " Reading Ca46 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca46.h5\n", + " Reading V50 from /home/lorenzo/nuclear_data/endfb80_hdf5/V50.h5\n", + " Reading Zn64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn64.h5\n", + " Reading Zn66 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn66.h5\n", + " Reading Zn67 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn67.h5\n", + " Reading Zn68 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn68.h5\n", + " Reading Zn70 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn70.h5\n", + " Reading Ag107 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag107.h5\n", + " Reading Ag109 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag109.h5\n", + " Reading Cd106 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd106.h5\n", + " Reading Cd108 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd108.h5\n", + " Reading Cd110 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd110.h5\n", + " Reading Cd111 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd111.h5\n", + " Reading Cd112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd112.h5\n", + " Reading Cd113 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd113.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n", + " 1200K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Cd114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd114.h5\n", + " Reading Cd116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd116.h5\n", + " Reading Sn112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn112.h5\n", + " Reading Sn114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn114.h5\n", + " Reading Sn115 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn115.h5\n", + " Reading Sn116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn116.h5\n", + " Reading Sn117 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn117.h5\n", + " Reading Sn118 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn118.h5\n", + " Reading Sn119 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn119.h5\n", + " Reading Sn120 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn120.h5\n", + " Reading Sn122 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn122.h5\n", + " Reading Sn124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn124.h5\n", + " Reading Ba130 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba130.h5\n", + " Reading Ba132 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba132.h5\n", + " Reading Ba134 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba134.h5\n", + " Reading Ba135 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba135.h5\n", + " Reading Ba136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba136.h5\n", + " Reading Ba137 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba137.h5\n", + " Reading Ba138 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba138.h5\n", + " Reading Ta180 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta180.h5\n", + " Reading Ta181 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta181.h5\n", + " Reading Gd152 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd152.h5\n", + " Reading Gd154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd154.h5\n", + " Reading Gd155 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd155.h5\n", + " Reading Gd156 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd156.h5\n", + " Reading Gd157 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd157.h5\n", + " Reading Gd158 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd158.h5\n", + " Reading Gd160 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd160.h5\n", + " Reading H1 from /home/lorenzo/nuclear_data/endfb80_hdf5/H1.h5\n", + " Reading H2 from /home/lorenzo/nuclear_data/endfb80_hdf5/H2.h5\n", + " Reading Na23 from /home/lorenzo/nuclear_data/endfb80_hdf5/Na23.h5\n", + " Reading K39 from /home/lorenzo/nuclear_data/endfb80_hdf5/K39.h5\n", + " Reading K40 from /home/lorenzo/nuclear_data/endfb80_hdf5/K40.h5\n", + " Reading K41 from /home/lorenzo/nuclear_data/endfb80_hdf5/K41.h5\n", + " Reading N14 from /home/lorenzo/nuclear_data/endfb80_hdf5/N14.h5\n", + " Reading N15 from /home/lorenzo/nuclear_data/endfb80_hdf5/N15.h5\n", + " Reading c_Graphite from /home/lorenzo/nuclear_data/endfb80_hdf5/c_Graphite.h5\n", + " Minimum neutron data temperature: 250 K\n", + " Maximum neutron data temperature: 2500 K\n", + " Preparing distributed cell instances...\n", + " Reading plot XML file...\n", + " Writing summary.h5 file...\n", + "[openmc.deplete] t=0.0 s, dt=432000 s, source=8000000.0\n", + " Reading H3 from /home/lorenzo/nuclear_data/endfb80_hdf5/H3.h5\n", + " Reading Be7 from /home/lorenzo/nuclear_data/endfb80_hdf5/Be7.h5\n", + " Reading Ne20 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ne20.h5\n", + " Reading Ne21 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ne21.h5\n", + " Reading Ne22 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ne22.h5\n", + " Reading Na22 from /home/lorenzo/nuclear_data/endfb80_hdf5/Na22.h5\n", + " Reading Al26_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Al26_m1.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Na22 at 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Si31 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si31.h5\n", + " Reading Si32 from /home/lorenzo/nuclear_data/endfb80_hdf5/Si32.h5\n", + " Reading S35 from /home/lorenzo/nuclear_data/endfb80_hdf5/S35.h5\n", + " Reading Cl35 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cl35.h5\n", + " Reading Cl36 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cl36.h5\n", + " Reading Cl37 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cl37.h5\n", + " Reading Ar36 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar36.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Ar37 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar37.h5\n", + " Reading Ar38 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar38.h5\n", + " Reading Ar39 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar39.h5\n", + " Reading Ar40 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar40.h5\n", + " Reading Ar41 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ar41.h5\n", + " Reading Ca41 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca41.h5\n", + " Reading Ca45 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca45.h5\n", + " Reading Ca47 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ca47.h5\n", + " Reading Sc45 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sc45.h5\n", + " Reading V49 from /home/lorenzo/nuclear_data/endfb80_hdf5/V49.h5\n", + " Reading Cr51 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cr51.h5\n", + " Reading Mn54 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mn54.h5\n", + " Reading Fe55 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fe55.h5\n", + " Reading Co58 from /home/lorenzo/nuclear_data/endfb80_hdf5/Co58.h5\n", + " Reading Co58_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Co58_m1.h5\n", + " Reading Ni59 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni59.h5\n", + " Reading Ni63 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ni63.h5\n", + " Reading Cu64 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cu64.h5\n", + " Reading Zn65 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn65.h5\n", + " Reading Zn69 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zn69.h5\n", + " Reading Ga69 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ga69.h5\n", + " Reading Ga70 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ga70.h5\n", + " Reading Ga71 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ga71.h5\n", + " Reading Ge70 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge70.h5\n", + " Reading Ge71 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge71.h5\n", + " Reading Ge72 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge72.h5\n", + " Reading Ge73 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge73.h5\n", + " Reading Ge74 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge74.h5\n", + " Reading Ge75 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge75.h5\n", + " Reading Ge76 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ge76.h5\n", + " Reading As73 from /home/lorenzo/nuclear_data/endfb80_hdf5/As73.h5\n", + " Reading As74 from /home/lorenzo/nuclear_data/endfb80_hdf5/As74.h5\n", + " Reading As75 from /home/lorenzo/nuclear_data/endfb80_hdf5/As75.h5\n", + " Reading Se74 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se74.h5\n", + " Reading Se75 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se75.h5\n", + " Reading Se76 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se76.h5\n", + " Reading Se77 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se77.h5\n", + " Reading Se78 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se78.h5\n", + " Reading Se79 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se79.h5\n", + " Reading Se80 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se80.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Se79 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Se79 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Se79 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Se79 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Se79 at 1200K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Se81 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se81.h5\n", + " Reading Se82 from /home/lorenzo/nuclear_data/endfb80_hdf5/Se82.h5\n", + " Reading Br79 from /home/lorenzo/nuclear_data/endfb80_hdf5/Br79.h5\n", + " Reading Br80 from /home/lorenzo/nuclear_data/endfb80_hdf5/Br80.h5\n", + " Reading Br81 from /home/lorenzo/nuclear_data/endfb80_hdf5/Br81.h5\n", + " Reading Kr78 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr78.h5\n", + " Reading Kr79 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr79.h5\n", + " Reading Kr80 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr80.h5\n", + " Reading Kr81 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr81.h5\n", + " Reading Kr82 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr82.h5\n", + " Reading Kr83 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr83.h5\n", + " Reading Kr84 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr84.h5\n", + " Reading Kr85 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr85.h5\n", + " Reading Kr86 from /home/lorenzo/nuclear_data/endfb80_hdf5/Kr86.h5\n", + " Reading Rb85 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rb85.h5\n", + " Reading Rb86 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rb86.h5\n", + " Reading Rb87 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rb87.h5\n", + " Reading Sr84 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr84.h5\n", + " Reading Sr85 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr85.h5\n", + " Reading Sr86 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr86.h5\n", + " Reading Sr87 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr87.h5\n", + " Reading Sr88 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr88.h5\n", + " Reading Sr89 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr89.h5\n", + " Reading Sr90 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sr90.h5\n", + " Reading Y89 from /home/lorenzo/nuclear_data/endfb80_hdf5/Y89.h5\n", + " Reading Y90 from /home/lorenzo/nuclear_data/endfb80_hdf5/Y90.h5\n", + " Reading Y91 from /home/lorenzo/nuclear_data/endfb80_hdf5/Y91.h5\n", + " Reading Zr93 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr93.h5\n", + " Reading Zr95 from /home/lorenzo/nuclear_data/endfb80_hdf5/Zr95.h5\n", + " Reading Nb93 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nb93.h5\n", + " Reading Nb94 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nb94.h5\n", + " Reading Nb95 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nb95.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 2500K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Mo93 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo93.h5\n", + " Reading Mo99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo99.h5\n", + " Reading Tc98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tc98.h5\n", + " Reading Tc99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tc99.h5\n", + " Reading Ru96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru96.h5\n", + " Reading Ru97 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru97.h5\n", + " Reading Ru98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru98.h5\n", + " Reading Ru99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru99.h5\n", + " Reading Ru100 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru100.h5\n", + " Reading Ru101 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru101.h5\n", + " Reading Ru102 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru102.h5\n", + " Reading Ru103 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru103.h5\n", + " Reading Ru104 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru104.h5\n", + " Reading Ru105 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru105.h5\n", + " Reading Ru106 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru106.h5\n", + " Reading Rh103 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rh103.h5\n", + " Reading Rh104 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rh104.h5\n", + " Reading Rh105 from /home/lorenzo/nuclear_data/endfb80_hdf5/Rh105.h5\n", + " Reading Pd102 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd102.h5\n", + " Reading Pd103 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd103.h5\n", + " Reading Pd104 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd104.h5\n", + " Reading Pd105 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd105.h5\n", + " Reading Pd106 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd106.h5\n", + " Reading Pd107 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd107.h5\n", + " Reading Pd108 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd108.h5\n", + " Reading Pd109 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd109.h5\n", + " Reading Pd110 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pd110.h5\n", + " Reading Ag108 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag108.h5\n", + " Reading Ag110_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag110_m1.h5\n", + " Reading Ag111 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag111.h5\n", + " Reading Ag112 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag112.h5\n", + " Reading Ag113 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag113.h5\n", + " Reading Ag114 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag114.h5\n", + " Reading Ag115 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag115.h5\n", + " Reading Ag116 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag116.h5\n", + " Reading Ag117 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag117.h5\n", + " Reading Ag118_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ag118_m1.h5\n", + " Reading Cd107 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd107.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Cd109 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd109.h5\n", + " Reading Cd115_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cd115_m1.h5\n", + " Reading In113 from /home/lorenzo/nuclear_data/endfb80_hdf5/In113.h5\n", + " Reading In114 from /home/lorenzo/nuclear_data/endfb80_hdf5/In114.h5\n", + " Reading In115 from /home/lorenzo/nuclear_data/endfb80_hdf5/In115.h5\n", + " Reading Sn113 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn113.h5\n", + " Reading Sn121_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn121_m1.h5\n", + " Reading Sn123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn123.h5\n", + " Reading Sn125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn125.h5\n", + " Reading Sn126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sn126.h5\n", + " Reading Sb121 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb121.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Sn123 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Sb122 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb122.h5\n", + " Reading Sb123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb123.h5\n", + " Reading Sb124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb124.h5\n", + " Reading Sb125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb125.h5\n", + " Reading Sb126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sb126.h5\n", + " Reading Te120 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te120.h5\n", + " Reading Te121 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te121.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Te120 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Te120 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Te120 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Te120 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Te121_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te121_m1.h5\n", + " Reading Te122 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te122.h5\n", + " Reading Te123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te123.h5\n", + " Reading Te124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te124.h5\n", + " Reading Te125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te125.h5\n", + " Reading Te126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te126.h5\n", + " Reading Te127_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te127_m1.h5\n", + " Reading Te128 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te128.h5\n", + " Reading Te129_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te129_m1.h5\n", + " Reading Te130 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te130.h5\n", + " Reading Te131 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te131.h5\n", + " Reading Te131_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te131_m1.h5\n", + " Reading Te132 from /home/lorenzo/nuclear_data/endfb80_hdf5/Te132.h5\n", + " Reading I127 from /home/lorenzo/nuclear_data/endfb80_hdf5/I127.h5\n", + " Reading I128 from /home/lorenzo/nuclear_data/endfb80_hdf5/I128.h5\n", + " Reading I129 from /home/lorenzo/nuclear_data/endfb80_hdf5/I129.h5\n", + " Reading I130 from /home/lorenzo/nuclear_data/endfb80_hdf5/I130.h5\n", + " Reading I131 from /home/lorenzo/nuclear_data/endfb80_hdf5/I131.h5\n", + " Reading I132 from /home/lorenzo/nuclear_data/endfb80_hdf5/I132.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide I131 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide I131 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide I131 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide I131 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide I131 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide I131 at 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading I132_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/I132_m1.h5\n", + " Reading I133 from /home/lorenzo/nuclear_data/endfb80_hdf5/I133.h5\n", + " Reading I134 from /home/lorenzo/nuclear_data/endfb80_hdf5/I134.h5\n", + " Reading I135 from /home/lorenzo/nuclear_data/endfb80_hdf5/I135.h5\n", + " Reading Xe123 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe123.h5\n", + " Reading Xe124 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe124.h5\n", + " Reading Xe125 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe125.h5\n", + " Reading Xe126 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe126.h5\n", + " Reading Xe127 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe127.h5\n", + " Reading Xe128 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe128.h5\n", + " Reading Xe129 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe129.h5\n", + " Reading Xe130 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe130.h5\n", + " Reading Xe131 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe131.h5\n", + " Reading Xe132 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe132.h5\n", + " Reading Xe133 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe133.h5\n", + " Reading Xe134 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe134.h5\n", + " Reading Xe135 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe135.h5\n", + " Reading Xe136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Xe136.h5\n", + " Reading Cs133 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs133.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Xe133 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Cs134 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs134.h5\n", + " Reading Cs135 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs135.h5\n", + " Reading Cs136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs136.h5\n", + " Reading Cs137 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cs137.h5\n", + " Reading Ba131 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba131.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cs136 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Ba133 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba133.h5\n", + " Reading Ba139 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba139.h5\n", + " Reading Ba140 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ba140.h5\n", + " Reading La138 from /home/lorenzo/nuclear_data/endfb80_hdf5/La138.h5\n", + " Reading La139 from /home/lorenzo/nuclear_data/endfb80_hdf5/La139.h5\n", + " Reading La140 from /home/lorenzo/nuclear_data/endfb80_hdf5/La140.h5\n", + " Reading Ce136 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce136.h5\n", + " Reading Ce137 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce137.h5\n", + " Reading Ce137_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce137_m1.h5\n", + " Reading Ce138 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce138.h5\n", + " Reading Ce139 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce139.h5\n", + " Reading Ce140 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce140.h5\n", + " Reading Ce141 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce141.h5\n", + " Reading Ce142 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce142.h5\n", + " Reading Ce143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce143.h5\n", + " Reading Ce144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ce144.h5\n", + " Reading Pr141 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pr141.h5\n", + " Reading Pr142 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pr142.h5\n", + " Reading Pr143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pr143.h5\n", + " Reading Nd142 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd142.h5\n", + " Reading Nd143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd143.h5\n", + " Reading Nd144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd144.h5\n", + " Reading Nd145 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd145.h5\n", + " Reading Nd146 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd146.h5\n", + " Reading Nd147 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd147.h5\n", + " Reading Nd148 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd148.h5\n", + " Reading Nd149 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd149.h5\n", + " Reading Nd150 from /home/lorenzo/nuclear_data/endfb80_hdf5/Nd150.h5\n", + " Reading Pm143 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm143.h5\n", + " Reading Pm144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm144.h5\n", + " Reading Pm145 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm145.h5\n", + " Reading Pm146 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm146.h5\n", + " Reading Pm147 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm147.h5\n", + " Reading Pm148 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm148.h5\n", + " Reading Pm148_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm148_m1.h5\n", + " Reading Pm149 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm149.h5\n", + " Reading Pm150 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm150.h5\n", + " Reading Pm151 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pm151.h5\n", + " Reading Sm144 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm144.h5\n", + " Reading Sm145 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm145.h5\n", + " Reading Sm146 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm146.h5\n", + " Reading Sm147 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm147.h5\n", + " Reading Sm148 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm148.h5\n", + " Reading Sm149 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm149.h5\n", + " Reading Sm150 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm150.h5\n", + " Reading Sm151 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm151.h5\n", + " Reading Sm152 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm152.h5\n", + " Reading Sm153 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm153.h5\n", + " Reading Sm154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Sm154.h5\n", + " Reading Eu151 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu151.h5\n", + " Reading Eu152 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu152.h5\n", + " Reading Eu153 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu153.h5\n", + " Reading Eu154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu154.h5\n", + " Reading Eu155 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu155.h5\n", + " Reading Eu156 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu156.h5\n", + " Reading Eu157 from /home/lorenzo/nuclear_data/endfb80_hdf5/Eu157.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Eu156 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Gd153 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd153.h5\n", + " Reading Gd159 from /home/lorenzo/nuclear_data/endfb80_hdf5/Gd159.h5\n", + " Reading Tb158 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb158.h5\n", + " Reading Tb159 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb159.h5\n", + " Reading Tb160 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb160.h5\n", + " Reading Tb161 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tb161.h5\n", + " Reading Dy154 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy154.h5\n", + " Reading Dy155 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy155.h5\n", + " Reading Dy156 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy156.h5\n", + " Reading Dy157 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy157.h5\n", + " Reading Dy158 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy158.h5\n", + " Reading Dy159 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy159.h5\n", + " Reading Dy160 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy160.h5\n", + " Reading Dy161 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy161.h5\n", + " Reading Dy162 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy162.h5\n", + " Reading Dy163 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy163.h5\n", + " Reading Dy164 from /home/lorenzo/nuclear_data/endfb80_hdf5/Dy164.h5\n", + " Reading Ho165 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ho165.h5\n", + " Reading Ho166_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ho166_m1.h5\n", + " Reading Er162 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er162.h5\n", + " Reading Er163 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er163.h5\n", + " Reading Er164 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er164.h5\n", + " Reading Er165 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er165.h5\n", + " Reading Er166 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er166.h5\n", + " Reading Er167 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er167.h5\n", + " Reading Er168 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er168.h5\n", + " Reading Er169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er169.h5\n", + " Reading Er170 from /home/lorenzo/nuclear_data/endfb80_hdf5/Er170.h5\n", + " Reading Tm168 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm168.h5\n", + " Reading Tm169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm169.h5\n", + " Reading Tm170 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm170.h5\n", + " Reading Tm171 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm171.h5\n", + " Reading Yb168 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb168.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb168 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Yb169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb169.h5\n", + " Reading Yb170 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb170.h5\n", + " Reading Yb171 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb171.h5\n", + " Reading Yb172 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb172.h5\n", + " Reading Yb173 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb173.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb170 at\n", + " 2500K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb171 at\n", + " 2500K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb172 at\n", + " 2500K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb173 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Yb174 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb174.h5\n", + " Reading Yb175 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb175.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb174 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Yb176 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb176.h5\n", + " Reading Lu175 from /home/lorenzo/nuclear_data/endfb80_hdf5/Lu175.h5\n", + " Reading Lu176 from /home/lorenzo/nuclear_data/endfb80_hdf5/Lu176.h5\n", + " Reading Hf175 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf175.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Yb176 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Hf181 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf181.h5\n", + " Reading Hf182 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hf182.h5\n", + " Reading Ta182 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ta182.h5\n", + " Reading W181 from /home/lorenzo/nuclear_data/endfb80_hdf5/W181.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf181 at\n", + " 2500K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 600K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at\n", + " 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Hf182 at\n", + " 2500K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading W185 from /home/lorenzo/nuclear_data/endfb80_hdf5/W185.h5\n", + " Reading Re185 from /home/lorenzo/nuclear_data/endfb80_hdf5/Re185.h5\n", + " Reading Re186_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Re186_m1.h5\n", + " Reading Re187 from /home/lorenzo/nuclear_data/endfb80_hdf5/Re187.h5\n", + " Reading Os184 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os184.h5\n", + " Reading Os185 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os185.h5\n", + " Reading Os186 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os186.h5\n", + " Reading Os187 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os187.h5\n", + " Reading Os188 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os188.h5\n", + " Reading Os189 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os189.h5\n", + " Reading Os190 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os190.h5\n", + " Reading Os191 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os191.h5\n", + " Reading Os192 from /home/lorenzo/nuclear_data/endfb80_hdf5/Os192.h5\n", + " Reading Ir191 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir191.h5\n", + " Reading Ir192 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir192.h5\n", + " Reading Ir193 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir193.h5\n", + " Reading Ir194_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ir194_m1.h5\n", + " Reading Pt190 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt190.h5\n", + " Reading Pt191 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt191.h5\n", + " Reading Pt192 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt192.h5\n", + " Reading Pt193 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt193.h5\n", + " Reading Pt194 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt194.h5\n", + " Reading Pt195 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt195.h5\n", + " Reading Pt196 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt196.h5\n", + " Reading Pt197 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt197.h5\n", + " Reading Pt198 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pt198.h5\n", + " Reading Au197 from /home/lorenzo/nuclear_data/endfb80_hdf5/Au197.h5\n", + " Reading Hg196 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg196.h5\n", + " Reading Hg197 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg197.h5\n", + " Reading Hg197_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg197_m1.h5\n", + " Reading Hg198 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg198.h5\n", + " Reading Hg199 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg199.h5\n", + " Reading Hg200 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg200.h5\n", + " Reading Hg201 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg201.h5\n", + " Reading Hg202 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg202.h5\n", + " Reading Hg203 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg203.h5\n", + " Reading Hg204 from /home/lorenzo/nuclear_data/endfb80_hdf5/Hg204.h5\n", + " Reading Tl203 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tl203.h5\n", + " Reading Tl204 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tl204.h5\n", + " Reading Tl205 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tl205.h5\n", + " Reading Pb204 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb204.h5\n", + " Reading Pb205 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb205.h5\n", + " Reading Pb206 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb206.h5\n", + " Reading Pb207 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb207.h5\n", + " Reading Pb208 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pb208.h5\n", + " Reading Bi209 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bi209.h5\n", + " Reading Bi210_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bi210_m1.h5\n", + " Reading Po208 from /home/lorenzo/nuclear_data/endfb80_hdf5/Po208.h5\n", + " Reading Po209 from /home/lorenzo/nuclear_data/endfb80_hdf5/Po209.h5\n", + " Reading Po210 from /home/lorenzo/nuclear_data/endfb80_hdf5/Po210.h5\n", + " Reading Ra223 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra223.h5\n", + " Reading Ra224 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra224.h5\n", + " Reading Ra225 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra225.h5\n", + " Reading Ra226 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ra226.h5\n", + " Reading Ac225 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ac225.h5\n", + " Reading Ac226 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ac226.h5\n", + " Reading Ac227 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ac227.h5\n", + " Reading Th227 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th227.h5\n", + " Reading Th228 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th228.h5\n", + " Reading Th229 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th229.h5\n", + " Reading Th230 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th230.h5\n", + " Reading Th231 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th231.h5\n", + " Reading Th232 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th232.h5\n", + " Reading Th233 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th233.h5\n", + " Reading Th234 from /home/lorenzo/nuclear_data/endfb80_hdf5/Th234.h5\n", + " Reading Pa229 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa229.h5\n", + " Reading Pa230 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa230.h5\n", + " Reading Pa231 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa231.h5\n", + " Reading Pa232 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa232.h5\n", + " Reading Pa233 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pa233.h5\n", + " Reading U230 from /home/lorenzo/nuclear_data/endfb80_hdf5/U230.h5\n", + " Reading U231 from /home/lorenzo/nuclear_data/endfb80_hdf5/U231.h5\n", + " Reading U232 from /home/lorenzo/nuclear_data/endfb80_hdf5/U232.h5\n", + " Reading U233 from /home/lorenzo/nuclear_data/endfb80_hdf5/U233.h5\n", + " Reading U237 from /home/lorenzo/nuclear_data/endfb80_hdf5/U237.h5\n", + " Reading U239 from /home/lorenzo/nuclear_data/endfb80_hdf5/U239.h5\n", + " Reading U240 from /home/lorenzo/nuclear_data/endfb80_hdf5/U240.h5\n", + " Reading U241 from /home/lorenzo/nuclear_data/endfb80_hdf5/U241.h5\n", + " Reading Np234 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np234.h5\n", + " Reading Np235 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np235.h5\n", + " Reading Np236 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np236.h5\n", + " Reading Np236_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np236_m1.h5\n", + " Reading Np237 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np237.h5\n", + " Reading Np238 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np238.h5\n", + " Reading Np239 from /home/lorenzo/nuclear_data/endfb80_hdf5/Np239.h5\n", + " Reading Pu236 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu236.h5\n", + " Reading Pu237 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu237.h5\n", + " Reading Pu238 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu238.h5\n", + " Reading Pu239 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu239.h5\n", + " Reading Pu240 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu240.h5\n", + " Reading Pu241 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu241.h5\n", + " Reading Pu242 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu242.h5\n", + " Reading Pu243 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu243.h5\n", + " Reading Pu244 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu244.h5\n", + " Reading Pu245 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu245.h5\n", + " Reading Pu246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Pu246.h5\n", + " Reading Am240 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am240.h5\n", + " Reading Am241 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am241.h5\n", + " Reading Am242 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am242.h5\n", + " Reading Am242_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am242_m1.h5\n", + " Reading Am243 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am243.h5\n", + " Reading Am244 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am244.h5\n", + " Reading Am244_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Am244_m1.h5\n", + " Reading Cm240 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm240.h5\n", + " Reading Cm241 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm241.h5\n", + " Reading Cm242 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm242.h5\n", + " Reading Cm243 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm243.h5\n", + " Reading Cm244 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm244.h5\n", + " Reading Cm245 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm245.h5\n", + " Reading Cm246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm246.h5\n", + " Reading Cm247 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm247.h5\n", + " Reading Cm248 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm248.h5\n", + " Reading Cm249 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm249.h5\n", + " Reading Cm250 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cm250.h5\n", + " Reading Bk245 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk245.h5\n", + " Reading Bk246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk246.h5\n", + " Reading Bk247 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk247.h5\n", + " Reading Bk248 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk248.h5\n", + " Reading Bk249 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk249.h5\n", + " Reading Bk250 from /home/lorenzo/nuclear_data/endfb80_hdf5/Bk250.h5\n", + " Reading Cf246 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf246.h5\n", + " Reading Cf247 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf247.h5\n", + " Reading Cf248 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf248.h5\n", + " Reading Cf249 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf249.h5\n", + " Reading Cf250 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf250.h5\n", + " Reading Cf251 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf251.h5\n", + " Reading Cf252 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf252.h5\n", + " Reading Cf253 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf253.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 250K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 294K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 900K\n", + " WARNING: Negative value(s) found on probability table for nuclide Cf250 at\n", + " 1200K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Cf254 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf254.h5\n", + " Reading Es251 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es251.h5\n", + " Reading Es252 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es252.h5\n", + " Reading Es253 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es253.h5\n", + " Reading Es254 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es254.h5\n", + " Reading Es254_m1 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es254_m1.h5\n", + " Reading Es255 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es255.h5\n", + " Reading Fm255 from /home/lorenzo/nuclear_data/endfb80_hdf5/Fm255.h5\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.60942\n", + " 2/1 0.89106\n", + " 3/1 0.97799\n", + " 4/1 0.99502\n", + " 5/1 0.99819\n", + " 6/1 1.00437\n", + " 7/1 0.99348\n", + " 8/1 1.00257\n", + " 9/1 1.01441\n", + " 10/1 1.01353\n", + " 11/1 1.00178\n", + " 12/1 1.01576\n", + " 13/1 1.01754\n", + " 14/1 1.00683\n", + " 15/1 1.00505\n", + " 16/1 0.99786\n", + " 17/1 1.01767\n", + " 18/1 1.00034\n", + " 19/1 1.00494\n", + " 20/1 1.01117\n", + " 21/1 1.00252\n", + " 22/1 1.00592 1.00422 +/- 0.00170\n", + " 23/1 1.01500 1.00781 +/- 0.00372\n", + " 24/1 1.01347 1.00923 +/- 0.00299\n", + " 25/1 1.01031 1.00944 +/- 0.00233\n", + " 26/1 1.00326 1.00841 +/- 0.00216\n", + " 27/1 1.00972 1.00860 +/- 0.00184\n", + " 28/1 1.01373 1.00924 +/- 0.00171\n", + " 29/1 1.00611 1.00889 +/- 0.00155\n", + " 30/1 1.01140 1.00914 +/- 0.00141\n", + " 31/1 0.99045 1.00744 +/- 0.00212\n", + " 32/1 1.00671 1.00738 +/- 0.00194\n", + " 33/1 1.01682 1.00811 +/- 0.00193\n", + " 34/1 1.00605 1.00796 +/- 0.00179\n", + " 35/1 1.00573 1.00781 +/- 0.00167\n", + " 36/1 1.00131 1.00741 +/- 0.00162\n", + " 37/1 1.00832 1.00746 +/- 0.00152\n", + " 38/1 1.01443 1.00785 +/- 0.00148\n", + " 39/1 1.00582 1.00774 +/- 0.00141\n", + " 40/1 0.98830 1.00677 +/- 0.00165\n", + " 41/1 0.99523 1.00622 +/- 0.00166\n", + " 42/1 1.00353 1.00610 +/- 0.00159\n", + " 43/1 1.00226 1.00593 +/- 0.00153\n", + " 44/1 1.01347 1.00624 +/- 0.00150\n", + " 45/1 1.00294 1.00611 +/- 0.00144\n", + " 46/1 1.01234 1.00635 +/- 0.00141\n", + " 47/1 1.01298 1.00660 +/- 0.00138\n", + " 48/1 1.00300 1.00647 +/- 0.00133\n", + " 49/1 1.01010 1.00659 +/- 0.00129\n", + " 50/1 0.99917 1.00635 +/- 0.00127\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 3.6673e+01 seconds\n", + " Reading cross sections = 1.5091e+01 seconds\n", + " Total time in simulation = 1.4075e+03 seconds\n", + " Time in transport only = 1.4071e+03 seconds\n", + " Time in inactive batches = 6.3276e+01 seconds\n", + " Time in active batches = 1.3442e+03 seconds\n", + " Time synchronizing fission bank = 1.9756e-01 seconds\n", + " Sampling source sites = 1.5885e-01 seconds\n", + " SEND/RECV source sites = 3.7839e-02 seconds\n", + " Time accumulating tallies = 6.4644e-02 seconds\n", + " Time writing statepoints = 9.7757e-03 seconds\n", + " Total time for finalization = 1.3423e-04 seconds\n", + " Total time elapsed = 1.4453e+03 seconds\n", + " Calculation Rate (inactive) = 9482.26 particles/second\n", + " Calculation Rate (active) = 669.536 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00599 +/- 0.00121\n", + " k-effective (Track-length) = 1.00635 +/- 0.00127\n", + " k-effective (Absorption) = 1.00602 +/- 0.00110\n", + " Combined k-effective = 1.00598 +/- 0.00110\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n0.h5...\n", + "[openmc.deplete] t=432000.0 s, dt=432000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.60115\n", + " 2/1 0.88928\n", + " 3/1 0.94715\n", + " 4/1 0.97733\n", + " 5/1 0.99749\n", + " 6/1 1.00479\n", + " 7/1 0.99471\n", + " 8/1 0.99993\n", + " 9/1 0.99051\n", + " 10/1 1.01665\n", + " 11/1 0.99415\n", + " 12/1 1.01018\n", + " 13/1 1.01106\n", + " 14/1 1.01588\n", + " 15/1 1.00526\n", + " 16/1 1.00077\n", + " 17/1 1.00117\n", + " 18/1 1.00009\n", + " 19/1 0.99434\n", + " 20/1 1.00243\n", + " 21/1 1.00150\n", + " 22/1 0.99410 0.99780 +/- 0.00370\n", + " 23/1 1.00698 1.00086 +/- 0.00373\n", + " 24/1 1.00999 1.00314 +/- 0.00349\n", + " 25/1 1.01735 1.00598 +/- 0.00392\n", + " 26/1 1.00480 1.00579 +/- 0.00321\n", + " 27/1 1.00722 1.00599 +/- 0.00272\n", + " 28/1 0.99473 1.00458 +/- 0.00274\n", + " 29/1 1.00419 1.00454 +/- 0.00242\n", + " 30/1 1.00796 1.00488 +/- 0.00219\n", + " 31/1 1.00857 1.00522 +/- 0.00201\n", + " 32/1 0.99732 1.00456 +/- 0.00195\n", + " 33/1 1.00652 1.00471 +/- 0.00180\n", + " 34/1 0.99580 1.00407 +/- 0.00178\n", + " 35/1 0.99530 1.00349 +/- 0.00176\n", + " 36/1 1.01447 1.00418 +/- 0.00178\n", + " 37/1 1.01762 1.00497 +/- 0.00185\n", + " 38/1 0.99450 1.00439 +/- 0.00184\n", + " 39/1 0.99973 1.00414 +/- 0.00176\n", + " 40/1 0.99579 1.00372 +/- 0.00172\n", + " 41/1 1.00942 1.00399 +/- 0.00166\n", + " 42/1 1.00841 1.00420 +/- 0.00159\n", + " 43/1 0.99084 1.00361 +/- 0.00163\n", + " 44/1 1.00087 1.00350 +/- 0.00156\n", + " 45/1 1.00956 1.00374 +/- 0.00152\n", + " 46/1 1.00058 1.00362 +/- 0.00147\n", + " 47/1 1.00990 1.00385 +/- 0.00143\n", + " 48/1 1.00305 1.00382 +/- 0.00138\n", + " 49/1 1.00704 1.00394 +/- 0.00133\n", + " 50/1 1.01104 1.00417 +/- 0.00131\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8414e+03 seconds\n", + " Time in transport only = 1.8410e+03 seconds\n", + " Time in inactive batches = 2.8855e+02 seconds\n", + " Time in active batches = 1.5529e+03 seconds\n", + " Time synchronizing fission bank = 1.8210e-01 seconds\n", + " Sampling source sites = 1.4562e-01 seconds\n", + " SEND/RECV source sites = 3.5746e-02 seconds\n", + " Time accumulating tallies = 7.8137e-02 seconds\n", + " Time writing statepoints = 1.1256e-02 seconds\n", + " Total time for finalization = 1.4153e-04 seconds\n", + " Total time elapsed = 1.8424e+03 seconds\n", + " Calculation Rate (inactive) = 2079.33 particles/second\n", + " Calculation Rate (active) = 579.574 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00479 +/- 0.00124\n", + " k-effective (Track-length) = 1.00417 +/- 0.00131\n", + " k-effective (Absorption) = 1.00426 +/- 0.00105\n", + " Combined k-effective = 1.00450 +/- 0.00092\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n1.h5...\n", + "[openmc.deplete] t=864000.0 s, dt=2592000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.59346\n", + " 2/1 0.87731\n", + " 3/1 0.96109\n", + " 4/1 0.97331\n", + " 5/1 0.98716\n", + " 6/1 0.99491\n", + " 7/1 0.99851\n", + " 8/1 1.00039\n", + " 9/1 0.99998\n", + " 10/1 0.99704\n", + " 11/1 1.00638\n", + " 12/1 1.00982\n", + " 13/1 1.01091\n", + " 14/1 1.01678\n", + " 15/1 0.98923\n", + " 16/1 0.99254\n", + " 17/1 1.00578\n", + " 18/1 1.01132\n", + " 19/1 1.01314\n", + " 20/1 1.01694\n", + " 21/1 1.00736\n", + " 22/1 1.01141 1.00939 +/- 0.00203\n", + " 23/1 1.00158 1.00678 +/- 0.00285\n", + " 24/1 1.00589 1.00656 +/- 0.00203\n", + " 25/1 1.00737 1.00672 +/- 0.00158\n", + " 26/1 1.01017 1.00730 +/- 0.00141\n", + " 27/1 0.99982 1.00623 +/- 0.00160\n", + " 28/1 1.01095 1.00682 +/- 0.00151\n", + " 29/1 1.01166 1.00736 +/- 0.00143\n", + " 30/1 1.00725 1.00735 +/- 0.00128\n", + " 31/1 0.99733 1.00643 +/- 0.00148\n", + " 32/1 1.01437 1.00710 +/- 0.00150\n", + " 33/1 1.00339 1.00681 +/- 0.00141\n", + " 34/1 0.98085 1.00496 +/- 0.00227\n", + " 35/1 1.01442 1.00559 +/- 0.00220\n", + " 36/1 1.00294 1.00542 +/- 0.00207\n", + " 37/1 0.99744 1.00495 +/- 0.00200\n", + " 38/1 1.01074 1.00527 +/- 0.00191\n", + " 39/1 0.99945 1.00497 +/- 0.00183\n", + " 40/1 1.00435 1.00494 +/- 0.00174\n", + " 41/1 1.00735 1.00505 +/- 0.00166\n", + " 42/1 1.00603 1.00510 +/- 0.00158\n", + " 43/1 1.00323 1.00501 +/- 0.00151\n", + " 44/1 0.99800 1.00472 +/- 0.00148\n", + " 45/1 1.01305 1.00506 +/- 0.00146\n", + " 46/1 1.00507 1.00506 +/- 0.00140\n", + " 47/1 1.02376 1.00575 +/- 0.00151\n", + " 48/1 1.00153 1.00560 +/- 0.00147\n", + " 49/1 1.00894 1.00571 +/- 0.00142\n", + " 50/1 1.01304 1.00596 +/- 0.00139\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8190e+03 seconds\n", + " Time in transport only = 1.8186e+03 seconds\n", + " Time in inactive batches = 2.8913e+02 seconds\n", + " Time in active batches = 1.5299e+03 seconds\n", + " Time synchronizing fission bank = 1.8696e-01 seconds\n", + " Sampling source sites = 1.4822e-01 seconds\n", + " SEND/RECV source sites = 3.7953e-02 seconds\n", + " Time accumulating tallies = 9.6822e-02 seconds\n", + " Time writing statepoints = 1.1563e-02 seconds\n", + " Total time for finalization = 1.2487e-04 seconds\n", + " Total time elapsed = 1.8201e+03 seconds\n", + " Calculation Rate (inactive) = 2075.23 particles/second\n", + " Calculation Rate (active) = 588.286 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00533 +/- 0.00143\n", + " k-effective (Track-length) = 1.00596 +/- 0.00139\n", + " k-effective (Absorption) = 1.00445 +/- 0.00114\n", + " Combined k-effective = 1.00527 +/- 0.00103\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n2.h5...\n", + "[openmc.deplete] t=3456000.0 s, dt=2592000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.61365\n", + " 2/1 0.90212\n", + " 3/1 0.95962\n", + " 4/1 1.00423\n", + " 5/1 1.00033\n", + " 6/1 1.00771\n", + " 7/1 1.00453\n", + " 8/1 1.00135\n", + " 9/1 1.01025\n", + " 10/1 1.01089\n", + " 11/1 1.00141\n", + " 12/1 1.00329\n", + " 13/1 1.00327\n", + " 14/1 1.00053\n", + " 15/1 1.00595\n", + " 16/1 1.00520\n", + " 17/1 1.00244\n", + " 18/1 1.01770\n", + " 19/1 1.00163\n", + " 20/1 1.01013\n", + " 21/1 1.01999\n", + " 22/1 1.00433 1.01216 +/- 0.00783\n", + " 23/1 1.00672 1.01035 +/- 0.00487\n", + " 24/1 0.99404 1.00627 +/- 0.00534\n", + " 25/1 1.01133 1.00728 +/- 0.00426\n", + " 26/1 1.00606 1.00708 +/- 0.00348\n", + " 27/1 0.99274 1.00503 +/- 0.00358\n", + " 28/1 1.00750 1.00534 +/- 0.00312\n", + " 29/1 1.00673 1.00549 +/- 0.00276\n", + " 30/1 1.01539 1.00648 +/- 0.00266\n", + " 31/1 1.00000 1.00589 +/- 0.00247\n", + " 32/1 1.01101 1.00632 +/- 0.00230\n", + " 33/1 1.01284 1.00682 +/- 0.00217\n", + " 34/1 0.99580 1.00603 +/- 0.00216\n", + " 35/1 1.00903 1.00623 +/- 0.00202\n", + " 36/1 1.01458 1.00676 +/- 0.00196\n", + " 37/1 1.00325 1.00655 +/- 0.00185\n", + " 38/1 0.99058 1.00566 +/- 0.00196\n", + " 39/1 1.00662 1.00571 +/- 0.00185\n", + " 40/1 0.98884 1.00487 +/- 0.00195\n", + " 41/1 1.01278 1.00525 +/- 0.00189\n", + " 42/1 0.99109 1.00460 +/- 0.00192\n", + " 43/1 0.99064 1.00400 +/- 0.00193\n", + " 44/1 1.01222 1.00434 +/- 0.00188\n", + " 45/1 1.01213 1.00465 +/- 0.00183\n", + " 46/1 1.00018 1.00448 +/- 0.00177\n", + " 47/1 0.99712 1.00421 +/- 0.00172\n", + " 48/1 1.00972 1.00440 +/- 0.00167\n", + " 49/1 1.00204 1.00432 +/- 0.00161\n", + " 50/1 0.98755 1.00376 +/- 0.00166\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8207e+03 seconds\n", + " Time in transport only = 1.8203e+03 seconds\n", + " Time in inactive batches = 2.8771e+02 seconds\n", + " Time in active batches = 1.5330e+03 seconds\n", + " Time synchronizing fission bank = 1.8680e-01 seconds\n", + " Sampling source sites = 1.4770e-01 seconds\n", + " SEND/RECV source sites = 3.8463e-02 seconds\n", + " Time accumulating tallies = 1.1871e-01 seconds\n", + " Time writing statepoints = 1.1733e-02 seconds\n", + " Total time for finalization = 1.0574e-04 seconds\n", + " Total time elapsed = 1.8217e+03 seconds\n", + " Calculation Rate (inactive) = 2085.41 particles/second\n", + " Calculation Rate (active) = 587.09 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00440 +/- 0.00161\n", + " k-effective (Track-length) = 1.00376 +/- 0.00166\n", + " k-effective (Absorption) = 1.00555 +/- 0.00094\n", + " Combined k-effective = 1.00539 +/- 0.00101\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n3.h5...\n", + "[openmc.deplete] t=6048000.0 s, dt=2592000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.60296\n", + " 2/1 0.88599\n", + " 3/1 0.95124\n", + " 4/1 0.98034\n", + " 5/1 0.99482\n", + " 6/1 0.99609\n", + " 7/1 1.00739\n", + " 8/1 0.99006\n", + " 9/1 1.00694\n", + " 10/1 1.00602\n", + " 11/1 1.00145\n", + " 12/1 1.00226\n", + " 13/1 1.01503\n", + " 14/1 1.01195\n", + " 15/1 1.01079\n", + " 16/1 1.00054\n", + " 17/1 0.99742\n", + " 18/1 1.00577\n", + " 19/1 1.02088\n", + " 20/1 1.01246\n", + " 21/1 1.01198\n", + " 22/1 0.99621 1.00409 +/- 0.00788\n", + " 23/1 1.01483 1.00767 +/- 0.00579\n", + " 24/1 1.00061 1.00591 +/- 0.00446\n", + " 25/1 1.00577 1.00588 +/- 0.00345\n", + " 26/1 1.02079 1.00836 +/- 0.00376\n", + " 27/1 0.99468 1.00641 +/- 0.00373\n", + " 28/1 1.01004 1.00686 +/- 0.00326\n", + " 29/1 1.00174 1.00629 +/- 0.00293\n", + " 30/1 1.00836 1.00650 +/- 0.00263\n", + " 31/1 1.00349 1.00623 +/- 0.00240\n", + " 32/1 1.00414 1.00605 +/- 0.00219\n", + " 33/1 1.00559 1.00602 +/- 0.00202\n", + " 34/1 0.99914 1.00553 +/- 0.00193\n", + " 35/1 0.99346 1.00472 +/- 0.00197\n", + " 36/1 1.01869 1.00559 +/- 0.00204\n", + " 37/1 1.00135 1.00534 +/- 0.00193\n", + " 38/1 1.02210 1.00628 +/- 0.00205\n", + " 39/1 1.01476 1.00672 +/- 0.00199\n", + " 40/1 0.99363 1.00607 +/- 0.00199\n", + " 41/1 1.00109 1.00583 +/- 0.00191\n", + " 42/1 1.01019 1.00603 +/- 0.00183\n", + " 43/1 1.01389 1.00637 +/- 0.00178\n", + " 44/1 1.00938 1.00650 +/- 0.00171\n", + " 45/1 0.99871 1.00618 +/- 0.00167\n", + " 46/1 0.99567 1.00578 +/- 0.00166\n", + " 47/1 1.00367 1.00570 +/- 0.00160\n", + " 48/1 1.00425 1.00565 +/- 0.00154\n", + " 49/1 1.00738 1.00571 +/- 0.00149\n", + " 50/1 0.98480 1.00501 +/- 0.00160\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8171e+03 seconds\n", + " Time in transport only = 1.8167e+03 seconds\n", + " Time in inactive batches = 2.8780e+02 seconds\n", + " Time in active batches = 1.5293e+03 seconds\n", + " Time synchronizing fission bank = 1.8174e-01 seconds\n", + " Sampling source sites = 1.4617e-01 seconds\n", + " SEND/RECV source sites = 3.4910e-02 seconds\n", + " Time accumulating tallies = 8.8733e-02 seconds\n", + " Time writing statepoints = 9.8809e-03 seconds\n", + " Total time for finalization = 9.3226e-05 seconds\n", + " Total time elapsed = 1.8182e+03 seconds\n", + " Calculation Rate (inactive) = 2084.77 particles/second\n", + " Calculation Rate (active) = 588.496 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00510 +/- 0.00149\n", + " k-effective (Track-length) = 1.00501 +/- 0.00160\n", + " k-effective (Absorption) = 1.00332 +/- 0.00142\n", + " Combined k-effective = 1.00403 +/- 0.00136\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n4.h5...\n", + "[openmc.deplete] t=8640000.0 s, dt=15552000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.60086\n", + " 2/1 0.86972\n", + " 3/1 0.96642\n", + " 4/1 0.97718\n", + " 5/1 0.99563\n", + " 6/1 0.99663\n", + " 7/1 0.99786\n", + " 8/1 1.00361\n", + " 9/1 0.99566\n", + " 10/1 1.01047\n", + " 11/1 0.99560\n", + " 12/1 1.01017\n", + " 13/1 1.00995\n", + " 14/1 1.00569\n", + " 15/1 0.99830\n", + " 16/1 1.00247\n", + " 17/1 1.00797\n", + " 18/1 1.01169\n", + " 19/1 0.98977\n", + " 20/1 1.00191\n", + " 21/1 1.00061\n", + " 22/1 1.01345 1.00703 +/- 0.00642\n", + " 23/1 0.99995 1.00467 +/- 0.00439\n", + " 24/1 1.00920 1.00580 +/- 0.00331\n", + " 25/1 0.99803 1.00425 +/- 0.00300\n", + " 26/1 1.01122 1.00541 +/- 0.00271\n", + " 27/1 0.98902 1.00307 +/- 0.00327\n", + " 28/1 0.99650 1.00225 +/- 0.00295\n", + " 29/1 1.01064 1.00318 +/- 0.00277\n", + " 30/1 1.00402 1.00326 +/- 0.00248\n", + " 31/1 0.99900 1.00288 +/- 0.00227\n", + " 32/1 1.01320 1.00374 +/- 0.00225\n", + " 33/1 1.00294 1.00367 +/- 0.00207\n", + " 34/1 1.01290 1.00433 +/- 0.00202\n", + " 35/1 1.00241 1.00421 +/- 0.00189\n", + " 36/1 0.99250 1.00347 +/- 0.00191\n", + " 37/1 0.99637 1.00306 +/- 0.00184\n", + " 38/1 1.01621 1.00379 +/- 0.00189\n", + " 39/1 1.00358 1.00378 +/- 0.00178\n", + " 40/1 1.00271 1.00372 +/- 0.00169\n", + " 41/1 1.01014 1.00403 +/- 0.00164\n", + " 42/1 1.00411 1.00403 +/- 0.00156\n", + " 43/1 1.01082 1.00433 +/- 0.00152\n", + " 44/1 1.01002 1.00456 +/- 0.00148\n", + " 45/1 1.01622 1.00503 +/- 0.00149\n", + " 46/1 0.99330 1.00458 +/- 0.00150\n", + " 47/1 1.00098 1.00445 +/- 0.00145\n", + " 48/1 1.01513 1.00483 +/- 0.00145\n", + " 49/1 1.00437 1.00481 +/- 0.00140\n", + " 50/1 1.00083 1.00468 +/- 0.00136\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8105e+03 seconds\n", + " Time in transport only = 1.8100e+03 seconds\n", + " Time in inactive batches = 2.8790e+02 seconds\n", + " Time in active batches = 1.5226e+03 seconds\n", + " Time synchronizing fission bank = 1.7775e-01 seconds\n", + " Sampling source sites = 1.4078e-01 seconds\n", + " SEND/RECV source sites = 3.6356e-02 seconds\n", + " Time accumulating tallies = 1.1440e-01 seconds\n", + " Time writing statepoints = 1.1593e-02 seconds\n", + " Total time for finalization = 1.8903e-04 seconds\n", + " Total time elapsed = 1.8115e+03 seconds\n", + " Calculation Rate (inactive) = 2084.08 particles/second\n", + " Calculation Rate (active) = 591.112 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.00489 +/- 0.00143\n", + " k-effective (Track-length) = 1.00468 +/- 0.00136\n", + " k-effective (Absorption) = 1.00411 +/- 0.00115\n", + " Combined k-effective = 1.00418 +/- 0.00112\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n5.h5...\n", + "[openmc.deplete] t=24192000.0 s, dt=8208000 s, source=8000000.0\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.59708\n", + " 2/1 0.88271\n", + " 3/1 0.95662\n", + " 4/1 0.96192\n", + " 5/1 1.00702\n", + " 6/1 0.97276\n", + " 7/1 1.01247\n", + " 8/1 0.99829\n", + " 9/1 0.99887\n", + " 10/1 0.99240\n", + " 11/1 1.01194\n", + " 12/1 0.99297\n", + " 13/1 1.01286\n", + " 14/1 1.00783\n", + " 15/1 1.01223\n", + " 16/1 0.99347\n", + " 17/1 1.00024\n", + " 18/1 0.99622\n", + " 19/1 1.00014\n", + " 20/1 1.00125\n", + " 21/1 0.99719\n", + " 22/1 0.97588 0.98654 +/- 0.01065\n", + " 23/1 1.00592 0.99300 +/- 0.00892\n", + " 24/1 1.00911 0.99703 +/- 0.00748\n", + " 25/1 1.00455 0.99853 +/- 0.00599\n", + " 26/1 0.99838 0.99851 +/- 0.00489\n", + " 27/1 1.00942 1.00007 +/- 0.00442\n", + " 28/1 0.99898 0.99993 +/- 0.00383\n", + " 29/1 0.98653 0.99844 +/- 0.00369\n", + " 30/1 1.00646 0.99924 +/- 0.00340\n", + " 31/1 0.99763 0.99910 +/- 0.00308\n", + " 32/1 1.00461 0.99956 +/- 0.00285\n", + " 33/1 1.00540 1.00001 +/- 0.00266\n", + " 34/1 1.00215 1.00016 +/- 0.00246\n", + " 35/1 1.01281 1.00100 +/- 0.00244\n", + " 36/1 0.99520 1.00064 +/- 0.00231\n", + " 37/1 1.00471 1.00088 +/- 0.00219\n", + " 38/1 0.99427 1.00051 +/- 0.00209\n", + " 39/1 1.01149 1.00109 +/- 0.00206\n", + " 40/1 1.00084 1.00108 +/- 0.00196\n", + " 41/1 1.00232 1.00114 +/- 0.00186\n", + " 42/1 1.00407 1.00127 +/- 0.00178\n", + " 43/1 1.00735 1.00153 +/- 0.00172\n", + " 44/1 0.98486 1.00084 +/- 0.00179\n", + " 45/1 0.99805 1.00073 +/- 0.00172\n", + " 46/1 0.99078 1.00035 +/- 0.00170\n", + " 47/1 1.00270 1.00043 +/- 0.00163\n", + " 48/1 0.99237 1.00015 +/- 0.00160\n", + " 49/1 0.99284 0.99989 +/- 0.00157\n", + " 50/1 0.99736 0.99981 +/- 0.00151\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8224e+03 seconds\n", + " Time in transport only = 1.8220e+03 seconds\n", + " Time in inactive batches = 2.8825e+02 seconds\n", + " Time in active batches = 1.5341e+03 seconds\n", + " Time synchronizing fission bank = 1.8792e-01 seconds\n", + " Sampling source sites = 1.5020e-01 seconds\n", + " SEND/RECV source sites = 3.6929e-02 seconds\n", + " Time accumulating tallies = 8.4246e-02 seconds\n", + " Time writing statepoints = 1.1513e-02 seconds\n", + " Total time for finalization = 1.3029e-04 seconds\n", + " Total time elapsed = 1.8235e+03 seconds\n", + " Calculation Rate (inactive) = 2081.5 particles/second\n", + " Calculation Rate (active) = 586.658 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 0.99966 +/- 0.00148\n", + " k-effective (Track-length) = 0.99981 +/- 0.00151\n", + " k-effective (Absorption) = 0.99907 +/- 0.00130\n", + " Combined k-effective = 0.99935 +/- 0.00121\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n6.h5...\n", + "[openmc.deplete] t=32400000.0 (final operator evaluation)\n", + " Maximum neutron transport energy: 20000000 eV for Li6\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.60121\n", + " 2/1 0.88427\n", + " 3/1 0.95151\n", + " 4/1 0.96807\n", + " 5/1 0.97437\n", + " 6/1 0.98574\n", + " 7/1 0.99351\n", + " 8/1 0.99847\n", + " 9/1 1.01221\n", + " 10/1 0.99740\n", + " 11/1 0.99622\n", + " 12/1 1.00647\n", + " 13/1 1.00011\n", + " 14/1 0.98830\n", + " 15/1 1.00141\n", + " 16/1 0.99886\n", + " 17/1 0.99064\n", + " 18/1 0.98695\n", + " 19/1 0.99649\n", + " 20/1 0.98039\n", + " 21/1 1.00017\n", + " 22/1 1.00425 1.00221 +/- 0.00204\n", + " 23/1 0.98771 0.99738 +/- 0.00498\n", + " 24/1 1.01080 1.00073 +/- 0.00486\n", + " 25/1 0.99855 1.00030 +/- 0.00379\n", + " 26/1 1.00262 1.00068 +/- 0.00312\n", + " 27/1 1.00268 1.00097 +/- 0.00265\n", + " 28/1 1.00079 1.00095 +/- 0.00230\n", + " 29/1 0.99842 1.00067 +/- 0.00205\n", + " 30/1 0.99646 1.00024 +/- 0.00188\n", + " 31/1 0.99804 1.00004 +/- 0.00171\n", + " 32/1 0.98251 0.99858 +/- 0.00214\n", + " 33/1 0.98509 0.99755 +/- 0.00222\n", + " 34/1 0.99858 0.99762 +/- 0.00206\n", + " 35/1 1.00299 0.99798 +/- 0.00195\n", + " 36/1 0.99394 0.99773 +/- 0.00184\n", + " 37/1 0.99468 0.99755 +/- 0.00174\n", + " 38/1 0.99469 0.99739 +/- 0.00165\n", + " 39/1 0.99689 0.99736 +/- 0.00156\n", + " 40/1 1.02072 0.99853 +/- 0.00188\n", + " 41/1 0.99978 0.99859 +/- 0.00179\n", + " 42/1 1.00440 0.99885 +/- 0.00173\n", + " 43/1 0.98447 0.99823 +/- 0.00177\n", + " 44/1 0.99226 0.99798 +/- 0.00171\n", + " 45/1 0.99634 0.99791 +/- 0.00164\n", + " 46/1 0.99276 0.99771 +/- 0.00159\n", + " 47/1 0.99572 0.99764 +/- 0.00153\n", + " 48/1 1.00497 0.99790 +/- 0.00150\n", + " 49/1 1.01988 0.99866 +/- 0.00163\n", + " 50/1 1.00250 0.99879 +/- 0.00158\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 0.0000e+00 seconds\n", + " Reading cross sections = 0.0000e+00 seconds\n", + " Total time in simulation = 1.8043e+03 seconds\n", + " Time in transport only = 1.8039e+03 seconds\n", + " Time in inactive batches = 2.8726e+02 seconds\n", + " Time in active batches = 1.5170e+03 seconds\n", + " Time synchronizing fission bank = 1.8194e-01 seconds\n", + " Sampling source sites = 1.4365e-01 seconds\n", + " SEND/RECV source sites = 3.7646e-02 seconds\n", + " Time accumulating tallies = 8.7450e-02 seconds\n", + " Time writing statepoints = 1.7304e-02 seconds\n", + " Total time for finalization = 1.0928e-04 seconds\n", + " Total time elapsed = 1.8054e+03 seconds\n", + " Calculation Rate (inactive) = 2088.7 particles/second\n", + " Calculation Rate (active) = 593.257 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 0.99850 +/- 0.00153\n", + " k-effective (Track-length) = 0.99879 +/- 0.00158\n", + " k-effective (Absorption) = 0.99839 +/- 0.00119\n", + " Combined k-effective = 0.99850 +/- 0.00118\n", + " Leakage Fraction = 0.00001 +/- 0.00000\n", + "\n", + " Creating state point openmc_simulation_n7.h5...\n" + ] + } + ], + "source": [ + "model = openmc.model.Model(geometry,mats,settings)\n", + "\n", + "#Depletion general settings\n", + "depletion_days = [5,5,30,30,30,180,95]\n", + "power = 8e6 #total thermal power [W]\n", + "salt_mass = 4590 * 10**3 #grams\n", + "salt_volume =salt_mass/salt_density #cm3\n", + "salt.volume = salt_volume\n", + "\n", + "# Initialize depletion operator\n", + "op = openmc.deplete.CoupledOperator(model, normalization_mode = \"energy-deposition\")\n", + "\n", + "# Initialize integrator object and start depletion calculation \n", + "integrator = openmc.deplete.PredictorIntegrator(op, depletion_days, timestep_units='d', power=power)\n", + "integrator.add_transfer_rate(salt, ['Xe','Kr'], 4.067e-5)\n", + "integrator.add_transfer_rate(salt, ['Se','Nb','Mo','Tc','Ru','Rh','Pd','Ag','Sb','Te'], 8.777e-3)\n", + "integrator.integrate()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "41d076d2", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's store some results that will be used later for comparison\n", + "results = openmc.deplete.Results('depletion_results.h5')\n", + "t, k = results.get_keff()\n", + "n_xe = 0\n", + "n_kr = 0\n", + "for nuc,_ in openmc.data.isotopes('Xe'):\n", + " n_xe += results.get_atoms(str(salt.id), nuc)[1]\n", + "for nuc,_ in openmc.data.isotopes('Kr'):\n", + " n_kr += results.get_atoms(str(salt.id), nuc)[1]" + ] + }, + { + "cell_type": "markdown", + "id": "415f0334", + "metadata": {}, + "source": [ + "# Critical Factor" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "826c26f9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Let's convert time from sec to days \n", + "t /= (3600 * 24)\n", + "\n", + "plt.figure()\n", + "ax = plt.subplot()\n", + "k1, = ax.plot(t, [k[0] for k in k], '--', c='red', label='keff wo removal rates')\n", + "ax1 = ax.twinx()\n", + "n1, = ax1.plot(t, n_xe, '--', c='green', label='Xe wo removal rates')\n", + "n3, = ax1.plot(t, n_kr, '--', c='blue', label='Kr wo removal rates')\n", + "ax.set_xlabel('Time[d]')\n", + "ax.set_ylabel(r'$k_{eff}$', color='r')\n", + "ax.tick_params(axis='y', colors='red')\n", + "ax1.set_yscale('log')\n", + "ax1.set_ylabel('Nuclides [atoms]')\n", + "ax1.legend(handles=[k1, n1, n3])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cc827041", + "metadata": {}, + "source": [ + "# Fission products\n", + "The removal rate for gaseous fission products has been tuned (see [here](https://info.ornl.gov/sites/publications/Files/Pub173113.pdf)) to obtain a Xenon poison fractions matching the measurements reported during the MSRE U235 operation, of 0.3%-0.4%. \n", + "\n", + "The Xenon poison fraction is defined as:\n", + "$FP = \\frac{\\Sigma_a^{135}Xe}{\\Sigma_a^{235}U}$\n", + "\n", + "Let's plot the same quantity and see if we obtain values that matches the reference :" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "dc07279e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Microscopic absorption cross section at 0.0253 eV\n", + "xs_xe135 = 2664214.0\n", + "xs_u235 = 686.006994850397\n", + "_, n_xe135 = results.get_atoms(str(salt.id), 'Xe135')\n", + "_, n_u235 = results.get_atoms(str(salt.id), 'U235')\n", + "# Poison fraction\n", + "pf = (xs_xe135*n_xe135)/(xs_u235*n_u235)*100\n", + "plt.figure()\n", + "plt.plot(t, pf)\n", + "plt.xlabel('Time [d]')\n", + "plt.ylabel('Xe posion fraction [%]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9d28beee", + "metadata": {}, + "source": [ + "# Inventory \n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "aa32c623", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "inventory = dict()\n", + "for nuc,_ in openmc.data.isotopes('U'):\n", + " inventory[nuc] = results.get_atoms(str(salt.id), nuc)[1] / openmc.data.AVOGADRO * openmc.data.atomic_mass(nuc) / 1000\n", + "\n", + "for nuc in ['Pu238','Pu239','Pu240','Pu241','Pu242']:\n", + " inventory[nuc] = results.get_atoms(str(salt.id), nuc)[1] / openmc.data.AVOGADRO * openmc.data.atomic_mass(nuc) / 1000\n", + "\n", + "plt.figure()\n", + "for nuc, mass in inventory.items():\n", + " plt.plot(t, mass, label=nuc)\n", + "plt.xlabel('Time [y]')\n", + "plt.ylabel('Mass [g]')\n", + "plt.yscale('log')\n", + "plt.ylim(1e-5)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7434b75d", + "metadata": {}, + "source": [ + "# Neutron absorption in the fuel" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0c5bd844", + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "import seaborn as sns\n", + "regex = re.compile(r'(\\d+|\\s+)')\n", + "\n", + "# All nuclides present in the fuel at last time-step\n", + "nucs = results.export_to_materials(-1)[0].get_nuclides()\n", + "\n", + "# Let's begin by making some useful groupings\n", + "gaseos = ['H', 'He', 'Ne', 'Ar', 'Kr', 'Xe', 'Rn'] #gaseous fission products\n", + "noble_metals = ['Se','Nb','Mo','Tc','Ru','Rh','Pd','Ag','Sb','Te'] # noble metals fission products\n", + "metals = ['Cr','Mn','Fe','Co','Ni','Cu','Zn','Hf','Zr','W',]\n", + "halogens = ['F','Cl','Br','I','At']\n", + "alkali_metals = ['Li','Na','K','Rb','Cs']\n", + "alkali_earths= ['Be','Mg','Ca','Sr','Ba','Ra']\n", + "lanthanides = ['Y','La','Ce','Pr','Nd','Pm','Sm','Eu','Gd','Tb','Dy','Ho','Er','Tm','Yb','Lu']\n", + "m_a = ['Ac','Th','Pa','Np','Am','Cm','Bk','Cf','Es','Fm','Md','No','Lr']\n", + "# Get fissile nuclides in the fuel, based on Ronen's rule for determining fissile isotopes\n", + "fissile = []\n", + "\n", + "for nuc in nucs:\n", + " elm = regex.split(nuc)[0]\n", + " a = round(openmc.data.atomic_mass(nuc))\n", + " z = openmc.data.ATOMIC_NUMBER[elm]\n", + " if 90 <= z <= 100:\n", + " ronen = 2*z -(a-z)\n", + " if ronen in [41,43,45]:\n", + " fissile.append(nuc) \n", + "\n", + "# Calculate totat absorption rate of fissile nuclides\n", + "tot_abs_rate = 0\n", + "for nuc in fissile:\n", + " tot_abs_rate += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " tot_abs_rate += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + "\n", + "nuclides_stack = dict()\n", + "groups_stack = {'Gaseos':0, 'Noble metals':0, 'Metals':0, 'Halogens':0 , 'Alkali metals':0, 'Alkali earths':0, 'Lanthanides':0, 'MA':0, 'Others':0}\n", + "for nuc in nucs:\n", + " \n", + " if regex.split(nuc)[0] in ['U','Pu']:\n", + " nuclides_stack[nuc] = results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " nuclides_stack[nuc] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " nuclides_stack[nuc] /= tot_abs_rate\n", + " \n", + " \n", + " elif regex.split(nuc)[0] in gaseos:\n", + " groups_stack['Gaseos'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Gaseos'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in noble_metals:\n", + " groups_stack['Noble metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Noble metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in metals:\n", + " groups_stack['Metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in halogens:\n", + " groups_stack['Halogens'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Halogens'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in alkali_metals:\n", + " groups_stack['Alkali metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Alkali metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in alkali_earths:\n", + " groups_stack['Alkali earths'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Alkali earths'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in lanthanides:\n", + " groups_stack['Lanthanides'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Lanthanides'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " elif regex.split(nuc)[0] in m_a:\n", + " groups_stack['MA'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['MA'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + " else:\n", + " groups_stack['Others'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n", + " groups_stack['Others'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n", + "\n", + "# Divide each array by the total absorption reaction rate of fissile nuclides\n", + "for g in groups_stack.keys():\n", + " groups_stack[g] /= tot_abs_rate\n", + "\n", + "# Sort dictionary groups\n", + "groups_stack=dict(reversed(sorted(groups_stack.items(), key=lambda item: item[1][len(item)])))\n", + "\n", + "# Create red color palette for groups_stack\n", + "colors = list(reversed(sns.color_palette(\"Reds\", len(groups_stack))))\n", + "\n", + "# Order uranium series\n", + "u_series = {key:value for key,value in nuclides_stack.items() if key.startswith('U')}\n", + "u_series = dict(reversed(sorted(u_series.items(), key=lambda item: item[1][len(item)])))\n", + "# Create green color palette for Uranium isotopes\n", + "colors += list(reversed(sns.color_palette(\"Greens\", len(u_series))))\n", + "\n", + "# Order plutionium series\n", + "pu_series = {key:value for key,value in nuclides_stack.items() if key.startswith('Pu')}\n", + "pu_series = dict(reversed(sorted(pu_series.items(), key=lambda item: item[1][len(item)])))\n", + "# Create blue color palette for plutonium isotopes\n", + "colors += list(reversed(sns.color_palette(\"Blues\", len(pu_series))))\n", + "# Add uramium and plutonium series to the stack\n", + "groups_stack.update(u_series)\n", + "groups_stack.update(pu_series)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cc4b980e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "plt.stackplot(t, groups_stack.values(), labels=groups_stack.keys(), \n", + " edgecolor=\"black\", linewidth=0.5,colors=colors, alpha=0.8)\n", + "handles, labels = plt.gca().get_legend_handles_labels()\n", + "legend = plt.legend([handles[idx] for idx in list(reversed(np.arange(0,len(handles),1)))],\n", + " [labels[idx] for idx in list(reversed(np.arange(0,len(handles),1)))],\n", + " bbox_to_anchor=(1.05,1), loc='upper left',\n", + " borderaxespad=0, ncol=2,\n", + " fontsize=15)\n", + "plt.xlabel('Time [d]',weight='bold',fontsize=17)\n", + "plt.title('Neutrons absorption distribution per neutron absorbed in fissile isotopes',\n", + " weight='bold', fontsize=17)\n", + "plt.xticks(fontsize=13)\n", + "plt.yticks(fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c0c7c200", + "metadata": {}, + "source": [ + "Here we can visualize the neutrons absorption for each nuclide present in the fuel per neutrons absorption by fissile isotopes. In other words we can see, out of the total neutrons generated, where and how many we end up losing.\n", + "\n", + "It is useful to group together those isotopes with similar characteristics that individually wouldn't represent a big contribution to capture.\n", + "\n", + "Due to the low burnup and relatively short simulation time, we are far from equilibrium. However, we can notice the quick increase of absorption in the Pu isotopes, created from neutron capture of U238, and in particular of Pu239, due to its higher absorption cross section than U235.\n", + "\n", + "**Note**: Neutrons lost to leakage out of the core and capture in other isotopes other than fuel are not represented. Thus, approximately 1 neutron is missing from the counting (we know that a fission event releases approximately 2.3 neutrons). This is simply due to the fact that we have defined the fuel salt as our only depletable material." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 69ae83b16f58bdfcd1c3901340104515f1902087 Mon Sep 17 00:00:00 2001 From: church89 Date: Tue, 23 Apr 2024 09:46:07 +0200 Subject: [PATCH 28/32] update script --- .../msre_depletion_transfer_rates.ipynb | 1049 +++++++++-------- 1 file changed, 535 insertions(+), 514 deletions(-) diff --git a/openmc_notebooks/msre_depletion_transfer_rates.ipynb b/openmc_notebooks/msre_depletion_transfer_rates.ipynb index 3834f53..7239204 100644 --- a/openmc_notebooks/msre_depletion_transfer_rates.ipynb +++ b/openmc_notebooks/msre_depletion_transfer_rates.ipynb @@ -16,17 +16,26 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "079fc782", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[ca-ws:1568059] mca_base_component_repository_open: unable to open mca_accelerator_rocm: libamdhip64.so.6: cannot open shared object file: No such file or directory (ignored)\n", + "rm: cannot remove '*.xml': No such file or directory\n", + "rm: cannot remove '*.h5': No such file or directory\n" + ] + }, { "data": { "text/plain": [ - "0" + "256" ] }, - "execution_count": 9, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -54,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "4bc6b094", "metadata": {}, "outputs": [], @@ -270,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "id": "edfa5bd1", "metadata": {}, "outputs": [ @@ -344,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "ba99fb4b", "metadata": {}, "outputs": [], @@ -377,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "id": "b9f8eada", "metadata": {}, "outputs": [ @@ -414,7 +423,7 @@ " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.14.1-dev\n", " Git SHA1 | d93ac83697e7fdb4fd4cc0f2705795398bf804bd\n", - " Date/Time | 2024-04-19 17:18:01\n", + " Date/Time | 2024-04-22 09:14:24\n", " MPI Processes | 1\n", " OpenMP Threads | 56\n", "\n", @@ -760,7 +769,22 @@ " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 294K\n", " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 600K\n", " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 900K\n", - " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 1200K\n", + " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 1200K\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Reading Mo93 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo93.h5\n", + " Reading Mo99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo99.h5\n", + " Reading Tc98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tc98.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 250K\n", " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 294K\n", " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 600K\n", @@ -773,9 +797,6 @@ "name": "stdout", "output_type": "stream", "text": [ - " Reading Mo93 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo93.h5\n", - " Reading Mo99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Mo99.h5\n", - " Reading Tc98 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tc98.h5\n", " Reading Tc99 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tc99.h5\n", " Reading Ru96 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru96.h5\n", " Reading Ru97 from /home/lorenzo/nuclear_data/endfb80_hdf5/Ru97.h5\n", @@ -1094,7 +1115,8 @@ " Reading Tm169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm169.h5\n", " Reading Tm170 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm170.h5\n", " Reading Tm171 from /home/lorenzo/nuclear_data/endfb80_hdf5/Tm171.h5\n", - " Reading Yb168 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb168.h5\n" + " Reading Yb168 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb168.h5\n", + " Reading Yb169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb169.h5\n" ] }, { @@ -1115,7 +1137,6 @@ "name": "stdout", "output_type": "stream", "text": [ - " Reading Yb169 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb169.h5\n", " Reading Yb170 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb170.h5\n", " Reading Yb171 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb171.h5\n", " Reading Yb172 from /home/lorenzo/nuclear_data/endfb80_hdf5/Yb172.h5\n", @@ -1369,7 +1390,8 @@ " Reading Cf250 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf250.h5\n", " Reading Cf251 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf251.h5\n", " Reading Cf252 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf252.h5\n", - " Reading Cf253 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf253.h5\n" + " Reading Cf253 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf253.h5\n", + " Reading Cf254 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf254.h5\n" ] }, { @@ -1387,7 +1409,6 @@ "name": "stdout", "output_type": "stream", "text": [ - " Reading Cf254 from /home/lorenzo/nuclear_data/endfb80_hdf5/Cf254.h5\n", " Reading Es251 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es251.h5\n", " Reading Es252 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es252.h5\n", " Reading Es253 from /home/lorenzo/nuclear_data/endfb80_hdf5/Es253.h5\n", @@ -1456,21 +1477,21 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.6673e+01 seconds\n", - " Reading cross sections = 1.5091e+01 seconds\n", - " Total time in simulation = 1.4075e+03 seconds\n", - " Time in transport only = 1.4071e+03 seconds\n", - " Time in inactive batches = 6.3276e+01 seconds\n", - " Time in active batches = 1.3442e+03 seconds\n", - " Time synchronizing fission bank = 1.9756e-01 seconds\n", - " Sampling source sites = 1.5885e-01 seconds\n", - " SEND/RECV source sites = 3.7839e-02 seconds\n", - " Time accumulating tallies = 6.4644e-02 seconds\n", - " Time writing statepoints = 9.7757e-03 seconds\n", - " Total time for finalization = 1.3423e-04 seconds\n", - " Total time elapsed = 1.4453e+03 seconds\n", - " Calculation Rate (inactive) = 9482.26 particles/second\n", - " Calculation Rate (active) = 669.536 particles/second\n", + " Total time for initialization = 3.6500e+01 seconds\n", + " Reading cross sections = 1.4960e+01 seconds\n", + " Total time in simulation = 7.9708e+02 seconds\n", + " Time in transport only = 7.9662e+02 seconds\n", + " Time in inactive batches = 6.4076e+01 seconds\n", + " Time in active batches = 7.3300e+02 seconds\n", + " Time synchronizing fission bank = 1.9497e-01 seconds\n", + " Sampling source sites = 1.5892e-01 seconds\n", + " SEND/RECV source sites = 3.5173e-02 seconds\n", + " Time accumulating tallies = 1.2986e-01 seconds\n", + " Time writing statepoints = 1.1006e-02 seconds\n", + " Total time for finalization = 9.3783e-05 seconds\n", + " Total time elapsed = 8.3474e+02 seconds\n", + " Calculation Rate (inactive) = 9363.88 particles/second\n", + " Calculation Rate (active) = 1227.83 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1489,82 +1510,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.60115\n", - " 2/1 0.88928\n", - " 3/1 0.94715\n", - " 4/1 0.97733\n", - " 5/1 0.99749\n", - " 6/1 1.00479\n", - " 7/1 0.99471\n", - " 8/1 0.99993\n", - " 9/1 0.99051\n", - " 10/1 1.01665\n", - " 11/1 0.99415\n", - " 12/1 1.01018\n", - " 13/1 1.01106\n", - " 14/1 1.01588\n", - " 15/1 1.00526\n", - " 16/1 1.00077\n", - " 17/1 1.00117\n", - " 18/1 1.00009\n", - " 19/1 0.99434\n", - " 20/1 1.00243\n", - " 21/1 1.00150\n", - " 22/1 0.99410 0.99780 +/- 0.00370\n", - " 23/1 1.00698 1.00086 +/- 0.00373\n", - " 24/1 1.00999 1.00314 +/- 0.00349\n", - " 25/1 1.01735 1.00598 +/- 0.00392\n", - " 26/1 1.00480 1.00579 +/- 0.00321\n", - " 27/1 1.00722 1.00599 +/- 0.00272\n", - " 28/1 0.99473 1.00458 +/- 0.00274\n", - " 29/1 1.00419 1.00454 +/- 0.00242\n", - " 30/1 1.00796 1.00488 +/- 0.00219\n", - " 31/1 1.00857 1.00522 +/- 0.00201\n", - " 32/1 0.99732 1.00456 +/- 0.00195\n", - " 33/1 1.00652 1.00471 +/- 0.00180\n", - " 34/1 0.99580 1.00407 +/- 0.00178\n", - " 35/1 0.99530 1.00349 +/- 0.00176\n", - " 36/1 1.01447 1.00418 +/- 0.00178\n", - " 37/1 1.01762 1.00497 +/- 0.00185\n", - " 38/1 0.99450 1.00439 +/- 0.00184\n", - " 39/1 0.99973 1.00414 +/- 0.00176\n", - " 40/1 0.99579 1.00372 +/- 0.00172\n", - " 41/1 1.00942 1.00399 +/- 0.00166\n", - " 42/1 1.00841 1.00420 +/- 0.00159\n", - " 43/1 0.99084 1.00361 +/- 0.00163\n", - " 44/1 1.00087 1.00350 +/- 0.00156\n", - " 45/1 1.00956 1.00374 +/- 0.00152\n", - " 46/1 1.00058 1.00362 +/- 0.00147\n", - " 47/1 1.00990 1.00385 +/- 0.00143\n", - " 48/1 1.00305 1.00382 +/- 0.00138\n", - " 49/1 1.00704 1.00394 +/- 0.00133\n", - " 50/1 1.01104 1.00417 +/- 0.00131\n", + " 1/1 0.59905\n", + " 2/1 0.89606\n", + " 3/1 0.95802\n", + " 4/1 0.99236\n", + " 5/1 0.99845\n", + " 6/1 0.99229\n", + " 7/1 0.98286\n", + " 8/1 0.97778\n", + " 9/1 0.99499\n", + " 10/1 1.01694\n", + " 11/1 1.00326\n", + " 12/1 1.01001\n", + " 13/1 0.98846\n", + " 14/1 1.01495\n", + " 15/1 1.00818\n", + " 16/1 1.00436\n", + " 17/1 0.99966\n", + " 18/1 1.00010\n", + " 19/1 1.00763\n", + " 20/1 1.00442\n", + " 21/1 0.98804\n", + " 22/1 0.99163 0.98983 +/- 0.00179\n", + " 23/1 1.00938 0.99635 +/- 0.00660\n", + " 24/1 1.01242 1.00037 +/- 0.00616\n", + " 25/1 1.00773 1.00184 +/- 0.00499\n", + " 26/1 0.99938 1.00143 +/- 0.00410\n", + " 27/1 1.00102 1.00137 +/- 0.00346\n", + " 28/1 1.00009 1.00121 +/- 0.00300\n", + " 29/1 0.98385 0.99928 +/- 0.00328\n", + " 30/1 0.99113 0.99847 +/- 0.00304\n", + " 31/1 0.99098 0.99779 +/- 0.00283\n", + " 32/1 0.99213 0.99731 +/- 0.00263\n", + " 33/1 0.99835 0.99739 +/- 0.00242\n", + " 34/1 0.99722 0.99738 +/- 0.00224\n", + " 35/1 0.98990 0.99688 +/- 0.00214\n", + " 36/1 1.00706 0.99752 +/- 0.00210\n", + " 37/1 0.99409 0.99732 +/- 0.00199\n", + " 38/1 0.99508 0.99719 +/- 0.00188\n", + " 39/1 0.99834 0.99725 +/- 0.00178\n", + " 40/1 1.00495 0.99764 +/- 0.00173\n", + " 41/1 0.99605 0.99756 +/- 0.00165\n", + " 42/1 1.01356 0.99829 +/- 0.00173\n", + " 43/1 1.00674 0.99866 +/- 0.00169\n", + " 44/1 1.01598 0.99938 +/- 0.00178\n", + " 45/1 1.00243 0.99950 +/- 0.00171\n", + " 46/1 0.99797 0.99944 +/- 0.00164\n", + " 47/1 1.01167 0.99989 +/- 0.00164\n", + " 48/1 1.00427 1.00005 +/- 0.00159\n", + " 49/1 1.01168 1.00045 +/- 0.00159\n", + " 50/1 1.00832 1.00071 +/- 0.00155\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 0.0000e+00 seconds\n", " Reading cross sections = 0.0000e+00 seconds\n", - " Total time in simulation = 1.8414e+03 seconds\n", - " Time in transport only = 1.8410e+03 seconds\n", - " Time in inactive batches = 2.8855e+02 seconds\n", - " Time in active batches = 1.5529e+03 seconds\n", - " Time synchronizing fission bank = 1.8210e-01 seconds\n", - " Sampling source sites = 1.4562e-01 seconds\n", - " SEND/RECV source sites = 3.5746e-02 seconds\n", - " Time accumulating tallies = 7.8137e-02 seconds\n", - " Time writing statepoints = 1.1256e-02 seconds\n", - " Total time for finalization = 1.4153e-04 seconds\n", - " Total time elapsed = 1.8424e+03 seconds\n", - " Calculation Rate (inactive) = 2079.33 particles/second\n", - " Calculation Rate (active) = 579.574 particles/second\n", + " Total time in simulation = 1.1922e+03 seconds\n", + " Time in transport only = 1.1918e+03 seconds\n", + " Time in inactive batches = 2.9643e+02 seconds\n", + " Time in active batches = 8.9582e+02 seconds\n", + " Time synchronizing fission bank = 1.6833e-01 seconds\n", + " Sampling source sites = 1.3534e-01 seconds\n", + " SEND/RECV source sites = 3.2420e-02 seconds\n", + " Time accumulating tallies = 1.2552e-01 seconds\n", + " Time writing statepoints = 1.0222e-02 seconds\n", + " Total time for finalization = 1.2883e-04 seconds\n", + " Total time elapsed = 1.1933e+03 seconds\n", + " Calculation Rate (inactive) = 2024.11 particles/second\n", + " Calculation Rate (active) = 1004.66 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.00479 +/- 0.00124\n", - " k-effective (Track-length) = 1.00417 +/- 0.00131\n", - " k-effective (Absorption) = 1.00426 +/- 0.00105\n", - " Combined k-effective = 1.00450 +/- 0.00092\n", + " k-effective (Collision) = 1.00028 +/- 0.00144\n", + " k-effective (Track-length) = 1.00071 +/- 0.00155\n", + " k-effective (Absorption) = 1.00027 +/- 0.00121\n", + " Combined k-effective = 0.99976 +/- 0.00115\n", " Leakage Fraction = 0.00001 +/- 0.00000\n", "\n", " Creating state point openmc_simulation_n1.h5...\n", @@ -1576,82 +1597,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.59346\n", - " 2/1 0.87731\n", - " 3/1 0.96109\n", - " 4/1 0.97331\n", - " 5/1 0.98716\n", - " 6/1 0.99491\n", - " 7/1 0.99851\n", - " 8/1 1.00039\n", - " 9/1 0.99998\n", - " 10/1 0.99704\n", - " 11/1 1.00638\n", - " 12/1 1.00982\n", - " 13/1 1.01091\n", - " 14/1 1.01678\n", - " 15/1 0.98923\n", - " 16/1 0.99254\n", - " 17/1 1.00578\n", - " 18/1 1.01132\n", - " 19/1 1.01314\n", - " 20/1 1.01694\n", - " 21/1 1.00736\n", - " 22/1 1.01141 1.00939 +/- 0.00203\n", - " 23/1 1.00158 1.00678 +/- 0.00285\n", - " 24/1 1.00589 1.00656 +/- 0.00203\n", - " 25/1 1.00737 1.00672 +/- 0.00158\n", - " 26/1 1.01017 1.00730 +/- 0.00141\n", - " 27/1 0.99982 1.00623 +/- 0.00160\n", - " 28/1 1.01095 1.00682 +/- 0.00151\n", - " 29/1 1.01166 1.00736 +/- 0.00143\n", - " 30/1 1.00725 1.00735 +/- 0.00128\n", - " 31/1 0.99733 1.00643 +/- 0.00148\n", - " 32/1 1.01437 1.00710 +/- 0.00150\n", - " 33/1 1.00339 1.00681 +/- 0.00141\n", - " 34/1 0.98085 1.00496 +/- 0.00227\n", - " 35/1 1.01442 1.00559 +/- 0.00220\n", - " 36/1 1.00294 1.00542 +/- 0.00207\n", - " 37/1 0.99744 1.00495 +/- 0.00200\n", - " 38/1 1.01074 1.00527 +/- 0.00191\n", - " 39/1 0.99945 1.00497 +/- 0.00183\n", - " 40/1 1.00435 1.00494 +/- 0.00174\n", - " 41/1 1.00735 1.00505 +/- 0.00166\n", - " 42/1 1.00603 1.00510 +/- 0.00158\n", - " 43/1 1.00323 1.00501 +/- 0.00151\n", - " 44/1 0.99800 1.00472 +/- 0.00148\n", - " 45/1 1.01305 1.00506 +/- 0.00146\n", - " 46/1 1.00507 1.00506 +/- 0.00140\n", - " 47/1 1.02376 1.00575 +/- 0.00151\n", - " 48/1 1.00153 1.00560 +/- 0.00147\n", - " 49/1 1.00894 1.00571 +/- 0.00142\n", - " 50/1 1.01304 1.00596 +/- 0.00139\n", + " 1/1 0.59951\n", + " 2/1 0.89383\n", + " 3/1 0.96249\n", + " 4/1 0.97631\n", + " 5/1 0.98446\n", + " 6/1 0.99669\n", + " 7/1 0.99834\n", + " 8/1 1.00950\n", + " 9/1 1.01054\n", + " 10/1 0.99328\n", + " 11/1 0.97327\n", + " 12/1 0.99273\n", + " 13/1 0.98182\n", + " 14/1 1.01219\n", + " 15/1 0.99945\n", + " 16/1 1.00978\n", + " 17/1 1.00249\n", + " 18/1 0.99919\n", + " 19/1 0.99317\n", + " 20/1 1.00091\n", + " 21/1 0.99973\n", + " 22/1 0.99912 0.99943 +/- 0.00030\n", + " 23/1 0.99789 0.99892 +/- 0.00054\n", + " 24/1 0.99658 0.99833 +/- 0.00070\n", + " 25/1 0.99600 0.99787 +/- 0.00071\n", + " 26/1 1.00965 0.99983 +/- 0.00205\n", + " 27/1 1.01420 1.00188 +/- 0.00269\n", + " 28/1 0.98691 1.00001 +/- 0.00299\n", + " 29/1 0.99490 0.99944 +/- 0.00269\n", + " 30/1 0.99755 0.99925 +/- 0.00242\n", + " 31/1 0.98216 0.99770 +/- 0.00268\n", + " 32/1 0.99123 0.99716 +/- 0.00251\n", + " 33/1 1.00182 0.99752 +/- 0.00233\n", + " 34/1 1.00869 0.99832 +/- 0.00230\n", + " 35/1 1.00672 0.99888 +/- 0.00222\n", + " 36/1 0.99972 0.99893 +/- 0.00207\n", + " 37/1 0.99412 0.99865 +/- 0.00197\n", + " 38/1 1.00012 0.99873 +/- 0.00186\n", + " 39/1 0.98822 0.99818 +/- 0.00184\n", + " 40/1 1.00183 0.99836 +/- 0.00176\n", + " 41/1 0.98764 0.99785 +/- 0.00175\n", + " 42/1 0.99374 0.99766 +/- 0.00168\n", + " 43/1 1.00587 0.99802 +/- 0.00164\n", + " 44/1 1.01337 0.99866 +/- 0.00170\n", + " 45/1 1.01178 0.99918 +/- 0.00171\n", + " 46/1 1.00958 0.99958 +/- 0.00169\n", + " 47/1 0.99599 0.99945 +/- 0.00163\n", + " 48/1 0.99515 0.99930 +/- 0.00158\n", + " 49/1 1.02132 1.00006 +/- 0.00170\n", + " 50/1 1.01018 1.00039 +/- 0.00168\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 0.0000e+00 seconds\n", " Reading cross sections = 0.0000e+00 seconds\n", - " Total time in simulation = 1.8190e+03 seconds\n", - " Time in transport only = 1.8186e+03 seconds\n", - " Time in inactive batches = 2.8913e+02 seconds\n", - " Time in active batches = 1.5299e+03 seconds\n", - " Time synchronizing fission bank = 1.8696e-01 seconds\n", - " Sampling source sites = 1.4822e-01 seconds\n", - " SEND/RECV source sites = 3.7953e-02 seconds\n", - " Time accumulating tallies = 9.6822e-02 seconds\n", - " Time writing statepoints = 1.1563e-02 seconds\n", - " Total time for finalization = 1.2487e-04 seconds\n", - " Total time elapsed = 1.8201e+03 seconds\n", - " Calculation Rate (inactive) = 2075.23 particles/second\n", - " Calculation Rate (active) = 588.286 particles/second\n", + " Total time in simulation = 1.1960e+03 seconds\n", + " Time in transport only = 1.1955e+03 seconds\n", + " Time in inactive batches = 2.9597e+02 seconds\n", + " Time in active batches = 8.9999e+02 seconds\n", + " Time synchronizing fission bank = 1.8753e-01 seconds\n", + " Sampling source sites = 1.4925e-01 seconds\n", + " SEND/RECV source sites = 3.7660e-02 seconds\n", + " Time accumulating tallies = 1.6143e-01 seconds\n", + " Time writing statepoints = 1.1208e-02 seconds\n", + " Total time for finalization = 1.3444e-04 seconds\n", + " Total time elapsed = 1.1971e+03 seconds\n", + " Calculation Rate (inactive) = 2027.2 particles/second\n", + " Calculation Rate (active) = 1000.01 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.00533 +/- 0.00143\n", - " k-effective (Track-length) = 1.00596 +/- 0.00139\n", - " k-effective (Absorption) = 1.00445 +/- 0.00114\n", - " Combined k-effective = 1.00527 +/- 0.00103\n", + " k-effective (Collision) = 1.00030 +/- 0.00176\n", + " k-effective (Track-length) = 1.00039 +/- 0.00168\n", + " k-effective (Absorption) = 1.00219 +/- 0.00116\n", + " Combined k-effective = 1.00173 +/- 0.00101\n", " Leakage Fraction = 0.00001 +/- 0.00000\n", "\n", " Creating state point openmc_simulation_n2.h5...\n", @@ -1663,82 +1684,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.61365\n", - " 2/1 0.90212\n", - " 3/1 0.95962\n", - " 4/1 1.00423\n", - " 5/1 1.00033\n", - " 6/1 1.00771\n", - " 7/1 1.00453\n", - " 8/1 1.00135\n", - " 9/1 1.01025\n", - " 10/1 1.01089\n", - " 11/1 1.00141\n", - " 12/1 1.00329\n", - " 13/1 1.00327\n", - " 14/1 1.00053\n", - " 15/1 1.00595\n", - " 16/1 1.00520\n", - " 17/1 1.00244\n", - " 18/1 1.01770\n", - " 19/1 1.00163\n", - " 20/1 1.01013\n", - " 21/1 1.01999\n", - " 22/1 1.00433 1.01216 +/- 0.00783\n", - " 23/1 1.00672 1.01035 +/- 0.00487\n", - " 24/1 0.99404 1.00627 +/- 0.00534\n", - " 25/1 1.01133 1.00728 +/- 0.00426\n", - " 26/1 1.00606 1.00708 +/- 0.00348\n", - " 27/1 0.99274 1.00503 +/- 0.00358\n", - " 28/1 1.00750 1.00534 +/- 0.00312\n", - " 29/1 1.00673 1.00549 +/- 0.00276\n", - " 30/1 1.01539 1.00648 +/- 0.00266\n", - " 31/1 1.00000 1.00589 +/- 0.00247\n", - " 32/1 1.01101 1.00632 +/- 0.00230\n", - " 33/1 1.01284 1.00682 +/- 0.00217\n", - " 34/1 0.99580 1.00603 +/- 0.00216\n", - " 35/1 1.00903 1.00623 +/- 0.00202\n", - " 36/1 1.01458 1.00676 +/- 0.00196\n", - " 37/1 1.00325 1.00655 +/- 0.00185\n", - " 38/1 0.99058 1.00566 +/- 0.00196\n", - " 39/1 1.00662 1.00571 +/- 0.00185\n", - " 40/1 0.98884 1.00487 +/- 0.00195\n", - " 41/1 1.01278 1.00525 +/- 0.00189\n", - " 42/1 0.99109 1.00460 +/- 0.00192\n", - " 43/1 0.99064 1.00400 +/- 0.00193\n", - " 44/1 1.01222 1.00434 +/- 0.00188\n", - " 45/1 1.01213 1.00465 +/- 0.00183\n", - " 46/1 1.00018 1.00448 +/- 0.00177\n", - " 47/1 0.99712 1.00421 +/- 0.00172\n", - " 48/1 1.00972 1.00440 +/- 0.00167\n", - " 49/1 1.00204 1.00432 +/- 0.00161\n", - " 50/1 0.98755 1.00376 +/- 0.00166\n", + " 1/1 0.61123\n", + " 2/1 0.87158\n", + " 3/1 0.95155\n", + " 4/1 0.99351\n", + " 5/1 1.00301\n", + " 6/1 0.98470\n", + " 7/1 0.99388\n", + " 8/1 0.99495\n", + " 9/1 0.99603\n", + " 10/1 0.99768\n", + " 11/1 1.01248\n", + " 12/1 0.98342\n", + " 13/1 0.98357\n", + " 14/1 0.99239\n", + " 15/1 1.00545\n", + " 16/1 1.00092\n", + " 17/1 1.00569\n", + " 18/1 1.00036\n", + " 19/1 0.98902\n", + " 20/1 1.01742\n", + " 21/1 1.00107\n", + " 22/1 1.00187 1.00147 +/- 0.00040\n", + " 23/1 1.01306 1.00533 +/- 0.00387\n", + " 24/1 1.01023 1.00656 +/- 0.00300\n", + " 25/1 0.99202 1.00365 +/- 0.00372\n", + " 26/1 1.00845 1.00445 +/- 0.00314\n", + " 27/1 0.98179 1.00121 +/- 0.00419\n", + " 28/1 1.00492 1.00168 +/- 0.00366\n", + " 29/1 0.99114 1.00051 +/- 0.00343\n", + " 30/1 0.99859 1.00031 +/- 0.00307\n", + " 31/1 1.01276 1.00145 +/- 0.00300\n", + " 32/1 0.99187 1.00065 +/- 0.00285\n", + " 33/1 1.00283 1.00082 +/- 0.00263\n", + " 34/1 0.99591 1.00047 +/- 0.00246\n", + " 35/1 0.98841 0.99966 +/- 0.00243\n", + " 36/1 1.00272 0.99985 +/- 0.00228\n", + " 37/1 1.00289 1.00003 +/- 0.00215\n", + " 38/1 0.99722 0.99988 +/- 0.00203\n", + " 39/1 0.99767 0.99976 +/- 0.00192\n", + " 40/1 0.99660 0.99960 +/- 0.00183\n", + " 41/1 0.99972 0.99961 +/- 0.00174\n", + " 42/1 1.00816 1.00000 +/- 0.00171\n", + " 43/1 0.99989 0.99999 +/- 0.00163\n", + " 44/1 0.98888 0.99953 +/- 0.00163\n", + " 45/1 1.01053 0.99997 +/- 0.00162\n", + " 46/1 1.00289 1.00008 +/- 0.00156\n", + " 47/1 1.00280 1.00018 +/- 0.00151\n", + " 48/1 0.99904 1.00014 +/- 0.00145\n", + " 49/1 1.00821 1.00042 +/- 0.00143\n", + " 50/1 1.00049 1.00042 +/- 0.00138\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 0.0000e+00 seconds\n", " Reading cross sections = 0.0000e+00 seconds\n", - " Total time in simulation = 1.8207e+03 seconds\n", - " Time in transport only = 1.8203e+03 seconds\n", - " Time in inactive batches = 2.8771e+02 seconds\n", - " Time in active batches = 1.5330e+03 seconds\n", - " Time synchronizing fission bank = 1.8680e-01 seconds\n", - " Sampling source sites = 1.4770e-01 seconds\n", - " SEND/RECV source sites = 3.8463e-02 seconds\n", - " Time accumulating tallies = 1.1871e-01 seconds\n", - " Time writing statepoints = 1.1733e-02 seconds\n", - " Total time for finalization = 1.0574e-04 seconds\n", - " Total time elapsed = 1.8217e+03 seconds\n", - " Calculation Rate (inactive) = 2085.41 particles/second\n", - " Calculation Rate (active) = 587.09 particles/second\n", + " Total time in simulation = 1.1738e+03 seconds\n", + " Time in transport only = 1.1734e+03 seconds\n", + " Time in inactive batches = 2.9980e+02 seconds\n", + " Time in active batches = 8.7403e+02 seconds\n", + " Time synchronizing fission bank = 1.7583e-01 seconds\n", + " Sampling source sites = 1.3959e-01 seconds\n", + " SEND/RECV source sites = 3.5731e-02 seconds\n", + " Time accumulating tallies = 9.2453e-02 seconds\n", + " Time writing statepoints = 1.1671e-02 seconds\n", + " Total time for finalization = 1.0126e-04 seconds\n", + " Total time elapsed = 1.1750e+03 seconds\n", + " Calculation Rate (inactive) = 2001.34 particles/second\n", + " Calculation Rate (active) = 1029.71 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.00440 +/- 0.00161\n", - " k-effective (Track-length) = 1.00376 +/- 0.00166\n", - " k-effective (Absorption) = 1.00555 +/- 0.00094\n", - " Combined k-effective = 1.00539 +/- 0.00101\n", + " k-effective (Collision) = 1.00044 +/- 0.00132\n", + " k-effective (Track-length) = 1.00042 +/- 0.00138\n", + " k-effective (Absorption) = 0.99942 +/- 0.00116\n", + " Combined k-effective = 0.99979 +/- 0.00108\n", " Leakage Fraction = 0.00001 +/- 0.00000\n", "\n", " Creating state point openmc_simulation_n3.h5...\n", @@ -1750,82 +1771,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.60296\n", - " 2/1 0.88599\n", - " 3/1 0.95124\n", - " 4/1 0.98034\n", - " 5/1 0.99482\n", - " 6/1 0.99609\n", - " 7/1 1.00739\n", - " 8/1 0.99006\n", - " 9/1 1.00694\n", - " 10/1 1.00602\n", - " 11/1 1.00145\n", - " 12/1 1.00226\n", - " 13/1 1.01503\n", - " 14/1 1.01195\n", - " 15/1 1.01079\n", - " 16/1 1.00054\n", - " 17/1 0.99742\n", - " 18/1 1.00577\n", - " 19/1 1.02088\n", - " 20/1 1.01246\n", - " 21/1 1.01198\n", - " 22/1 0.99621 1.00409 +/- 0.00788\n", - " 23/1 1.01483 1.00767 +/- 0.00579\n", - " 24/1 1.00061 1.00591 +/- 0.00446\n", - " 25/1 1.00577 1.00588 +/- 0.00345\n", - " 26/1 1.02079 1.00836 +/- 0.00376\n", - " 27/1 0.99468 1.00641 +/- 0.00373\n", - " 28/1 1.01004 1.00686 +/- 0.00326\n", - " 29/1 1.00174 1.00629 +/- 0.00293\n", - " 30/1 1.00836 1.00650 +/- 0.00263\n", - " 31/1 1.00349 1.00623 +/- 0.00240\n", - " 32/1 1.00414 1.00605 +/- 0.00219\n", - " 33/1 1.00559 1.00602 +/- 0.00202\n", - " 34/1 0.99914 1.00553 +/- 0.00193\n", - " 35/1 0.99346 1.00472 +/- 0.00197\n", - " 36/1 1.01869 1.00559 +/- 0.00204\n", - " 37/1 1.00135 1.00534 +/- 0.00193\n", - " 38/1 1.02210 1.00628 +/- 0.00205\n", - " 39/1 1.01476 1.00672 +/- 0.00199\n", - " 40/1 0.99363 1.00607 +/- 0.00199\n", - " 41/1 1.00109 1.00583 +/- 0.00191\n", - " 42/1 1.01019 1.00603 +/- 0.00183\n", - " 43/1 1.01389 1.00637 +/- 0.00178\n", - " 44/1 1.00938 1.00650 +/- 0.00171\n", - " 45/1 0.99871 1.00618 +/- 0.00167\n", - " 46/1 0.99567 1.00578 +/- 0.00166\n", - " 47/1 1.00367 1.00570 +/- 0.00160\n", - " 48/1 1.00425 1.00565 +/- 0.00154\n", - " 49/1 1.00738 1.00571 +/- 0.00149\n", - " 50/1 0.98480 1.00501 +/- 0.00160\n", + " 1/1 0.60920\n", + " 2/1 0.88238\n", + " 3/1 0.95497\n", + " 4/1 0.97401\n", + " 5/1 0.98079\n", + " 6/1 1.00531\n", + " 7/1 0.98860\n", + " 8/1 0.99611\n", + " 9/1 1.01045\n", + " 10/1 0.99669\n", + " 11/1 0.98725\n", + " 12/1 0.98875\n", + " 13/1 0.99812\n", + " 14/1 1.00441\n", + " 15/1 0.98289\n", + " 16/1 0.99336\n", + " 17/1 1.00776\n", + " 18/1 0.99696\n", + " 19/1 0.99040\n", + " 20/1 0.99449\n", + " 21/1 0.99139\n", + " 22/1 0.99801 0.99470 +/- 0.00331\n", + " 23/1 0.98397 0.99112 +/- 0.00405\n", + " 24/1 0.99908 0.99311 +/- 0.00349\n", + " 25/1 0.97751 0.98999 +/- 0.00413\n", + " 26/1 0.99818 0.99136 +/- 0.00364\n", + " 27/1 1.00552 0.99338 +/- 0.00368\n", + " 28/1 0.99768 0.99392 +/- 0.00323\n", + " 29/1 0.99227 0.99373 +/- 0.00286\n", + " 30/1 0.98219 0.99258 +/- 0.00280\n", + " 31/1 0.99263 0.99258 +/- 0.00254\n", + " 32/1 0.98981 0.99235 +/- 0.00233\n", + " 33/1 1.00244 0.99313 +/- 0.00228\n", + " 34/1 0.98570 0.99260 +/- 0.00217\n", + " 35/1 0.98772 0.99227 +/- 0.00205\n", + " 36/1 1.00208 0.99289 +/- 0.00201\n", + " 37/1 0.99180 0.99282 +/- 0.00189\n", + " 38/1 0.99222 0.99279 +/- 0.00178\n", + " 39/1 1.00119 0.99323 +/- 0.00174\n", + " 40/1 0.98831 0.99298 +/- 0.00167\n", + " 41/1 0.99386 0.99303 +/- 0.00159\n", + " 42/1 0.99413 0.99308 +/- 0.00152\n", + " 43/1 0.99594 0.99320 +/- 0.00146\n", + " 44/1 0.99504 0.99328 +/- 0.00140\n", + " 45/1 0.99611 0.99339 +/- 0.00134\n", + " 46/1 0.99503 0.99345 +/- 0.00129\n", + " 47/1 0.99561 0.99353 +/- 0.00125\n", + " 48/1 0.98118 0.99309 +/- 0.00128\n", + " 49/1 0.99013 0.99299 +/- 0.00124\n", + " 50/1 0.99924 0.99320 +/- 0.00121\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 0.0000e+00 seconds\n", " Reading cross sections = 0.0000e+00 seconds\n", - " Total time in simulation = 1.8171e+03 seconds\n", - " Time in transport only = 1.8167e+03 seconds\n", - " Time in inactive batches = 2.8780e+02 seconds\n", - " Time in active batches = 1.5293e+03 seconds\n", - " Time synchronizing fission bank = 1.8174e-01 seconds\n", - " Sampling source sites = 1.4617e-01 seconds\n", - " SEND/RECV source sites = 3.4910e-02 seconds\n", - " Time accumulating tallies = 8.8733e-02 seconds\n", - " Time writing statepoints = 9.8809e-03 seconds\n", - " Total time for finalization = 9.3226e-05 seconds\n", - " Total time elapsed = 1.8182e+03 seconds\n", - " Calculation Rate (inactive) = 2084.77 particles/second\n", - " Calculation Rate (active) = 588.496 particles/second\n", + " Total time in simulation = 1.1641e+03 seconds\n", + " Time in transport only = 1.1637e+03 seconds\n", + " Time in inactive batches = 2.8967e+02 seconds\n", + " Time in active batches = 8.7445e+02 seconds\n", + " Time synchronizing fission bank = 1.7591e-01 seconds\n", + " Sampling source sites = 1.4104e-01 seconds\n", + " SEND/RECV source sites = 3.4345e-02 seconds\n", + " Time accumulating tallies = 1.3762e-01 seconds\n", + " Time writing statepoints = 1.0267e-02 seconds\n", + " Total time for finalization = 1.0079e-04 seconds\n", + " Total time elapsed = 1.1652e+03 seconds\n", + " Calculation Rate (inactive) = 2071.3 particles/second\n", + " Calculation Rate (active) = 1029.21 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.00510 +/- 0.00149\n", - " k-effective (Track-length) = 1.00501 +/- 0.00160\n", - " k-effective (Absorption) = 1.00332 +/- 0.00142\n", - " Combined k-effective = 1.00403 +/- 0.00136\n", + " k-effective (Collision) = 0.99299 +/- 0.00117\n", + " k-effective (Track-length) = 0.99320 +/- 0.00121\n", + " k-effective (Absorption) = 0.99592 +/- 0.00103\n", + " Combined k-effective = 0.99467 +/- 0.00090\n", " Leakage Fraction = 0.00001 +/- 0.00000\n", "\n", " Creating state point openmc_simulation_n4.h5...\n", @@ -1837,82 +1858,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.60086\n", - " 2/1 0.86972\n", - " 3/1 0.96642\n", - " 4/1 0.97718\n", - " 5/1 0.99563\n", - " 6/1 0.99663\n", - " 7/1 0.99786\n", - " 8/1 1.00361\n", - " 9/1 0.99566\n", - " 10/1 1.01047\n", - " 11/1 0.99560\n", - " 12/1 1.01017\n", - " 13/1 1.00995\n", - " 14/1 1.00569\n", - " 15/1 0.99830\n", - " 16/1 1.00247\n", - " 17/1 1.00797\n", - " 18/1 1.01169\n", - " 19/1 0.98977\n", - " 20/1 1.00191\n", - " 21/1 1.00061\n", - " 22/1 1.01345 1.00703 +/- 0.00642\n", - " 23/1 0.99995 1.00467 +/- 0.00439\n", - " 24/1 1.00920 1.00580 +/- 0.00331\n", - " 25/1 0.99803 1.00425 +/- 0.00300\n", - " 26/1 1.01122 1.00541 +/- 0.00271\n", - " 27/1 0.98902 1.00307 +/- 0.00327\n", - " 28/1 0.99650 1.00225 +/- 0.00295\n", - " 29/1 1.01064 1.00318 +/- 0.00277\n", - " 30/1 1.00402 1.00326 +/- 0.00248\n", - " 31/1 0.99900 1.00288 +/- 0.00227\n", - " 32/1 1.01320 1.00374 +/- 0.00225\n", - " 33/1 1.00294 1.00367 +/- 0.00207\n", - " 34/1 1.01290 1.00433 +/- 0.00202\n", - " 35/1 1.00241 1.00421 +/- 0.00189\n", - " 36/1 0.99250 1.00347 +/- 0.00191\n", - " 37/1 0.99637 1.00306 +/- 0.00184\n", - " 38/1 1.01621 1.00379 +/- 0.00189\n", - " 39/1 1.00358 1.00378 +/- 0.00178\n", - " 40/1 1.00271 1.00372 +/- 0.00169\n", - " 41/1 1.01014 1.00403 +/- 0.00164\n", - " 42/1 1.00411 1.00403 +/- 0.00156\n", - " 43/1 1.01082 1.00433 +/- 0.00152\n", - " 44/1 1.01002 1.00456 +/- 0.00148\n", - " 45/1 1.01622 1.00503 +/- 0.00149\n", - " 46/1 0.99330 1.00458 +/- 0.00150\n", - " 47/1 1.00098 1.00445 +/- 0.00145\n", - " 48/1 1.01513 1.00483 +/- 0.00145\n", - " 49/1 1.00437 1.00481 +/- 0.00140\n", - " 50/1 1.00083 1.00468 +/- 0.00136\n", + " 1/1 0.59395\n", + " 2/1 0.87478\n", + " 3/1 0.94880\n", + " 4/1 0.97846\n", + " 5/1 0.98156\n", + " 6/1 0.99089\n", + " 7/1 0.99707\n", + " 8/1 0.99833\n", + " 9/1 0.98616\n", + " 10/1 0.99305\n", + " 11/1 1.01009\n", + " 12/1 1.00400\n", + " 13/1 1.00343\n", + " 14/1 1.01125\n", + " 15/1 0.99997\n", + " 16/1 0.99101\n", + " 17/1 0.98479\n", + " 18/1 1.00612\n", + " 19/1 0.98939\n", + " 20/1 1.00571\n", + " 21/1 0.99917\n", + " 22/1 0.98535 0.99226 +/- 0.00691\n", + " 23/1 0.97807 0.98753 +/- 0.00619\n", + " 24/1 0.98580 0.98710 +/- 0.00440\n", + " 25/1 1.01019 0.99171 +/- 0.00574\n", + " 26/1 0.99703 0.99260 +/- 0.00477\n", + " 27/1 0.99612 0.99310 +/- 0.00406\n", + " 28/1 1.00438 0.99451 +/- 0.00379\n", + " 29/1 1.00792 0.99600 +/- 0.00366\n", + " 30/1 0.99877 0.99628 +/- 0.00328\n", + " 31/1 0.98893 0.99561 +/- 0.00304\n", + " 32/1 0.99071 0.99520 +/- 0.00281\n", + " 33/1 1.01313 0.99658 +/- 0.00293\n", + " 34/1 0.99102 0.99618 +/- 0.00274\n", + " 35/1 0.99006 0.99577 +/- 0.00258\n", + " 36/1 0.99945 0.99600 +/- 0.00243\n", + " 37/1 0.99126 0.99573 +/- 0.00230\n", + " 38/1 1.00254 0.99610 +/- 0.00220\n", + " 39/1 1.01296 0.99699 +/- 0.00226\n", + " 40/1 0.98790 0.99654 +/- 0.00219\n", + " 41/1 0.99339 0.99639 +/- 0.00209\n", + " 42/1 1.00031 0.99657 +/- 0.00200\n", + " 43/1 0.99268 0.99640 +/- 0.00192\n", + " 44/1 0.99117 0.99618 +/- 0.00185\n", + " 45/1 0.99900 0.99629 +/- 0.00178\n", + " 46/1 0.97923 0.99564 +/- 0.00183\n", + " 47/1 0.99626 0.99566 +/- 0.00176\n", + " 48/1 0.99515 0.99564 +/- 0.00170\n", + " 49/1 0.98663 0.99533 +/- 0.00167\n", + " 50/1 1.00867 0.99577 +/- 0.00167\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 0.0000e+00 seconds\n", " Reading cross sections = 0.0000e+00 seconds\n", - " Total time in simulation = 1.8105e+03 seconds\n", - " Time in transport only = 1.8100e+03 seconds\n", - " Time in inactive batches = 2.8790e+02 seconds\n", - " Time in active batches = 1.5226e+03 seconds\n", - " Time synchronizing fission bank = 1.7775e-01 seconds\n", - " Sampling source sites = 1.4078e-01 seconds\n", - " SEND/RECV source sites = 3.6356e-02 seconds\n", - " Time accumulating tallies = 1.1440e-01 seconds\n", - " Time writing statepoints = 1.1593e-02 seconds\n", - " Total time for finalization = 1.8903e-04 seconds\n", - " Total time elapsed = 1.8115e+03 seconds\n", - " Calculation Rate (inactive) = 2084.08 particles/second\n", - " Calculation Rate (active) = 591.112 particles/second\n", + " Total time in simulation = 1.1668e+03 seconds\n", + " Time in transport only = 1.1664e+03 seconds\n", + " Time in inactive batches = 2.9723e+02 seconds\n", + " Time in active batches = 8.6962e+02 seconds\n", + " Time synchronizing fission bank = 1.7613e-01 seconds\n", + " Sampling source sites = 1.3993e-01 seconds\n", + " SEND/RECV source sites = 3.5642e-02 seconds\n", + " Time accumulating tallies = 1.1405e-01 seconds\n", + " Time writing statepoints = 1.0900e-02 seconds\n", + " Total time for finalization = 9.8751e-05 seconds\n", + " Total time elapsed = 1.1681e+03 seconds\n", + " Calculation Rate (inactive) = 2018.65 particles/second\n", + " Calculation Rate (active) = 1034.94 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.00489 +/- 0.00143\n", - " k-effective (Track-length) = 1.00468 +/- 0.00136\n", - " k-effective (Absorption) = 1.00411 +/- 0.00115\n", - " Combined k-effective = 1.00418 +/- 0.00112\n", + " k-effective (Collision) = 0.99546 +/- 0.00162\n", + " k-effective (Track-length) = 0.99577 +/- 0.00167\n", + " k-effective (Absorption) = 0.99554 +/- 0.00093\n", + " Combined k-effective = 0.99558 +/- 0.00097\n", " Leakage Fraction = 0.00001 +/- 0.00000\n", "\n", " Creating state point openmc_simulation_n5.h5...\n", @@ -1924,82 +1945,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.59708\n", - " 2/1 0.88271\n", - " 3/1 0.95662\n", - " 4/1 0.96192\n", - " 5/1 1.00702\n", - " 6/1 0.97276\n", - " 7/1 1.01247\n", - " 8/1 0.99829\n", - " 9/1 0.99887\n", - " 10/1 0.99240\n", - " 11/1 1.01194\n", - " 12/1 0.99297\n", - " 13/1 1.01286\n", - " 14/1 1.00783\n", - " 15/1 1.01223\n", - " 16/1 0.99347\n", - " 17/1 1.00024\n", - " 18/1 0.99622\n", - " 19/1 1.00014\n", - " 20/1 1.00125\n", - " 21/1 0.99719\n", - " 22/1 0.97588 0.98654 +/- 0.01065\n", - " 23/1 1.00592 0.99300 +/- 0.00892\n", - " 24/1 1.00911 0.99703 +/- 0.00748\n", - " 25/1 1.00455 0.99853 +/- 0.00599\n", - " 26/1 0.99838 0.99851 +/- 0.00489\n", - " 27/1 1.00942 1.00007 +/- 0.00442\n", - " 28/1 0.99898 0.99993 +/- 0.00383\n", - " 29/1 0.98653 0.99844 +/- 0.00369\n", - " 30/1 1.00646 0.99924 +/- 0.00340\n", - " 31/1 0.99763 0.99910 +/- 0.00308\n", - " 32/1 1.00461 0.99956 +/- 0.00285\n", - " 33/1 1.00540 1.00001 +/- 0.00266\n", - " 34/1 1.00215 1.00016 +/- 0.00246\n", - " 35/1 1.01281 1.00100 +/- 0.00244\n", - " 36/1 0.99520 1.00064 +/- 0.00231\n", - " 37/1 1.00471 1.00088 +/- 0.00219\n", - " 38/1 0.99427 1.00051 +/- 0.00209\n", - " 39/1 1.01149 1.00109 +/- 0.00206\n", - " 40/1 1.00084 1.00108 +/- 0.00196\n", - " 41/1 1.00232 1.00114 +/- 0.00186\n", - " 42/1 1.00407 1.00127 +/- 0.00178\n", - " 43/1 1.00735 1.00153 +/- 0.00172\n", - " 44/1 0.98486 1.00084 +/- 0.00179\n", - " 45/1 0.99805 1.00073 +/- 0.00172\n", - " 46/1 0.99078 1.00035 +/- 0.00170\n", - " 47/1 1.00270 1.00043 +/- 0.00163\n", - " 48/1 0.99237 1.00015 +/- 0.00160\n", - " 49/1 0.99284 0.99989 +/- 0.00157\n", - " 50/1 0.99736 0.99981 +/- 0.00151\n", + " 1/1 0.60126\n", + " 2/1 0.87245\n", + " 3/1 0.95624\n", + " 4/1 0.97141\n", + " 5/1 0.99008\n", + " 6/1 0.98539\n", + " 7/1 0.97347\n", + " 8/1 0.98761\n", + " 9/1 0.98760\n", + " 10/1 0.98178\n", + " 11/1 0.98329\n", + " 12/1 0.98290\n", + " 13/1 0.99169\n", + " 14/1 0.98385\n", + " 15/1 0.98329\n", + " 16/1 0.98600\n", + " 17/1 1.01095\n", + " 18/1 0.99684\n", + " 19/1 0.96558\n", + " 20/1 0.97788\n", + " 21/1 0.99445\n", + " 22/1 0.98070 0.98757 +/- 0.00688\n", + " 23/1 0.97964 0.98493 +/- 0.00477\n", + " 24/1 0.98686 0.98541 +/- 0.00341\n", + " 25/1 0.98554 0.98544 +/- 0.00264\n", + " 26/1 0.98777 0.98583 +/- 0.00219\n", + " 27/1 0.98990 0.98641 +/- 0.00194\n", + " 28/1 0.99333 0.98727 +/- 0.00189\n", + " 29/1 0.99888 0.98856 +/- 0.00211\n", + " 30/1 0.99786 0.98949 +/- 0.00210\n", + " 31/1 0.98268 0.98887 +/- 0.00200\n", + " 32/1 0.99090 0.98904 +/- 0.00183\n", + " 33/1 0.99132 0.98922 +/- 0.00170\n", + " 34/1 0.97072 0.98790 +/- 0.00205\n", + " 35/1 0.97238 0.98686 +/- 0.00217\n", + " 36/1 0.97156 0.98590 +/- 0.00225\n", + " 37/1 0.99956 0.98671 +/- 0.00226\n", + " 38/1 0.96919 0.98574 +/- 0.00234\n", + " 39/1 0.99332 0.98613 +/- 0.00225\n", + " 40/1 0.97164 0.98541 +/- 0.00225\n", + " 41/1 0.99085 0.98567 +/- 0.00216\n", + " 42/1 0.98472 0.98563 +/- 0.00206\n", + " 43/1 0.99120 0.98587 +/- 0.00198\n", + " 44/1 0.99101 0.98608 +/- 0.00191\n", + " 45/1 0.98239 0.98593 +/- 0.00184\n", + " 46/1 0.99074 0.98612 +/- 0.00178\n", + " 47/1 0.98124 0.98594 +/- 0.00172\n", + " 48/1 0.98247 0.98581 +/- 0.00166\n", + " 49/1 0.99072 0.98598 +/- 0.00161\n", + " 50/1 0.99106 0.98615 +/- 0.00157\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 0.0000e+00 seconds\n", " Reading cross sections = 0.0000e+00 seconds\n", - " Total time in simulation = 1.8224e+03 seconds\n", - " Time in transport only = 1.8220e+03 seconds\n", - " Time in inactive batches = 2.8825e+02 seconds\n", - " Time in active batches = 1.5341e+03 seconds\n", - " Time synchronizing fission bank = 1.8792e-01 seconds\n", - " Sampling source sites = 1.5020e-01 seconds\n", - " SEND/RECV source sites = 3.6929e-02 seconds\n", - " Time accumulating tallies = 8.4246e-02 seconds\n", - " Time writing statepoints = 1.1513e-02 seconds\n", - " Total time for finalization = 1.3029e-04 seconds\n", - " Total time elapsed = 1.8235e+03 seconds\n", - " Calculation Rate (inactive) = 2081.5 particles/second\n", - " Calculation Rate (active) = 586.658 particles/second\n", + " Total time in simulation = 1.1755e+03 seconds\n", + " Time in transport only = 1.1751e+03 seconds\n", + " Time in inactive batches = 2.9584e+02 seconds\n", + " Time in active batches = 8.7965e+02 seconds\n", + " Time synchronizing fission bank = 1.7278e-01 seconds\n", + " Sampling source sites = 1.3887e-01 seconds\n", + " SEND/RECV source sites = 3.3359e-02 seconds\n", + " Time accumulating tallies = 1.4017e-01 seconds\n", + " Time writing statepoints = 1.1532e-02 seconds\n", + " Total time for finalization = 1.0987e-04 seconds\n", + " Total time elapsed = 1.1766e+03 seconds\n", + " Calculation Rate (inactive) = 2028.14 particles/second\n", + " Calculation Rate (active) = 1023.13 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.99966 +/- 0.00148\n", - " k-effective (Track-length) = 0.99981 +/- 0.00151\n", - " k-effective (Absorption) = 0.99907 +/- 0.00130\n", - " Combined k-effective = 0.99935 +/- 0.00121\n", + " k-effective (Collision) = 0.98672 +/- 0.00143\n", + " k-effective (Track-length) = 0.98615 +/- 0.00157\n", + " k-effective (Absorption) = 0.98630 +/- 0.00088\n", + " Combined k-effective = 0.98668 +/- 0.00086\n", " Leakage Fraction = 0.00001 +/- 0.00000\n", "\n", " Creating state point openmc_simulation_n6.h5...\n", @@ -2011,83 +2032,83 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.60121\n", - " 2/1 0.88427\n", - " 3/1 0.95151\n", - " 4/1 0.96807\n", - " 5/1 0.97437\n", - " 6/1 0.98574\n", - " 7/1 0.99351\n", - " 8/1 0.99847\n", - " 9/1 1.01221\n", - " 10/1 0.99740\n", - " 11/1 0.99622\n", - " 12/1 1.00647\n", - " 13/1 1.00011\n", - " 14/1 0.98830\n", - " 15/1 1.00141\n", - " 16/1 0.99886\n", - " 17/1 0.99064\n", - " 18/1 0.98695\n", - " 19/1 0.99649\n", - " 20/1 0.98039\n", - " 21/1 1.00017\n", - " 22/1 1.00425 1.00221 +/- 0.00204\n", - " 23/1 0.98771 0.99738 +/- 0.00498\n", - " 24/1 1.01080 1.00073 +/- 0.00486\n", - " 25/1 0.99855 1.00030 +/- 0.00379\n", - " 26/1 1.00262 1.00068 +/- 0.00312\n", - " 27/1 1.00268 1.00097 +/- 0.00265\n", - " 28/1 1.00079 1.00095 +/- 0.00230\n", - " 29/1 0.99842 1.00067 +/- 0.00205\n", - " 30/1 0.99646 1.00024 +/- 0.00188\n", - " 31/1 0.99804 1.00004 +/- 0.00171\n", - " 32/1 0.98251 0.99858 +/- 0.00214\n", - " 33/1 0.98509 0.99755 +/- 0.00222\n", - " 34/1 0.99858 0.99762 +/- 0.00206\n", - " 35/1 1.00299 0.99798 +/- 0.00195\n", - " 36/1 0.99394 0.99773 +/- 0.00184\n", - " 37/1 0.99468 0.99755 +/- 0.00174\n", - " 38/1 0.99469 0.99739 +/- 0.00165\n", - " 39/1 0.99689 0.99736 +/- 0.00156\n", - " 40/1 1.02072 0.99853 +/- 0.00188\n", - " 41/1 0.99978 0.99859 +/- 0.00179\n", - " 42/1 1.00440 0.99885 +/- 0.00173\n", - " 43/1 0.98447 0.99823 +/- 0.00177\n", - " 44/1 0.99226 0.99798 +/- 0.00171\n", - " 45/1 0.99634 0.99791 +/- 0.00164\n", - " 46/1 0.99276 0.99771 +/- 0.00159\n", - " 47/1 0.99572 0.99764 +/- 0.00153\n", - " 48/1 1.00497 0.99790 +/- 0.00150\n", - " 49/1 1.01988 0.99866 +/- 0.00163\n", - " 50/1 1.00250 0.99879 +/- 0.00158\n", + " 1/1 0.59303\n", + " 2/1 0.86391\n", + " 3/1 0.93364\n", + " 4/1 0.96444\n", + " 5/1 0.97242\n", + " 6/1 0.97278\n", + " 7/1 0.98454\n", + " 8/1 0.97761\n", + " 9/1 0.98113\n", + " 10/1 0.98741\n", + " 11/1 0.98869\n", + " 12/1 0.98135\n", + " 13/1 0.98958\n", + " 14/1 0.98251\n", + " 15/1 0.97528\n", + " 16/1 0.98259\n", + " 17/1 0.99675\n", + " 18/1 0.98021\n", + " 19/1 0.98825\n", + " 20/1 0.96487\n", + " 21/1 0.97379\n", + " 22/1 0.98972 0.98175 +/- 0.00797\n", + " 23/1 0.98301 0.98217 +/- 0.00462\n", + " 24/1 0.98410 0.98265 +/- 0.00330\n", + " 25/1 0.97599 0.98132 +/- 0.00288\n", + " 26/1 0.97146 0.97968 +/- 0.00287\n", + " 27/1 0.98196 0.98000 +/- 0.00245\n", + " 28/1 0.98602 0.98076 +/- 0.00225\n", + " 29/1 0.97612 0.98024 +/- 0.00205\n", + " 30/1 0.98126 0.98034 +/- 0.00184\n", + " 31/1 0.99264 0.98146 +/- 0.00200\n", + " 32/1 0.97451 0.98088 +/- 0.00192\n", + " 33/1 0.98932 0.98153 +/- 0.00188\n", + " 34/1 0.98116 0.98150 +/- 0.00174\n", + " 35/1 0.97452 0.98104 +/- 0.00169\n", + " 36/1 0.98064 0.98101 +/- 0.00158\n", + " 37/1 0.97813 0.98084 +/- 0.00149\n", + " 38/1 0.98740 0.98121 +/- 0.00145\n", + " 39/1 0.99051 0.98170 +/- 0.00146\n", + " 40/1 0.98332 0.98178 +/- 0.00139\n", + " 41/1 0.98448 0.98191 +/- 0.00132\n", + " 42/1 0.97983 0.98181 +/- 0.00127\n", + " 43/1 0.97592 0.98156 +/- 0.00124\n", + " 44/1 1.00545 0.98255 +/- 0.00155\n", + " 45/1 0.99607 0.98309 +/- 0.00158\n", + " 46/1 1.00015 0.98375 +/- 0.00165\n", + " 47/1 0.97365 0.98338 +/- 0.00163\n", + " 48/1 0.98990 0.98361 +/- 0.00159\n", + " 49/1 0.98849 0.98378 +/- 0.00155\n", + " 50/1 1.00661 0.98454 +/- 0.00168\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 0.0000e+00 seconds\n", " Reading cross sections = 0.0000e+00 seconds\n", - " Total time in simulation = 1.8043e+03 seconds\n", - " Time in transport only = 1.8039e+03 seconds\n", - " Time in inactive batches = 2.8726e+02 seconds\n", - " Time in active batches = 1.5170e+03 seconds\n", - " Time synchronizing fission bank = 1.8194e-01 seconds\n", - " Sampling source sites = 1.4365e-01 seconds\n", - " SEND/RECV source sites = 3.7646e-02 seconds\n", - " Time accumulating tallies = 8.7450e-02 seconds\n", - " Time writing statepoints = 1.7304e-02 seconds\n", - " Total time for finalization = 1.0928e-04 seconds\n", - " Total time elapsed = 1.8054e+03 seconds\n", - " Calculation Rate (inactive) = 2088.7 particles/second\n", - " Calculation Rate (active) = 593.257 particles/second\n", + " Total time in simulation = 1.2161e+03 seconds\n", + " Time in transport only = 1.2157e+03 seconds\n", + " Time in inactive batches = 3.0233e+02 seconds\n", + " Time in active batches = 9.1382e+02 seconds\n", + " Time synchronizing fission bank = 1.9335e-01 seconds\n", + " Sampling source sites = 1.5557e-01 seconds\n", + " SEND/RECV source sites = 3.7010e-02 seconds\n", + " Time accumulating tallies = 1.3974e-01 seconds\n", + " Time writing statepoints = 1.3431e-02 seconds\n", + " Total time for finalization = 1.3210e-04 seconds\n", + " Total time elapsed = 1.2174e+03 seconds\n", + " Calculation Rate (inactive) = 1984.6 particles/second\n", + " Calculation Rate (active) = 984.875 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.99850 +/- 0.00153\n", - " k-effective (Track-length) = 0.99879 +/- 0.00158\n", - " k-effective (Absorption) = 0.99839 +/- 0.00119\n", - " Combined k-effective = 0.99850 +/- 0.00118\n", - " Leakage Fraction = 0.00001 +/- 0.00000\n", + " k-effective (Collision) = 0.98444 +/- 0.00153\n", + " k-effective (Track-length) = 0.98454 +/- 0.00168\n", + " k-effective (Absorption) = 0.98138 +/- 0.00115\n", + " Combined k-effective = 0.98218 +/- 0.00120\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n", " Creating state point openmc_simulation_n7.h5...\n" ] @@ -2115,7 +2136,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "id": "41d076d2", "metadata": {}, "outputs": [], @@ -2141,13 +2162,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "826c26f9", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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          " ] @@ -2162,10 +2183,10 @@ "\n", "plt.figure()\n", "ax = plt.subplot()\n", - "k1, = ax.plot(t, [k[0] for k in k], '--', c='red', label='keff wo removal rates')\n", + "k1, = ax.plot(t, [k[0] for k in k], 'o-', c='red', label='keff wo removal rates')\n", "ax1 = ax.twinx()\n", - "n1, = ax1.plot(t, n_xe, '--', c='green', label='Xe wo removal rates')\n", - "n3, = ax1.plot(t, n_kr, '--', c='blue', label='Kr wo removal rates')\n", + "n1, = ax1.plot(t, n_xe, '--', c='black', label='Xe wo removal rates')\n", + "n3, = ax1.plot(t, n_kr, c='black', label='Kr wo removal rates')\n", "ax.set_xlabel('Time[d]')\n", "ax.set_ylabel(r'$k_{eff}$', color='r')\n", "ax.tick_params(axis='y', colors='red')\n", @@ -2191,13 +2212,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "id": "dc07279e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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gWQoAAMswpRBBbAUHAMB6jLoRFCgopuYGAADLEG4iyD9zkxClNUMAAEQjRt0I8hcUO6m5AQDAMoSbCAoUFLMsBQCAZQg3EeT2sRUcAACrhbQV/Lvf/W7YL7x48WJlZGSE/bxY4vVxV3AAAKwWUrh56aWX9P3vf1/Jyckhveizzz6rQ4cOnfPhpnFZipkbAACsEvJF/H75y1+GHFZWrFhx2g2KJW62ggMAYLmQphTWrFmjDh06hPyiK1euVNeuXU+7UbHCw7IUAACWC2nm5tJLLw3rRf/jP/7jtBoTawIzN1znBgAAy5zRvaVeeeUVrV27Vl6vV9/61rc0btw4s9oVE/w1NyxLAQBgndOeUnjwwQd13333yeFwyDAMTZ8+XXfddZeZbYt6nuNbwSkoBgDAOiHP3GzevFlDhw4N/Lx8+XK98847gR1Ut9xyi0aNGqUnn3zS/FZGKWpuAACwXshTCrfffrumTZumI0eOSJJ69uypJ554QhUVFdq+fbsWLVqkb3zjGxFraDRiKzgAANYLedTduHGjOnfurIsuukh/+9vftHTpUm3btk0jRozQJZdcok8//VTPPvtsJNsaddgKDgCA9UJelnI6nZoxY4a+973vacqUKTrvvPP01FNPqUuXLpFsX1TzL0tx40wAAKwT9npJz5499dprr+n666/XyJEjtXDhwki0KyZ4vBQUAwBgtZBH3YMHD+q+++7TNddcowceeEDXX3+9Nm7cqLffflvDhw/X9u3bI9nOqOT2UlAMAIDVQg43kyZN0saNG/Wd73xHFRUVmjJlijp27Khly5bpZz/7mcaPH68ZM2ZEsq1Rh63gAABYL+Sam9dff13btm1T7969VVRUpN69ewceu/zyy7V161Y98sgjEWlktArcFZyCYgAALBPylEKfPn309NNP68MPP9TixYvVrVu3oMeTkpL06KOPmt7AaNa4LMXMDQAAVgl51F26dKlef/11DRkyRM8++6wWLVoUyXbFhMaCYmZuAACwSsjLUoMHD9bmzZsj2ZaY42YrOAAAlmO9JILYCg4AgPVCGnU7dOigAwcOhPyiF1xwgT755JPTblQs8PkMHZ+4YSs4AAAWCmlZ6uDBg1q5cqXS0tJCetEvv/xSXq/3jBoW7dzHt4FLUjwzNwAAWCbkmptJkyZFsh0xx78NXKKgGAAAK4UUbnxNZiEQGv82cImt4AAAWIlRN0L8xcQSNTcAAFiJcBMh/juCxzmkOMINAACWIdxEiPv4zA3FxAAAWIuRN0I8x2tuEpi1AQDAUoSbCPEEbprJrxgAACuFvBW8KZ/Pp127dmnfvn3NdlKNHDnSlIZFO4+P+0oBAGCHsMPNhg0bdOONN+qTTz6RYRhBjzkcjnP+4n1+Hu4IDgCALcION7fffruGDh2qV155RZ07d5bDwcxESxoLivn9AABgpbDDzUcffaQVK1aod+/ekWhPzAjU3FBQDACApcJeM8nLy9OuXbsi0ZaYwlZwAADsEfbMzV133aV7771XVVVVGjhwoBISEoIeHzRokGmNi2aNNTfM3AAAYKWww824ceMkSZMnTw4cczgcMgyDguIm/DfOTGDmBgAAS4Udbvbs2ROJdsQcCooBALBH2OGmW7dukWhHzPEXFCewFRwAAEud1kX8du/erQULFmjnzp2SpH79+umee+5Rr169TG1cNGPmBgAAe4Q9rfDaa6+pX79+2rRpkwYNGqRBgwZp48aN6t+/v1avXh2JNkYlf0Gxk4JiAAAsFfbMzcyZMzV9+nTNnTu32fEZM2boiiuuMK1x0azx9gssSwEAYKWwR96dO3fq1ltvbXZ88uTJev/9901pVCxwsxUcAABbhB1uOnXqpPLy8mbHy8vLlZGRYUabYgJbwQEAsEfYy1JFRUW67bbb9K9//UsjRoyQJL355pt67LHHVFxcbHoDoxUFxQAA2CPscPPggw+qbdu2euKJJzRr1ixJUpcuXfTQQw/p7rvvNr2B0arx3lLM3AAAYKWww43D4dD06dM1ffp01dXVSZLatm1resOincfrLyhm5gYAACud1nVu/Ag1rXOzFRwAAFuEFG4uuugilZaWqn379hoyZIgcjtYH7K1bt5rWuGjGVnAAAOwRUri57rrr5HK5At+fLNycjoULF+rxxx9XVVWVcnJy9OSTT2rYsGEtnvvCCy/o0Ucf1a5du+R2u9WnTx/de++9uvnmm01t05niruAAANgjpHAzZ86cwPcPPfSQqQ1Yvny5iouLtXjxYuXl5WnBggUaPXq0KioqWtxa3qFDB91///3q27evEhMT9fe//12FhYXKyMjQ6NGjTW3bmQgUFDNzAwCApcIeeXv27Kkvv/yy2fGDBw+qZ8+eYTdg/vz5KioqUmFhofr166fFixerTZs2Wrp0aYvnjxo1Stdff72++c1vqlevXrrnnns0aNAgrV+/vsXz6+vrVVtbG/RlBQqKAQCwR9jh5uOPP5bX6212vL6+Xp9++mlYr9XQ0KAtW7aooKCgsUFxcSooKFBZWdkpn28YhkpLS1VRUaGRI0e2eE5JSYnS0tICX9nZ2WG18XS52QoOAIAtQt4t9fLLLwe+f+2115SWlhb42ev1qrS0VD169AjrzQ8cOCCv16vMzMyg45mZmfrggw9afV5NTY26du2q+vp6OZ1O/epXv2r1nlazZs0KurhgbW2tJQHHw0X8AACwRcjhZuzYsZKOXedm0qRJQY8lJCSoe/fueuKJJ0xtXGvatm2r8vJyHTp0SKWlpSouLlbPnj01atSoZue6XK5AMbSVKCgGAMAeIYcb3/GtzT169NDbb7+t9PT0M37z9PR0OZ1OVVdXBx2vrq5WVlZWq8+Li4tT7969JUmDBw/Wzp07VVJS0mK4sYubgmIAAGwR9si7Z88eU4KNJCUmJio3N1elpaWBYz6fT6WlpcrPzw/5dXw+n+rr601pk1koKAYAwB5hX6H47rvvVu/evZvdR+qpp57Srl27tGDBgrBer7i4WJMmTdLQoUM1bNgwLViwQIcPH1ZhYaEkaeLEieratatKSkokHSsQHjp0qHr16qX6+nq9+uqr+sMf/qBFixaF25WI4t5SAADYI+xw8/zzzwcVF/uNGDFCc+fODTvcjB8/Xvv379fs2bNVVVWlwYMHa9WqVYEi48rKSsU1CQiHDx/WHXfcoU8//VTJycnq27ev/vjHP2r8+PHhdiWiKCgGAMAeDsMwjHCekJSUpB07dgRqXvx27dqlAQMG6OjRo6Y20Gy1tbVKS0tTTU2NUlNTI/Y+N/92o9746ID+Z3yOrh9yfsTeBwCAc0E443fYaya9e/fWqlWrmh1fuXLlaV3EL1a5j8/cOFmWAgDAUmEvSxUXF2vq1Knav3+/LrvsMklSaWmpnnjiibCXpGKZfyt4AlvBAQCwVNjhZvLkyaqvr9fPfvYz/fSnP5Ukde/eXYsWLdLEiRNNb2C0Yis4AAD2CDvcSNKUKVM0ZcoU7d+/X8nJyUpJSTG7XVHP66OgGAAAO5xWuPHr1KmTWe2IOY3LUszcAABgpdMKNytWrNCf//xnVVZWqqGhIeixrVu3mtKwaOdmKzgAALYIe1rhl7/8pQoLC5WZmalt27Zp2LBh6tixo/71r39pzJgxkWhjVPJfxI8rFAMAYK2ww82vfvUrPf3003ryySeVmJio++67T6tXr9bdd9+tmpqaSLQxKvmXpdgKDgCAtcIeeSsrKzVixAhJUnJysurq6iRJN998s/70pz+Z27ooFliWYis4AACWCjvcZGVl6auvvpIkXXDBBdqwYYOkYzfUDPNixzGtcVmKmRsAAKwU9sh72WWXBe4tVVhYqOnTp+uKK67Q+PHjdf3115vewGjFvaUAALBH2Lulnn76afmOX8PlzjvvVMeOHfXWW2/p2muv1Q9/+EPTGxitAjM31NwAAGCpsMKNx+PRo48+qsmTJ+v884/dDPKGG27QDTfcEJHGRTN/QTEzNwAAWCusaYX4+Hj9/Oc/l8fjiVR7YoabKxQDAGCLsNdMLr/8cq1bty4SbYkZXp8hf211PMtSAABYKuyamzFjxmjmzJnavn27cnNzdd555wU9fu2115rWuGjl3wYuMXMDAIDVwg43d9xxhyRp/vz5zR5zOBzyer1n3qoo5y8mligoBgDAamGHG/9OKbTO620MN8zcAABgrZCmFTp06KADBw5IkiZPnhy4KjFa5m4SALlCMQAA1gop3DQ0NKi2tlaS9Pvf/15Hjx6NaKOiXWAbeJxDDgfhBgAAK4W0LJWfn6+xY8cqNzdXhmHo7rvvVnJycovnLl261NQGRiM3VycGAMA2IYWbP/7xj/qf//kf7d69Ww6HQzU1NczenIS/oJht4AAAWC+kcJOZmam5c+dKknr06KE//OEP6tixY0QbFs24rxQAAPYJe7fUnj17ItGOmOL2MnMDAIBdGH0jwOu/aSYzNwAAWI5wEwHcVwoAAPsQbiLAvxWcqxMDAGA9Rt8IoKAYAAD7hF1QLB27BcOuXbu0b9++ZrdjGDlypCkNi2bu4zU3TmZuAACwXNjhZsOGDbrxxhv1ySefyDCMoMe4ceYx/pkbCooBALBe2OHm9ttv19ChQ/XKK6+oc+fO3F6gBY0X8eN3AwCA1cIONx999JFWrFih3r17R6I9MSFwbykny1IAAFgt7NE3Ly9Pu3btikRbYobHx7IUAAB2CXvm5q677tK9996rqqoqDRw4UAkJCUGPDxo0yLTGRSuuUAwAgH3CDjfjxo2TJE2ePDlwzOFwyDAMCoqPo6AYAAD7cG+pCGjcCk64AQDAamGHm27dukWiHTGl8SJ+LEsBAGC107qI3+7du7VgwQLt3LlTktSvXz/dc8896tWrl6mNi1aBG2cycwMAgOXCnlp47bXX1K9fP23atEmDBg3SoEGDtHHjRvXv31+rV6+ORBujjput4AAA2CbsmZuZM2dq+vTpmjt3brPjM2bM0BVXXGFa46IVBcUAANgn7KmFnTt36tZbb212fPLkyXr//fdNaVS0c/vYCg4AgF3CHn07deqk8vLyZsfLy8uVkZFhRpuiHncFBwDAPmEvSxUVFem2227Tv/71L40YMUKS9Oabb+qxxx5TcXGx6Q2MRtxbCgAA+4Qdbh588EG1bdtWTzzxhGbNmiVJ6tKlix566CHdfffdpjcwGrnZCg4AgG3CDjcOh0PTp0/X9OnTVVdXJ0lq27at6Q2LZmwFBwDAPqd1nRs/Qk3L2AoOAIB9Qgo3F110kUpLS9W+fXsNGTJEDkfrMxJbt241rXHRioJiAADsE1K4ue666+RyuSRJY8eOjWR7YoInsCzFzA0AAFYLKdzMmTOnxe/RMjczNwAA2CbsqYW9e/fq008/Dfy8adMmTZs2TU8//bSpDYtmHi9bwQEAsEvY4ebGG2/UmjVrJElVVVUqKCjQpk2bdP/99+uRRx4xvYHRyONjKzgAAHYJe/TdsWOHhg0bJkn685//rIEDB+qtt97SM888o2XLlpndvqjERfwAALBP2OHG7XYHiov/+c9/6tprr5Uk9e3bV1988cVpNWLhwoXq3r27kpKSlJeXp02bNrV67pIlS3TJJZeoffv2at++fWDm6GziX5ZKYOYGAADLhT369u/fX4sXL9Ybb7yh1atX66qrrpIkff755+rYsWPYDVi+fLmKi4s1Z84cbd26VTk5ORo9erT27dvX4vlr167VhAkTtGbNGpWVlSk7O1tXXnmlPvvss7DfO1IoKAYAwD5hh5vHHntMv/71rzVq1ChNmDBBOTk5kqSXX345sFwVjvnz56uoqEiFhYXq16+fFi9erDZt2mjp0qUtnv/MM8/ojjvu0ODBg9W3b1/95je/kc/nU2lpadjvHSke7goOAIBtwr5C8ahRo3TgwAHV1taqffv2geO33Xab2rRpE9ZrNTQ0aMuWLYF7VElSXFycCgoKVFZWFtJrHDlyRG63Wx06dGjx8fr6etXX1wd+rq2tDauNpyNwET9qbgAAsNxpTS04nU55PB6tX79e69ev1/79+9W9e3dlZGSE9ToHDhyQ1+tVZmZm0PHMzExVVVWF9BozZsxQly5dVFBQ0OLjJSUlSktLC3xlZ2eH1cbT0Xj7BcINAABWCzvcHD58WJMnT1bnzp01cuRIjRw5Ul26dNGtt96qI0eORKKNrZo7d66ee+45vfjii0pKSmrxnFmzZqmmpibwtXfv3oi3y78VnIJiAACsF/boW1xcrHXr1ulvf/ubDh48qIMHD+qvf/2r1q1bp3vvvTes10pPT5fT6VR1dXXQ8erqamVlZZ30ufPmzdPcuXP1j3/8Q4MGDWr1PJfLpdTU1KCvSGMrOAAA9gk73Dz//PP67W9/qzFjxgTCwtVXX60lS5ZoxYoVYb1WYmKicnNzg4qB/cXB+fn5rT7v5z//uX76059q1apVGjp0aLhdiDgPdwUHAMA2YRcUHzlypFmNjCRlZGSc1rJUcXGxJk2apKFDh2rYsGFasGCBDh8+rMLCQknSxIkT1bVrV5WUlEg6tltr9uzZevbZZ9W9e/dAbU5KSopSUlLCfv9I8BcUJ1BzAwCA5cKeWsjPz9ecOXN09OjRwLGvv/5aDz/88ElnW1ozfvx4zZs3T7Nnz9bgwYNVXl6uVatWBQJUZWVl0MUBFy1apIaGBv3nf/6nOnfuHPiaN29e2O8dKW62ggMAYBuHYRhGOE/YsWOHRo8erfr6+sA1bt555x0lJSXptddeU//+/SPSULPU1tYqLS1NNTU1Eau/GfLIP/TvI279Y/pIfSOzbUTeAwCAc0k443fYy1IDBgzQRx99pGeeeUYffPCBJGnChAm66aablJycfHotjjHcFRwAAPuEHW4kqU2bNioqKjK7LTHDv1uKreAAAFjvtMJNRUWFnnzySe3cuVOS9M1vflNTp05V3759TW1ctPJf54aL+AEAYL3T2go+YMAAbdmyRTk5OcrJydHWrVs1cOBAPf/885FoY1QxDKPxCsUUFAMAYLmwZ27uu+8+zZo1S4888kjQ8Tlz5ui+++7TuHHjTGtcNPL6Guuz2QoOAID1wp5a+OKLLzRx4sRmx//rv/4raMv2ucrTJNxwET8AAKwX9ug7atQovfHGG82Or1+/XpdccokpjYpm7uMX8JPYLQUAgB3CXpa69tprNWPGDG3ZskXDhw+XJG3YsEF/+ctf9PDDD+vll18OOvdc498GLhFuAACwQ9gX8YsLsUjW4XDI6/WeVqMiKdIX8dtfV6+Lf/ZPSdKekqvlcBBwAAA4UxG9iJ/P5zv1Secw/zbwBKeDYAMAgA2oeDWZh23gAADYihHYZP6CYi7gBwCAPQg3JuPWCwAA2IsR2GT+mRsnO6UAALAF4cZk/pqbBMINAAC2OK1ws3v3bj3wwAOaMGGC9u3bJ0lauXKl3nvvPVMbF438y1JcnRgAAHuEPQKvW7dOAwcO1MaNG/XCCy/o0KFDkqR33nlHc+bMMb2B0cZDQTEAALYKO9zMnDlT//3f/63Vq1crMTExcPyyyy7Thg0bTG1cNAoUFLMVHAAAW4Q9Am/fvl3XX399s+MZGRk6cOCAKY2KZmwFBwDAXmGHm3bt2rV49+9t27apa9eupjQqmgUu4kfNDQAAtgh7BL7hhhs0Y8YMVVVVyeFwyOfz6c0339SPfvQjTZw4MRJtjCr+2y9w00wAAOwRdrh59NFH1bdvX2VnZ+vQoUPq16+fRo4cqREjRuj++++PRBujijtw+wXCDQAAdgj7xpmJiYlasmSJZs+ere3bt+vQoUMaMmSI+vTpE4n2RR0vVygGAMBWIY/ADz74oDweT+Dn7OxsXX311fr+97+vPn36qLKyUldccUVEGhlNKCgGAMBeIYeb3//+97r44ou1Y8eOZo/9+te/1oABAxQfH/ZEUMwJXMSPreAAANgi5BF4x44dGjhwoIYOHaqSkhL5fD5VVlaqoKBA9913n+bNm6eVK1dGsq1RwX8RvwRmbgAAsEXIUy2pqan63//9X40bN04//OEPtXz5cu3Zs0fDhg3Tu+++q27dukWynVHDzVZwAABsFfYIPHz4cA0cOFDvvvuufD6fHnjgAYJNE2wFBwDAXmGFmz/96U/q16+ffD6fdu7cqSlTpujKK6/U9OnTdfTo0Ui1Mao01twQbgAAsEPI4WbcuHEqKirSQw89pNLSUl144YX6+c9/rjVr1ujVV19VTk6OysrKItnWqMAVigEAsFfINTdVVVXatm1bs+vZjBgxQuXl5Zo5c6YuvfRSNTQ0mN7IaEJBMQAA9go53LzxxhuKa2V7c3Jysn7xi19o3LhxpjUsWrnZCg4AgK1CHoFbCzZNjRw58owaEwuYuQEAwF5ML5iscSs44QYAADsQbkzm3wruZFkKAABbMAKbLHDjTLaCAwBgC8KNybhCMQAA9mIENhkFxQAA2ItwYzI3VygGAMBWhBuT+WduWJYCAMAejMAm899+gWUpAADsQbgxmX9Ziq3gAADYgxHYZF4fBcUAANiJcGOywFZwZm4AALAFI7DJGguKmbkBAMAOhBuTeXwUFAMAYCfCjclYlgIAwF6MwCYLLEtxET8AAGxBuDGZf1mKi/gBAGAPRmCTeXwUFAMAYCfCjckCVyim5gYAAFswApssUFDMzA0AALYg3JjMwxWKAQCwle3hZuHCherevbuSkpKUl5enTZs2tXrue++9p3Hjxql79+5yOBxasGCBdQ0NkYet4AAA2MrWEXj58uUqLi7WnDlztHXrVuXk5Gj06NHat29fi+cfOXJEPXv21Ny5c5WVlWVxa0PjPr4V3MlWcAAAbGFruJk/f76KiopUWFiofv36afHixWrTpo2WLl3a4vkXX3yxHn/8cd1www1yuVwWtzY0jVcoZuYGAAA72DYCNzQ0aMuWLSooKGhsTFycCgoKVFZWZtr71NfXq7a2NugrUgzDkNdHQTEAAHayLdwcOHBAXq9XmZmZQcczMzNVVVVl2vuUlJQoLS0t8JWdnW3aa5/IP2sjsRUcAAC7xPwIPGvWLNXU1AS+9u7dG7H38hcTS8zcAABgl3i73jg9PV1Op1PV1dVBx6urq00tFna5XJbV57iPbwOXCDcAANjFtpmbxMRE5ebmqrS0NHDM5/OptLRU+fn5djXrjDSduWFZCgAAe9g2cyNJxcXFmjRpkoYOHaphw4ZpwYIFOnz4sAoLCyVJEydOVNeuXVVSUiLpWBHy+++/H/j+s88+U3l5uVJSUtS7d2/b+uHnvyO4wyHFsRUcAABb2Bpuxo8fr/3792v27NmqqqrS4MGDtWrVqkCRcWVlpeKazIB8/vnnGjJkSODnefPmad68ebr00ku1du1aq5vfTGAbOLM2AADYxmEYhnHq02JHbW2t0tLSVFNTo9TUVFNfu/LLIxr5+Bq1SXTq/UeuMvW1AQA4l4UzfjPFYCJ/QXE8S1IAANiGcGMif0ExVycGAMA+jMIm8t9Xim3gAADYh3BjIn9BMXcEBwDAPozCJvIwcwMAgO0INyZqnLkh3AAAYBfCjYkoKAYAwH6MwiYKbAVnWQoAANsQbkzkn7mhoBgAAPswCpvIX1CcwMwNAAC2IdyYyM1WcAAAbMcobCK2ggMAYD/CjYnYCg4AgP0INyYKFBSzFRwAANswCpvI46OgGAAAuxFuTORmKzgAALZjFDYRBcUAANiPcGMif0FxAjM3AADYhlHYRO7jMzdOZm4AALAN4cZE3sDMDeEGAAC7EG5M5GYrOAAAtmMUNhEFxQAA2I9wYyIKigEAsB+jsInczNwAAGA7wo2J/LdfSKDmBgAA2zAKm8i/LOVktxQAALYh3JjIf28p7goOAIB9CDcmYlkKAAD7MQqbiIJiAADsR7gxEVvBAQCwH6OwiZi5AQDAfoQbE3m4/QIAALZjFDaR/8aZ7JYCAMA+hBsTudkKDgCA7Qg3JmIrOAAA9mMUNhEFxQAA2I9wYyJPoOaGXysAAHZhFDaR5/jMTQIzNwAA2IZwYyK3lxtnAgBgN8KNifxbwSkoBgDAPozCJgrcFZxlKQAAbEO4MZF/WYqCYgAA7MMobCIKigEAsB/hxkRuH/eWAgDAbozCJgrM3LBbCgAA2xBuTOLzGTo+ccNWcAAAbES4MYn/6sQSy1IAANiJUdgk/m3gEgXFAADYiXBjEv82cImt4AAA2IlR2CT+YmKJmRsAAOxEuDGJv+bGGeeQw0G4AQDALoQbk7iPz9zEs1MKAABbEW5M4gnceoFwAwCAnc6KcLNw4UJ1795dSUlJysvL06ZNm056/l/+8hf17dtXSUlJGjhwoF599VWLWto6D1cnBgDgrGD7SLx8+XIVFxdrzpw52rp1q3JycjR69Gjt27evxfPfeustTZgwQbfeequ2bdumsWPHauzYsdqxY4fFLQ/m3wpOMTEAAPZyGIZhnPq0yMnLy9PFF1+sp556SpLk8/mUnZ2tu+66SzNnzmx2/vjx43X48GH9/e9/DxwbPny4Bg8erMWLF5/y/Wpra5WWlqaamhqlpqaa1o8dn9Xo/z25XlmpSdrwk8tNe10AABDe+G3rzE1DQ4O2bNmigoKCwLG4uDgVFBSorKysxeeUlZUFnS9Jo0ePbvX8+vp61dbWBn1FQqCgmJkbAABsZWu4OXDggLxerzIzM4OOZ2ZmqqqqqsXnVFVVhXV+SUmJ0tLSAl/Z2dnmNP4EhqTkBKeSE5wReX0AABAa22tuIm3WrFmqqakJfO3duzci73PRBe2186dXaXXxpRF5fQAAEJp4O988PT1dTqdT1dXVQcerq6uVlZXV4nOysrLCOt/lcsnlcpnTYAAAcNazdeYmMTFRubm5Ki0tDRzz+XwqLS1Vfn5+i8/Jz88POl+SVq9e3er5AADg3GLrzI0kFRcXa9KkSRo6dKiGDRumBQsW6PDhwyosLJQkTZw4UV27dlVJSYkk6Z577tGll16qJ554Qt/5znf03HPPafPmzXr66aft7AYAADhL2B5uxo8fr/3792v27NmqqqrS4MGDtWrVqkDRcGVlpeKa3GV7xIgRevbZZ/XAAw/oJz/5ifr06aOXXnpJAwYMsKsLAADgLGL7dW6sFqnr3AAAgMiJmuvcAAAAmI1wAwAAYgrhBgAAxBTCDQAAiCmEGwAAEFMINwAAIKYQbgAAQEwh3AAAgJhCuAEAADHF9tsvWM1/Qeba2lqbWwIAAELlH7dDubHCORdu6urqJEnZ2dk2twQAAISrrq5OaWlpJz3nnLu3lM/n0+eff662bdvK4XCY+tq1tbXKzs7W3r17z4n7VtHf2EZ/Y9u51l/p3OtzrPXXMAzV1dWpS5cuQTfUbsk5N3MTFxen888/P6LvkZqaGhN/SKGiv7GN/sa2c62/0rnX51jq76lmbPwoKAYAADGFcAMAAGIK4cZELpdLc+bMkcvlsrsplqC/sY3+xrZzrb/Sudfnc62/TZ1zBcUAACC2MXMDAABiCuEGAADEFMINAACIKYQbAAAQUwg3Jlm4cKG6d++upKQk5eXladOmTXY3yRQPPfSQHA5H0Fffvn0Djx89elR33nmnOnbsqJSUFI0bN07V1dU2tjg8//d//6drrrlGXbp0kcPh0EsvvRT0uGEYmj17tjp37qzk5GQVFBToo48+Cjrnq6++0k033aTU1FS1a9dOt956qw4dOmRhL8Jzqj7fcsstzT7zq666KuicaOlzSUmJLr74YrVt21YZGRkaO3asKioqgs4J5W+4srJS3/nOd9SmTRtlZGToxz/+sTwej5VdCUko/R01alSzz/f2228POida+rto0SINGjQocJG6/Px8rVy5MvB4LH22fqfqcyx9vmfEwBl77rnnjMTERGPp0qXGe++9ZxQVFRnt2rUzqqur7W7aGZszZ47Rv39/44svvgh87d+/P/D47bffbmRnZxulpaXG5s2bjeHDhxsjRoywscXhefXVV43777/feOGFFwxJxosvvhj0+Ny5c420tDTjpZdeMt555x3j2muvNXr06GF8/fXXgXOuuuoqIycnx9iwYYPxxhtvGL179zYmTJhgcU9Cd6o+T5o0ybjqqquCPvOvvvoq6Jxo6fPo0aON3/3ud8aOHTuM8vJy4+qrrzYuuOAC49ChQ4FzTvU37PF4jAEDBhgFBQXGtm3bjFdffdVIT083Zs2aZUeXTiqU/l566aVGUVFR0OdbU1MTeDya+vvyyy8br7zyivHhhx8aFRUVxk9+8hMjISHB2LFjh2EYsfXZ+p2qz7H0+Z4Jwo0Jhg0bZtx5552Bn71er9GlSxejpKTExlaZY86cOUZOTk6Ljx08eNBISEgw/vKXvwSO7dy505BklJWVWdRC85w40Pt8PiMrK8t4/PHHA8cOHjxouFwu409/+pNhGIbx/vvvG5KMt99+O3DOypUrDYfDYXz22WeWtf10tRZurrvuulafE8193rdvnyHJWLdunWEYof0Nv/rqq0ZcXJxRVVUVOGfRokVGamqqUV9fb20HwnRifw3j2OB3zz33tPqcaO6vYRhG+/btjd/85jcx/9k25e+zYcT+5xsqlqXOUENDg7Zs2aKCgoLAsbi4OBUUFKisrMzGlpnno48+UpcuXdSzZ0/ddNNNqqyslCRt2bJFbrc7qO99+/bVBRdcEBN937Nnj6qqqoL6l5aWpry8vED/ysrK1K5dOw0dOjRwTkFBgeLi4rRx40bL22yWtWvXKiMjQxdeeKGmTJmiL7/8MvBYNPe5pqZGktShQwdJof0Nl5WVaeDAgcrMzAycM3r0aNXW1uq9996zsPXhO7G/fs8884zS09M1YMAAzZo1S0eOHAk8Fq399Xq9eu6553T48GHl5+fH/GcrNe+zXyx+vuE6526cabYDBw7I6/UG/aFIUmZmpj744AObWmWevLw8LVu2TBdeeKG++OILPfzww7rkkku0Y8cOVVVVKTExUe3atQt6TmZmpqqqquxpsIn8fWjps/U/VlVVpYyMjKDH4+Pj1aFDh6j9HVx11VX67ne/qx49emj37t36yU9+ojFjxqisrExOpzNq++zz+TRt2jR961vf0oABAyQppL/hqqqqFv8G/I+drVrqryTdeOON6tatm7p06aJ3331XM2bMUEVFhV544QVJ0dff7du3Kz8/X0ePHlVKSopefPFF9evXT+Xl5TH72bbWZyn2Pt/TRbjBSY0ZMybw/aBBg5SXl6du3brpz3/+s5KTk21sGSLlhhtuCHw/cOBADRo0SL169dLatWt1+eWX29iyM3PnnXdqx44dWr9+vd1NsURr/b3tttsC3w8cOFCdO3fW5Zdfrt27d6tXr15WN/OMXXjhhSovL1dNTY1WrFihSZMmad26dXY3K6Ja63O/fv1i7vM9XSxLnaH09HQ5nc5mFfjV1dXKysqyqVWR065dO33jG9/Qrl27lJWVpYaGBh08eDDonFjpu78PJ/tss7KytG/fvqDHPR6Pvvrqq5j4HUhSz549lZ6erl27dkmKzj5PnTpVf//737VmzRqdf/75geOh/A1nZWW1+Dfgf+xs1Fp/W5KXlydJQZ9vNPU3MTFRvXv3Vm5urkpKSpSTk6Nf/OIXMfvZSq33uSXR/vmeLsLNGUpMTFRubq5KS0sDx3w+n0pLS4PWQGPFoUOHtHv3bnXu3Fm5ublKSEgI6ntFRYUqKytjou89evRQVlZWUP9qa2u1cePGQP/y8/N18OBBbdmyJXDO66+/Lp/PF/iPSrT79NNP9eWXX6pz586SoqvPhmFo6tSpevHFF/X666+rR48eQY+H8jecn5+v7du3BwW61atXKzU1NbAUcLY4VX9bUl5eLklBn2+09LclPp9P9fX1MffZnoy/zy2Jtc83ZHZXNMeC5557znC5XMayZcuM999/37jtttuMdu3aBVWjR6t7773XWLt2rbFnzx7jzTffNAoKCoz09HRj3759hmEc22p5wQUXGK+//rqxefNmIz8/38jPz7e51aGrq6sztm3bZmzbts2QZMyfP9/Ytm2b8cknnxiGcWwreLt27Yy//vWvxrvvvmtcd911LW4FHzJkiLFx40Zj/fr1Rp8+fc7KbdF+J+tzXV2d8aMf/cgoKysz9uzZY/zzn/80LrroIqNPnz7G0aNHA68RLX2eMmWKkZaWZqxduzZoa+yRI0cC55zqb9i/dfbKK680ysvLjVWrVhmdOnU6K7fOnqq/u3btMh555BFj8+bNxp49e4y//vWvRs+ePY2RI0cGXiOa+jtz5kxj3bp1xp49e4x3333XmDlzpuFwOIx//OMfhmHE1mfrd7I+x9rneyYINyZ58sknjQsuuMBITEw0hg0bZmzYsMHuJpli/PjxRufOnY3ExESja9euxvjx441du3YFHv/666+NO+64w2jfvr3Rpk0b4/rrrze++OILG1scnjVr1hiSmn1NmjTJMIxj28EffPBBIzMz03C5XMbll19uVFRUBL3Gl19+aUyYMMFISUkxUlNTjcLCQqOurs6G3oTmZH0+cuSIceWVVxqdOnUyEhISjG7duhlFRUXNgnq09Lmlfkoyfve73wXOCeVv+OOPPzbGjBljJCcnG+np6ca9995ruN1ui3tzaqfqb2VlpTFy5EijQ4cOhsvlMnr37m38+Mc/DroOimFET38nT55sdOvWzUhMTDQ6depkXH755YFgYxix9dn6nazPsfb5ngmHYRiGdfNEAAAAkUXNDQAAiCmEGwAAEFMINwAAIKYQbgAAQEwh3AAAgJhCuAEAADGFcAMAAGIK4QYAAMQUwg0AW91yyy0aO3as5e+7bNkyORwOORwOTZs27aTndu/eXQsWLAj62f/cE2/MCMB+8XY3AEDscjgcJ318zpw5+sUvfiG7LpSempqqiooKnXfeeWE97+2339Ybb7yhcePGRahlAM4E4QZAxHzxxReB75cvX67Zs2eroqIicCwlJUUpKSl2NE3SsfCVlZUV9vM6deqkDh06RKBFAMzAshSAiMnKygp8paWlBcKE/yslJaXZstSoUaN01113adq0aWrfvr0yMzO1ZMkSHT58WIWFhWrbtq169+6tlStXBr3Xjh07NGbMGKWkpCgzM1M333yzDhw4EHab9+3bp2uuuUbJycnq0aOHnnnmmTP9NQCwGOEGwFnn97//vdLT07Vp0ybdddddmjJlir73ve9pxIgR2rp1q6688krdfPPNOnLkiCTp4MGDuuyyyzRkyBBt3rxZq1atUnV1tb7//e+H/d633HKL9u7dqzVr1mjFihX61a9+pX379pndRQARRLgBcNbJycnRAw88oD59+mjWrFlKSkpSenq6ioqK1KdPH82ePVtffvml3n33XUnSU089pSFDhujRRx9V3759NWTIEC1dulRr1qzRhx9+GPL7fvjhh1q5cqWWLFmi4cOHKzc3V7/97W/19ddfR6qrACKAmhsAZ51BgwYFvnc6nerYsaMGDhwYOJaZmSlJgRmVd955R2vWrGmxfmf37t36xje+EdL77ty5U/Hx8crNzQ0c69u3r9q1a3c63QBgE8INgLNOQkJC0M8OhyPomH8Xls/nkyQdOnRI11xzjR577LFmr9W5c+cIthTA2YhwAyDqXXTRRXr++efVvXt3xcef/n/W+vbtK4/Hoy1btujiiy+WJFVUVHAtGyDKUHMDIOrdeeed+uqrrzRhwgS9/fbb2r17t1577TUVFhbK6/WG/DoXXnihrrrqKv3whz/Uxo0btWXLFv3gBz9QcnJyBFsPwGyEGwBRr0uXLnrzzTfl9Xp15ZVXauDAgZo2bZratWunuLjw/jP3u9/9Tl26dNGll16q7373u7rtttuUkZERoZYDiASHYdelQQHARsuWLdO0adNOe8lp7dq1+va3v61///vfFBwDZxlmbgCcs2pqapSSkqIZM2aE9bz+/ftrzJgxEWoVgDPFzA2Ac1JdXZ2qq6slSe3atVN6enrIz/3kk0/kdrslST179gx76QtAZBFuAABATOF/NwAAQEwh3AAAgJhCuAEAADGFcAMAAGIK4QYAAMQUwg0AAIgphBsAABBTCDcAACCm/H+huwm3LmMf7wAAAABJRU5ErkJggg==", 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          " ] @@ -2231,13 +2252,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "id": "aa32c623", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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          " ] @@ -2275,7 +2296,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "id": "0c5bd844", "metadata": {}, "outputs": [], @@ -2380,13 +2401,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "id": "cc4b980e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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          " ] From cade121c45a34b51268fae9d9132bdcee6cd61eb Mon Sep 17 00:00:00 2001 From: lukelabrie Date: Thu, 20 Feb 2025 14:33:22 +0100 Subject: [PATCH 29/32] msre pump transient notebook --- .../parameters_U235.cpython-312.pyc | Bin 0 -> 5216 bytes .../data/ornl_spindown.csv | 73 ++++ .../data/ornl_spinup.csv | 48 +++ .../data/spindown.csv | 29 ++ .../pump_transient_benchmark/data/spinup.csv | 37 ++ .../pump_transient_benchmark/model.ipynb | 348 ++++++++++++++++++ .../parameters_U233.py | 154 ++++++++ .../parameters_U235.py | 202 ++++++++++ .../parameters_U235_pump_transient.py | 217 +++++++++++ .../pump_transients.ipynb | 315 ++++++++++++++++ .../steady_state.ipynb | 22 ++ 11 files changed, 1445 insertions(+) create mode 100644 dynamic_model/pump_transient_benchmark/__pycache__/parameters_U235.cpython-312.pyc create mode 100644 dynamic_model/pump_transient_benchmark/data/ornl_spindown.csv create mode 100644 dynamic_model/pump_transient_benchmark/data/ornl_spinup.csv create mode 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+5.401733613855988, 95.42839605383773 +5.623250939898963, 96.11909559711347 +5.802209317821131, 96.29645483437183 +5.9982130814552175, 96.47446018907011 +6.245316658119071, 96.99555390436655 +6.509483792547842, 97.3467187329637 +6.765146406173652, 97.5269854987017 +6.995349973549566, 96.68283306344469 +7.268003456728303, 97.37547095904019 +7.498079819733233, 97.72534355275751 +7.770787819070964, 97.90625643593543 +7.992341489219934, 98.25580597093278 +8.239445065883785, 98.77689968622921 diff --git a/dynamic_model/pump_transient_benchmark/model.ipynb b/dynamic_model/pump_transient_benchmark/model.ipynb new file mode 100644 index 0000000..9f7af70 --- /dev/null +++ b/dynamic_model/pump_transient_benchmark/model.ipynb @@ -0,0 +1,348 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from parameters_U235_pump_transient import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from jitcdde import t\n", + "from msrDynamics import Node, System\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "MSRE = System()\n", + "\n", + "# radiator\n", + "T_out_rc = Node(m = mn_rp, scp = mcp_rpn/mn_rp, W = W_rp, y0 = T0_rp)\n", + "T_out_air = Node(m = mn_rs, scp = mcp_rsn/mn_rs, W = W_rs, y0 = T0_rs)\n", + "\n", + "# heat exchanger\n", + "T_hf1 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p1)\n", + "T_hf2 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p2)\n", + "T_hf3 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p3)\n", + "T_hf4 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p4)\n", + "T_ht1 = Node(m = m_tn, scp = scp_t, y0 = T0_t1)\n", + "T_ht2 = Node(m = m_tn, scp = scp_t, y0 = T0_t2)\n", + "T_hc1 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s1)\n", + "T_hc2 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s2)\n", + "T_hc3 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s3)\n", + "T_hc4 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s4)\n", + "\n", + "# core \n", + "n = Node(y0 = n_frac0)\n", + "C1 = Node(y0 = C0[0])\n", + "C2 = Node(y0 = C0[1])\n", + "C3 = Node(y0 = C0[2])\n", + "C4 = Node(y0 = C0[3])\n", + "C5 = Node(y0 = C0[4])\n", + "C6 = Node(y0 = C0[5])\n", + "rho = Node(y0 = rho_0)\n", + "\n", + "T_cg = Node(m = mcp_g1/scp_g, scp = scp_g, y0 = T0_g1)\n", + "T_cf1 = Node(m = mn_f, scp = scp_f, W = W_f, y0 = T0_f1)\n", + "T_cf2 = Node(m = mn_f, scp = scp_f, W = W_f, y0 = T0_f1)\n", + "\n", + "MSRE.add_nodes([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n", + " T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho])\n", + "\n", + "\n", + "# dynamics \n", + "\n", + "# radiator\n", + "T_out_rc.set_dTdt_advective(source = T_hc4.y(t-tau_hx_r))\n", + "T_out_rc.set_dTdt_convective(source = [T_out_air.y()], hA = [hA_rpn])\n", + "\n", + "T_out_air.set_dTdt_advective(source = Trs_in)\n", + "T_out_air.set_dTdt_convective(source = [T_out_rc.y()], hA = [hA_rsn])\n", + "\n", + "# heat exchanger\n", + "T_hf1.set_dTdt_advective(source = T_cf2.y(t-tau_c_hx))\n", + "T_hf1.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_pn])\n", + "\n", + "T_hf2.set_dTdt_advective(source = T_hf1.y())\n", + "T_hf2.dTdt_convective = T_hf1.dTdt_convective\n", + "\n", + "T_hf3.set_dTdt_advective(source = T_hf2.y())\n", + "T_hf3.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn])\n", + "\n", + "T_hf4.set_dTdt_advective(source = T_hf3.y())\n", + "# T_hf4.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn])\n", + "T_hf4.dTdt_convective = T_hf3.dTdt_convective\n", + "\n", + "# T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf2.y(),T_hc3.y(),T_hc4.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n", + "# T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf4.y(),T_hc1.y(),T_hc2.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n", + "T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf1.y(),T_hc3.y(),T_hc3.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n", + "T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf3.y(),T_hc1.y(),T_hc1.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn])\n", + "\n", + "T_hc1.set_dTdt_advective(source = T_out_rc.y(t-tau_r_hx))\n", + "T_hc1.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_sn])\n", + "\n", + "T_hc2.set_dTdt_advective(source = T_hc1.y())\n", + "T_hc2.dTdt_convective = T_hc1.dTdt_convective\n", + "\n", + "T_hc3.set_dTdt_advective(source = T_hc2.y())\n", + "T_hc3.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_sn])\n", + "\n", + "T_hc4.set_dTdt_advective(source = T_hc3.y())\n", + "T_hc4.dTdt_convective = T_hc3.dTdt_convective\n", + "\n", + "# core\n", + "n.set_dndt(r = rho.y(), beta_eff = beta_t, Lambda = Lam, lam = lam, C = [C1.y(),C2.y(),C3.y(),C4.y(),C5.y(),C6.y()])\n", + "C1.set_dcdt(n.y(),beta = beta[0],Lambda = Lam,lam = lam[0],t_c=tau_c,t_l = tau_l, flow = True)\n", + "C2.set_dcdt(n.y(), beta = beta[1],Lambda = Lam,lam = lam[1],t_c=tau_c,t_l = tau_l, flow = True)\n", + "C3.set_dcdt(n.y(),beta = beta[2],Lambda = Lam,lam = lam[2],t_c=tau_c,t_l = tau_l, flow = True)\n", + "C4.set_dcdt(n.y(),beta = beta[3],Lambda = Lam,lam = lam[3],t_c=tau_c,t_l = tau_l, flow = True)\n", + "C5.set_dcdt(n.y(),beta = beta[4],Lambda = Lam,lam = lam[4],t_c=tau_c,t_l = tau_l, flow = True)\n", + "C6.set_dcdt(n.y(),beta = beta[5],Lambda = Lam,lam = lam[5],t_c=tau_c,t_l = tau_l, flow = True)\n", + "\n", + "T_cg.set_dTdt_convective(source = [T_cf1.y()], hA = [hA_fg])\n", + "T_cg.set_dTdt_internal(source = [n.y()], k = [k_g*P])\n", + "\n", + "T_cf1.set_dTdt_advective(source = T_hf4.y(t-tau_hx_c))\n", + "T_cf1.set_dTdt_convective(source = [T_cg.y()], hA = [k_1*hA_fg])\n", + "T_cf1.set_dTdt_internal(source = [n.y()], k = [k_f1*P])\n", + "\n", + "T_cf2.set_dTdt_advective(source = T_cf1.y())\n", + "T_cf2.dTdt_convective = T_cf1.dTdt_convective\n", + "T_cf2.set_dTdt_internal(source = [n.y()], k = [k_f2*P])\n", + "\n", + "rho.set_drdt(sources = [T_cf1.dydt, T_cg.dydt], coeffs = [a_f/2,a_g])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finalizing integrator...\n", + "integrating...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Integration progress: 0%| | 0/500000 [00:00" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(3,1)\n", + "\n", + "axs[0].set_ylim([0,20])\n", + "# axs[0].set_xlim([0,100])\n", + "axs[0].plot(T, P*n.y_out*(1e6), label = 'Power (W)')\n", + "\n", + "# axs[1].set_ylim([600,700])\n", + "# axs[1].set_xlim([0,100])\n", + "axs[1].plot(T, T_hf4.y_out, label = 'Core Fuel Inlet')\n", + "axs[1].plot(T, T_cf2.y_out, label = 'Core Fuel Outlet')\n", + "axs[1].plot(T, T_cg.y_out, label = 'Graphite Temp')\n", + "axs[1].plot(T, T_hf2.y_out, label = 'Hx Fuel Temp')\n", + "axs[1].plot(T, T_hc2.y_out, label = 'Hx Coolant Temp')\n", + "axs[1].legend()\n", + "\n", + "axs[2].plot(T, rho.y_out, label = 'feedback')\n", + "axs[2].legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# fig,axs = plt.subplots(2,3,figsize=(18,12))\n", + "\n", + "# # Set a professional color scheme\n", + "# colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n", + "\n", + "# # Function to update the style of each axis\n", + "# def update_axis_style(ax, title, x_label='', y_label='', x_ticks=True):\n", + "# ax.set_xlim([t0,tf])\n", + "# ax.set_title(title)\n", + "# ax.set_xlabel(x_label)\n", + "# ax.set_ylabel(y_label)\n", + "# ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n", + "# ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n", + "# ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)\n", + "\n", + "# # Applying the updated style to the subplots\n", + "# # Fuel temperatures\n", + "# sol_jit = np.array(sol_jit)\n", + "# update_axis_style(axs[0, 0], \"Fuel Node Temperatures (C)\", y_label=r\"$^\\circ$ C\")\n", + "# axs[0,0].plot(T,[s[20] for s in sol_jit],label=\"core 1\",color=colors[0]) \n", + "# axs[0,0].plot(T,[s[21] for s in sol_jit],label=\"core 2\",color=colors[1]) \n", + "# axs[0,0].plot(T,[s[2] for s in sol_jit],label=\"hx 1\",color=colors[2]) \n", + "# axs[0,0].plot(T,[s[3] for s in sol_jit],label=\"hx 2\",color=colors[3])\n", + "# axs[0,0].plot(T,[s[4] for s in sol_jit],label=\"hx 3\",color=colors[4])\n", + "# axs[0,0].plot(T,[s[5] for s in sol_jit],label=\"hx 4\",color=colors[5]) \n", + "\n", + "# # Coolant temperatures\n", + "# update_axis_style(axs[0, 1], \"Coolant Node Temperatures (C)\", y_label=r\"$^\\circ$ C\")\n", + "# axs[0, 1].plot(T, sol_jit[:, 6], label=f\"hx 1\", color=colors[0])\n", + "# axs[0, 1].plot(T, sol_jit[:, 7], label=f\"hx 1\", color=colors[1])\n", + "# axs[0, 1].plot(T, sol_jit[:, 8], label=f\"hx 1\", color=colors[2])\n", + "# axs[0, 1].plot(T, sol_jit[:, 9], label=f\"hx 1\", color=colors[3])\n", + "# axs[0, 1].plot(T, sol_jit[:, 0], label=f\"r 1\", color=colors[4])\n", + "\n", + "# # Tube node temperatures\n", + "# update_axis_style(axs[0, 2], \"Tube Node Temperatures (C)\", y_label=r\"$^\\circ$ C\")\n", + "# axs[0,2].plot(T,[s[6] for s in sol_jit],label=\"hx 1\",color=colors[0]) \n", + "# axs[0,2].plot(T,[s[7] for s in sol_jit],label=\"hx 2\",color=colors[1]) \n", + "\n", + "# # Precursor concentrations\n", + "# update_axis_style(axs[1, 2], \"Precursor Concentrations\", x_label=\"t (s)\", y_label=r\"concentration (1/cm$^3$)\")\n", + "# for i in range(6):\n", + "# axs[1, 2].plot(T, sol_jit[:, i+13], label=f\"C{i+1}\", color=colors[i])\n", + "# axs[1, 2].set_yscale(\"log\")\n", + "\n", + "# # Multiplication factor temp\n", + "# update_axis_style(axs[1, 0], r\"$n$\", x_label=\"t (s)\", y_label=r\"$\\frac{n}{n_0}$\")\n", + "# axs[1, 0].plot(T, sol_jit[:, 12], label=\"n\", color='tab:blue')\n", + "\n", + "# # Reactivity\n", + "# update_axis_style(axs[1, 1], r\"$\\rho$\", x_label=\"t (s)\")\n", + "# axs[1, 1].plot(T, sol_jit[:, 22], label=\"n\", color='tab:orange')\n", + "\n", + "# # Adding legends\n", + "# for ax in axs.flat:\n", + "# ax.legend()\n", + "\n", + "# plt.tight_layout()\n", + "# plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "onion_dynamics_py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/dynamic_model/pump_transient_benchmark/parameters_U233.py b/dynamic_model/pump_transient_benchmark/parameters_U233.py new file mode 100644 index 0000000..aab95ea --- /dev/null +++ b/dynamic_model/pump_transient_benchmark/parameters_U233.py @@ -0,0 +1,154 @@ +import numpy as np + + + +# domain +t0 = 0.0 +tf = 1000.00 +T = np.arange(t0,tf,0.01) + +# REACTIVITY INSERTION +inserted = 1.39e-4 # 1MW +# inserted = 1.96e-4 # 5MW +# inserted = 2.48e-4 # 8MW + +# NEUTRONICS DATA +tau_l = 16.73 +tau_c = 8.46 +# P = 0.1 +P = 1 +# P = 5 +# P = 8 +n_frac0 = 1 # initial fractional neutron density n/n0 +Lam = 4.0E-04 +lam = np.array([1.260E-02, 3.370E-02, 1.390E-01, 3.250E-01, 1.130E+00, 2.500E+00]) +beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009]) +beta_t = np.sum(beta) # total delayed neutron fraction MSRE +rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel +C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c)) + +# Feedback coefficients +a_f = -11.034E-5 +a_g = -05.814E-5 + +# CORE HEAT TRANSFER PARAMETERS +vdot_f = 7.5708E-02 +rho_f = 2.14647E+03 +W_f = 1.623879934566580e+02 +m_f = W_f * tau_c +nn_f = 2 +mn_f = m_f / nn_f +scp_f = 1.9665E-3 + +# Core Upflow +v_g = 1.95386 +rho_g = 1.860E3 +m_g = v_g * rho_g +scp_g = 1.773E-3 +mcp_g1 = m_g * scp_g +mcp_f1 = mn_f * scp_f +mcp_f2 = mn_f * scp_f +hA_fg = 0.02 * 9 / 5 +k_g = 0.07 +k_1 = 0.5 +k_2 = 0.5 +k_f = 0.93 +k_f1 = k_f / nn_f +k_f2 = k_f / nn_f + +# Heat Exchanger +d_he = 16 +h_he = 72 +od_tube = 0.5 +id_tube = od_tube - 2 * 0.042 +n_tube = 159 +a_tube = 254 * 144 +l_tube = a_tube / n_tube / (np.pi * od_tube) +v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube +v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube +v_he = (d_he / 2) ** 2 * np.pi * h_he +v_he_fuel = v_he - v_tube +in_m = 1.63871e-5 +W_p = W_f +m_p = v_he_fuel * in_m * rho_f +nn_p = 4 +mn_p = m_p / nn_p +cp_p = scp_f +vdot_s = 5.36265E-02 +rho_s = 1.922e3 +W_s = 1.005793369810108e+02 +m_s = v_cool * in_m * rho_s +nn_s = 4 +mn_s = m_s / nn_s +scp_s = 2.39E-3 +A_phe = 2.359E+01 +ha_p = 6.480E-01 +ha_s = 3.060E-01 +mcp_pn = mn_p * cp_p +hA_pn = ha_p / nn_s +nn_t = 2 +rho_tube = 8.7745E+03 +m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t +scp_t = 5.778E-04 +mcp_tn = m_tn * scp_t +mcp_sn = mn_s * scp_s +hA_sn = ha_s / nn_s + +# Initial conditions +Tf_in = 6.3222E+02 +T0_f2 = 6.5727E+02 +T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2 +T0_g1 = T0_f1 + (k_g * P / hA_fg) +Tp_in = T0_f2 +T0_p4 = Tf_in +T0_p1 = Tp_in - (Tp_in - T0_p4) / 4 +T0_p2 = Tp_in - 2 * (Tp_in - T0_p4) / 4 +T0_p3 = Tp_in - 3 * (Tp_in - T0_p4) / 4 +Ts_in = 5.4611E+02 +T0_s4 = 5.7939E+02 +T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s +T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s +T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s +T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn) +T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn) + +# Radiator Parameters +Trp_in = T0_s4 +T0_rp = Ts_in +Trs_in = 37.78 +T0_rs = 148.9 +od_rad = 0.01905 +tube_wall_thick = 0.0018288 +id_rad = od_rad - 2 * tube_wall_thick +n_rtubes = 120 +l_rtube = 9.144 +v_rp = np.pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes +n_tpr = 12 +n_row = 10 +tube_space = 0.0381 +v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube +W_rp = W_s +m_rp = v_rp * rho_s +nn_rp = 1 +mn_rp = m_rp / nn_rp +cp_rp = scp_s +vdot_rs = 94.389 +rho_rs = 1.1237 +W_rs = vdot_rs * rho_rs +m_rs = v_rs * rho_rs +nn_rs = 1 +mn_rs = m_rs / nn_rs +scp_rs = 1.0085E-3 +A_rad = 6.503E1 +h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2) +mcp_rpn = mn_rp * cp_rp +hA_rpn = h_roverall * A_rad / nn_rs +mcp_rsn = mn_rs * scp_rs +hA_rsn = h_roverall * A_rad / nn_rs + +# Pure time delays between components +tau_hx_c = 8.67 #+2.145 +tau_c_hx = 3.77 #+2.145 +tau_hx_r = 4.71 +tau_r_hx = 8.24 + diff --git a/dynamic_model/pump_transient_benchmark/parameters_U235.py b/dynamic_model/pump_transient_benchmark/parameters_U235.py new file mode 100644 index 0000000..95e6c15 --- /dev/null +++ b/dynamic_model/pump_transient_benchmark/parameters_U235.py @@ -0,0 +1,202 @@ +import numpy as np +import math +pi = math.pi + +# domain +t0 = 0.0 +tf = 50000.00 +T = np.arange(t0,tf,1.0) + +# NEUTRONICS DATA +tau_l = 16.73 # ORNL-TM-0728 %16.44; % (s) +tau_c = 8.46 # ORNL-TM-0728 %8.460; % (s) +# P = 8.0 # Thermal Power in MW ORNL-TM-1070, p.2 +P = 1.0e-5 +n_frac0 = 1.0 # initial fractional neutron density n/n0 (n/cm^3/s) +Lam = 2.400E-04 # mean generation time ORNL-TM-1070 p.15 U235 +# Lam = 4.0E-04; # mean generation time ORNL-TM-1070 p.15 U233 +lam = np.array([1.240E-02, 3.05E-02, 1.11E-01, 3.01E-01, 1.140E+00, 3.014E+00]) +beta = np.array([0.000223, 0.001457, 0.001307, 0.002628, 0.000766, 0.00023]) # U235 +# beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009]) # U233 +beta_t = np.sum(beta) # total delayed neutron fraction MSRE +rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel +C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c)) + +# Feedback co-efficients +a_f = -8.71E-05 # U235 (drho/°C) fuel salt temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -5.904E-05; % ORNL-TM-0728 p. 101 % +a_g = -6.66E-05 # U235 (drho/°C) graphite temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -6.624E-05; % ORNL-TM-0728 p.101 + +# CORE HEAT TRANSFER PARAMETERS +# FUEL PARAMETERS - DONE +vdot_f = 7.5708E-02 # ORNL-TM-0728 % 7.571e-2; % vol. flow rate (m^3/s) ORNL-TM-1647 p.3, ORNL-TM-0728 p.12 +rho_f = 2.14647E+03 # (partially enriched U-235)ORNL-TM-0728 p.8 2.243E+03; % (Th-U) density of fuel salt (kg/m^3) ORNL-TM-0728 p.8 +W_f = 1.623879934566580e+02 # 1.83085e+02;%vdot_f*rho_f; % 182.78; % calcd from m_dot*cp*delT=P; vdot_f*rho_f; % fuel flow rate (kg/s) +# tau_f_c = tau_c; % ORNL-TM-0728 % 8.45; % transit time of fuel in core (s) ORNL-TM-1070 p.15, TDAMSRE p.5 +m_f = W_f * tau_c # fuel mass in core (kg) +nn_f = 2 # number of fuel nodes in core model +mn_f = m_f / nn_f # fuel mass per node (kg) +# cp_f = 4.2*9/5; % (MJ/deg-C) total fuel heat capacity TDAMSRE p.5 +scp_f = 1.9665E-3 # specific heat capacity of fuel salt (MJ/kg-C) ORNL-TM-0728 p.8 + +# Core Upflow - DONE +v_g = 1.95386 # graphite volume(m^3) ORNL-TM-0728 p. 101 +rho_g = 1.860E3 # graphite density (kg/m^3) ORNL-3812 p.77, ORNL-TM-0728 p.87 +m_g = v_g * rho_g # graphite mass (kg) +cp_g = 3.6 * 9 / 5 # TDAMSRE p.5 graphite total heat capacity (MW-s/C) ORNL-TM-1647 p.3 +scp_g = 1.773E-3 # cp_g/m_g; % graphite specific heat capacity (MW-s/kg-C) ORNL-TM-1647 p.3 +mcp_g1 = m_g * scp_g # (mass of material x heat capacity of material) of graphite per lump (MW-s/°C) +mcp_f1 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) +mcp_f2 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) +hA_fg = 0.02 * 9 / 5 # (fuel to graphite heat transfer coeff x heat transfer area) (MW/°C) ORNL-TM-1647 p.3, TDAMSRE p.5 +k_g = 0.07 # fraction of total power generated in the graphite ORNL-TM-0728 p.9 +k_1 = 0.5 # fraction of heat transferred from graphite which goes to the first fuel lump +k_2 = 0.5 # fraction of heat transferred from graphite which goes to the second fuel lump +k_f = 0.93 # fraction of heat generated in fuel - that generated in the external loop ORNL-TM-0728 p.9 +k_f1 = k_f / nn_f # fraction of total power generated in lump f1 +k_f2 = k_f / nn_f # fraction of total power generated in lump f2 + +# New node for power deposited in fuel outside the core +k_out = 1 - (k_g + k_f) # fraction of power generated in fuel in external loop ORNL-TM-0728 p.9 +m_out = W_f # (kg) Mass of node such that resident time is 1 sec (W_f needs to be defined) + +# Initial conditions - DONE +Tf_in = 6.3222E+02 # in °C ORNL-TM-1647 p.2 +T0_f2 = 6.5727E+02 # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 +T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2 # 6.405952380952389e+02; in °C +T0_g1 = T0_f1 + (k_g * P / hA_fg) # 6.589285714285924e+02; in °C +# T0_out = k_out * P / m_out / scp_f + T0_f2 # in °C (scp_f needs to be defined) + + +# Heat Exchanger - DONE +# Geometry +d_he = 16 # (in) he diameter ORNL-TM-0728 p. 164 +h_he = 72 # (in) active height % 96; %(in) he height ORNL-TM-0728 p. 164 +od_tube = 0.5 # (in) coolant tube OD ORNL-TM-0728 p. 164 +id_tube = od_tube - 2 * 0.042 # (in) coolant tube ID ORNL-TM-0728 p. 164 +n_tube = 159 # number of coolant tubes ORNL-TM-0728 p. 164 +a_tube = 254 * 144 # (in^2) total area of tubes ORNL-TM-0728 p. 164 +l_tube = a_tube / n_tube / (np.pi * od_tube) # (in) tube length +v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube # (in^3) hx shell volume occupied by tubes +v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube # (in^3) hx volume occupied by coolant +v_he = (d_he / 2) ** 2 * np.pi * h_he # (in^3) volume of heat exchanger shell +v_he_fuel = v_he - v_tube # (in^3) volume available to fuel in shell + +# Unit conversions +in_m = 1.63871e-5 # 1 cubic inch = 1.63871e-5 cubic meters + +# PRIMARY FLOW PARAMETERS - DONE +W_p = W_f # fuel flow rate (kg/s) + +m_p = v_he_fuel * in_m * rho_f # fuel mass in PHE (kg) +nn_p = 4 # number of fuel nodes in PHE +mn_p = m_p / nn_p # fuel mass per node (kg) +cp_p = scp_f # fuel heat capacity (MJ/(kg-C)) + +# SECONDARY FLOW PARAMETERS - DONE +vdot_s = 5.36265E-02 # ORNL-TM-0728 p. 164 % 5.236E-02; % coolant volume flow rate (m^3/s) ORNL-TM-1647 p.3 +rho_s = 1.922e3 # coolant salt density (kg/m^3) ORNL-TM-0728 p.8 +W_s = 1.005793369810108e+02 # vdot_s*rho_s; % calcd from mdot*cp*delT; vdot_s*rho_s; % coolant flow rate (kg/s) ORNL-TM-1647 p.3 + +m_s = v_cool * in_m * rho_s # coolant mass in PHE (kg) +nn_s = 4 # number of coolant nodes in PHE +mn_s = m_s / nn_s # coolant mass per node (kg) +scp_s = 2.39E-3 # cp_s/m_s; % specific heat capacity of coolant (MJ/(kg-C) ORNL-TM-0728 p.8 + +A_phe = 2.359E+01 # effective area for heat transfer (primary and secondary, m^2) ORNL-TM-0728 p.164 + +ha_p = 6.480E-01 # heat transfer*area coefficient from primary to tubes (MW/C) ORNL-TM-1647 p.3 +ha_s = 3.060E-01 # heat transfer*area coefficient from tubes to secondary (MW/C) ORNL-TM-1647 p.3 + +# Primary Side +mcp_pn = mn_p * cp_p # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C +hA_pn = ha_p / nn_s # 3.030; % (primary to tube heat transfer coeff x heat transfer area) in MW/°C + +# Tubes - DONE +nn_t = 2 # number of nodes of tubes in the model +rho_tube = 8.7745E+03 # (kg/m^3) density of INOR-8 ORNL-TM-0728 p.20 +m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t # mass of tubes (kg) +scp_t = 5.778E-04 # specific heat capacity of tubes (MJ/(kg-C)) ORNL-TM-0728 p.20 +mcp_tn = m_tn * scp_t # mass*(heat capacity) of tubes per lump in MW-s/°C + +# Secondary Side - DONE +mcp_sn = mn_s * scp_s # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C +hA_sn = ha_s / nn_s # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Initial conditions - DONE +# Primary nodes +Tp_in = T0_f2 # in °C ORNL-TM-1647 p.2 +T0_p4 = Tf_in # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 +T0_p1 = Tp_in + (T0_p4 - Tp_in) / 4 # in °C +T0_p2 = Tp_in + 2 * (T0_p4 - Tp_in) / 4 # in °C +T0_p3 = Tp_in + 3 * (T0_p4 - Tp_in) / 4 # in °C + +# Secondary nodes +Ts_in = 5.4611E+02 # in °C ORNL-TM-1647 p.2 +T0_s4 = 5.7939E+02 # in °C ORNL-TM-1647 p.2 +T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s # in °C +T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s # in °C +T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s # in °C +# Tube nodes +T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn) # in °C +T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn) # in °C + +# Radiator Parameters - DONE + +# Initial conditions - DONE +# Primary nodes +Trp_in = T0_s4 # 5.933E+02; % in °C ORNL-TM-1647 p.2 +T0_rp = Ts_in # in °C ORNL-TM-1647 p.2 + +# Secondary nodes - DONE +Trs_in = 37.78 # (C) air inlet temperature ORNL-TM-1647 p.2 +T0_rs = 148.9 # (C) air exit temperature ORNL-TM-1647 p.2 + +# Radiator Geometry +od_rad = 0.01905 # (m) outer diameter of tubes in the radiator ORNL-TM-0728 p.296 +tube_wall_thick = 0.0018288 # (m) thickness of tubes in the radiator ORNL-TM-0728 p.296 +id_rad = od_rad - 2 * tube_wall_thick +n_rtubes = 120 # number of tubes in the radiator (rows times tubes per row) ORNL-TM-0728 p.296 +l_rtube = 9.144 # (m) length of tubes in the radiator ORNL-TM-0728 p.296 +v_rp = pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes # volume available to salt in the radiator +# v_rtube = pi * (od_rad / 2) ** 2 * l_rtube * n_rtubes - v_rp # volume of metal in radiator tubes *TUBES NOT MODELED + +n_tpr = 12 # number of tubes per row in the radiator matrix +n_row = 10 # number rows in the radiator matrix +tube_space = 0.0381 # (m) spacing between tubes and rows of matrix +v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube # volume of air inside radiator + +# PRIMARY FLOW PARAMETERS - DONE +W_rp = W_s # coolant salt flow rate (kg/s) +m_rp = v_rp * rho_s # coolant salt mass in rad (kg) +nn_rp = 1 # number of coolant salt nodes in the radiator +mn_rp = m_rp / nn_rp # coolant mass per node (kg) +cp_rp = scp_s # coolant specific heat capacity (MJ/(kg-C)) + +# SECONDARY FLOW PARAMETERS - DONE +vdot_rs = 94.389 # ORNL-TM-0728 p. 296; 78.82; % air volume flow rate (m^3/s) ORNL-TM-1647 p.2 +rho_rs = 1.1237 # air density (kg/m^3) REFPROP (310K and 0.1MPa) +W_rs = vdot_rs * rho_rs # air flow rate (kg/s) + +m_rs = v_rs * rho_rs # coolant air mass in rad (kg) +nn_rs = 1 # number of coolant nodes in rad +mn_rs = m_rs / nn_rs # coolant mass per node (kg) +scp_rs = 1.0085E-3 # (MJ/kg-C) specific heat capacity of air at (air_out+air_in)/2 REFPROP + +A_rad = 6.503E1 # (m^2) surface area of the radiator ORNL-TM-0728 p.14 +h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2) # cald as: P/A_rad/((T0_rp+Trp_in)/2-(T0_rs+Trs_in)/2) 3.168E-4; % (MW/m^2-C) polimi thesis + +# Primary Side +mcp_rpn = mn_rp * cp_rp # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C +hA_rpn = h_roverall * A_rad / nn_rs # 3.030; % (primary to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Secondary Side - DONE +mcp_rsn = mn_rs * scp_rs # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C +hA_rsn = h_roverall * A_rad / nn_rs # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Pure time delays between components - DONE +tau_hx_c = 8.67 # (sec) delay from hx to core TDAMSRE p.6 +tau_c_hx = 3.77 # (sec) subtracted 1 sec for external loop power generation node resident time; delay from core to fuel hx TDAMSRE p.6 +tau_hx_r = 4.71 # (sec) fertile hx to core TDAMSRE p.6 +tau_r_hx = 8.24 # (sec) core to fertile hx TDAMSRE p.6 + +first_val = (rho_0 - beta_t) * n_frac0 / Lam + lam[0] * C0[0] + lam[1] * C0[1] + lam[2] * C0[2] + lam[3] * C0[3] + lam[4] * C0[4] + lam[5] * C0[5] \ No newline at end of file diff --git a/dynamic_model/pump_transient_benchmark/parameters_U235_pump_transient.py b/dynamic_model/pump_transient_benchmark/parameters_U235_pump_transient.py new file mode 100644 index 0000000..0dc743f --- /dev/null +++ b/dynamic_model/pump_transient_benchmark/parameters_U235_pump_transient.py @@ -0,0 +1,217 @@ +import numpy as np +import math +pi = math.pi + +# domain +t0 = 0.0 +tf = 10000.00 +T = np.arange(t0,tf,0.01) + +# NEUTRONICS DATA +tau_l = 16.73 # ORNL-TM-0728 %16.44; % (s) +tau_c = 8.46 # ORNL-TM-0728 %8.460; % (s) +# P = 8.0 # Thermal Power in MW ORNL-TM-1070, p.2 +P = 1.0e-5 # 10 W +n_frac0 = 1.0 # initial fractional neutron density n/n0 (n/cm^3/s) +Lam = 2.400E-04 # mean generation time ORNL-TM-1070 p.15 U235 +# Lam = 4.0E-04; # mean generation time ORNL-TM-1070 p.15 U233 +lam = np.array([1.240E-02, 3.05E-02, 1.11E-01, 3.01E-01, 1.140E+00, 3.014E+00]) +beta = np.array([0.000223, 0.001457, 0.001307, 0.002628, 0.000766, 0.00023]) # U235 +# beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009]) # U233 +beta_t = np.sum(beta) # total delayed neutron fraction MSRE +rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel +C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c)) + +# Feedback co-efficients +# a_f = -8.71E-05 # U235 (drho/°C) fuel salt temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -5.904E-05; % ORNL-TM-0728 p. 101 % +# a_g = -6.66E-05 # U235 (drho/°C) graphite temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -6.624E-05; % ORNL-TM-0728 p.101 + +# low power coefficients +a_f = (-4.1e-5)*5/9 +a_g = (-4.0e-5)*5/9 + +# CORE HEAT TRANSFER PARAMETERS +# FUEL PARAMETERS - DONE +vdot_f = 7.5708E-02 # ORNL-TM-0728 % 7.571e-2; % vol. flow rate (m^3/s) ORNL-TM-1647 p.3, ORNL-TM-0728 p.12 +rho_f = 2.14647E+03 # (partially enriched U-235)ORNL-TM-0728 p.8 2.243E+03; % (Th-U) density of fuel salt (kg/m^3) ORNL-TM-0728 p.8 + +# W_f = 1.623879934566580e+02 # 1.83085e+02;%vdot_f*rho_f; % 182.78; % calcd from m_dot*cp*delT=P; vdot_f*rho_f; % fuel flow rate (kg/s) + +W_f = 1200*(1/264.172)*(1/60)*(rho_f) # gpm -> kg/s + +# tau_f_c = tau_c; % ORNL-TM-0728 % 8.45; % transit time of fuel in core (s) ORNL-TM-1070 p.15, TDAMSRE p.5 +m_f = W_f * tau_c # fuel mass in core (kg) +nn_f = 2 # number of fuel nodes in core model +mn_f = m_f / nn_f # fuel mass per node (kg) +# cp_f = 4.2*9/5; % (MJ/deg-C) total fuel heat capacity TDAMSRE p.5 +scp_f = 1.9665E-3 # specific heat capacity of fuel salt (MJ/kg-C) ORNL-TM-0728 p.8 + +# Core Upflow - DONE +v_g = 1.95386 # graphite volume(m^3) ORNL-TM-0728 p. 101 +rho_g = 1.860E3 # graphite density (kg/m^3) ORNL-3812 p.77, ORNL-TM-0728 p.87 +m_g = v_g * rho_g # graphite mass (kg) +cp_g = 3.6 * 9 / 5 # TDAMSRE p.5 graphite total heat capacity (MW-s/C) ORNL-TM-1647 p.3 +scp_g = 1.773E-3 # cp_g/m_g; % graphite specific heat capacity (MW-s/kg-C) ORNL-TM-1647 p.3 +mcp_g1 = m_g * scp_g # (mass of material x heat capacity of material) of graphite per lump (MW-s/°C) +mcp_f1 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) +mcp_f2 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) +hA_fg = 0.02 * 9 / 5 # (fuel to graphite heat transfer coeff x heat transfer area) (MW/°C) ORNL-TM-1647 p.3, TDAMSRE p.5 +k_g = 0.07 # fraction of total power generated in the graphite ORNL-TM-0728 p.9 +k_1 = 0.5 # fraction of heat transferred from graphite which goes to the first fuel lump +k_2 = 0.5 # fraction of heat transferred from graphite which goes to the second fuel lump +k_f = 0.93 # fraction of heat generated in fuel - that generated in the external loop ORNL-TM-0728 p.9 +k_f1 = k_f / nn_f # fraction of total power generated in lump f1 +k_f2 = k_f / nn_f # fraction of total power generated in lump f2 + +# New node for power deposited in fuel outside the core +k_out = 1 - (k_g + k_f) # fraction of power generated in fuel in external loop ORNL-TM-0728 p.9 +m_out = W_f # (kg) Mass of node such that resident time is 1 sec (W_f needs to be defined) + +# Initial conditions - DONE +Tf_in = 648.85 # in °C ORNL-TM-1647 p.2 +T0_f2 = 648.85 # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 +T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2 # 6.405952380952389e+02; in °C +T0_g1 = T0_f1 + (k_g * P / hA_fg) # 6.589285714285924e+02; in °C +# T0_out = k_out * P / m_out / scp_f + T0_f2 # in °C (scp_f needs to be defined) + + +# Heat Exchanger - DONE +# Geometry +d_he = 16 # (in) he diameter ORNL-TM-0728 p. 164 +h_he = 72 # (in) active height % 96; %(in) he height ORNL-TM-0728 p. 164 +od_tube = 0.5 # (in) coolant tube OD ORNL-TM-0728 p. 164 +id_tube = od_tube - 2 * 0.042 # (in) coolant tube ID ORNL-TM-0728 p. 164 +n_tube = 159 # number of coolant tubes ORNL-TM-0728 p. 164 +a_tube = 254 * 144 # (in^2) total area of tubes ORNL-TM-0728 p. 164 +l_tube = a_tube / n_tube / (np.pi * od_tube) # (in) tube length +v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube # (in^3) hx shell volume occupied by tubes +v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube # (in^3) hx volume occupied by coolant +v_he = (d_he / 2) ** 2 * np.pi * h_he # (in^3) volume of heat exchanger shell +v_he_fuel = v_he - v_tube # (in^3) volume available to fuel in shell + +# Unit conversions +in_m = 1.63871e-5 # 1 cubic inch = 1.63871e-5 cubic meters + +# PRIMARY FLOW PARAMETERS - DONE +W_p = W_f # fuel flow rate (kg/s) + +m_p = v_he_fuel * in_m * rho_f # fuel mass in PHE (kg) +nn_p = 4 # number of fuel nodes in PHE +mn_p = m_p / nn_p # fuel mass per node (kg) +cp_p = scp_f # fuel heat capacity (MJ/(kg-C)) + +# SECONDARY FLOW PARAMETERS - DONE +vdot_s = 5.36265E-02 # ORNL-TM-0728 p. 164 % 5.236E-02; % coolant volume flow rate (m^3/s) ORNL-TM-1647 p.3 +rho_s = 1.922e3 # coolant salt density (kg/m^3) ORNL-TM-0728 p.8 +W_s = 1.005793369810108e+02 # vdot_s*rho_s; % calcd from mdot*cp*delT; vdot_s*rho_s; % coolant flow rate (kg/s) ORNL-TM-1647 p.3 + +m_s = v_cool * in_m * rho_s # coolant mass in PHE (kg) +nn_s = 4 # number of coolant nodes in PHE +mn_s = m_s / nn_s # coolant mass per node (kg) +scp_s = 2.39E-3 # cp_s/m_s; % specific heat capacity of coolant (MJ/(kg-C) ORNL-TM-0728 p.8 + +A_phe = 2.359E+01 # effective area for heat transfer (primary and secondary, m^2) ORNL-TM-0728 p.164 + +ha_p = 6.480E-01 # heat transfer*area coefficient from primary to tubes (MW/C) ORNL-TM-1647 p.3 +ha_s = 3.060E-01 # heat transfer*area coefficient from tubes to secondary (MW/C) ORNL-TM-1647 p.3 + +# Primary Side +mcp_pn = mn_p * cp_p # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C +hA_pn = ha_p / nn_s # 3.030; % (primary to tube heat transfer coeff x heat transfer area) in MW/°C + +# Tubes - DONE +nn_t = 2 # number of nodes of tubes in the model +rho_tube = 8.7745E+03 # (kg/m^3) density of INOR-8 ORNL-TM-0728 p.20 +m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t # mass of tubes (kg) +scp_t = 5.778E-04 # specific heat capacity of tubes (MJ/(kg-C)) ORNL-TM-0728 p.20 +mcp_tn = m_tn * scp_t # mass*(heat capacity) of tubes per lump in MW-s/°C + +# Secondary Side - DONE +mcp_sn = mn_s * scp_s # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C +hA_sn = ha_s / nn_s # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Initial conditions - DONE +# Primary nodes +Tp_in = T0_f2 # in °C ORNL-TM-1647 p.2 +T0_p4 = Tf_in # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 +T0_p1 = Tp_in + (T0_p4 - Tp_in) / 4 # in °C +T0_p2 = Tp_in + 2 * (T0_p4 - Tp_in) / 4 # in °C +T0_p3 = Tp_in + 3 * (T0_p4 - Tp_in) / 4 # in °C + +# Secondary nodes +hx_f_temp = 648.85-(3e-4) +Ts_in = hx_f_temp # in °C ORNL-TM-1647 p.2 +T0_s4 = hx_f_temp # in °C ORNL-TM-1647 p.2 +T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s # in °C +T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s # in °C +T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s # in °C +# Tube nodes +T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn) # in °C +T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn) # in °C + +# Radiator Parameters - DONE + +# Initial conditions - DONE +# Primary nodes +Trp_in = T0_s4 # 5.933E+02; % in °C ORNL-TM-1647 p.2 +T0_rp = Ts_in # in °C ORNL-TM-1647 p.2 + +# Secondary nodes - DONE +Trs_in = 37.78 # (C) air inlet temperature ORNL-TM-1647 p.2 +T0_rs = 148.9 # (C) air exit temperature ORNL-TM-1647 p.2 + +# Radiator Geometry +od_rad = 0.01905 # (m) outer diameter of tubes in the radiator ORNL-TM-0728 p.296 +tube_wall_thick = 0.0018288 # (m) thickness of tubes in the radiator ORNL-TM-0728 p.296 +id_rad = od_rad - 2 * tube_wall_thick +n_rtubes = 120 # number of tubes in the radiator (rows times tubes per row) ORNL-TM-0728 p.296 +l_rtube = 9.144 # (m) length of tubes in the radiator ORNL-TM-0728 p.296 +v_rp = pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes # volume available to salt in the radiator +# v_rtube = pi * (od_rad / 2) ** 2 * l_rtube * n_rtubes - v_rp # volume of metal in radiator tubes *TUBES NOT MODELED + +n_tpr = 12 # number of tubes per row in the radiator matrix +n_row = 10 # number rows in the radiator matrix +tube_space = 0.0381 # (m) spacing between tubes and rows of matrix +v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube # volume of air inside radiator + +# PRIMARY FLOW PARAMETERS - DONE +W_rp = W_s # coolant salt flow rate (kg/s) +m_rp = v_rp * rho_s # coolant salt mass in rad (kg) +nn_rp = 1 # number of coolant salt nodes in the radiator +mn_rp = m_rp / nn_rp # coolant mass per node (kg) +cp_rp = scp_s # coolant specific heat capacity (MJ/(kg-C)) + +# SECONDARY FLOW PARAMETERS - DONE +# assume only convection airflow +# vdot_rs = 94.389 # ORNL-TM-0728 p. 296; 78.82; % air volume flow rate (m^3/s) ORNL-TM-1647 p.2 +vdot_rs = 1.0 +rho_rs = 1.1237 # air density (kg/m^3) REFPROP (310K and 0.1MPa) +W_rs = vdot_rs * rho_rs # air flow rate (kg/s) + + + +m_rs = v_rs * rho_rs # coolant air mass in rad (kg) +nn_rs = 1 # number of coolant nodes in rad +mn_rs = m_rs / nn_rs # coolant mass per node (kg) +scp_rs = 1.0085E-3 # (MJ/kg-C) specific heat capacity of air at (air_out+air_in)/2 REFPROP + +A_rad = 6.503E1 # (m^2) surface area of the radiator ORNL-TM-0728 p.14 +h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2) # cald as: P/A_rad/((T0_rp+Trp_in)/2-(T0_rs+Trs_in)/2) 3.168E-4; % (MW/m^2-C) polimi thesis +# h_roverall = 3.0 + +# Primary Side +mcp_rpn = mn_rp * cp_rp # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C +hA_rpn = h_roverall * A_rad / nn_rs # 3.030; % (primary to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Secondary Side - DONE +mcp_rsn = mn_rs * scp_rs # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C +hA_rsn = h_roverall * A_rad / nn_rs # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C + +# Pure time delays between components - DONE +tau_hx_c = 8.67 # (sec) delay from hx to core TDAMSRE p.6 +tau_c_hx = 3.77 # (sec) subtracted 1 sec for external loop power generation node resident time; delay from core to fuel hx TDAMSRE p.6 +tau_hx_r = 4.71 # (sec) fertile hx to core TDAMSRE p.6 +tau_r_hx = 8.24 # (sec) core to fertile hx TDAMSRE p.6 + +first_val = (rho_0 - beta_t) * n_frac0 / Lam + lam[0] * C0[0] + lam[1] * C0[1] + lam[2] * C0[2] + lam[3] * C0[3] + lam[4] * C0[4] + lam[5] * C0[5] + diff --git a/dynamic_model/pump_transient_benchmark/pump_transients.ipynb b/dynamic_model/pump_transient_benchmark/pump_transients.ipynb new file mode 100644 index 0000000..2eacef8 --- /dev/null +++ b/dynamic_model/pump_transient_benchmark/pump_transients.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from parameters_U235_pump_transient import *\n", + "from jitcdde import t\n", + "from msrDynamics import Node, System\n", + "import pandas as pd\n", + "import os\n", + "from scipy.interpolate import interp1d\n", + "import sympy as sp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook simulates pump transients for the MSRE at zero power (10W). The system is run at steady-state until $t = 2500$, where the fuel and coolant pumps are spun down at the rate defined by the ORNL data. The model includes a reactivity response, $\\texttt{rho_control}$, which models the response of the MSRE control rod to maintain constant power. It is modeled as a pure integrator of $\\frac{dn}{dt}$ from the point-kinetics equations with a coefficient of -1.0, i.e. it tries to exactly cancel reactivity changes introduced by flow changes. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# plotting style \n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams[\"font.family\"] = \"monospace\"\n", + "\n", + "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown']\n", + "\n", + "# function to update the style of each axis\n", + "def update_axis_style(ax, title = None, x_label='', y_label='', x_ticks=True,fsl = 12, fsb = 12, fst = 14):\n", + " if title:\n", + " ax.set_title(title,fontsize=fst)\n", + " ax.set_xlabel(x_label,fontsize=fsb)\n", + " ax.set_ylabel(y_label,fontsize=fsl)\n", + " ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n", + " ax.tick_params(axis='x', which='both', bottom=x_ticks, top=False, labelbottom=x_ticks)\n", + " ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# set up system \n", + "\n", + "MSRE = System()\n", + "\n", + "# define flow events\n", + "df_spindown = pd.read_csv(os.getcwd() + '/data/spindown.csv', names = ['time', 'pct'], header = None)\n", + "df_spinup = pd.read_csv(os.getcwd() + '/data/spinup.csv', names = ['time', 'pct'], header = None)\n", + "spindown_fit = interp1d(df_spindown['time'], df_spindown['pct'])\n", + "spinup_fit = interp1d(df_spinup['time'], df_spinup['pct'])\n", + "t_spindown = 2500\n", + "t_spinup = 7500\n", + "\n", + "def flow_fac(t):\n", + " if (t <= t_spindown):\n", + " return 1.0\n", + " elif (t < (t_spindown + 20.0)):\n", + " return max(spindown_fit(t-t_spindown)/100.0, 0.02)\n", + " elif (t <= t_spinup):\n", + " return 0.02\n", + " elif (t < t_spinup + 8.20):\n", + " return max(spinup_fit(t-t_spinup)/100.0, 0.02)\n", + " else:\n", + " return 1.0\n", + "\n", + "flow_pct = MSRE.add_input(flow_fac, T, max_anchors = 5000, input_tol = 32)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define nodes\n", + "\n", + "# radiator\n", + "T_out_rc = Node(m=mn_rp, scp=mcp_rpn/mn_rp, W=W_rp, y0=T0_rp, name='T_out_rc')\n", + "T_out_air = Node(m=mn_rs, scp=mcp_rsn/mn_rs, W=W_rs, y0=T0_rs, name='T_out_air')\n", + "\n", + "# heat exchanger\n", + "T_hf1 = Node(m=mn_p, scp=mcp_pn/mn_p, W=W_p*flow_pct, y0=T0_p1, name='T_hf1')\n", + "T_hf2 = Node(m=mn_p, scp=mcp_pn/mn_p, W=W_p*flow_pct, y0=T0_p2, name='T_hf2')\n", + "T_hf3 = Node(m=mn_p, scp=mcp_pn/mn_p, W=W_p*flow_pct, y0=T0_p3, name='T_hf3')\n", + "T_hf4 = Node(m=mn_p, scp=mcp_pn/mn_p, W=W_p*flow_pct, y0=T0_p4, name='T_hf4')\n", + "T_ht1 = Node(m=m_tn, scp=scp_t, y0=T0_t1, name='T_ht1')\n", + "T_ht2 = Node(m=m_tn, scp=scp_t, y0=T0_t2, name='T_ht2')\n", + "T_hc1 = Node(m=mn_s, scp=mcp_sn/mn_s, W=W_s*flow_pct, y0=T0_s1, name='T_hc1')\n", + "T_hc2 = Node(m=mn_s, scp=mcp_sn/mn_s, W=W_s*flow_pct, y0=T0_s2, name='T_hc2')\n", + "T_hc3 = Node(m=mn_s, scp=mcp_sn/mn_s, W=W_s*flow_pct, y0=T0_s3, name='T_hc3')\n", + "T_hc4 = Node(m=mn_s, scp=mcp_sn/mn_s, W=W_s*flow_pct, y0=T0_s4, name='T_hc4')\n", + "\n", + "# core \n", + "n = Node(y0=n_frac0, name='n')\n", + "C1 = Node(y0=C0[0], name='C1')\n", + "C2 = Node(y0=C0[1], name='C2')\n", + "C3 = Node(y0=C0[2], name='C3')\n", + "C4 = Node(y0=C0[3], name='C4')\n", + "C5 = Node(y0=C0[4], name='C5')\n", + "C6 = Node(y0=C0[5], name='C6')\n", + "rho = Node(y0=rho_0, name='rho')\n", + "\n", + "rho_control = Node(y0=0.0, name='rho_control')\n", + "\n", + "T_cg = Node(m=mcp_g1/scp_g, scp=scp_g, y0=T0_g1, name='T_cg')\n", + "T_cf1 = Node(m=mn_f, scp=scp_f, W=W_f*flow_pct, y0=T0_f1, name='T_cf1')\n", + "T_cf2 = Node(m=mn_f, scp=scp_f, W=W_f*flow_pct, y0=T0_f1, name='T_cf2')\n", + "\n", + "MSRE.add_nodes([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1,\n", + " T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho, rho_control])\n", + "\n", + "# dynamics\n", + "\n", + "# radiator\n", + "T_out_rc.set_dTdt_advective(source = T_hc4.y(t-tau_hx_r/flow_pct))\n", + "T_out_rc.set_dTdt_convective(source = [T_out_air.y()], hA = [hA_rpn*flow_pct])\n", + "\n", + "T_out_air.set_dTdt_advective(source = Trs_in)\n", + "T_out_air.set_dTdt_convective(source = [T_out_rc.y()], hA = [hA_rsn])\n", + "\n", + "# heat exchanger\n", + "T_hf1.set_dTdt_advective(source = T_cf2.y(t-tau_c_hx/flow_pct))\n", + "T_hf1.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_pn*flow_pct])\n", + "\n", + "T_hf2.set_dTdt_advective(source = T_hf1.y())\n", + "T_hf2.dTdt_convective = T_hf1.dTdt_convective\n", + "\n", + "T_hf3.set_dTdt_advective(source = T_hf2.y())\n", + "T_hf3.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn*flow_pct])\n", + "\n", + "T_hf4.set_dTdt_advective(source = T_hf3.y())\n", + "T_hf4.dTdt_convective = T_hf3.dTdt_convective\n", + "\n", + "T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf1.y(),T_hc3.y(),T_hc3.y()], hA = [hA_pn*flow_pct,hA_pn*flow_pct,hA_sn*flow_pct,hA_sn*flow_pct])\n", + "T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf3.y(),T_hc1.y(),T_hc1.y()], hA = [hA_pn*flow_pct,hA_pn*flow_pct,hA_sn*flow_pct,hA_sn*flow_pct])\n", + "\n", + "T_hc1.set_dTdt_advective(source = T_out_rc.y(t-tau_r_hx/flow_pct))\n", + "T_hc1.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_sn*flow_pct])\n", + "\n", + "T_hc2.set_dTdt_advective(source = T_hc1.y())\n", + "T_hc2.dTdt_convective = T_hc1.dTdt_convective\n", + "\n", + "T_hc3.set_dTdt_advective(source = T_hc2.y())\n", + "T_hc3.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_sn*flow_pct])\n", + "\n", + "T_hc4.set_dTdt_advective(source = T_hc3.y())\n", + "T_hc4.dTdt_convective = T_hc3.dTdt_convective\n", + "\n", + "# core\n", + "n.set_dndt(r = rho.y() + rho_control.y(), beta_eff = beta_t, Lambda = Lam, lam = lam, C = [C1.y(),C2.y(),C3.y(),C4.y(),C5.y(),C6.y()])\n", + "C1.set_dcdt(n.y(), beta = beta[0],Lambda = Lam, lam = lam[0], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n", + "C2.set_dcdt(n.y(), beta = beta[1],Lambda = Lam, lam = lam[1], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n", + "C3.set_dcdt(n.y(), beta = beta[2],Lambda = Lam, lam = lam[2], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n", + "C4.set_dcdt(n.y(), beta = beta[3],Lambda = Lam, lam = lam[3], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n", + "C5.set_dcdt(n.y(), beta = beta[4],Lambda = Lam, lam = lam[4], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n", + "C6.set_dcdt(n.y(), beta = beta[5],Lambda = Lam, lam = lam[5], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n", + "\n", + "rho_control.dydt = -sp.Min(29.0e-5, sp.Max(-29.0e-5, n.dydt))\n", + "\n", + "\n", + "T_cg.set_dTdt_convective(source = [T_cf1.y()], hA = [hA_fg*flow_pct])\n", + "T_cg.set_dTdt_internal(source = [n.y()], k = [k_g*P])\n", + "\n", + "T_cf1.set_dTdt_advective(source = T_hf4.y(t-tau_hx_c/flow_pct))\n", + "T_cf1.set_dTdt_convective(source = [T_cg.y()], hA = [k_1*hA_fg*flow_pct])\n", + "T_cf1.set_dTdt_internal(source = [n.y()], k = [k_f1*P])\n", + "\n", + "T_cf2.set_dTdt_advective(source = T_cf1.y())\n", + "T_cf2.dTdt_convective = T_cf1.dTdt_convective\n", + "T_cf2.set_dTdt_internal(source = [n.y()], k = [k_f2*P])\n", + "\n", + "rho.set_drdt(sources = [T_cf1.dydt, T_cg.dydt], coeffs = [a_f,a_g])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "finalizing integrator...\n", + "integrating...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Integration progress: 0%| | 0/1000000 [00:00" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
          " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_ornl_spindown = pd.read_csv(os.getcwd() + '/data/ornl_spindown.csv', header = None, names = ['time', 'pcm'])\n", + "df_ornl_spinup = pd.read_csv(os.getcwd() + '/data/ornl_spinup.csv', header = None, names = ['time', 'pcm'])\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize = (12,5))\n", + "\n", + "T_compare_spindown = T[(T >= 2500.0) & (T <= 2572.0)] -2500\n", + "rho_compare_spindown = 200+(rho_control.y_out[(T >= 2500.0) & (T <= 2572.0)])*1e5\n", + "\n", + "T_compare_spinup = T[(T >= 7500.0) & (T <= 7547.0)] -7500\n", + "rho_compare_spinup = 200+(rho_control.y_out[(T >= 7500.0) & (T <= 7547.0)])*1e5\n", + "\n", + "update_axis_style(axs[0], title = 'MSRE Pump Coastdown Test')\n", + "axs[0].plot(T_compare_spindown, rho_compare_spindown, label = 'simulated, msrDynamics')\n", + "axs[0].scatter(df_ornl_spindown['time'], df_ornl_spindown['pcm'], marker=\"2\",color=colors[2],label=\"measured, ORNL\")\n", + "axs[0].set_xlim([0,72])\n", + "axs[0].legend()\n", + "\n", + "update_axis_style(axs[1], title = 'MSRE Pump Startup Test')\n", + "axs[1].plot(T_compare_spinup, rho_compare_spinup-rho_compare_spinup[0], label = 'simulated, msrDynamics')\n", + "axs[1].scatter(df_ornl_spinup['time'], df_ornl_spinup['pcm'], marker=\"2\",color=colors[2],label=\"measured, ORNL\")\n", + "axs[1].set_xlim([0,47])\n", + "axs[1].legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "onion_dynamics_py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/dynamic_model/pump_transient_benchmark/steady_state.ipynb b/dynamic_model/pump_transient_benchmark/steady_state.ipynb new file mode 100644 index 0000000..7ad2046 --- /dev/null +++ b/dynamic_model/pump_transient_benchmark/steady_state.ipynb @@ -0,0 +1,22 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 691d8f30aa22efcdb90391f8d51bfe5f7d574c1f Mon Sep 17 00:00:00 2001 From: lukelabrie Date: Thu, 20 Feb 2025 14:36:15 +0100 Subject: [PATCH 30/32] clean up pump transient writeup --- .../parameters_U233.py | 154 ------------- .../parameters_U235.py | 202 ------------------ .../pump_transients.ipynb | 3 +- .../steady_state.ipynb | 22 -- 4 files changed, 2 insertions(+), 379 deletions(-) delete mode 100644 dynamic_model/pump_transient_benchmark/parameters_U233.py delete mode 100644 dynamic_model/pump_transient_benchmark/parameters_U235.py delete mode 100644 dynamic_model/pump_transient_benchmark/steady_state.ipynb diff --git a/dynamic_model/pump_transient_benchmark/parameters_U233.py b/dynamic_model/pump_transient_benchmark/parameters_U233.py deleted file mode 100644 index aab95ea..0000000 --- a/dynamic_model/pump_transient_benchmark/parameters_U233.py +++ /dev/null @@ -1,154 +0,0 @@ -import numpy as np - - - -# domain -t0 = 0.0 -tf = 1000.00 -T = np.arange(t0,tf,0.01) - -# REACTIVITY INSERTION -inserted = 1.39e-4 # 1MW -# inserted = 1.96e-4 # 5MW -# inserted = 2.48e-4 # 8MW - -# NEUTRONICS DATA -tau_l = 16.73 -tau_c = 8.46 -# P = 0.1 -P = 1 -# P = 5 -# P = 8 -n_frac0 = 1 # initial fractional neutron density n/n0 -Lam = 4.0E-04 -lam = np.array([1.260E-02, 3.370E-02, 1.390E-01, 3.250E-01, 1.130E+00, 2.500E+00]) -beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009]) -beta_t = np.sum(beta) # total delayed neutron fraction MSRE -rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel -C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c)) - -# Feedback coefficients -a_f = -11.034E-5 -a_g = -05.814E-5 - -# CORE HEAT TRANSFER PARAMETERS -vdot_f = 7.5708E-02 -rho_f = 2.14647E+03 -W_f = 1.623879934566580e+02 -m_f = W_f * tau_c -nn_f = 2 -mn_f = m_f / nn_f -scp_f = 1.9665E-3 - -# Core Upflow -v_g = 1.95386 -rho_g = 1.860E3 -m_g = v_g * rho_g -scp_g = 1.773E-3 -mcp_g1 = m_g * scp_g -mcp_f1 = mn_f * scp_f -mcp_f2 = mn_f * scp_f -hA_fg = 0.02 * 9 / 5 -k_g = 0.07 -k_1 = 0.5 -k_2 = 0.5 -k_f = 0.93 -k_f1 = k_f / nn_f -k_f2 = k_f / nn_f - -# Heat Exchanger -d_he = 16 -h_he = 72 -od_tube = 0.5 -id_tube = od_tube - 2 * 0.042 -n_tube = 159 -a_tube = 254 * 144 -l_tube = a_tube / n_tube / (np.pi * od_tube) -v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube -v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube -v_he = (d_he / 2) ** 2 * np.pi * h_he -v_he_fuel = v_he - v_tube -in_m = 1.63871e-5 -W_p = W_f -m_p = v_he_fuel * in_m * rho_f -nn_p = 4 -mn_p = m_p / nn_p -cp_p = scp_f -vdot_s = 5.36265E-02 -rho_s = 1.922e3 -W_s = 1.005793369810108e+02 -m_s = v_cool * in_m * rho_s -nn_s = 4 -mn_s = m_s / nn_s -scp_s = 2.39E-3 -A_phe = 2.359E+01 -ha_p = 6.480E-01 -ha_s = 3.060E-01 -mcp_pn = mn_p * cp_p -hA_pn = ha_p / nn_s -nn_t = 2 -rho_tube = 8.7745E+03 -m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t -scp_t = 5.778E-04 -mcp_tn = m_tn * scp_t -mcp_sn = mn_s * scp_s -hA_sn = ha_s / nn_s - -# Initial conditions -Tf_in = 6.3222E+02 -T0_f2 = 6.5727E+02 -T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2 -T0_g1 = T0_f1 + (k_g * P / hA_fg) -Tp_in = T0_f2 -T0_p4 = Tf_in -T0_p1 = Tp_in - (Tp_in - T0_p4) / 4 -T0_p2 = Tp_in - 2 * (Tp_in - T0_p4) / 4 -T0_p3 = Tp_in - 3 * (Tp_in - T0_p4) / 4 -Ts_in = 5.4611E+02 -T0_s4 = 5.7939E+02 -T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s -T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s -T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s -T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn) -T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn) - -# Radiator Parameters -Trp_in = T0_s4 -T0_rp = Ts_in -Trs_in = 37.78 -T0_rs = 148.9 -od_rad = 0.01905 -tube_wall_thick = 0.0018288 -id_rad = od_rad - 2 * tube_wall_thick -n_rtubes = 120 -l_rtube = 9.144 -v_rp = np.pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes -n_tpr = 12 -n_row = 10 -tube_space = 0.0381 -v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube -W_rp = W_s -m_rp = v_rp * rho_s -nn_rp = 1 -mn_rp = m_rp / nn_rp -cp_rp = scp_s -vdot_rs = 94.389 -rho_rs = 1.1237 -W_rs = vdot_rs * rho_rs -m_rs = v_rs * rho_rs -nn_rs = 1 -mn_rs = m_rs / nn_rs -scp_rs = 1.0085E-3 -A_rad = 6.503E1 -h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2) -mcp_rpn = mn_rp * cp_rp -hA_rpn = h_roverall * A_rad / nn_rs -mcp_rsn = mn_rs * scp_rs -hA_rsn = h_roverall * A_rad / nn_rs - -# Pure time delays between components -tau_hx_c = 8.67 #+2.145 -tau_c_hx = 3.77 #+2.145 -tau_hx_r = 4.71 -tau_r_hx = 8.24 - diff --git a/dynamic_model/pump_transient_benchmark/parameters_U235.py b/dynamic_model/pump_transient_benchmark/parameters_U235.py deleted file mode 100644 index 95e6c15..0000000 --- a/dynamic_model/pump_transient_benchmark/parameters_U235.py +++ /dev/null @@ -1,202 +0,0 @@ -import numpy as np -import math -pi = math.pi - -# domain -t0 = 0.0 -tf = 50000.00 -T = np.arange(t0,tf,1.0) - -# NEUTRONICS DATA -tau_l = 16.73 # ORNL-TM-0728 %16.44; % (s) -tau_c = 8.46 # ORNL-TM-0728 %8.460; % (s) -# P = 8.0 # Thermal Power in MW ORNL-TM-1070, p.2 -P = 1.0e-5 -n_frac0 = 1.0 # initial fractional neutron density n/n0 (n/cm^3/s) -Lam = 2.400E-04 # mean generation time ORNL-TM-1070 p.15 U235 -# Lam = 4.0E-04; # mean generation time ORNL-TM-1070 p.15 U233 -lam = np.array([1.240E-02, 3.05E-02, 1.11E-01, 3.01E-01, 1.140E+00, 3.014E+00]) -beta = np.array([0.000223, 0.001457, 0.001307, 0.002628, 0.000766, 0.00023]) # U235 -# beta = np.array([0.00023, 0.00079, 0.00067, 0.00073, 0.00013, 0.00009]) # U233 -beta_t = np.sum(beta) # total delayed neutron fraction MSRE -rho_0 = beta_t-sum(np.divide(beta,1+np.divide(1-np.exp(-lam*tau_l),lam*tau_c))) # reactivity change in going from stationary to circulating fuel -C0 = beta / Lam * (1.0 / (lam - (np.exp(-lam * tau_l) - 1.0) / tau_c)) - -# Feedback co-efficients -a_f = -8.71E-05 # U235 (drho/°C) fuel salt temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -5.904E-05; % ORNL-TM-0728 p. 101 % -a_g = -6.66E-05 # U235 (drho/°C) graphite temperature-reactivity feedback coefficient ORNL-TM-1647 p.3 % -6.624E-05; % ORNL-TM-0728 p.101 - -# CORE HEAT TRANSFER PARAMETERS -# FUEL PARAMETERS - DONE -vdot_f = 7.5708E-02 # ORNL-TM-0728 % 7.571e-2; % vol. flow rate (m^3/s) ORNL-TM-1647 p.3, ORNL-TM-0728 p.12 -rho_f = 2.14647E+03 # (partially enriched U-235)ORNL-TM-0728 p.8 2.243E+03; % (Th-U) density of fuel salt (kg/m^3) ORNL-TM-0728 p.8 -W_f = 1.623879934566580e+02 # 1.83085e+02;%vdot_f*rho_f; % 182.78; % calcd from m_dot*cp*delT=P; vdot_f*rho_f; % fuel flow rate (kg/s) -# tau_f_c = tau_c; % ORNL-TM-0728 % 8.45; % transit time of fuel in core (s) ORNL-TM-1070 p.15, TDAMSRE p.5 -m_f = W_f * tau_c # fuel mass in core (kg) -nn_f = 2 # number of fuel nodes in core model -mn_f = m_f / nn_f # fuel mass per node (kg) -# cp_f = 4.2*9/5; % (MJ/deg-C) total fuel heat capacity TDAMSRE p.5 -scp_f = 1.9665E-3 # specific heat capacity of fuel salt (MJ/kg-C) ORNL-TM-0728 p.8 - -# Core Upflow - DONE -v_g = 1.95386 # graphite volume(m^3) ORNL-TM-0728 p. 101 -rho_g = 1.860E3 # graphite density (kg/m^3) ORNL-3812 p.77, ORNL-TM-0728 p.87 -m_g = v_g * rho_g # graphite mass (kg) -cp_g = 3.6 * 9 / 5 # TDAMSRE p.5 graphite total heat capacity (MW-s/C) ORNL-TM-1647 p.3 -scp_g = 1.773E-3 # cp_g/m_g; % graphite specific heat capacity (MW-s/kg-C) ORNL-TM-1647 p.3 -mcp_g1 = m_g * scp_g # (mass of material x heat capacity of material) of graphite per lump (MW-s/°C) -mcp_f1 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) -mcp_f2 = mn_f * scp_f # (mass of material x heat capacity of material) of fuel salt per lump (MW-s/°C) -hA_fg = 0.02 * 9 / 5 # (fuel to graphite heat transfer coeff x heat transfer area) (MW/°C) ORNL-TM-1647 p.3, TDAMSRE p.5 -k_g = 0.07 # fraction of total power generated in the graphite ORNL-TM-0728 p.9 -k_1 = 0.5 # fraction of heat transferred from graphite which goes to the first fuel lump -k_2 = 0.5 # fraction of heat transferred from graphite which goes to the second fuel lump -k_f = 0.93 # fraction of heat generated in fuel - that generated in the external loop ORNL-TM-0728 p.9 -k_f1 = k_f / nn_f # fraction of total power generated in lump f1 -k_f2 = k_f / nn_f # fraction of total power generated in lump f2 - -# New node for power deposited in fuel outside the core -k_out = 1 - (k_g + k_f) # fraction of power generated in fuel in external loop ORNL-TM-0728 p.9 -m_out = W_f # (kg) Mass of node such that resident time is 1 sec (W_f needs to be defined) - -# Initial conditions - DONE -Tf_in = 6.3222E+02 # in °C ORNL-TM-1647 p.2 -T0_f2 = 6.5727E+02 # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 -T0_f1 = Tf_in + (T0_f2 - Tf_in) / 2 # 6.405952380952389e+02; in °C -T0_g1 = T0_f1 + (k_g * P / hA_fg) # 6.589285714285924e+02; in °C -# T0_out = k_out * P / m_out / scp_f + T0_f2 # in °C (scp_f needs to be defined) - - -# Heat Exchanger - DONE -# Geometry -d_he = 16 # (in) he diameter ORNL-TM-0728 p. 164 -h_he = 72 # (in) active height % 96; %(in) he height ORNL-TM-0728 p. 164 -od_tube = 0.5 # (in) coolant tube OD ORNL-TM-0728 p. 164 -id_tube = od_tube - 2 * 0.042 # (in) coolant tube ID ORNL-TM-0728 p. 164 -n_tube = 159 # number of coolant tubes ORNL-TM-0728 p. 164 -a_tube = 254 * 144 # (in^2) total area of tubes ORNL-TM-0728 p. 164 -l_tube = a_tube / n_tube / (np.pi * od_tube) # (in) tube length -v_tube = n_tube * np.pi * (od_tube / 2) ** 2 * l_tube # (in^3) hx shell volume occupied by tubes -v_cool = n_tube * np.pi * (id_tube / 2) ** 2 * l_tube # (in^3) hx volume occupied by coolant -v_he = (d_he / 2) ** 2 * np.pi * h_he # (in^3) volume of heat exchanger shell -v_he_fuel = v_he - v_tube # (in^3) volume available to fuel in shell - -# Unit conversions -in_m = 1.63871e-5 # 1 cubic inch = 1.63871e-5 cubic meters - -# PRIMARY FLOW PARAMETERS - DONE -W_p = W_f # fuel flow rate (kg/s) - -m_p = v_he_fuel * in_m * rho_f # fuel mass in PHE (kg) -nn_p = 4 # number of fuel nodes in PHE -mn_p = m_p / nn_p # fuel mass per node (kg) -cp_p = scp_f # fuel heat capacity (MJ/(kg-C)) - -# SECONDARY FLOW PARAMETERS - DONE -vdot_s = 5.36265E-02 # ORNL-TM-0728 p. 164 % 5.236E-02; % coolant volume flow rate (m^3/s) ORNL-TM-1647 p.3 -rho_s = 1.922e3 # coolant salt density (kg/m^3) ORNL-TM-0728 p.8 -W_s = 1.005793369810108e+02 # vdot_s*rho_s; % calcd from mdot*cp*delT; vdot_s*rho_s; % coolant flow rate (kg/s) ORNL-TM-1647 p.3 - -m_s = v_cool * in_m * rho_s # coolant mass in PHE (kg) -nn_s = 4 # number of coolant nodes in PHE -mn_s = m_s / nn_s # coolant mass per node (kg) -scp_s = 2.39E-3 # cp_s/m_s; % specific heat capacity of coolant (MJ/(kg-C) ORNL-TM-0728 p.8 - -A_phe = 2.359E+01 # effective area for heat transfer (primary and secondary, m^2) ORNL-TM-0728 p.164 - -ha_p = 6.480E-01 # heat transfer*area coefficient from primary to tubes (MW/C) ORNL-TM-1647 p.3 -ha_s = 3.060E-01 # heat transfer*area coefficient from tubes to secondary (MW/C) ORNL-TM-1647 p.3 - -# Primary Side -mcp_pn = mn_p * cp_p # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C -hA_pn = ha_p / nn_s # 3.030; % (primary to tube heat transfer coeff x heat transfer area) in MW/°C - -# Tubes - DONE -nn_t = 2 # number of nodes of tubes in the model -rho_tube = 8.7745E+03 # (kg/m^3) density of INOR-8 ORNL-TM-0728 p.20 -m_tn = (v_tube - v_cool) * in_m * rho_tube / nn_t # mass of tubes (kg) -scp_t = 5.778E-04 # specific heat capacity of tubes (MJ/(kg-C)) ORNL-TM-0728 p.20 -mcp_tn = m_tn * scp_t # mass*(heat capacity) of tubes per lump in MW-s/°C - -# Secondary Side - DONE -mcp_sn = mn_s * scp_s # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C -hA_sn = ha_s / nn_s # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C - -# Initial conditions - DONE -# Primary nodes -Tp_in = T0_f2 # in °C ORNL-TM-1647 p.2 -T0_p4 = Tf_in # 6.5444E+02; % in °C 6.461904761904777e+02; ORNL-TM-1647 p.2 -T0_p1 = Tp_in + (T0_p4 - Tp_in) / 4 # in °C -T0_p2 = Tp_in + 2 * (T0_p4 - Tp_in) / 4 # in °C -T0_p3 = Tp_in + 3 * (T0_p4 - Tp_in) / 4 # in °C - -# Secondary nodes -Ts_in = 5.4611E+02 # in °C ORNL-TM-1647 p.2 -T0_s4 = 5.7939E+02 # in °C ORNL-TM-1647 p.2 -T0_s1 = Ts_in + (T0_s4 - Ts_in) / nn_s # in °C -T0_s2 = Ts_in + 2 * (T0_s4 - Ts_in) / nn_s # in °C -T0_s3 = Ts_in + 3 * (T0_s4 - Ts_in) / nn_s # in °C -# Tube nodes -T0_t1 = (T0_p1 * hA_pn + T0_s3 * hA_sn) / (hA_pn + hA_sn) # in °C -T0_t2 = (T0_p3 * hA_pn + T0_s1 * hA_sn) / (hA_pn + hA_sn) # in °C - -# Radiator Parameters - DONE - -# Initial conditions - DONE -# Primary nodes -Trp_in = T0_s4 # 5.933E+02; % in °C ORNL-TM-1647 p.2 -T0_rp = Ts_in # in °C ORNL-TM-1647 p.2 - -# Secondary nodes - DONE -Trs_in = 37.78 # (C) air inlet temperature ORNL-TM-1647 p.2 -T0_rs = 148.9 # (C) air exit temperature ORNL-TM-1647 p.2 - -# Radiator Geometry -od_rad = 0.01905 # (m) outer diameter of tubes in the radiator ORNL-TM-0728 p.296 -tube_wall_thick = 0.0018288 # (m) thickness of tubes in the radiator ORNL-TM-0728 p.296 -id_rad = od_rad - 2 * tube_wall_thick -n_rtubes = 120 # number of tubes in the radiator (rows times tubes per row) ORNL-TM-0728 p.296 -l_rtube = 9.144 # (m) length of tubes in the radiator ORNL-TM-0728 p.296 -v_rp = pi * (id_rad / 2) ** 2 * l_rtube * n_rtubes # volume available to salt in the radiator -# v_rtube = pi * (od_rad / 2) ** 2 * l_rtube * n_rtubes - v_rp # volume of metal in radiator tubes *TUBES NOT MODELED - -n_tpr = 12 # number of tubes per row in the radiator matrix -n_row = 10 # number rows in the radiator matrix -tube_space = 0.0381 # (m) spacing between tubes and rows of matrix -v_rs = (n_row * od_rad + (n_row - 1) * tube_space) * (n_tpr * od_rad + (n_tpr - 1) * tube_space) * l_rtube # volume of air inside radiator - -# PRIMARY FLOW PARAMETERS - DONE -W_rp = W_s # coolant salt flow rate (kg/s) -m_rp = v_rp * rho_s # coolant salt mass in rad (kg) -nn_rp = 1 # number of coolant salt nodes in the radiator -mn_rp = m_rp / nn_rp # coolant mass per node (kg) -cp_rp = scp_s # coolant specific heat capacity (MJ/(kg-C)) - -# SECONDARY FLOW PARAMETERS - DONE -vdot_rs = 94.389 # ORNL-TM-0728 p. 296; 78.82; % air volume flow rate (m^3/s) ORNL-TM-1647 p.2 -rho_rs = 1.1237 # air density (kg/m^3) REFPROP (310K and 0.1MPa) -W_rs = vdot_rs * rho_rs # air flow rate (kg/s) - -m_rs = v_rs * rho_rs # coolant air mass in rad (kg) -nn_rs = 1 # number of coolant nodes in rad -mn_rs = m_rs / nn_rs # coolant mass per node (kg) -scp_rs = 1.0085E-3 # (MJ/kg-C) specific heat capacity of air at (air_out+air_in)/2 REFPROP - -A_rad = 6.503E1 # (m^2) surface area of the radiator ORNL-TM-0728 p.14 -h_roverall = P / A_rad / ((T0_rp + Trp_in) / 2 - (T0_rs + Trs_in) / 2) # cald as: P/A_rad/((T0_rp+Trp_in)/2-(T0_rs+Trs_in)/2) 3.168E-4; % (MW/m^2-C) polimi thesis - -# Primary Side -mcp_rpn = mn_rp * cp_rp # (mass of material x heat capacity of material) of fuel salt per lump in MW-s/°C -hA_rpn = h_roverall * A_rad / nn_rs # 3.030; % (primary to secondary heat transfer coeff x heat transfer area) in MW/°C - -# Secondary Side - DONE -mcp_rsn = mn_rs * scp_rs # (mass of material x heat capacity of material) of coolant salt per lump in MW-s/°C -hA_rsn = h_roverall * A_rad / nn_rs # (tube to secondary heat transfer coeff x heat transfer area) in MW/°C - -# Pure time delays between components - DONE -tau_hx_c = 8.67 # (sec) delay from hx to core TDAMSRE p.6 -tau_c_hx = 3.77 # (sec) subtracted 1 sec for external loop power generation node resident time; delay from core to fuel hx TDAMSRE p.6 -tau_hx_r = 4.71 # (sec) fertile hx to core TDAMSRE p.6 -tau_r_hx = 8.24 # (sec) core to fertile hx TDAMSRE p.6 - -first_val = (rho_0 - beta_t) * n_frac0 / Lam + lam[0] * C0[0] + lam[1] * C0[1] + lam[2] * C0[2] + lam[3] * C0[3] + lam[4] * C0[4] + lam[5] * C0[5] \ No newline at end of file diff --git a/dynamic_model/pump_transient_benchmark/pump_transients.ipynb b/dynamic_model/pump_transient_benchmark/pump_transients.ipynb index 2eacef8..5b49482 100644 --- a/dynamic_model/pump_transient_benchmark/pump_transients.ipynb +++ b/dynamic_model/pump_transient_benchmark/pump_transients.ipynb @@ -26,7 +26,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook simulates pump transients for the MSRE at zero power (10W). The system is run at steady-state until $t = 2500$, where the fuel and coolant pumps are spun down at the rate defined by the ORNL data. The model includes a reactivity response, $\\texttt{rho_control}$, which models the response of the MSRE control rod to maintain constant power. It is modeled as a pure integrator of $\\frac{dn}{dt}$ from the point-kinetics equations with a coefficient of -1.0, i.e. it tries to exactly cancel reactivity changes introduced by flow changes. " + "This notebook simulates pump transients for the MSRE at zero power (10W). The system is run at steady-state until $t = 2500$, where the fuel and coolant pumps are spun down at the rate defined by the ORNL data. After allowing the system to settle to a new equilibrium, at $t = 7500$, the pumps are spun up again. The model includes a reactivity response, `rho_control`, which models the response of the MSRE control rod to maintain constant power. It is modeled as a pure integrator of $\\frac{dn}{dt}$ from the point-kinetics equations with a coefficient of -1.0, i.e. it tries to exactly cancel reactivity changes introduced by flow changes, with the caveat that changes are limited to $\\pm$29 pcm/s, which was the maximum rate achievable by the MSRE control rods. " ] }, { @@ -174,6 +174,7 @@ "C5.set_dcdt(n.y(), beta = beta[4],Lambda = Lam, lam = lam[4], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n", "C6.set_dcdt(n.y(), beta = beta[5],Lambda = Lam, lam = lam[5], t_c = tau_c/flow_pct, t_l = tau_l/flow_pct, flow = True)\n", "\n", + "# control rod response\n", "rho_control.dydt = -sp.Min(29.0e-5, sp.Max(-29.0e-5, n.dydt))\n", "\n", "\n", diff --git a/dynamic_model/pump_transient_benchmark/steady_state.ipynb b/dynamic_model/pump_transient_benchmark/steady_state.ipynb deleted file mode 100644 index 7ad2046..0000000 --- a/dynamic_model/pump_transient_benchmark/steady_state.ipynb +++ /dev/null @@ -1,22 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From f938f50e60ee028585584bf6e958ccbcacd17ea8 Mon Sep 17 00:00:00 2001 From: lukelabrie Date: Thu, 20 Feb 2025 14:38:37 +0100 Subject: [PATCH 31/32] add reference to msrDynamics --- dynamic_model/pump_transient_benchmark/pump_transients.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dynamic_model/pump_transient_benchmark/pump_transients.ipynb b/dynamic_model/pump_transient_benchmark/pump_transients.ipynb index 5b49482..eb7dcaf 100644 --- a/dynamic_model/pump_transient_benchmark/pump_transients.ipynb +++ b/dynamic_model/pump_transient_benchmark/pump_transients.ipynb @@ -26,7 +26,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook simulates pump transients for the MSRE at zero power (10W). The system is run at steady-state until $t = 2500$, where the fuel and coolant pumps are spun down at the rate defined by the ORNL data. After allowing the system to settle to a new equilibrium, at $t = 7500$, the pumps are spun up again. The model includes a reactivity response, `rho_control`, which models the response of the MSRE control rod to maintain constant power. It is modeled as a pure integrator of $\\frac{dn}{dt}$ from the point-kinetics equations with a coefficient of -1.0, i.e. it tries to exactly cancel reactivity changes introduced by flow changes, with the caveat that changes are limited to $\\pm$29 pcm/s, which was the maximum rate achievable by the MSRE control rods. " + "This notebook simulates pump transients for the MSRE at zero power (10W). A dynamical system representing the MSRE is built using the [msrDynamics](https://github.com/LukeLabrie/msrDynamics) tool. The system is run at steady-state until $t = 2500$, where the fuel and coolant pumps are spun down at the rate defined by the ORNL data. After allowing the system to settle to a new equilibrium, at $t = 7500$, the pumps are spun up again. The model includes a reactivity response, `rho_control`, which models the response of the MSRE control rod to maintain constant power. It is modeled as a pure integrator of $\\frac{dn}{dt}$ from the point-kinetics equations with a coefficient of -1.0, i.e. it tries to exactly cancel reactivity changes introduced by flow changes, with the caveat that changes are limited to $\\pm$29 pcm/s, which was the maximum rate achievable by the MSRE control rods. " ] }, { From b0f73669b771d2fb82da890ecbd45fdea1b6cd08 Mon Sep 17 00:00:00 2001 From: lukelabrie Date: Thu, 20 Feb 2025 14:49:41 +0100 Subject: [PATCH 32/32] more writeup updates --- .../pump_transients.ipynb | 44 ++++++++++++++----- 1 file changed, 33 insertions(+), 11 deletions(-) diff --git a/dynamic_model/pump_transient_benchmark/pump_transients.ipynb b/dynamic_model/pump_transient_benchmark/pump_transients.ipynb index eb7dcaf..25b110d 100644 --- a/dynamic_model/pump_transient_benchmark/pump_transients.ipynb +++ b/dynamic_model/pump_transient_benchmark/pump_transients.ipynb @@ -4,7 +4,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Imports" + "# MSRE Pump Transient Benchmark" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook simulates pump transients for the MSRE at zero power (10W). A dynamical system representing the MSRE is built using the [msrDynamics](https://github.com/LukeLabrie/msrDynamics) tool. The system is run at steady-state until $t = 2500$, where the fuel and coolant pumps are spun down at the rate defined by the ORNL data. After allowing the system to settle to a new equilibrium, at $t = 7500$, the pumps are spun up again. The model includes a reactivity response, `rho_control`, which models the response of the MSRE control rod to maintain constant power. It is modeled as a pure integrator of $\\frac{dn}{dt}$ from the point-kinetics equations with a coefficient of -1.0, i.e. it tries to exactly cancel reactivity changes introduced by flow changes, with the caveat that changes are limited to $\\pm$29 pcm/s, which was the maximum rate achievable by the MSRE control rods. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" ] }, { @@ -26,7 +40,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook simulates pump transients for the MSRE at zero power (10W). A dynamical system representing the MSRE is built using the [msrDynamics](https://github.com/LukeLabrie/msrDynamics) tool. The system is run at steady-state until $t = 2500$, where the fuel and coolant pumps are spun down at the rate defined by the ORNL data. After allowing the system to settle to a new equilibrium, at $t = 7500$, the pumps are spun up again. The model includes a reactivity response, `rho_control`, which models the response of the MSRE control rod to maintain constant power. It is modeled as a pure integrator of $\\frac{dn}{dt}$ from the point-kinetics equations with a coefficient of -1.0, i.e. it tries to exactly cancel reactivity changes introduced by flow changes, with the caveat that changes are limited to $\\pm$29 pcm/s, which was the maximum rate achievable by the MSRE control rods. " + "## Plotting Style " ] }, { @@ -52,6 +66,13 @@ " ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sytem Definition using `msrDynamics`" + ] + }, { "cell_type": "code", "execution_count": null, @@ -70,6 +91,7 @@ "t_spindown = 2500\n", "t_spinup = 7500\n", "\n", + "# defining flow events\n", "def flow_fac(t):\n", " if (t <= t_spindown):\n", " return 1.0\n", @@ -82,15 +104,8 @@ " else:\n", " return 1.0\n", "\n", - "flow_pct = MSRE.add_input(flow_fac, T, max_anchors = 5000, input_tol = 32)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "flow_pct = MSRE.add_input(flow_fac, T, max_anchors = 5000, input_tol = 32)\n", + "\n", "# define nodes\n", "\n", "# radiator\n", @@ -192,6 +207,13 @@ "rho.set_drdt(sources = [T_cf1.dydt, T_cg.dydt], coeffs = [a_f,a_g])" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null,

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jitcdde import t +from msrDynamics.system_objects import Node, System +import pandas as pd +import sympy as sp +from concurrent.futures import ProcessPoolExecutor +from scipy.signal import find_peaks + +f_range = np.logspace(-2, 1, num=100) +# tau_l = 2 * tau_l +# tau_c = 2*tau_c +# tau_c_hx = 2 * tau_c_hx +# tau_hx_c = 2 * tau_hx_c +def process_frequency(f): + + MSRE = System() + + # radiator + T_out_rc = Node(m = mn_rp, scp = mcp_rpn/mn_rp, W = W_rp, y0 = T0_rp) + T_out_air = Node(m = mn_rs, scp = mcp_rsn/mn_rs, W = W_rs, y0 = T0_rs) + + # heat exchanger + T_hf1 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p1) + T_hf2 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p2) + T_hf3 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p3) + T_hf4 = Node(m = mn_p, scp = mcp_pn/mn_p, W = W_p, y0 = T0_p4) + T_ht1 = Node(m = m_tn, scp = scp_t, y0 = T0_t1) + T_ht2 = Node(m = m_tn, scp = scp_t, y0 = T0_t2) + T_hc1 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s1) + T_hc2 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s2) + T_hc3 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s3) + T_hc4 = Node(m = mn_s, scp = mcp_sn/mn_s, W = W_s, y0 = T0_s4) + + # core + n = Node(y0 = n_frac0) + C1 = Node(y0 = C0[0]) + C2 = Node(y0 = C0[1]) + C3 = Node(y0 = C0[2]) + C4 = Node(y0 = C0[3]) + C5 = Node(y0 = C0[4]) + C6 = Node(y0 = C0[5]) + rho = Node(y0 = 0.0) + + # add reactivity input + r = 1e-5 + def rho_insert(t): + return r*sp.sin(f*t) + + rho_ext = MSRE.add_input(rho_insert, T) + + T_cg = Node(m = mcp_g1/scp_g, scp = scp_g, y0 = T0_g1) + T_cf1 = Node(m = mn_f, scp = scp_f, W = W_f, y0 = T0_f1) + T_cf2 = Node(m = mn_f, scp = scp_f, W = W_f, y0 = T0_f2) + + MSRE.add_nodes([T_out_rc,T_out_air,T_hf1,T_hf2,T_hf3,T_hf4,T_ht1,T_ht2,T_hc1, + T_hc2,T_hc3,T_hc4,n,C1,C2,C3,C4,C5,C6,T_cg,T_cf1,T_cf2,rho]) + + # dynamics + + # radiator + T_out_rc.set_dTdt_advective(source = T_hc4.y(t-tau_hx_r)) + T_out_rc.set_dTdt_convective(source = [T_out_air.y()], hA = [hA_rpn]) + + T_out_air.set_dTdt_advective(source = Trs_in) + T_out_air.set_dTdt_convective(source = [T_out_rc.y()], hA = [hA_rsn]) + + # heat exchanger + T_hf1.set_dTdt_advective(source = T_cf2.y(t-tau_c_hx)) + T_hf1.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_pn]) + + T_hf2.set_dTdt_advective(source = T_hf1.y()) + T_hf2.dTdt_convective = T_hf1.dTdt_convective + + T_hf3.set_dTdt_advective(source = T_hf2.y()) + T_hf3.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn]) + + T_hf4.set_dTdt_advective(source = T_hf3.y()) + # T_hf4.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_pn]) + T_hf4.dTdt_convective = T_hf3.dTdt_convective + + # T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf2.y(),T_hc3.y(),T_hc4.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn]) + # T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf4.y(),T_hc1.y(),T_hc2.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn]) + T_ht1.set_dTdt_convective(source = [T_hf1.y(),T_hf1.y(),T_hc3.y(),T_hc3.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn]) + T_ht2.set_dTdt_convective(source = [T_hf3.y(),T_hf3.y(),T_hc1.y(),T_hc1.y()], hA = [hA_pn,hA_pn,hA_sn,hA_sn]) + + T_hc1.set_dTdt_advective(source = T_out_rc.y(t-tau_r_hx)) + T_hc1.set_dTdt_convective(source = [T_ht2.y()], hA = [hA_sn]) + + T_hc2.set_dTdt_advective(source = T_hc1.y()) + T_hc2.dTdt_convective = T_hc1.dTdt_convective + + T_hc3.set_dTdt_advective(source = T_hc2.y()) + T_hc3.set_dTdt_convective(source = [T_ht1.y()], hA = [hA_sn]) + + T_hc4.set_dTdt_advective(source = T_hc3.y()) + T_hc4.dTdt_convective = T_hc3.dTdt_convective + + # core + n.set_dndt(r = rho.y()+rho_ext, beta_eff = beta_t, Lambda = Lam, lam = lam, C = [C1.y(),C2.y(),C3.y(),C4.y(),C5.y(),C6.y()]) + C1.set_dcdt(n.y(),beta[0],Lam,lam[0],tau_c,tau_l) + C2.set_dcdt(n.y(),beta[1],Lam,lam[1],tau_c,tau_l) + C3.set_dcdt(n.y(),beta[2],Lam,lam[2],tau_c,tau_l) + C4.set_dcdt(n.y(),beta[3],Lam,lam[3],tau_c,tau_l) + C5.set_dcdt(n.y(),beta[4],Lam,lam[4],tau_c,tau_l) + C6.set_dcdt(n.y(),beta[5],Lam,lam[5],tau_c,tau_l) + + T_cg.set_dTdt_convective(source = [T_cf1.y()], hA = [hA_fg]) + T_cg.set_dTdt_internal(source = n.y(), k = k_g*P) + + T_cf1.set_dTdt_advective(source = T_hf4.y(t-tau_hx_c)) + T_cf1.set_dTdt_convective(source = [T_cg.y()], hA = [k_1*hA_fg]) + T_cf1.set_dTdt_internal(source = n.y(), k = k_f1*P) + + T_cf2.set_dTdt_advective(source = T_cf1.y()) + T_cf2.dTdt_convective = T_cf1.dTdt_convective + T_cf2.set_dTdt_internal(source = n.y(), k = k_f2*P) + + rho.set_drdt(sources = [T_cf1.dydt(), T_cf2.dydt(), T_cg.dydt()], coeffs = [a_f/2,a_f/2,a_g]) + + MSRE.solve(T) + + i_out = [i for i in range(len(T)) if T[i] >= 500] + n0 = n.y_out[i_out[0]-25] + n_out = np.array(n.y_out)[i_out] + + # calculate output amplitude + peaks, _ = find_peaks(n_out) + troughs, _ = find_peaks(-n_out) + amplitude = (np.mean(n_out[peaks]) - np.mean(n_out[troughs]))/2 + + # calculate Gain + input_amplitude = r + gain = amplitude / (input_amplitude*n0) + + # calculate Phase Shift + peak_times = T[i_out][peaks] + input_period = 2 * np.pi / f + input_signal = [i[0] for i in MSRE.input.get_state(T)] + input_peaks, _ = find_peaks(input_signal) + time_differences = [abs(T[input_peaks[i]] - peak_times[0]) for i in range(len(input_peaks))] + closest_peak_index = np.argmin(time_differences) + closest_peak_time = T[input_peaks[closest_peak_index]] + + # calculate Phase Shift + phase_shift = 360*(closest_peak_time-peak_times[0])/input_period + + if (phase_shift>180): + phase_shift = -360+phase_shift + elif (phase_shift<-180): + phase_shift = 360-phase_shift + + return f, gain, phase_shift + +with ProcessPoolExecutor() as executor: + results = list(executor.map(process_frequency, f_range)) + +# Process the results +results_df = pd.DataFrame(results, columns=['Frequency', 'Gain', 'Phase Shift']) + +# Write to CSV file +# csv_filename = f"frequency_response_results_{P}_MW_double_tau.csv" +csv_filename = f"frequency_response_results_{P}_MW.csv" +results_df.to_csv(csv_filename, index=False) + +print(f"Results written to {csv_filename}") \ No newline at end of file diff --git a/dynamic_model/msrDynamics_implementation/model_frq_response.ipynb b/dynamic_model/msrDynamics_implementation/model_frq_response.ipynb new file mode 100644 index 0000000..7c69724 --- /dev/null +++ b/dynamic_model/msrDynamics_implementation/model_frq_response.ipynb @@ -0,0 +1,297 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from parameters_U233 import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from jitcdde import t\n", + "from msrDynamics import Node, System\n", + "import pandas as pd\n", + "import math\n", + "import matplotlib.patches as mpatches" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "P = 8\n", + "\n", + "# unpack ORNL data\n", + "df_ORNL_mag = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_magnitude.csv\",names=['f','mag'])\n", + "df_ORNL_mag = df_ORNL_mag.sort_values(df_ORNL_mag.columns[0])\n", + "df_ORNL_phase = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_phase.csv\",names=['f','phase'])\n", + "df_ORNL_phase = df_ORNL_phase.sort_values(df_ORNL_phase.columns[0])\n", + "df_ORNL_mag_t = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_magnitude_theoretical.csv\",names=['f','mag'])\n", + "df_ORNL_mag_t = df_ORNL_mag_t.sort_values(df_ORNL_mag_t.columns[0])\n", + "df_ORNL_phase_t = pd.read_csv(f\"./data/ORNL_msre_{int(P)}MW_U233_phase_theoretical.csv\",names=['f','phase'])\n", + "df_ORNL_phase_t = df_ORNL_phase_t.sort_values(df_ORNL_phase_t.columns[0])\n", + "df_jit = pd.read_csv(f\"./data/frequency_response_results_{P}_MW.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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