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 a/dynamic_model/msrDynamics_implementation/__pycache__/parameters_U233.cpython-311.pyc b/dynamic_model/msrDynamics_implementation/__pycache__/parameters_U233.cpython-311.pyc
new file mode 100644
index 0000000..df09c81
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_insertion.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_insertion.csv
new file mode 100644
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_magnitude.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_magnitude.csv
new file mode 100644
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_magnitude_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_magnitude_theoretical.csv
new file mode 100644
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_phase.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_phase.csv
new file mode 100644
index 0000000..c716740
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_phase_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_1MW_U233_phase_theoretical.csv
new file mode 100644
index 0000000..f3af70a
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_insertion.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_insertion.csv
new file mode 100644
index 0000000..cf3d932
--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude.csv
new file mode 100644
index 0000000..96fe61a
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude_theoretical.csv
new file mode 100644
index 0000000..c07967a
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_magnitude_theoretical.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase.csv
new file mode 100644
index 0000000..84303c8
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase.csv
@@ -0,0 +1,54 @@
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_5MW_U233_phase_theoretical.csv
new file mode 100644
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_insertion.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_insertion.csv
new file mode 100644
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_magnitude.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_magnitude.csv
new file mode 100644
index 0000000..17ff279
--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_magnitude_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_magnitude_theoretical.csv
new file mode 100644
index 0000000..16ce917
--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_phase.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_phase.csv
new file mode 100644
index 0000000..283564c
--- /dev/null
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diff --git a/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_phase_theoretical.csv b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_phase_theoretical.csv
new file mode 100644
index 0000000..3bada9f
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/ORNL_msre_8MW_U233_phase_theoretical.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/frequency_response_results_1_MW.csv b/dynamic_model/msrDynamics_implementation/data/frequency_response_results_1_MW.csv
new file mode 100644
index 0000000..5a8c4c0
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/frequency_response_results_1_MW.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/frequency_response_results_5_MW.csv b/dynamic_model/msrDynamics_implementation/data/frequency_response_results_5_MW.csv
new file mode 100644
index 0000000..b2b5bd5
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/frequency_response_results_5_MW.csv
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diff --git a/dynamic_model/msrDynamics_implementation/data/frequency_response_results_8_MW.csv b/dynamic_model/msrDynamics_implementation/data/frequency_response_results_8_MW.csv
new file mode 100644
index 0000000..a4a69d7
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/data/frequency_response_results_8_MW.csv
@@ -0,0 +1,100 @@
+Frequency,Gain,Phase Shift
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diff --git a/dynamic_model/msrDynamics_implementation/data/simulink_msre_1MW_U233_insertion.xlsx b/dynamic_model/msrDynamics_implementation/data/simulink_msre_1MW_U233_insertion.xlsx
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diff --git a/dynamic_model/msrDynamics_implementation/data/simulink_msre_5MW_U233_insertion.xlsx b/dynamic_model/msrDynamics_implementation/data/simulink_msre_5MW_U233_insertion.xlsx
new file mode 100644
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Binary files /dev/null and b/dynamic_model/msrDynamics_implementation/data/simulink_msre_5MW_U233_insertion.xlsx differ
diff --git a/dynamic_model/msrDynamics_implementation/data/simulink_msre_8MW_U233_insertion.xlsx b/dynamic_model/msrDynamics_implementation/data/simulink_msre_8MW_U233_insertion.xlsx
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diff --git a/dynamic_model/msrDynamics_implementation/frq_script.py b/dynamic_model/msrDynamics_implementation/frq_script.py
new file mode 100644
index 0000000..837b318
--- /dev/null
+++ b/dynamic_model/msrDynamics_implementation/frq_script.py
@@ -0,0 +1,168 @@
+# Imports
+from parameters_U233 import *
+import numpy as np
+import matplotlib.pyplot as plt
+from 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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",
+      "text/plain": [
+       "<Figure size 600x1200 with 2 Axes>"
+      ]
+     },
+     "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):\n",
+    "        return 0.0\n",
+    "    else:\n",
+    "        return inserted\n",
+    "\n",
+    "rho_ext = MSRE.add_input(rho_insert, T)\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_f2)\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",
+    "# 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()+rho_ext, 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[0],Lam,lam[0],True,tau_c,tau_l)\n",
+    "C2.set_dcdt(n.y(),beta[1],Lam,lam[1],True,tau_c,tau_l)\n",
+    "C3.set_dcdt(n.y(),beta[2],Lam,lam[2],True,tau_c,tau_l)\n",
+    "C4.set_dcdt(n.y(),beta[3],Lam,lam[3],True,tau_c,tau_l)\n",
+    "C5.set_dcdt(n.y(),beta[4],Lam,lam[4],True,tau_c,tau_l)\n",
+    "C6.set_dcdt(n.y(),beta[5],Lam,lam[5],True,tau_c,tau_l)\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_cf2.dydt, T_cg.dydt], coeffs = [a_f/2,a_f/2,a_g])"
+   ]
+  },
+  {
+   "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"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/luke/git/envs/onion_env/lib/python3.11/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",
+      "/home/luke/git/envs/onion_env/lib/python3.11/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",
+      "/home/luke/git/envs/onion_env/lib/python3.11/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": [
+    "sol_jit = MSRE.solve(T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_199186/112360540.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  duration = df_ORNL.iloc[-1][0]-df_ORNL.iloc[0][0]\n",
+      "/tmp/ipykernel_199186/112360540.py:8: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
+      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
+      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
+      "\n",
+      "df[\"col\"][row_indexer] = value\n",
+      "\n",
+      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "  df_ORNL['t'][i] += delta\n"
+     ]
+    }
+   ],
+   "source": [
+    "duration = df_ORNL.iloc[-1][0]-df_ORNL.iloc[0][0]\n",
+    "i_insert = [i for i in range(len(T)) if (T[i] > 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": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "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