MSRE/dynamic_model/pump_transient_benchmark/model.ipynb

{
 "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<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating, compiling, and loading C code.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Integration progress:   0%|          | 1/500000 [00:00<111:03:32,  1.25it/s]/home/luke/miniconda3/envs/onion_dynamics_py312/lib/python3.12/site-packages/jitcdde/_jitcdde.py:820: 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 (though you should think about increasing your sampling time).\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 (though you should think about increasing your sampling time).\")\n",
      "Integration progress: 100%|██████████| 500000/500000 [00:01<00:00, 255515.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "populating nodes objects solution vectors...\n"
     ]
    }
   ],
   "source": [
    "sol_jit = MSRE.solve(T, max_delay = 30.0, abs_tol = 1e-14, rel_tol = 1e-12, populate_nodes = True)\n",
    "\n",
    "# T, sol_jit = MSRE.equilibrium_search(dT = 0.01, \n",
    "#                                      max_delay = tau_l, \n",
    "#                                      populate_nodes = False, \n",
    "#                                      md_step = 0.0001, \n",
    "#                                      abs_tol_eq = 1.0e-12, \n",
    "#                                      rel_tol_eq = 1.0e-10,\n",
    "#                                      show_conv_metrics = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, axs = plt.subplots(3,2, figsize = (8,8))\n",
    "\n",
    "# axs[0,0].set_xlim([0,20])\n",
    "# axs[0,0].plot(T, T_hf4.y_out, label = 'core inlet')\n",
    "# axs[0,0].plot(T, T_cf2.y_out, label = 'core outlet')\n",
    "# axs[0,0].plot(T, T_cg.y_out, label = 'graphite')\n",
    "# axs[0,0].set_ylim([647,649])\n",
    "# axs[0,0].legend()\n",
    "\n",
    "# axs[1,0].set_xlim([0,20])\n",
    "# axs[1,0].plot(T, T_cf2.y_out, label = 'hx fuel inlet')\n",
    "# axs[1,0].plot(T, T_hf4.y_out, label = 'hx fuel outlet')\n",
    "# axs[1,0].plot(T, (T_ht1.y_out+T_ht1.y_out)/2, label = 'hx tubes')\n",
    "# axs[1,0].legend()\n",
    "\n",
    "# axs[2,0].set_xlim([0,20])\n",
    "# axs[2,0].plot(T, T_out_rc.y_out, label = 'hx coolant inlet')\n",
    "# axs[2,0].plot(T, T_hc4.y_out, label = 'hx coolant outlet')\n",
    "# axs[2,0].plot(T, (T_ht1.y_out+T_ht1.y_out)/2, label = 'hx tubes')\n",
    "# axs[2,0].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x750c0c9332c0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "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
}