MSRE/dynamic_model/msrDynamics_implementation/model_step.ipynb

{
 "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
}