examples/are/model_mann_insertion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jitcdde import t\n",
    "from parameters_insertion import *\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.family'] = 'DejaVu Sans Mono'\n",
    "from msrDynamics import Node, System\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate System Object & Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "T0_c_f1 = F_to_K(1217)   # ORNL-1845 p. 80 \n",
    "T0_c_f2 = F_to_K(1573)   # ORNL-1845 p. 80\n",
    "T0_hfh_f1 = F_to_K(1573) # ORNL-1845 p. 80\n",
    "T0_hfh_f2 = F_to_K(1217) # ORNL-1845 p. 80\n",
    "\n",
    "# params = [-5.68629049e-05, -5.00000000e-05,  4.34964894e-05]\n",
    "# params = [-6.08617335e-05,  0.14e-04,  2.25540322e-05]\n",
    "# params = [-5.64624071e-05, 0.14e-04, 2.11060064e-05]\n",
    "# params = [-5.63564018e-05, 5.85140680e-05, 2.07492117e-05]\n",
    "# a_f, a_b, a_c = params\n",
    "\n",
    "# params = [ 5.53544724e-02,  9.73750875e-04,  6.14606090e-05,  9.13354480e-02,\n",
    "#            2.14807587e-02,  1.54981189e-01,  6.73477510e-03,  7.08833109e-02,\n",
    "#            3.41616261e+00,  7.08460818e-03,  6.92607123e-01, -1.22447661e-05,\n",
    "#            -4.67969807e-05,  7.82730830e-05]\n",
    "\n",
    "# params = [4.11758530e-02,  9.54123339e-02,  4.02648742e-03,  3.69268870e-03,\n",
    "#            1.49931526e-03,  7.12000364e-02,  7.02430066e-05,  7.39697568e-03,\n",
    "#            3.67285525e-01,  2.65592007e-04,  4.05877564e-02, -1.99737362e-04,\n",
    "#            -2.94696580e-06,  1.99997944e-04]\n",
    "\n",
    "# hA_ft_c, hA_tc_c, hA_mc_c, hA_ft_hx, hA_ht_hx, hA_ct_hx, hA_th_hxch, \\\n",
    "# hA_ht_hxhw, hA_tw_hxhw, hA_ht_hxhwc, hA_tw_hxhwc, a_f, a_b, a_c = params\n",
    "\n",
    "# a_f, a_b, a_c = [-0.3e-4,-0.3e-4,0.07e-4]\n",
    "\n",
    "# ARE system        \n",
    "ARE = System()\n",
    "\n",
    "# CORE NODES\n",
    "c_f1 = Node(m = m_f_c/2, scp = scp_f, W = W_f, y0 = T0_c_f1)\n",
    "c_f2 = Node(m = m_f_c/2, scp = scp_f, W = W_f, y0 = T0_c_f2)\n",
    "c_t1 = Node(m = m_t, scp = scp_t, y0 = T0_c_t1)\n",
    "c_c1 = Node(m = m_c_c/2, scp = scp_c, W = W_c, y0 = T0_c_c1)\n",
    "c_c2 = Node(m = m_c_c/2, scp = scp_c, W = W_c, y0 = T0_c_c2) \n",
    "c_m1 = Node(m = m_m_c, scp = scp_m, y0 = T0_c_m+50)\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 = 0.00)\n",
    "\n",
    "# add reactivity input\n",
    "inserted = 4e-3\n",
    "def rho_insert(t):\n",
    "    if (t<t_ins):\n",
    "        return 0.0\n",
    "    elif (t<(t_ins+insert_duration)):\n",
    "        return ((t-t_ins))*(inserted/insert_duration) # linear\n",
    "    elif (t < t_wd):\n",
    "        return inserted\n",
    "    elif (t < t_wd+insert_duration):\n",
    "        return inserted-((t-t_wd))*(inserted/insert_duration) # linear\n",
    "    else:\n",
    "        return 0.0\n",
    "\n",
    "rho_ext = ARE.add_input(rho_insert, T)\n",
    "\n",
    "# FUEL-HELIUM HX1\n",
    "hx_fh1_f1 = Node(m = m_f_hx/2, scp = scp_f, W = W_f/2, y0 = T0_hfh_f1)\n",
    "hx_fh1_f2 = Node(m = m_f_hx/2, scp = scp_f, W = W_f/2, y0 = T0_hfh_f2)\n",
    "hx_fh1_t1 = Node(m = m_t_hxfh, scp = scp_t, y0 = T0_hfh_t1)\n",
    "hx_fh1_h1 = Node(m = m_h_hxfh/2, scp = scp_h, W = W_h_fh, y0 = T0_hfh_h1)\n",
    "hx_fh1_h2 = Node(m = m_h_hxfh/2, scp = scp_h, W = W_h_fh, y0 = T0_hfh_h2)\n",
    "\n",
    "# FUEL-HELIUM HX2\n",
    "hx_fh2_f1 = Node(m = m_f_hx/2, scp = scp_f, W = W_f/2, y0 = T0_hfh_f1)\n",
    "hx_fh2_f2 = Node(m = m_f_hx/2, scp = scp_f, W = W_f/2, y0 = T0_hfh_f2)\n",
    "hx_fh2_t1 = Node(m = m_t_hxfh, scp = scp_t, y0 = T0_hfh_t1)\n",
    "hx_fh2_h1 = Node(m = m_h_hxfh/2, scp = scp_h, W = W_h_fh, y0 = T0_hfh_h1)\n",
    "hx_fh2_h2 = Node(m = m_h_hxfh/2, scp = scp_h, W = W_h_fh, y0 = T0_hfh_h2)\n",
    "\n",
    "# COOLANT-HELIUM HX1\n",
    "hx_ch1_c1 = Node(m = m_c_hx/2, scp = scp_c, W = W_c/2, y0 = T0_hch_c1)\n",
    "hx_ch1_c2 = Node(m = m_c_hx/2, scp = scp_c, W = W_c/2, y0 = T0_hch_c2)\n",
    "hx_ch1_t1 = Node(m = m_t_hxch, scp = scp_t, y0 = T0_hch_t1)\n",
    "hx_ch1_h1 = Node(m = m_h_hxch/2, scp = scp_h, W = W_h_ch, y0 = T0_hch_h1)\n",
    "hx_ch1_h2 = Node(m = m_h_hxch/2, scp = scp_h, W = W_h_ch, y0 = T0_hch_h2)\n",
    "\n",
    "# COOLANT-HELIUM HX2\n",
    "hx_ch2_c1 = Node(m = m_c_hx/2, scp = scp_c, W = W_c/2, y0 = T0_hch_c1)\n",
    "hx_ch2_c2 = Node(m = m_c_hx/2, scp = scp_c, W = W_c/2, y0 = T0_hch_c2)\n",
    "hx_ch2_t1 = Node(m = m_t_hxch, scp = scp_t, y0 = T0_hfh_t1)\n",
    "hx_ch2_h1 = Node(m = m_h_hxch/2, scp = scp_h, W = W_h_ch, y0 = T0_hch_h1)\n",
    "hx_ch2_h2 = Node(m = m_h_hxch/2, scp = scp_h, W = W_h_ch, y0 = T0_hch_h2)\n",
    "\n",
    "# HELIUM-WATER HX1 (FUEL LOOP)\n",
    "hx_hwf1_h1 = Node(m = m_h_hxhwf/2, scp = scp_h, W = W_h_fh, y0 = T0_hhwf_h1)\n",
    "hx_hwf1_h2 = Node(m = m_h_hxhwf/2, scp = scp_h, W = W_h_fh, y0 = T0_hhwf_h2)\n",
    "hx_hwf1_t1 = Node(m = m_t_hxhwf, scp = scp_t, y0 = T0_hhwf_t1)\n",
    "hx_hwf1_w1 = Node(m = m_w_hxhwf/2, scp = scp_w, W = W_hhwf_w, y0 = T0_hhwf_w1)\n",
    "hx_hwf1_w2 = Node(m = m_w_hxhwf/2, scp = scp_w, W = W_hhwf_w, y0 = T0_hhwf_w2)\n",
    "\n",
    "# HELIUM-WATER HX2 (FUEL LOOP)\n",
    "hx_hwf2_h1 = Node(m = m_h_hxhwf/2, scp = scp_h, W = W_h_fh, y0 = T0_hhwf_h1)\n",
    "hx_hwf2_h2 = Node(m = m_h_hxhwf/2, scp = scp_h, W = W_h_fh, y0 = T0_hhwf_h2)\n",
    "hx_hwf2_t1 = Node(m = m_t_hxhwf, scp = scp_t, y0 = T0_hhwf_t1)\n",
    "hx_hwf2_w1 = Node(m = m_w_hxhwf/2, scp = scp_w, W = W_hhwf_w, y0 = T0_hhwf_w1)\n",
    "hx_hwf2_w2 = Node(m = m_w_hxhwf/2, scp = scp_w, W = W_hhwf_w, y0 = T0_hhwf_w2)\n",
    "\n",
    "# HELIUM-WATER HX1 (COOLANT LOOP)\n",
    "hx_hwc1_h1 = Node(m = m_h_hxhwc/2, scp = scp_h, W = W_h_ch, y0 = T0_hhwc_h1)\n",
    "hx_hwc1_h2 = Node(m = m_h_hxhwc/2, scp = scp_h, W = W_h_ch, y0 = T0_hhwc_h2)\n",
    "hx_hwc1_t1 = Node(m = m_t_hxhwc, scp = scp_t, y0 = T0_hhwf_t1)\n",
    "hx_hwc1_w1 = Node(m = m_w_hxhwc/2, scp = scp_w, W = W_hhwc_w, y0 = T0_hhwc_w1)\n",
    "hx_hwc1_w2 = Node(m = m_w_hxhwc/2, scp = scp_w, W = W_hhwc_w, y0 = T0_hhwc_w2)\n",
    "\n",
    "# HELIUM-WATER HX2 (COOLANT LOOP)\n",
    "hx_hwc2_h1 = Node(m = m_h_hxhwc/2, scp = scp_h, W = W_h_ch, y0 = T0_hhwc_h1)\n",
    "hx_hwc2_h2 = Node(m = m_h_hxhwc/2, scp = scp_h, W = W_h_ch, y0 = T0_hhwc_h2)\n",
    "hx_hwc2_t1 = Node(m = m_t_hxhwc, scp = scp_t, y0 = T0_hhwf_t1)\n",
    "hx_hwc2_w1 = Node(m = m_w_hxhwc/2, scp = scp_w, W = W_hhwc_w, y0 = T0_hhwc_w1)\n",
    "hx_hwc2_w2 = Node(m = m_w_hxhwc/2, scp = scp_w, W = W_hhwc_w, y0 = T0_hhwc_w2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "ARE.add_nodes([c_f1,c_f2,c_t1,c_c1,c_c2,c_m1,n,C1,C2,C3,C4,C5,C6,rho,\n",
    "              hx_fh1_f1,hx_fh1_f2,hx_fh1_t1,hx_fh1_h1,hx_fh1_h2,\n",
    "              hx_fh2_f1,hx_fh2_f2,hx_fh2_t1,hx_fh2_h1,hx_fh2_h2,\n",
    "              hx_ch1_c1,hx_ch1_c2,hx_ch1_t1,hx_ch1_h1,hx_ch1_h2,\n",
    "              hx_ch2_c1,hx_ch2_c2,hx_ch2_t1,hx_ch2_h1,hx_ch2_h2,\n",
    "              hx_hwf1_h1,hx_hwf1_h2,hx_hwf1_t1,hx_hwf1_w1,hx_hwf1_w2,\n",
    "              hx_hwf2_h1,hx_hwf2_h2,hx_hwf2_t1,hx_hwf2_w1,hx_hwf2_w2,\n",
    "              hx_hwc1_h1,hx_hwc1_h2,hx_hwc1_t1,hx_hwc1_w1,hx_hwc1_w2,\n",
    "              hx_hwc2_h1,hx_hwc2_h2,hx_hwc2_t1,hx_hwc2_w1,hx_hwc2_w2,\n",
    "              ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define Dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CORE\n",
    "c_f1.set_dTdt_advective(source = (hx_fh1_f2.y(t-tau_hx_c_f)+hx_fh2_f2.y(t-tau_hx_c_f))/2) \n",
    "c_f1.set_dTdt_internal(source = n.y(), k = k_f1*P)\n",
    "c_f1.set_dTdt_convective(source = [c_t1.y()], hA = [hA_ft_c/2])\n",
    "\n",
    "c_f2.set_dTdt_advective(source = c_f1.y()) \n",
    "c_f2.set_dTdt_internal(source = n.y(), k = k_f2*P)\n",
    "c_f2.dTdt_convective = c_f1.dTdt_convective \n",
    "\n",
    "c_t1.set_dTdt_convective(source = [c_f1.y(), c_c1.y()], hA = [hA_ft_c,hA_tc_c])\n",
    "\n",
    "c_c1.set_dTdt_advective(source = (hx_ch1_c2.y(t-tau_c_hx_f)+hx_ch2_c2.y(t-tau_c_hx_f))/2)\n",
    "c_c1.set_dTdt_convective(source = [c_t1.y(),c_m1.y()], hA = [hA_tc_c/2,hA_mc_c/2])\n",
    "\n",
    "c_c2.set_dTdt_advective(source = c_c1.y())\n",
    "c_c2.dTdt_convective = c_c1.dTdt_convective\n",
    "\n",
    "c_m1.set_dTdt_internal(source = n.y(), k = k_m*P)\n",
    "c_m1.set_dTdt_convective(source = [c_c1.y()], hA = [hA_mc_c])\n",
    "\n",
    "n.set_dndt(rho.y()+rho_ext, beta_t, Lam, lam, [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",
    "rho.set_drdt([c_f1.dydt(),c_f2.dydt(),c_m1.dydt(),c_c1.dydt(),c_c2.dydt()],[a_f/2,a_f/2,a_b,a_c/2,a_c/2])\n",
    "\n",
    "# FUEL-HELIUM HX1\n",
    "hx_fh1_f1.set_dTdt_advective(source = c_f2.y(t-tau_c_hx_f))\n",
    "hx_fh1_f1.set_dTdt_convective(source = [hx_fh1_t1.y()], hA = [hA_ft_hx/2])\n",
    "\n",
    "hx_fh1_f2.set_dTdt_advective(source = hx_fh1_f1.y())\n",
    "hx_fh1_f2.dTdt_convective = hx_fh1_f1.dTdt_convective\n",
    "\n",
    "hx_fh1_t1.set_dTdt_convective(source = [hx_fh1_f1.y(),hx_fh1_h1.y()],hA = [hA_ft_hx,hA_ht_hx])\n",
    "\n",
    "hx_fh1_h1.set_dTdt_advective(source = hx_hwf2_h2.y(t-tau_h))\n",
    "hx_fh1_h1.set_dTdt_convective(source = [hx_fh1_t1.y()], hA = [hA_ht_hx/2])\n",
    "\n",
    "hx_fh1_h2.set_dTdt_advective(source = hx_fh1_h1.y())\n",
    "hx_fh1_h2.dTdt_convective = hx_fh1_h1.dTdt_convective\n",
    "\n",
    "# FUEL-HELIUM HX2\n",
    "hx_fh2_f1.set_dTdt_advective(source = c_f2.y(t-tau_c_hx_f))\n",
    "hx_fh2_f1.set_dTdt_convective(source = [hx_fh2_t1.y()], hA = [hA_ft_hx/2])\n",
    "\n",
    "hx_fh2_f2.set_dTdt_advective(source = hx_fh2_f1.y())\n",
    "hx_fh2_f2.dTdt_convective = hx_fh2_f1.dTdt_convective\n",
    "\n",
    "hx_fh2_t1.set_dTdt_convective(source = [hx_fh2_f1.y(),hx_fh2_h1.y()],hA = [hA_ft_hx,hA_ht_hx])\n",
    "\n",
    "hx_fh2_h1.set_dTdt_advective(source = hx_hwf1_h2.y())\n",
    "hx_fh2_h1.set_dTdt_convective(source = [hx_fh2_t1.y()], hA = [hA_ht_hx/2])\n",
    "\n",
    "hx_fh2_h2.set_dTdt_advective(source = hx_fh2_h1.y())\n",
    "hx_fh2_h2.dTdt_convective = hx_fh2_h1.dTdt_convective\n",
    "\n",
    "# COOLANT-HELIUM HX1\n",
    "hx_ch1_c1.set_dTdt_advective(source = c_c2.y(t-tau_c_hx_f))\n",
    "hx_ch1_c1.set_dTdt_convective(source = [hx_ch1_t1.y()], hA = [hA_ct_hx/2])\n",
    "\n",
    "hx_ch1_c2.set_dTdt_advective(source = hx_ch1_c1.y())\n",
    "hx_ch1_c2.dTdt_convective = hx_ch1_c1.dTdt_convective\n",
    "\n",
    "hx_ch1_t1.set_dTdt_convective(source = [hx_ch1_c1.y(),hx_ch1_h1.y()],hA = [hA_ct_hx,hA_th_hxch])\n",
    "\n",
    "hx_ch1_h1.set_dTdt_advective(source = hx_hwc1_h2.y(t-tau_h))\n",
    "hx_ch1_h1.set_dTdt_convective(source = [hx_ch1_t1.y()], hA = [hA_th_hxch/2])\n",
    "\n",
    "hx_ch1_h2.set_dTdt_advective(source = hx_ch1_h1.y())\n",
    "hx_ch1_h2.dTdt_convective = hx_ch1_h1.dTdt_convective\n",
    "\n",
    "# COOLANT-HELIUM HX2\n",
    "hx_ch2_c1.set_dTdt_advective(source = c_c2.y(t-tau_c_hx_f))\n",
    "hx_ch2_c1.set_dTdt_convective(source = [hx_ch2_t1.y()], hA = [hA_ct_hx/2])\n",
    "\n",
    "hx_ch2_c2.set_dTdt_advective(source = hx_ch2_c1.y())\n",
    "hx_ch2_c2.dTdt_convective = hx_ch2_c1.dTdt_convective\n",
    "\n",
    "hx_ch2_t1.set_dTdt_convective(source = [hx_ch2_c1.y(),hx_ch2_h1.y()],hA = [hA_ct_hx,hA_th_hxch])\n",
    "\n",
    "hx_ch2_h1.set_dTdt_advective(source = hx_hwc2_h2.y(t-tau_h))\n",
    "hx_ch2_h1.set_dTdt_convective(source = [hx_ch2_t1.y()], hA = [hA_th_hxch/2])\n",
    "\n",
    "hx_ch2_h2.set_dTdt_advective(source = hx_ch2_h1.y())\n",
    "hx_ch2_h2.dTdt_convective = hx_ch2_h1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX1 (FUEL LOOP)\n",
    "hx_hwf1_h1.set_dTdt_advective(source = hx_fh1_h2.y())\n",
    "hx_hwf1_h1.set_dTdt_convective(source = [hx_hwf1_t1.y()], hA = [hA_ht_hxhw/2])\n",
    "\n",
    "hx_hwf1_h2.set_dTdt_advective(source = hx_hwf1_h1.y())\n",
    "hx_hwf1_h2.dTdt_convective = hx_hwf1_h1.dTdt_convective\n",
    "\n",
    "hx_hwf1_t1.set_dTdt_convective(source = [hx_hwf1_h1.y(),hx_hwf1_w1.y()],hA = [hA_ht_hxhw,hA_tw_hxhw])\n",
    "\n",
    "hx_hwf1_w1.set_dTdt_advective(source = T0_hhwf_w1)\n",
    "hx_hwf1_w1.set_dTdt_convective(source = [hx_hwf1_t1.y()], hA = [hA_tw_hxhw/2])\n",
    "\n",
    "hx_hwf1_w2.set_dTdt_advective(source = hx_hwf1_w1.y())\n",
    "hx_hwf1_w2.dTdt_convective = hx_hwf1_w1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX2 (FUEL LOOP)\n",
    "hx_hwf2_h1.set_dTdt_advective(source = hx_fh2_h2.y())\n",
    "hx_hwf2_h1.set_dTdt_convective(source = [hx_hwf2_t1.y()], hA = [hA_ht_hxhw/2])\n",
    "\n",
    "hx_hwf2_h2.set_dTdt_advective(source = hx_hwf2_h1.y())\n",
    "hx_hwf2_h2.dTdt_convective = hx_hwf2_h1.dTdt_convective\n",
    "\n",
    "hx_hwf2_t1.set_dTdt_convective(source = [hx_hwf2_h1.y(),hx_hwf2_w1.y()],hA = [hA_ht_hxhw,hA_tw_hxhw])\n",
    "\n",
    "hx_hwf2_w1.set_dTdt_advective(source = T0_hhwf_w1)\n",
    "hx_hwf2_w1.set_dTdt_convective(source = [hx_hwf2_t1.y()], hA = [hA_tw_hxhw/2])\n",
    "\n",
    "hx_hwf2_w2.set_dTdt_advective(source = hx_hwf2_w1.y())\n",
    "hx_hwf2_w2.dTdt_convective = hx_hwf2_w1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX1 (COOLANT LOOP)\n",
    "hx_hwc1_h1.set_dTdt_advective(source = hx_ch1_h2.y())\n",
    "hx_hwc1_h1.set_dTdt_convective(source = [hx_hwc1_t1.y()], hA = [hA_ht_hxhwc/2])\n",
    "\n",
    "hx_hwc1_h2.set_dTdt_advective(source = hx_hwc1_h1.y())\n",
    "hx_hwc1_h2.dTdt_convective = hx_hwc1_h1.dTdt_convective\n",
    "\n",
    "hx_hwc1_t1.set_dTdt_convective(source = [hx_hwc1_h1.y(),hx_hwc1_w1.y()],hA = [hA_ht_hxhwc,hA_tw_hxhwc])\n",
    "\n",
    "hx_hwc1_w1.set_dTdt_advective(source = T0_hhwc_w1)\n",
    "hx_hwc1_w1.set_dTdt_convective(source = [hx_hwc1_t1.y()], hA = [hA_tw_hxhwc/2])\n",
    "\n",
    "hx_hwc1_w2.set_dTdt_advective(source = hx_hwc1_w1.y())\n",
    "hx_hwc1_w2.dTdt_convective = hx_hwc1_w1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX2 (COOLANT LOOP)\n",
    "hx_hwc2_h1.set_dTdt_advective(source = hx_ch2_h2.y())\n",
    "hx_hwc2_h1.set_dTdt_convective(source = [hx_hwc2_t1.y()], hA = [hA_ht_hxhwc/2]) \n",
    "\n",
    "hx_hwc2_h2.set_dTdt_advective(source = hx_hwc2_h1.y())\n",
    "hx_hwc2_h2.dTdt_convective = hx_hwc2_h1.dTdt_convective\n",
    "\n",
    "hx_hwc2_t1.set_dTdt_convective(source = [hx_hwc2_h1.y(),hx_hwc2_w1.y()],hA = [hA_ht_hxhwc,hA_tw_hxhwc])\n",
    "\n",
    "hx_hwc2_w1.set_dTdt_advective(source = T0_hhwc_w1)\n",
    "hx_hwc2_w1.set_dTdt_convective(source = [hx_hwc2_t1.y()], hA = [hA_tw_hxhwc/2])\n",
    "\n",
    "hx_hwc2_w2.set_dTdt_advective(source = hx_hwc2_w1.y())\n",
    "hx_hwc2_w2.dTdt_convective = hx_hwc2_w1.dTdt_convective\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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/thesis_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/thesis_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/thesis_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": [
    "# solve system \n",
    "sol_jit = ARE.solve(T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unpack ORNL data \n",
    "df_power_reversed = pd.read_csv(\"./data/insertion.csv\",header=None)\n",
    "df_inlet_reversed = pd.read_csv(\"./data/fuel_inlet_temp.csv\",header=None)\n",
    "df_outlet_reversed = pd.read_csv(\"./data/fuel_outlet_temp.csv\",header=None)\n",
    "df_power = df_power_reversed.iloc[::-1] \n",
    "df_inlet = df_inlet_reversed.iloc[::-1]\n",
    "df_outlet = df_outlet_reversed.iloc[::-1]\n",
    "df_power = df_power.reset_index(drop=True)\n",
    "df_inlet = df_inlet.reset_index(drop=True)\n",
    "df_outlet = df_outlet.reset_index(drop=True)\n",
    "df_inlet[1] = [F_to_K(t) for t in df_inlet[1]]\n",
    "df_outlet[1] = [F_to_K(t) for t in df_outlet[1]]\n",
    "\n",
    "\n",
    "# get indices for simulation data\n",
    "t_before_data = (1110-df_inlet[0][0])*60\n",
    "duration_data = (df_inlet.iloc[-1][0]-df_inlet[0][0])*60\n",
    "t_end_data = df_inlet.iloc[-1][0]\n",
    "t_before_sim = t_ins-t_before_data\n",
    "T_insert = [t for t in T if (t > (t_before_sim)) and (t < (t_before_sim)+(duration_data))]\n",
    "i_insert = [t[0] for t in enumerate(T) if (t[1] > (t_before_sim)) and (t[1] < (t_before_sim)+(duration_data))]\n",
    "adj = (df_inlet[0][0])*60-T_insert[0]\n",
    "df_outlet = df_outlet[:-2]\n",
    "\n",
    "# adjust to reported initial values (graphical interpretaion correction)\n",
    "dp = df_power[1][0]-2.34\n",
    "df_power[1] = [p-dp for p in df_power[1]]\n",
    "\n",
    "dt_fin = df_inlet[1][0]-F_to_K(1217)\n",
    "df_inlet[1] = [tf-dt_fin for tf in df_inlet[1]]\n",
    "\n",
    "dt_fout = df_outlet[1][0]-F_to_K(1573)\n",
    "df_outlet[1] = [tf-dt_fout for tf in df_outlet[1]]\n",
    "\n",
    "# avg temp\n",
    "ornl_diff = [df_outlet[1][i]-df_inlet[1][i] for i in range(len(df_inlet))]\n",
    "ornl_avg = sum(ornl_diff)/len(ornl_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_power)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f78bc44bdd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(2,2,figsize=(20,16))\n",
    "\n",
    "# Set a professional color scheme\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', \n",
    "          'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n",
    "\n",
    "# Function to update the style of each axis\n",
    "def update_axis_style(ax, title = '', x_label='', y_label='', x_ticks=True, y_scale='linear'):\n",
    "    ax.set_title(title,fontsize=14)\n",
    "    ax.set_xlabel(x_label,fontsize=12)\n",
    "    ax.set_ylabel(y_label,fontsize=12)\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",
    "    ax.set_yscale(y_scale)\n",
    "\n",
    "update_axis_style(axs[0,0],\"ARE: Reactivity Insertion, Power(MW)\")\n",
    "axs[0,0].plot(df_power[0]*60-adj,df_power[1],label=\"ORNL-1845\")\n",
    "axs[0,0].plot(T_insert,[s*P for s in n.y_out[i_insert[0]:(i_insert[-1]+1)]],label=\"msrDynamics\")\n",
    "axs[0,0].set_xlabel(r\"$t$ (s)\")\n",
    "axs[0,0].set_ylabel(\"Power (MW)\")\n",
    "axs[0,0].legend()\n",
    "\n",
    "ornl_avg = (df_inlet[1]+df_outlet[1])/2\n",
    "jitcdde_avg = (((np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2)+(np.array(c_f2.y_out)))/2\n",
    "jit_inlet = (np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2\n",
    "\n",
    "update_axis_style(axs[0,1],\"ARE: Reactivity Insertion, Fuel Inlet Temperature\")\n",
    "axs[0,1].plot(df_inlet[0]*60-adj,df_inlet[1],label=\"inlet, ORNL-1845\",color=\"green\")\n",
    "axs[0,1].plot(df_outlet[0]*60-adj,df_outlet[1],label=\"outlet, ORNL-1845\",color=\"green\")\n",
    "axs[0,1].plot(df_outlet[0]*60-adj,ornl_avg,label=\"avg, ORNL-1845\",color=\"green\",linestyle=\"--\")\n",
    "axs[0,1].plot(T_insert,jit_inlet[i_insert[0]:(i_insert[-1]+1)],label=\"inlet, msrDynamics\",color=\"blue\")\n",
    "axs[0,1].plot(T_insert,c_f2.y_out[i_insert[0]:(i_insert[-1]+1)],label=\"outlet, msrDynamics\",color=\"blue\")\n",
    "axs[0,1].plot(T_insert,jitcdde_avg[i_insert[0]:(i_insert[-1]+1)],label=\"average, msrDynamics\",color=\"blue\",linestyle=\"--\")\n",
    "axs[0,1].set_xlabel(r\"$t$ (s)\")\n",
    "axs[0,1].set_ylabel(r\"Temperature ($^\\circ$K)\")\n",
    "axs[0,1].legend()\n",
    "\n",
    "# delta\n",
    "update_axis_style(axs[1,0],r\"ARE: Reactivity Insertion, $\\Delta$ P(MW)\")\n",
    "axs[1,0].plot(df_power[0]*60-adj,df_power[1]-P,label=\"ORNL-1845\")\n",
    "axs[1,0].plot(T_insert,[P*(s-n.y_out[i_insert[0]-1]) for s in n.y_out[i_insert]],label=\"msrDynamics\")\n",
    "axs[1,0].set_xlabel(r\"$t$ (s)\")\n",
    "axs[1,0].set_ylabel(r\"$\\Delta P$ (MW)\")\n",
    "axs[1,0].legend()\n",
    "\n",
    "ornl_avg = (df_inlet[1]+df_outlet[1])/2\n",
    "jitcdde_avg = (((np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2)+(np.array(c_f2.y_out)))/2\n",
    "jit_inlet = (np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2\n",
    "\n",
    "update_axis_style(axs[1,1],r\"ARE: Reactivity Insertion, $\\Delta$T\")\n",
    "axs[1,1].plot(T_insert,jit_inlet[i_insert]-jit_inlet[i_insert[0]-1],label=\"inlet, msrDynamics\",color=colors[0])\n",
    "axs[1,1].plot(df_inlet[0]*60-adj,df_inlet[1]-df_inlet[1][0],linestyle =\"--\",label=\"inlet, ORNL-1845 \",color=colors[0])\n",
    "axs[1,1].plot(T_insert,c_f2.y_out[i_insert]-c_f2.y_out[i_insert[0]-1],label=\"outlet, msrDynamics\",color=colors[1])\n",
    "axs[1,1].plot(df_outlet[0]*60-adj,df_outlet[1]-df_outlet[1][0],linestyle=\"--\",label=\"outlet, ORNL-1845 \",color=colors[1])\n",
    "axs[1,1].plot(T_insert,jitcdde_avg[i_insert]-jitcdde_avg[i_insert[0]-1],label=\"average, msrDynamics\",color=colors[2])\n",
    "axs[1,1].plot(df_outlet[0]*60-adj,ornl_avg-ornl_avg[0],label=\"average, ORNL-1845\",color=colors[2],linestyle=\"--\")\n",
    "axs[1,1].set_xlabel(r\"$t$ (s)\")\n",
    "axs[1,1].set_ylabel(r\" $\\Delta$ T ($^\\circ$K)\")\n",
    "axs[1,1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "When flow is True, both 't_c' (core transit time) and 't_l' (loop transit time) must                                         be greater than 0 to avoid division by zero.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 48\u001b[0m\n\u001b[1;32m     45\u001b[0m c_m1\u001b[38;5;241m.\u001b[39mset_dTdt_convective(source \u001b[38;5;241m=\u001b[39m [c_c1\u001b[38;5;241m.\u001b[39my()], hA \u001b[38;5;241m=\u001b[39m [hA_mc_c])\n\u001b[1;32m     47\u001b[0m n\u001b[38;5;241m.\u001b[39mset_dndt(rho\u001b[38;5;241m.\u001b[39my()\u001b[38;5;241m+\u001b[39mrho_ext, beta_t, Lam, lam, [C1\u001b[38;5;241m.\u001b[39my(), C2\u001b[38;5;241m.\u001b[39my(), C3\u001b[38;5;241m.\u001b[39my(), C4\u001b[38;5;241m.\u001b[39my(), C5\u001b[38;5;241m.\u001b[39my(), C6\u001b[38;5;241m.\u001b[39my()])\n\u001b[0;32m---> 48\u001b[0m \u001b[43mC1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_dcdt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbeta\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mLam\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlam\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtau_c\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtau_l\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     49\u001b[0m C2\u001b[38;5;241m.\u001b[39mset_dcdt(n\u001b[38;5;241m.\u001b[39my(), beta[\u001b[38;5;241m1\u001b[39m], Lam, lam[\u001b[38;5;241m1\u001b[39m], tau_c, tau_l)\n\u001b[1;32m     50\u001b[0m C3\u001b[38;5;241m.\u001b[39mset_dcdt(n\u001b[38;5;241m.\u001b[39my(), beta[\u001b[38;5;241m2\u001b[39m], Lam, lam[\u001b[38;5;241m2\u001b[39m], tau_c, tau_l)\n",
      "File \u001b[0;32m~/git/msrDynamics/src/msrDynamics/system_objects.py:231\u001b[0m, in \u001b[0;36mNode.set_dcdt\u001b[0;34m(self, n, beta, Lambda, lam, flow, t_c, t_l)\u001b[0m\n\u001b[1;32m    229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flow:\n\u001b[1;32m    230\u001b[0m      \u001b[38;5;28;01mif\u001b[39;00m (t_c \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (t_l \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m):\n\u001b[0;32m--> 231\u001b[0m           \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen flow is True, both \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt_c\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m (core transit time) and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt_l\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m (loop transit time) must \u001b[39m\u001b[38;5;130;01m\\\u001b[39;00m\n\u001b[1;32m    232\u001b[0m \u001b[38;5;124m                              be greater than 0 to avoid division by zero.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    233\u001b[0m      outflow \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my() \u001b[38;5;241m/\u001b[39m t_c\n\u001b[1;32m    234\u001b[0m      inflow \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my(t\u001b[38;5;241m-\u001b[39mt_l) \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mexp(\u001b[38;5;241m-\u001b[39mlam \u001b[38;5;241m*\u001b[39m t_l) \u001b[38;5;241m/\u001b[39m t_c\n",
      "\u001b[0;31mValueError\u001b[0m: When flow is True, both 't_c' (core transit time) and 't_l' (loop transit time) must                                         be greater than 0 to avoid division by zero."
     ]
    }
   ],
   "source": [
    "# power fraction as calculated from steady state\n",
    "hx_hwf1 = 0.4054600395507873\n",
    "hx_hwf2 = 0.40545967824513574\n",
    "hx_hwc1 = 0.09454014110203846\n",
    "hx_hwc2 = 0.09454014110203846\n",
    "\n",
    "dT_lm_f = ((F_to_K(1450)-F_to_K(620))-(F_to_K(1150)-F_to_K(180)))/math.log((F_to_K(1450)-F_to_K(620))/(F_to_K(1150)-F_to_K(180)))\n",
    "h_overall_f = P*(hx_hwf1+hx_hwf2)/dT_lm_f\n",
    "hA_ft_hx = h_overall_f\n",
    "hA_ht_hx = h_overall_f\n",
    "\n",
    "dT_lm_c = ((F_to_K(1235)-F_to_K(1020))-(F_to_K(1105)-F_to_K(170)))/math.log((F_to_K(1235)-F_to_K(1020))/(F_to_K(1105)-F_to_K(170)))\n",
    "h_overall_c = P*(hx_hwc1+hx_hwc2)/dT_lm_c\n",
    "hA_ct_hx = h_overall_c\n",
    "hA_th_hxch = h_overall_c\n",
    "\n",
    "dT_lm_hf = ((F_to_K(620)-F_to_K(135))-(F_to_K(180)-F_to_K(70)))/math.log((F_to_K(620)-F_to_K(135))/(F_to_K(180)-F_to_K(70)))\n",
    "h_overall_hf = P*(hx_hwf1+hx_hwf2)/dT_lm_hf\n",
    "hA_ht_hxhw = h_overall_hf\n",
    "hA_tw_hxhw = h_overall_hf\n",
    "\n",
    "dT_lm_hc = ((F_to_K(1020)-F_to_K(100))-(F_to_K(170)-F_to_K(70)))/math.log((F_to_K(1020)-F_to_K(100))/(F_to_K(170)-F_to_K(70)))\n",
    "h_overall_hc = P*(hx_hwc1+hx_hwc2)/dT_lm_hc\n",
    "hA_ht_hxhwc = h_overall_hc\n",
    "hA_tw_hxhwc = h_overall_hc\n",
    "\n",
    "# CORE\n",
    "c_f1.set_dTdt_advective(source = (hx_fh1_f2.y(t-tau_hx_c_f)+hx_fh2_f2.y(t-tau_hx_c_f))/2) \n",
    "c_f1.set_dTdt_internal(source = n.y(), k = k_f1*P)\n",
    "c_f1.set_dTdt_convective(source = [c_t1.y()], hA = [hA_ft_c/2])\n",
    "\n",
    "c_f2.set_dTdt_advective(source = c_f1.y()) \n",
    "c_f2.set_dTdt_internal(source = n.y(), k = k_f2*P)\n",
    "c_f2.dTdt_convective = c_f1.dTdt_convective \n",
    "\n",
    "c_t1.set_dTdt_convective(source = [c_f1.y(), c_c1.y()], hA = [hA_ft_c,hA_tc_c])\n",
    "\n",
    "c_c1.set_dTdt_advective(source = (hx_ch1_c2.y(t-tau_c_hx_f)+hx_ch2_c2.y(t-tau_c_hx_f))/2)\n",
    "c_c1.set_dTdt_convective(source = [c_t1.y(),c_m1.y()], hA = [hA_tc_c/2,hA_mc_c/2])\n",
    "\n",
    "c_c2.set_dTdt_advective(source = c_c1.y())\n",
    "c_c2.dTdt_convective = c_c1.dTdt_convective\n",
    "\n",
    "c_m1.set_dTdt_internal(source = n.y(), k = k_m*P)\n",
    "c_m1.set_dTdt_convective(source = [c_c1.y()], hA = [hA_mc_c])\n",
    "\n",
    "n.set_dndt(rho.y()+rho_ext, beta_t, Lam, lam, [C1.y(), C2.y(), C3.y(), C4.y(), C5.y(), C6.y()])\n",
    "C1.set_dcdt(n.y(), beta[0], Lam, lam[0], tau_c, tau_l)\n",
    "C2.set_dcdt(n.y(), beta[1], Lam, lam[1], tau_c, tau_l)\n",
    "C3.set_dcdt(n.y(), beta[2], Lam, lam[2], tau_c, tau_l)\n",
    "C4.set_dcdt(n.y(), beta[3], Lam, lam[3], tau_c, tau_l)\n",
    "C5.set_dcdt(n.y(), beta[4], Lam, lam[4], tau_c, tau_l)\n",
    "C6.set_dcdt(n.y(), beta[5], Lam, lam[5], tau_c, tau_l)\n",
    "rho.set_drdt([c_f1.dydt(),c_f2.dydt(),c_m1.dydt(),c_c1.dydt(),c_c2.dydt()],[a_f/2,a_f/2,a_b,a_c/2,a_c/2])\n",
    "\n",
    "# FUEL-HELIUM HX1\n",
    "hx_fh1_f1.set_dTdt_advective(source = c_f2.y(t-tau_c_hx_f))\n",
    "hx_fh1_f1.set_dTdt_convective(source = [hx_fh1_t1.y()], hA = [hA_ft_hx/2])\n",
    "\n",
    "hx_fh1_f2.set_dTdt_advective(source = hx_fh1_f1.y())\n",
    "hx_fh1_f2.dTdt_convective = hx_fh1_f1.dTdt_convective\n",
    "\n",
    "hx_fh1_t1.set_dTdt_convective(source = [hx_fh1_f1.y(),hx_fh1_h1.y()],hA = [hA_ft_hx,hA_ht_hx])\n",
    "\n",
    "hx_fh1_h1.set_dTdt_advective(source = hx_hwf2_h2.y(t-tau_h))\n",
    "hx_fh1_h1.set_dTdt_convective(source = [hx_fh1_t1.y()], hA = [hA_ht_hx/2])\n",
    "\n",
    "hx_fh1_h2.set_dTdt_advective(source = hx_fh1_h1.y())\n",
    "hx_fh1_h2.dTdt_convective = hx_fh1_h1.dTdt_convective\n",
    "\n",
    "# FUEL-HELIUM HX2\n",
    "hx_fh2_f1.set_dTdt_advective(source = c_f2.y(t-tau_c_hx_f))\n",
    "hx_fh2_f1.set_dTdt_convective(source = [hx_fh2_t1.y()], hA = [hA_ft_hx/2])\n",
    "\n",
    "hx_fh2_f2.set_dTdt_advective(source = hx_fh2_f1.y())\n",
    "hx_fh2_f2.dTdt_convective = hx_fh2_f1.dTdt_convective\n",
    "\n",
    "hx_fh2_t1.set_dTdt_convective(source = [hx_fh2_f1.y(),hx_fh2_h1.y()],hA = [hA_ft_hx,hA_ht_hx])\n",
    "\n",
    "hx_fh2_h1.set_dTdt_advective(source = hx_hwf1_h2.y())\n",
    "hx_fh2_h1.set_dTdt_convective(source = [hx_fh2_t1.y()], hA = [hA_ht_hx/2])\n",
    "\n",
    "hx_fh2_h2.set_dTdt_advective(source = hx_fh2_h1.y())\n",
    "hx_fh2_h2.dTdt_convective = hx_fh2_h1.dTdt_convective\n",
    "\n",
    "# COOLANT-HELIUM HX1\n",
    "hx_ch1_c1.set_dTdt_advective(source = c_c2.y(t-tau_c_hx_f))\n",
    "hx_ch1_c1.set_dTdt_convective(source = [hx_ch1_t1.y()], hA = [hA_ct_hx/2])\n",
    "\n",
    "hx_ch1_c2.set_dTdt_advective(source = hx_ch1_c1.y())\n",
    "hx_ch1_c2.dTdt_convective = hx_ch1_c1.dTdt_convective\n",
    "\n",
    "hx_ch1_t1.set_dTdt_convective(source = [hx_ch1_c1.y(),hx_ch1_h1.y()],hA = [hA_ct_hx,hA_th_hxch])\n",
    "\n",
    "hx_ch1_h1.set_dTdt_advective(source = hx_hwc1_h2.y(t-tau_h))\n",
    "hx_ch1_h1.set_dTdt_convective(source = [hx_ch1_t1.y()], hA = [hA_th_hxch/2])\n",
    "\n",
    "hx_ch1_h2.set_dTdt_advective(source = hx_ch1_h1.y())\n",
    "hx_ch1_h2.dTdt_convective = hx_ch1_h1.dTdt_convective\n",
    "\n",
    "# COOLANT-HELIUM HX2\n",
    "hx_ch2_c1.set_dTdt_advective(source = c_c2.y(t-tau_c_hx_f))\n",
    "hx_ch2_c1.set_dTdt_convective(source = [hx_ch2_t1.y()], hA = [hA_ct_hx/2])\n",
    "\n",
    "hx_ch2_c2.set_dTdt_advective(source = hx_ch2_c1.y())\n",
    "hx_ch2_c2.dTdt_convective = hx_ch2_c1.dTdt_convective\n",
    "\n",
    "hx_ch2_t1.set_dTdt_convective(source = [hx_ch2_c1.y(),hx_ch2_h1.y()],hA = [hA_ct_hx,hA_th_hxch])\n",
    "\n",
    "hx_ch2_h1.set_dTdt_advective(source = hx_hwc2_h2.y(t-tau_h))\n",
    "hx_ch2_h1.set_dTdt_convective(source = [hx_ch2_t1.y()], hA = [hA_th_hxch/2])\n",
    "\n",
    "hx_ch2_h2.set_dTdt_advective(source = hx_ch2_h1.y())\n",
    "hx_ch2_h2.dTdt_convective = hx_ch2_h1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX1 (FUEL LOOP)\n",
    "hx_hwf1_h1.set_dTdt_advective(source = hx_fh1_h2.y())\n",
    "hx_hwf1_h1.set_dTdt_convective(source = [hx_hwf1_t1.y()], hA = [hA_ht_hxhw/2])\n",
    "\n",
    "hx_hwf1_h2.set_dTdt_advective(source = hx_hwf1_h1.y())\n",
    "hx_hwf1_h2.dTdt_convective = hx_hwf1_h1.dTdt_convective\n",
    "\n",
    "hx_hwf1_t1.set_dTdt_convective(source = [hx_hwf1_h1.y(),hx_hwf1_w1.y()],hA = [hA_ht_hxhw,hA_tw_hxhw])\n",
    "\n",
    "hx_hwf1_w1.set_dTdt_advective(source = T0_hhwf_w1)\n",
    "hx_hwf1_w1.set_dTdt_convective(source = [hx_hwf1_t1.y()], hA = [hA_tw_hxhw/2])\n",
    "\n",
    "hx_hwf1_w2.set_dTdt_advective(source = hx_hwf1_w1.y())\n",
    "hx_hwf1_w2.dTdt_convective = hx_hwf1_w1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX2 (FUEL LOOP)\n",
    "hx_hwf2_h1.set_dTdt_advective(source = hx_fh2_h2.y())\n",
    "hx_hwf2_h1.set_dTdt_convective(source = [hx_hwf2_t1.y()], hA = [hA_ht_hxhw/2])\n",
    "\n",
    "hx_hwf2_h2.set_dTdt_advective(source = hx_hwf2_h1.y())\n",
    "hx_hwf2_h2.dTdt_convective = hx_hwf2_h1.dTdt_convective\n",
    "\n",
    "hx_hwf2_t1.set_dTdt_convective(source = [hx_hwf2_h1.y(),hx_hwf2_w1.y()],hA = [hA_ht_hxhw,hA_tw_hxhw])\n",
    "\n",
    "hx_hwf2_w1.set_dTdt_advective(source = T0_hhwf_w1)\n",
    "hx_hwf2_w1.set_dTdt_convective(source = [hx_hwf2_t1.y()], hA = [hA_tw_hxhw/2])\n",
    "\n",
    "hx_hwf2_w2.set_dTdt_advective(source = hx_hwf2_w1.y())\n",
    "hx_hwf2_w2.dTdt_convective = hx_hwf2_w1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX1 (COOLANT LOOP)\n",
    "hx_hwc1_h1.set_dTdt_advective(source = hx_ch1_h2.y())\n",
    "hx_hwc1_h1.set_dTdt_convective(source = [hx_hwc1_t1.y()], hA = [hA_ht_hxhwc/2])\n",
    "\n",
    "hx_hwc1_h2.set_dTdt_advective(source = hx_hwc1_h1.y())\n",
    "hx_hwc1_h2.dTdt_convective = hx_hwc1_h1.dTdt_convective\n",
    "\n",
    "hx_hwc1_t1.set_dTdt_convective(source = [hx_hwc1_h1.y(),hx_hwc1_w1.y()],hA = [hA_ht_hxhwc,hA_tw_hxhwc])\n",
    "\n",
    "hx_hwc1_w1.set_dTdt_advective(source = T0_hhwc_w1)\n",
    "hx_hwc1_w1.set_dTdt_convective(source = [hx_hwc1_t1.y()], hA = [hA_tw_hxhwc/2])\n",
    "\n",
    "hx_hwc1_w2.set_dTdt_advective(source = hx_hwc1_w1.y())\n",
    "hx_hwc1_w2.dTdt_convective = hx_hwc1_w1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX2 (COOLANT LOOP)\n",
    "hx_hwc2_h1.set_dTdt_advective(source = hx_ch2_h2.y())\n",
    "hx_hwc2_h1.set_dTdt_convective(source = [hx_hwc2_t1.y()], hA = [hA_ht_hxhwc/2]) \n",
    "\n",
    "hx_hwc2_h2.set_dTdt_advective(source = hx_hwc2_h1.y())\n",
    "hx_hwc2_h2.dTdt_convective = hx_hwc2_h1.dTdt_convective\n",
    "\n",
    "hx_hwc2_t1.set_dTdt_convective(source = [hx_hwc2_h1.y(),hx_hwc2_w1.y()],hA = [hA_ht_hxhwc,hA_tw_hxhwc])\n",
    "\n",
    "hx_hwc2_w1.set_dTdt_advective(source = T0_hhwc_w1)\n",
    "hx_hwc2_w1.set_dTdt_convective(source = [hx_hwc2_t1.y()], hA = [hA_tw_hxhwc/2])\n",
    "\n",
    "hx_hwc2_w2.set_dTdt_advective(source = hx_hwc2_w1.y())\n",
    "hx_hwc2_w2.dTdt_convective = hx_hwc2_w1.dTdt_convective\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# solve system \n",
    "sol_jit = ARE.solve(T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "P*n.y_out[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,axs = plt.subplots(2,2,figsize=(24,16))\n",
    "\n",
    "# Set a professional color scheme\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', \n",
    "          'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n",
    "\n",
    "# Function to update the style of each axis\n",
    "def update_axis_style(ax, title = '', x_label='', y_label='', x_ticks=True, y_scale='linear'):\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",
    "    ax.set_yscale(y_scale)\n",
    "\n",
    "update_axis_style(axs[0,0],\"ARE: Reactivity Insertion, Power(MW)\")\n",
    "axs[0,0].plot(df_power[0]*60-adj,df_power[1],label=\"ORNL-1845\")\n",
    "axs[0,0].plot(T_insert,[s*P for s in n.y_out[i_insert[0]:(i_insert[-1]+1)]],label=\"msrDynamics\")\n",
    "axs[0,0].set_xlabel(r\"$t$ (s)\")\n",
    "axs[0,0].set_ylabel(\"Power (MW)\")\n",
    "axs[0,0].legend()\n",
    "\n",
    "ornl_avg = (df_inlet[1]+df_outlet[1])/2\n",
    "jitcdde_avg = (((np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2)+(np.array(c_f2.y_out)))/2\n",
    "jit_inlet = (np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2\n",
    "\n",
    "update_axis_style(axs[0,1],\"ARE: Reactivity Insertion, Fuel Inlet Temperature\")\n",
    "axs[0,1].plot(df_inlet[0]*60-adj,df_inlet[1],label=\"inlet, ORNL-1845\",color=\"green\")\n",
    "axs[0,1].plot(df_outlet[0]*60-adj,df_outlet[1],label=\"outlet, ORNL-1845\",color=\"green\")\n",
    "axs[0,1].plot(df_outlet[0]*60-adj,ornl_avg,label=\"avg, ORNL-1845\",color=\"green\",linestyle=\"--\")\n",
    "axs[0,1].plot(T_insert,jit_inlet[i_insert[0]:(i_insert[-1]+1)],label=\"inlet, msrDynamics\",color=\"blue\")\n",
    "axs[0,1].plot(T_insert,c_f2.y_out[i_insert[0]:(i_insert[-1]+1)],label=\"outlet, msrDynamics\",color=\"blue\")\n",
    "axs[0,1].plot(T_insert,jitcdde_avg[i_insert[0]:(i_insert[-1]+1)],label=\"average, msrDynamics\",color=\"blue\",linestyle=\"--\")\n",
    "axs[0,1].set_xlabel(r\"$t$ (s)\")\n",
    "axs[0,1].set_ylabel(r\"Temperature ($^\\circ$K)\")\n",
    "axs[0,1].legend()\n",
    "\n",
    "# delta\n",
    "update_axis_style(axs[1,0],r\"ARE: Reactivity Insertion, $\\Delta$ P(MW)\")\n",
    "axs[1,0].plot(df_power[0]*60-adj,df_power[1]-P,label=\"ORNL-1845\")\n",
    "axs[1,0].plot(T_insert,[P*(s-n.y_out[i_insert[0]-1]) for s in n.y_out[i_insert]],label=\"msrDynamics\")\n",
    "axs[1,0].set_xlabel(r\"$t$ (s)\")\n",
    "axs[1,0].set_ylabel(r\"$\\Delta P$ (MW)\")\n",
    "axs[1,0].legend()\n",
    "\n",
    "ornl_avg = (df_inlet[1]+df_outlet[1])/2\n",
    "jitcdde_avg = (((np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2)+(np.array(c_f2.y_out)))/2\n",
    "jit_inlet = (np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2\n",
    "\n",
    "update_axis_style(axs[1,1],r\"ARE: Reactivity Insertion, $\\Delta$T\")\n",
    "axs[1,1].plot(T_insert,jit_inlet[i_insert]-jit_inlet[i_insert[0]-1],label=\"inlet, msrDynamics\",color=colors[0])\n",
    "axs[1,1].plot(df_inlet[0]*60-adj,df_inlet[1]-df_inlet[1][0],linestyle =\"--\",label=\"inlet, ORNL-1845 \",color=colors[0])\n",
    "axs[1,1].plot(T_insert,c_f2.y_out[i_insert]-c_f2.y_out[i_insert[0]-1],label=\"outlet, msrDynamics\",color=colors[1])\n",
    "axs[1,1].plot(df_outlet[0]*60-adj,df_outlet[1]-df_outlet[1][0],linestyle=\"--\",label=\"outlet, ORNL-1845 \",color=colors[1])\n",
    "axs[1,1].plot(T_insert,jitcdde_avg[i_insert]-jitcdde_avg[i_insert[0]-1],label=\"average, msrDynamics\",color=colors[2])\n",
    "axs[1,1].plot(df_outlet[0]*60-adj,ornl_avg-ornl_avg[0],label=\"average, ORNL-1845\",color=colors[2],linestyle=\"--\")\n",
    "axs[1,1].set_xlabel(r\"$t$ (s)\")\n",
    "axs[1,1].set_ylabel(r\" $\\Delta$ T ($^\\circ$K)\")\n",
    "axs[1,1].legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated coefficients "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = [1.76062602e-01, 1.33401256e-02, 8.84481706e-04, 2.17107326e-02, \n",
    "          1.06450686e-03, 2.75078295e-02, 4.66376746e-04, 2.23549383e-02,\n",
    "          7.38227668e-01, 1.83339068e-03, 6.83131280e-02]\n",
    "\n",
    "\n",
    "hA_ft_c, hA_tc_c, hA_mc_c, hA_ft_hx, hA_ht_hx, hA_ct_hx, hA_th_hxch, \\\n",
    "hA_ht_hxhw, hA_tw_hxhw, hA_ht_hxhwc, hA_tw_hxhwc = params\n",
    "\n",
    "# CORE\n",
    "c_f1.set_dTdt_advective(source = (hx_fh1_f2.y(t-tau_hx_c_f)+hx_fh2_f2.y(t-tau_hx_c_f))/2) \n",
    "c_f1.set_dTdt_internal(source = n.y(), k = k_f1*P)\n",
    "c_f1.set_dTdt_convective(source = [c_t1.y()], hA = [hA_ft_c/2])\n",
    "\n",
    "c_f2.set_dTdt_advective(source = c_f1.y()) \n",
    "c_f2.set_dTdt_internal(source = n.y(), k = k_f2*P)\n",
    "c_f2.dTdt_convective = c_f1.dTdt_convective \n",
    "\n",
    "c_t1.set_dTdt_convective(source = [c_f1.y(), c_c1.y()], hA = [hA_ft_c,hA_tc_c])\n",
    "\n",
    "c_c1.set_dTdt_advective(source = (hx_ch1_c2.y(t-tau_c_hx_f)+hx_ch2_c2.y(t-tau_c_hx_f))/2)\n",
    "c_c1.set_dTdt_convective(source = [c_t1.y(),c_m1.y()], hA = [hA_tc_c/2,hA_mc_c/2])\n",
    "\n",
    "c_c2.set_dTdt_advective(source = c_c1.y())\n",
    "c_c2.dTdt_convective = c_c1.dTdt_convective\n",
    "\n",
    "c_m1.set_dTdt_internal(source = n.y(), k = k_m*P)\n",
    "c_m1.set_dTdt_convective(source = [c_c1.y()], hA = [hA_mc_c])\n",
    "\n",
    "n.set_dndt(rho.y()+rho_ext, beta_t, Lam, lam, [C1.y(), C2.y(), C3.y(), C4.y(), C5.y(), C6.y()])\n",
    "C1.set_dcdt(n.y(), beta[0], Lam, lam[0], tau_c, tau_l)\n",
    "C2.set_dcdt(n.y(), beta[1], Lam, lam[1], tau_c, tau_l)\n",
    "C3.set_dcdt(n.y(), beta[2], Lam, lam[2], tau_c, tau_l)\n",
    "C4.set_dcdt(n.y(), beta[3], Lam, lam[3], tau_c, tau_l)\n",
    "C5.set_dcdt(n.y(), beta[4], Lam, lam[4], tau_c, tau_l)\n",
    "C6.set_dcdt(n.y(), beta[5], Lam, lam[5], tau_c, tau_l)\n",
    "rho.set_drdt([c_f1.dydt(),c_f2.dydt(),c_m1.dydt(),c_c1.dydt(),c_c2.dydt()],[a_f/2,a_f/2,a_b,a_c/2,a_c/2])\n",
    "\n",
    "# FUEL-HELIUM HX1\n",
    "hx_fh1_f1.set_dTdt_advective(source = c_f2.y(t-tau_c_hx_f))\n",
    "hx_fh1_f1.set_dTdt_convective(source = [hx_fh1_t1.y()], hA = [hA_ft_hx/2])\n",
    "\n",
    "hx_fh1_f2.set_dTdt_advective(source = hx_fh1_f1.y())\n",
    "hx_fh1_f2.dTdt_convective = hx_fh1_f1.dTdt_convective\n",
    "\n",
    "hx_fh1_t1.set_dTdt_convective(source = [hx_fh1_f1.y(),hx_fh1_h1.y()],hA = [hA_ft_hx,hA_ht_hx])\n",
    "\n",
    "hx_fh1_h1.set_dTdt_advective(source = hx_hwf2_h2.y(t-tau_h))\n",
    "hx_fh1_h1.set_dTdt_convective(source = [hx_fh1_t1.y()], hA = [hA_ht_hx/2])\n",
    "\n",
    "hx_fh1_h2.set_dTdt_advective(source = hx_fh1_h1.y())\n",
    "hx_fh1_h2.dTdt_convective = hx_fh1_h1.dTdt_convective\n",
    "\n",
    "# FUEL-HELIUM HX2\n",
    "hx_fh2_f1.set_dTdt_advective(source = c_f2.y(t-tau_c_hx_f))\n",
    "hx_fh2_f1.set_dTdt_convective(source = [hx_fh2_t1.y()], hA = [hA_ft_hx/2])\n",
    "\n",
    "hx_fh2_f2.set_dTdt_advective(source = hx_fh2_f1.y())\n",
    "hx_fh2_f2.dTdt_convective = hx_fh2_f1.dTdt_convective\n",
    "\n",
    "hx_fh2_t1.set_dTdt_convective(source = [hx_fh2_f1.y(),hx_fh2_h1.y()],hA = [hA_ft_hx,hA_ht_hx])\n",
    "\n",
    "hx_fh2_h1.set_dTdt_advective(source = hx_hwf1_h2.y())\n",
    "hx_fh2_h1.set_dTdt_convective(source = [hx_fh2_t1.y()], hA = [hA_ht_hx/2])\n",
    "\n",
    "hx_fh2_h2.set_dTdt_advective(source = hx_fh2_h1.y())\n",
    "hx_fh2_h2.dTdt_convective = hx_fh2_h1.dTdt_convective\n",
    "\n",
    "# COOLANT-HELIUM HX1\n",
    "hx_ch1_c1.set_dTdt_advective(source = c_c2.y(t-tau_c_hx_f))\n",
    "hx_ch1_c1.set_dTdt_convective(source = [hx_ch1_t1.y()], hA = [hA_ct_hx/2])\n",
    "\n",
    "hx_ch1_c2.set_dTdt_advective(source = hx_ch1_c1.y())\n",
    "hx_ch1_c2.dTdt_convective = hx_ch1_c1.dTdt_convective\n",
    "\n",
    "hx_ch1_t1.set_dTdt_convective(source = [hx_ch1_c1.y(),hx_ch1_h1.y()],hA = [hA_ct_hx,hA_th_hxch])\n",
    "\n",
    "hx_ch1_h1.set_dTdt_advective(source = hx_hwc1_h2.y(t-tau_h))\n",
    "hx_ch1_h1.set_dTdt_convective(source = [hx_ch1_t1.y()], hA = [hA_th_hxch/2])\n",
    "\n",
    "hx_ch1_h2.set_dTdt_advective(source = hx_ch1_h1.y())\n",
    "hx_ch1_h2.dTdt_convective = hx_ch1_h1.dTdt_convective\n",
    "\n",
    "# COOLANT-HELIUM HX2\n",
    "hx_ch2_c1.set_dTdt_advective(source = c_c2.y(t-tau_c_hx_f))\n",
    "hx_ch2_c1.set_dTdt_convective(source = [hx_ch2_t1.y()], hA = [hA_ct_hx/2])\n",
    "\n",
    "hx_ch2_c2.set_dTdt_advective(source = hx_ch2_c1.y())\n",
    "hx_ch2_c2.dTdt_convective = hx_ch2_c1.dTdt_convective\n",
    "\n",
    "hx_ch2_t1.set_dTdt_convective(source = [hx_ch2_c1.y(),hx_ch2_h1.y()],hA = [hA_ct_hx,hA_th_hxch])\n",
    "\n",
    "hx_ch2_h1.set_dTdt_advective(source = hx_hwc2_h2.y(t-tau_h))\n",
    "hx_ch2_h1.set_dTdt_convective(source = [hx_ch2_t1.y()], hA = [hA_th_hxch/2])\n",
    "\n",
    "hx_ch2_h2.set_dTdt_advective(source = hx_ch2_h1.y())\n",
    "hx_ch2_h2.dTdt_convective = hx_ch2_h1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX1 (FUEL LOOP)\n",
    "hx_hwf1_h1.set_dTdt_advective(source = hx_fh1_h2.y())\n",
    "hx_hwf1_h1.set_dTdt_convective(source = [hx_hwf1_t1.y()], hA = [hA_ht_hxhw/2])\n",
    "\n",
    "hx_hwf1_h2.set_dTdt_advective(source = hx_hwf1_h1.y())\n",
    "hx_hwf1_h2.dTdt_convective = hx_hwf1_h1.dTdt_convective\n",
    "\n",
    "hx_hwf1_t1.set_dTdt_convective(source = [hx_hwf1_h1.y(),hx_hwf1_w1.y()],hA = [hA_ht_hxhw,hA_tw_hxhw])\n",
    "\n",
    "hx_hwf1_w1.set_dTdt_advective(source = T0_hhwf_w1)\n",
    "hx_hwf1_w1.set_dTdt_convective(source = [hx_hwf1_t1.y()], hA = [hA_tw_hxhw/2])\n",
    "\n",
    "hx_hwf1_w2.set_dTdt_advective(source = hx_hwf1_w1.y())\n",
    "hx_hwf1_w2.dTdt_convective = hx_hwf1_w1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX2 (FUEL LOOP)\n",
    "hx_hwf2_h1.set_dTdt_advective(source = hx_fh2_h2.y())\n",
    "hx_hwf2_h1.set_dTdt_convective(source = [hx_hwf2_t1.y()], hA = [hA_ht_hxhw/2])\n",
    "\n",
    "hx_hwf2_h2.set_dTdt_advective(source = hx_hwf2_h1.y())\n",
    "hx_hwf2_h2.dTdt_convective = hx_hwf2_h1.dTdt_convective\n",
    "\n",
    "hx_hwf2_t1.set_dTdt_convective(source = [hx_hwf2_h1.y(),hx_hwf2_w1.y()],hA = [hA_ht_hxhw,hA_tw_hxhw])\n",
    "\n",
    "hx_hwf2_w1.set_dTdt_advective(source = T0_hhwf_w1)\n",
    "hx_hwf2_w1.set_dTdt_convective(source = [hx_hwf2_t1.y()], hA = [hA_tw_hxhw/2])\n",
    "\n",
    "hx_hwf2_w2.set_dTdt_advective(source = hx_hwf2_w1.y())\n",
    "hx_hwf2_w2.dTdt_convective = hx_hwf2_w1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX1 (COOLANT LOOP)\n",
    "hx_hwc1_h1.set_dTdt_advective(source = hx_ch1_h2.y())\n",
    "hx_hwc1_h1.set_dTdt_convective(source = [hx_hwc1_t1.y()], hA = [hA_ht_hxhwc/2])\n",
    "\n",
    "hx_hwc1_h2.set_dTdt_advective(source = hx_hwc1_h1.y())\n",
    "hx_hwc1_h2.dTdt_convective = hx_hwc1_h1.dTdt_convective\n",
    "\n",
    "hx_hwc1_t1.set_dTdt_convective(source = [hx_hwc1_h1.y(),hx_hwc1_w1.y()],hA = [hA_ht_hxhwc,hA_tw_hxhwc])\n",
    "\n",
    "hx_hwc1_w1.set_dTdt_advective(source = T0_hhwc_w1)\n",
    "hx_hwc1_w1.set_dTdt_convective(source = [hx_hwc1_t1.y()], hA = [hA_tw_hxhwc/2])\n",
    "\n",
    "hx_hwc1_w2.set_dTdt_advective(source = hx_hwc1_w1.y())\n",
    "hx_hwc1_w2.dTdt_convective = hx_hwc1_w1.dTdt_convective\n",
    "\n",
    "# HELIUM-WATER HX2 (COOLANT LOOP)\n",
    "hx_hwc2_h1.set_dTdt_advective(source = hx_ch2_h2.y())\n",
    "hx_hwc2_h1.set_dTdt_convective(source = [hx_hwc2_t1.y()], hA = [hA_ht_hxhwc/2]) \n",
    "\n",
    "hx_hwc2_h2.set_dTdt_advective(source = hx_hwc2_h1.y())\n",
    "hx_hwc2_h2.dTdt_convective = hx_hwc2_h1.dTdt_convective\n",
    "\n",
    "hx_hwc2_t1.set_dTdt_convective(source = [hx_hwc2_h1.y(),hx_hwc2_w1.y()],hA = [hA_ht_hxhwc,hA_tw_hxhwc])\n",
    "\n",
    "hx_hwc2_w1.set_dTdt_advective(source = T0_hhwc_w1)\n",
    "hx_hwc2_w1.set_dTdt_convective(source = [hx_hwc2_t1.y()], hA = [hA_tw_hxhwc/2])\n",
    "\n",
    "hx_hwc2_w2.set_dTdt_advective(source = hx_hwc2_w1.y())\n",
    "hx_hwc2_w2.dTdt_convective = hx_hwc2_w1.dTdt_convective\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol_jit = ARE.solve(T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,axs = plt.subplots(2,2,figsize=(24,16))\n",
    "\n",
    "# Set a professional color scheme\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', \n",
    "          'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n",
    "\n",
    "# Function to update the style of each axis\n",
    "def update_axis_style(ax, title = '', x_label='', y_label='', x_ticks=True, y_scale='linear'):\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",
    "    ax.set_yscale(y_scale)\n",
    "\n",
    "update_axis_style(axs[0,0],\"ARE: Reactivity Insertion, Power(MW)\")\n",
    "axs[0,0].plot(df_power[0]*60-adj,df_power[1],label=\"ORNL-1845\")\n",
    "axs[0,0].plot(T_insert,[s*P for s in n.y_out[i_insert[0]:(i_insert[-1]+1)]],label=\"msrDynamics\")\n",
    "axs[0,0].set_xlabel(r\"$t$ (s)\")\n",
    "axs[0,0].set_ylabel(\"Power (MW)\")\n",
    "axs[0,0].legend()\n",
    "\n",
    "ornl_avg = (df_inlet[1]+df_outlet[1])/2\n",
    "jitcdde_avg = (((np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2)+(np.array(c_f2.y_out)))/2\n",
    "jit_inlet = (np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2\n",
    "\n",
    "update_axis_style(axs[0,1],\"ARE: Reactivity Insertion, Fuel Inlet Temperature\")\n",
    "axs[0,1].plot(df_inlet[0]*60-adj,df_inlet[1],label=\"inlet, ORNL-1845\",color=\"green\")\n",
    "axs[0,1].plot(df_outlet[0]*60-adj,df_outlet[1],label=\"outlet, ORNL-1845\",color=\"green\")\n",
    "axs[0,1].plot(df_outlet[0]*60-adj,ornl_avg,label=\"avg, ORNL-1845\",color=\"green\",linestyle=\"--\")\n",
    "axs[0,1].plot(T_insert,jit_inlet[i_insert[0]:(i_insert[-1]+1)],label=\"inlet, msrDynamics\",color=\"blue\")\n",
    "axs[0,1].plot(T_insert,c_f2.y_out[i_insert[0]:(i_insert[-1]+1)],label=\"outlet, msrDynamics\",color=\"blue\")\n",
    "axs[0,1].plot(T_insert,jitcdde_avg[i_insert[0]:(i_insert[-1]+1)],label=\"average, msrDynamics\",color=\"blue\",linestyle=\"--\")\n",
    "axs[0,1].set_xlabel(r\"$t$ (s)\")\n",
    "axs[0,1].set_ylabel(r\"Temperature ($^\\circ$K)\")\n",
    "axs[0,1].legend()\n",
    "\n",
    "# delta\n",
    "update_axis_style(axs[1,0],r\"ARE: Reactivity Insertion, $\\Delta$ P(MW)\")\n",
    "axs[1,0].plot(df_power[0]*60-adj,df_power[1]-P,label=\"ORNL-1845\")\n",
    "axs[1,0].plot(T_insert,[P*(s-n.y_out[i_insert[0]-1]) for s in n.y_out[i_insert]],label=\"msrDynamics\")\n",
    "axs[1,0].set_xlabel(r\"$t$ (s)\")\n",
    "axs[1,0].set_ylabel(r\"$\\Delta P$ (MW)\")\n",
    "axs[1,0].legend()\n",
    "\n",
    "ornl_avg = (df_inlet[1]+df_outlet[1])/2\n",
    "jitcdde_avg = (((np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2)+(np.array(c_f2.y_out)))/2\n",
    "jit_inlet = (np.array(hx_fh1_f2.y_out)+np.array(hx_fh1_f2.y_out))/2\n",
    "\n",
    "update_axis_style(axs[1,1],r\"ARE: Reactivity Insertion, $\\Delta$T\")\n",
    "axs[1,1].plot(T_insert,jit_inlet[i_insert]-jit_inlet[i_insert[0]-1],label=\"inlet, msrDynamics\",color=colors[0])\n",
    "axs[1,1].plot(df_inlet[0]*60-adj,df_inlet[1]-df_inlet[1][0],linestyle =\"--\",label=\"inlet, ORNL-1845 \",color=colors[0])\n",
    "axs[1,1].plot(T_insert,c_f2.y_out[i_insert]-c_f2.y_out[i_insert[0]-1],label=\"outlet, msrDynamics\",color=colors[1])\n",
    "axs[1,1].plot(df_outlet[0]*60-adj,df_outlet[1]-df_outlet[1][0],linestyle=\"--\",label=\"outlet, ORNL-1845 \",color=colors[1])\n",
    "axs[1,1].plot(T_insert,jitcdde_avg[i_insert]-jitcdde_avg[i_insert[0]-1],label=\"average, msrDynamics\",color=colors[2])\n",
    "axs[1,1].plot(df_outlet[0]*60-adj,ornl_avg-ornl_avg[0],label=\"average, ORNL-1845\",color=colors[2],linestyle=\"--\")\n",
    "axs[1,1].set_xlabel(r\"$t$ (s)\")\n",
    "axs[1,1].set_ylabel(r\" $\\Delta$ T ($^\\circ$K)\")\n",
    "axs[1,1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "P*n.y_out[i_insert][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import CubicSpline\n",
    "\n",
    "in_spline = CubicSpline(df_inlet[0], df_inlet[1]) \n",
    "new_in = in_spline(df_power[0])\n",
    "\n",
    "out_spline = CubicSpline(df_outlet[0], df_outlet[1]) \n",
    "new_out = out_spline(df_power[0])\n",
    "\n",
    "df_inlet = df_power.copy()\n",
    "df_outlet = df_power.copy()\n",
    "\n",
    "df_inlet[1] = new_in\n",
    "df_outlet[1] = new_out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(1,2,figsize=(16,6))\n",
    "\n",
    "# Set a professional color scheme\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', \n",
    "          'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n",
    "\n",
    "# Function to update the style of each axis\n",
    "def update_axis_style(ax, title = '', x_label='', y_label='', x_ticks=True, y_scale='linear'):\n",
    "    ax.set_title(title,fontsize=16)\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",
    "    ax.set_yscale(y_scale)\n",
    "\n",
    "# Core Node Temperatures\n",
    "update_axis_style(ax[0], r\"ARE: Reactivity Insertion, Fractional Heat Extraction\")\n",
    "ax[0].plot(df_power[0]*60-adj,((df_outlet[1]-df_inlet[1])*scp_f*W_f/df_power[1]),linestyle=\"--\",label=\"Fractional Power Extraction: Fuel, ORNL-1845\",color=colors[0])\n",
    "ax[0].plot(T_insert,np.array([(s[c_f2.index]-(s[hx_fh1_f2.index]+s[hx_fh2_f2.index])/2)*W_f*scp_f/(P*s[n.index]) for s in sol_jit])[i_insert],label=\"Fractional Power Extraction: Fuel, msrDynamics\",color=colors[0])\n",
    "ax[0].plot(df_power[0]*60-adj,(df_power[1]-(df_outlet[1]-df_inlet[1])*scp_f*W_f)/df_power[1],label=\"Fractional Power Extraction: Coolant, Inferred\",color=colors[1],linestyle=\"--\")\n",
    "ax[0].plot(T_insert,np.array([(s[c_c2.index]-(s[hx_ch1_c2.index]+s[hx_ch2_c2.index])/2)*W_f*scp_f/(P*s[n.index]) for s in sol_jit])[i_insert],label=\"Fractional Power Extraction: Coolant, msrDynamics\",color=colors[1])\n",
    "ax[0].set_xlabel(r\"$t$(s)\")\n",
    "ax[0].legend()\n",
    "\n",
    "update_axis_style(ax[1], r\"ARE: Reactivity Insertion, Temperature Response\")\n",
    "c0_jit = sol_jit[i_insert[0]-1][c_c2.index]-(sol_jit[i_insert[0]-1][hx_ch1_c2.index]+sol_jit[i_insert[0]-1][hx_ch2_c2.index])/2\n",
    "c0_ornl = (df_power[1][0]-(df_outlet[1][0]-df_inlet[1][0])*scp_f*W_f)/(W_c*scp_c)\n",
    "f0_jit = sol_jit[i_insert[0]-1][c_f2.index]-(sol_jit[i_insert[0]-1][hx_fh1_f2.index]+sol_jit[i_insert[0]-1][hx_fh2_f2.index])/2\n",
    "ax[1].plot(T_insert,np.array([(s[c_f2.index]-(s[hx_fh1_f2.index]+s[hx_fh2_f2.index])/2)-f0_jit for s in sol_jit])[i_insert],label=\"Fuel Temperature Response, msrDynamics\",color=colors[0])\n",
    "ax[1].plot(df_power[0]*60-adj,(df_power[1]-(df_outlet[1]-df_inlet[1])*scp_f*W_f)/(W_c*scp_c) - c0_ornl,label=\"Coolant Temperature Response, ORNL (Inferred)\",color=colors[1],linestyle=\"--\")\n",
    "ax[1].plot(T_insert,np.array([(s[c_c2.index]-(s[hx_ch1_c2.index]+s[hx_ch2_c2.index])/2)-c0_jit for s in sol_jit])[i_insert],label=\"Coolant Temperature Response, msrDynamics\",color=colors[1])\n",
    "ax[1].plot(df_power[0]*60-adj,(df_outlet[1]-df_inlet[1])-(df_outlet[1][0]-df_inlet[1][0]),label=\"Fuel Temperature Response, ORNL\",color=colors[0],linestyle=\"--\")\n",
    "ax[1].set_xlabel(r\"$t$(s)\")\n",
    "ax[1].set_ylabel(r\"$\\Delta T$ $(^\\circ$K)\")\n",
    "ax[1].legend()\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(df_inlet[1])\n",
    "plt.plot(df_outlet[1])"
   ]
  }
 ],
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