reactor_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AZOG.reactor import Reactor_Core\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "from CoolProp.CoolProp import PropsSI\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "reactor=Reactor_Core(TemperatureFuel=np.linspace(800,810,num=5)+273.16,TemperatureModerator=np.linspace(310,345,num=10)+273.16,Power=1000e6,Precursor=2000,Pressure=15e6,Temp_hotleg=350+273.16,Temp_coldleg=300+273.16,Temp_lowerplenum=305+273.16,Temp_upplerplenum=345+273.16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2720.09386535244"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reactor.total_delayed_const/(reactor.NGT*reactor.Lamda)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xlsxwriter \n",
    "work_book=xlsxwriter.Workbook('test_case_reactor_a.excel')\n",
    "data_sheet=work_book.add_worksheet(\"test\")\n",
    "\n",
    "row=0\n",
    "data_sheet.write(row,0,'T')\n",
    "data_sheet.write(row,1,'Tf1')\n",
    "data_sheet.write(row,2,'Tf2')\n",
    "data_sheet.write(row,3,'Tf3')\n",
    "data_sheet.write(row,4,'Tf4')\n",
    "data_sheet.write(row,5,'Tf5')\n",
    "\n",
    "data_sheet.write(row,6,'Tmo1')\n",
    "data_sheet.write(row,7,'Tmo2')\n",
    "data_sheet.write(row,8,'Tmo3')\n",
    "data_sheet.write(row,9,'Tmo4')\n",
    "data_sheet.write(row,10,'Tmo5')\n",
    "data_sheet.write(row,11,'Tmo6')\n",
    "data_sheet.write(row,12,'Tmo7')\n",
    "data_sheet.write(row,13,'Tmo8')\n",
    "data_sheet.write(row,14,'Tmo9')\n",
    "data_sheet.write(row,15,'Tmo10')\n",
    "data_sheet.write(row,16,'Power')\n",
    "data_sheet.write(row,17,'Thl')\n",
    "data_sheet.write(row,18,'Tcl')\n",
    "data_sheet.write(row,19,'Tlp')\n",
    "data_sheet.write(row,20,'Tup')\n",
    "data_sheet.write(row,21,'Precursor')\n",
    "data_sheet.write(row,22,'Reactivity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation running... ... ..\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 737/500000 [00:00<10:14, 812.80it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500000/500000 [04:03<00:00, 2052.47it/s]\n"
     ]
    }
   ],
   "source": [
    "Simulation_time=500\n",
    "dt=0.001\n",
    "\n",
    "row=1\n",
    "t=np.linspace(0,Simulation_time,num=int(Simulation_time/dt))\n",
    "\n",
    "print(\"Simulation running... ... ..\\n\")\n",
    "for i in tqdm(range(len(t))):\n",
    "\n",
    "\n",
    "    PowerRatio=reactor.integrator(reactor.DPowerRatio,argsforfunction=[],intitial_cond=reactor.PowerRatio,time_step=dt)\n",
    "    Precursor=reactor.integrator(reactor.DPrecoursor,argsforfunction=[],intitial_cond=reactor.Precursor,time_step=dt)\n",
    "    #print(PowerRatio)\n",
    "\n",
    "    Tf1=reactor.integrator(reactor.DTf1,argsforfunction=[],intitial_cond=reactor.Tf1,time_step=dt)\n",
    "    Tf2=reactor.integrator(reactor.DTf2,argsforfunction=[],intitial_cond=reactor.Tf2,time_step=dt)\n",
    "    Tf3=reactor.integrator(reactor.DTf3,argsforfunction=[],intitial_cond=reactor.Tf3,time_step=dt)\n",
    "    Tf4=reactor.integrator(reactor.DTf4,argsforfunction=[],intitial_cond=reactor.Tf4,time_step=dt)\n",
    "    Tf5=reactor.integrator(reactor.DTf5,argsforfunction=[],intitial_cond=reactor.Tf5,time_step=dt)\n",
    "\n",
    "    Tmo1=reactor.integrator(reactor.DTmo1,argsforfunction=[],intitial_cond=reactor.Tmo1,time_step=dt)\n",
    "    Tmo2=reactor.integrator(reactor.DTmo2,argsforfunction=[],intitial_cond=reactor.Tmo2,time_step=dt)\n",
    "    Tmo3=reactor.integrator(reactor.DTmo3,argsforfunction=[],intitial_cond=reactor.Tmo3,time_step=dt)\n",
    "    Tmo4=reactor.integrator(reactor.DTmo4,argsforfunction=[],intitial_cond=reactor.Tmo4,time_step=dt)\n",
    "    Tmo5=reactor.integrator(reactor.DTmo5,argsforfunction=[],intitial_cond=reactor.Tmo5,time_step=dt)\n",
    "    Tmo6=reactor.integrator(reactor.DTmo6,argsforfunction=[],intitial_cond=reactor.Tmo6,time_step=dt)\n",
    "    Tmo7=reactor.integrator(reactor.DTmo7,argsforfunction=[],intitial_cond=reactor.Tmo7,time_step=dt)\n",
    "    Tmo8=reactor.integrator(reactor.DTmo8,argsforfunction=[],intitial_cond=reactor.Tmo8,time_step=dt)\n",
    "    Tmo9=reactor.integrator(reactor.DTmo9,argsforfunction=[],intitial_cond=reactor.Tmo9,time_step=dt)\n",
    "    Tmo10=reactor.integrator(reactor.DTmo10,argsforfunction=[],intitial_cond=reactor.Tmo10,time_step=dt)\n",
    "\n",
    "    Thl=reactor.integrator(reactor.DThl,argsforfunction=[],intitial_cond=reactor.Thl,time_step=dt)\n",
    "    Tcl=reactor.integrator(reactor.DTcl,argsforfunction=[380],intitial_cond=reactor.Tcl,time_step=dt)\n",
    "    Tlp=reactor.integrator(reactor.DTlp,argsforfunction=[],intitial_cond=reactor.Tlp,time_step=dt)\n",
    "    Tup=reactor.integrator(reactor.DTup,argsforfunction=[],intitial_cond=reactor.Tup,time_step=dt)\n",
    "\n",
    "    fuel_temp=Tf1+Tf2+Tf3+Tf4+Tf5\n",
    "    mod_temp=Tmo1+Tmo2+Tmo3+Tmo4+Tmo5+Tmo6+Tmo7+Tmo8+Tmo9+Tmo10\n",
    "\n",
    "    reactor.Reactivity(FuelTempSum=fuel_temp,ModeratorTempSum=mod_temp)\n",
    "\n",
    "    reactor.PowerRatio=PowerRatio\n",
    "    reactor.Precursor=Precursor\n",
    "    reactor.Tf1=Tf1\n",
    "    reactor.Tf2=Tf2\n",
    "    reactor.Tf3=Tf3\n",
    "    reactor.Tf4=Tf4\n",
    "    reactor.Tf5=Tf5\n",
    "    \n",
    "    reactor.Tmo1=Tmo1\n",
    "    reactor.Tmo2=Tmo2\n",
    "    reactor.Tmo3=Tmo3\n",
    "    reactor.Tmo4=Tmo4\n",
    "    reactor.Tmo5=Tmo5\n",
    "    reactor.Tmo6=Tmo6\n",
    "    reactor.Tmo7=Tmo7\n",
    "    reactor.Tmo8=Tmo8\n",
    "    reactor.Tmo9=Tmo9\n",
    "    reactor.Tmo10=Tmo10\n",
    "\n",
    "    reactor.Thl=Thl\n",
    "    reactor.Tup=Tup\n",
    "    reactor.Tlp=Tcl\n",
    "    reactor.Tcl=Tcl\n",
    "    \n",
    "    if (i%1000)==0:\n",
    "        data_sheet.write(row,0,t[i])\n",
    "        \n",
    "        data_sheet.write(row,1,Tf1)\n",
    "        data_sheet.write(row,2,Tf2)\n",
    "        data_sheet.write(row,3,Tf3)\n",
    "        data_sheet.write(row,4,Tf4)\n",
    "        data_sheet.write(row,5,Tf5)\n",
    "\n",
    "        data_sheet.write(row,6,Tmo1)\n",
    "        data_sheet.write(row,7,Tmo2)\n",
    "        data_sheet.write(row,8,Tmo3)\n",
    "        data_sheet.write(row,9,Tmo4)\n",
    "        data_sheet.write(row,10,Tmo5)\n",
    "        data_sheet.write(row,11,Tmo6)\n",
    "        data_sheet.write(row,12,Tmo7)\n",
    "        data_sheet.write(row,13,Tmo8)\n",
    "        data_sheet.write(row,14,Tmo9)\n",
    "        data_sheet.write(row,15,Tmo10)\n",
    "        data_sheet.write(row,16,reactor.PowerRatio)\n",
    "        data_sheet.write(row,17,Thl)\n",
    "        data_sheet.write(row,18,Tcl)\n",
    "        data_sheet.write(row,19,Tlp)\n",
    "        data_sheet.write(row,20,Tup)\n",
    "        data_sheet.write(row,21,reactor.Precursor)\n",
    "        data_sheet.write(row,22,reactor.reactivity)\n",
    "        row=row+1\n",
    "\n",
    "    \n",
    "work_book.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Reactivity</th>\n",
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       "      <td>563.935016</td>\n",
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       "      <td>-2.316792e-04</td>\n",
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       "      <th>2</th>\n",
       "      <td>2.000004</td>\n",
       "      <td>1092.559780</td>\n",
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       "      <td>1133.063288</td>\n",
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       "      <td>526.122621</td>\n",
       "      <td>549.102720</td>\n",
       "      <td>572.046937</td>\n",
       "      <td>...</td>\n",
       "      <td>657.670796</td>\n",
       "      <td>677.556179</td>\n",
       "      <td>697.371219</td>\n",
       "      <td>0.696341</td>\n",
       "      <td>643.276074</td>\n",
       "      <td>478.141535</td>\n",
       "      <td>478.174750</td>\n",
       "      <td>687.664194</td>\n",
       "      <td>1990.515326</td>\n",
       "      <td>-3.265119e-04</td>\n",
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       "      <th>3</th>\n",
       "      <td>3.000006</td>\n",
       "      <td>1081.857673</td>\n",
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       "      <td>686.519759</td>\n",
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       "      <td>-3.409085e-04</td>\n",
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       "      <td>...</td>\n",
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       "      <td>402.447215</td>\n",
       "      <td>424.894429</td>\n",
       "      <td>447.341644</td>\n",
       "      <td>469.788858</td>\n",
       "      <td>...</td>\n",
       "      <td>559.577717</td>\n",
       "      <td>582.024931</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>0.722229</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>1964.531283</td>\n",
       "      <td>2.843365e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>486.000972</td>\n",
       "      <td>990.151318</td>\n",
       "      <td>1035.045747</td>\n",
       "      <td>1079.940176</td>\n",
       "      <td>1124.834605</td>\n",
       "      <td>1169.729035</td>\n",
       "      <td>402.447215</td>\n",
       "      <td>424.894429</td>\n",
       "      <td>447.341644</td>\n",
       "      <td>469.788858</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.722229</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>1964.531283</td>\n",
       "      <td>2.747510e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>487.000974</td>\n",
       "      <td>990.151318</td>\n",
       "      <td>1035.045747</td>\n",
       "      <td>1079.940176</td>\n",
       "      <td>1124.834605</td>\n",
       "      <td>1169.729035</td>\n",
       "      <td>402.447215</td>\n",
       "      <td>424.894429</td>\n",
       "      <td>447.341644</td>\n",
       "      <td>469.788858</td>\n",
       "      <td>...</td>\n",
       "      <td>559.577717</td>\n",
       "      <td>582.024931</td>\n",
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       "      <td>604.472146</td>\n",
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       "      <td>380.000000</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>1964.531283</td>\n",
       "      <td>2.654957e-12</td>\n",
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       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>488.000976</td>\n",
       "      <td>990.151318</td>\n",
       "      <td>1035.045747</td>\n",
       "      <td>1079.940176</td>\n",
       "      <td>1124.834605</td>\n",
       "      <td>1169.729035</td>\n",
       "      <td>402.447215</td>\n",
       "      <td>424.894429</td>\n",
       "      <td>447.341644</td>\n",
       "      <td>469.788858</td>\n",
       "      <td>...</td>\n",
       "      <td>559.577717</td>\n",
       "      <td>582.024931</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>0.722229</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>1964.531283</td>\n",
       "      <td>2.565489e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>489.000978</td>\n",
       "      <td>990.151318</td>\n",
       "      <td>1035.045747</td>\n",
       "      <td>1079.940176</td>\n",
       "      <td>1124.834606</td>\n",
       "      <td>1169.729035</td>\n",
       "      <td>402.447215</td>\n",
       "      <td>424.894429</td>\n",
       "      <td>447.341644</td>\n",
       "      <td>469.788858</td>\n",
       "      <td>...</td>\n",
       "      <td>559.577717</td>\n",
       "      <td>582.024931</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>0.722229</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>604.472146</td>\n",
       "      <td>1964.531283</td>\n",
       "      <td>2.479041e-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>490 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              T          Tf1          Tf2          Tf3          Tf4  \\\n",
       "0      0.000000  1073.256842  1075.758421  1078.260000  1080.761578   \n",
       "1      1.000002  1093.385836  1105.692790  1116.658577  1126.399280   \n",
       "2      2.000004  1092.559780  1113.613271  1133.063288  1150.999682   \n",
       "3      3.000006  1081.857673  1109.731127  1135.954877  1160.587713   \n",
       "4      4.000008  1067.434815  1100.454502  1131.910986  1161.833699   \n",
       "..          ...          ...          ...          ...          ...   \n",
       "485  485.000970   990.151318  1035.045747  1079.940176  1124.834605   \n",
       "486  486.000972   990.151318  1035.045747  1079.940176  1124.834605   \n",
       "487  487.000974   990.151318  1035.045747  1079.940176  1124.834605   \n",
       "488  488.000976   990.151318  1035.045747  1079.940176  1124.834605   \n",
       "489  489.000978   990.151318  1035.045747  1079.940176  1124.834606   \n",
       "\n",
       "             Tf5        Tmo1        Tmo2        Tmo3        Tmo4  ...  \\\n",
       "0    1083.263157  583.471543  587.385114  591.269637  595.158526  ...   \n",
       "1    1135.027133  540.828048  563.935016  585.753715  607.496526  ...   \n",
       "2    1167.512324  502.143252  526.122621  549.102720  572.046937  ...   \n",
       "3    1183.691164  474.198067  498.426713  521.904384  545.368905  ...   \n",
       "4    1190.256358  454.029519  478.185549  501.792071  525.397719  ...   \n",
       "..           ...         ...         ...         ...         ...  ...   \n",
       "485  1169.729035  402.447215  424.894429  447.341644  469.788858  ...   \n",
       "486  1169.729035  402.447215  424.894429  447.341644  469.788858  ...   \n",
       "487  1169.729035  402.447215  424.894429  447.341644  469.788858  ...   \n",
       "488  1169.729035  402.447215  424.894429  447.341644  469.788858  ...   \n",
       "489  1169.729035  402.447215  424.894429  447.341644  469.788858  ...   \n",
       "\n",
       "           Tmo8        Tmo9       Tmo10     Power         Thl         Tcl  \\\n",
       "0    610.705349  614.589872  618.478761  0.943445  623.158308  573.094649   \n",
       "1    686.659873  704.574032  722.368399  0.708719  628.391723  517.661197   \n",
       "2    657.670796  677.556179  697.371219  0.696341  643.276074  478.141535   \n",
       "3    634.512364  655.599251  676.643738  0.692100  656.177557  449.967145   \n",
       "4    616.290888  638.089851  659.864164  0.692147  663.477999  429.881035   \n",
       "..          ...         ...         ...       ...         ...         ...   \n",
       "485  559.577717  582.024931  604.472146  0.722229  604.472146  380.000000   \n",
       "486  559.577717  582.024931  604.472146  0.722229  604.472146  380.000000   \n",
       "487  559.577717  582.024931  604.472146  0.722229  604.472146  380.000000   \n",
       "488  559.577717  582.024931  604.472146  0.722229  604.472146  380.000000   \n",
       "489  559.577717  582.024931  604.472146  0.722229  604.472146  380.000000   \n",
       "\n",
       "            Tlp         Tup    Precursor    Reactivity  \n",
       "0    578.091468  618.160000  2000.056555 -6.474746e-07  \n",
       "1    517.707788  666.111454  1997.235433 -2.316792e-04  \n",
       "2    478.174750  687.664194  1990.515326 -3.265119e-04  \n",
       "3    449.990824  686.519759  1982.693351 -3.409085e-04  \n",
       "4    429.897917  676.463065  1975.084410 -3.144131e-04  \n",
       "..          ...         ...          ...           ...  \n",
       "485  380.000000  604.472146  1964.531283  2.843365e-12  \n",
       "486  380.000000  604.472146  1964.531283  2.747510e-12  \n",
       "487  380.000000  604.472146  1964.531283  2.654957e-12  \n",
       "488  380.000000  604.472146  1964.531283  2.565489e-12  \n",
       "489  380.000000  604.472146  1964.531283  2.479041e-12  \n",
       "\n",
       "[490 rows x 23 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path='/media/ebny_walid/New Volume/Thesis/thesis_final_code/test_case_reactor_a.excel'\n",
    "from pandas import read_excel\n",
    "data1=read_excel(path)\n",
    "data1.head(-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Transient Power Factor')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(data1['T'],data1['Power'])\n",
    "\n",
    "plt.xlabel(\"Time (sec)\")\n",
    "plt.ylabel('Transient Power Factor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(np.array(data1[\"T\"]),np.array(data1[\"Tf1\"])-273.16)\n",
    "plt.plot(np.array(data1[\"T\"]),np.array(data1[\"Tf2\"])-273.16 )\n",
    "plt.plot(np.array(data1[\"T\"]),np.array(data1[\"Tf3\"])-273.16 )\n",
    "plt.plot(np.array(data1[\"T\"]),np.array(data1[\"Tf4\"])-273.16 )\n",
    "plt.plot(np.array(data1[\"T\"]),np.array(data1[\"Tf5\"])-273.16 )\n",
    "\n",
    "\n",
    "plt.legend(['Fuel node 1','Fuel node 2','Fuel node 3','Fuel node 4','Fuel node 5','Fuel node 1','Fuel node 2','Fuel node 3','Fuel node 4','Fuel node 5'])\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel(\"fuel_temp (C)\")\n",
    "\n",
    "TEMP=(np.array(data1[\"Tf1\"])+np.array(data1[\"Tf2\"])+np.array(data1[\"Tf3\"])+np.array(data1[\"Tf4\"])+np.array(data1[\"Tf5\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1080.420408681405"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(np.array(data1[\"Tf1\"])+np.array(data1[\"Tf2\"])+np.array(data1[\"Tf3\"])+np.array(data1[\"Tf4\"])+np.array(data1[\"Tf5\"]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data1['T'],data1['Precursor'],color='red')\n",
    "plt.ylabel(\"Precursor\\n Concentration\")\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9120a99a80>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.plot(data1['T'],data1['Tmo1'])\n",
    "plt.plot(data1['T'],data1['Tmo2'])\n",
    "plt.plot(data1['T'],data1['Tmo3'])\n",
    "plt.plot(data1['T'],data1['Tmo4'])\n",
    "plt.plot(data1['T'],data1['Tmo5'])\n",
    "plt.plot(data1['T'],data1['Tmo6'])\n",
    "plt.plot(data1['T'],data1['Tmo7'])\n",
    "plt.plot(data1['T'],data1['Tmo8'])\n",
    "plt.plot(data1['T'],data1['Tmo9'])\n",
    "plt.plot(data1['T'],data1['Tmo10'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Reactivity, $\\\\rho$')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(data1['T'],data1['Reactivity'])\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.ylabel(r\"Reactivity, $\\rho$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot (TEMP/5-273.16)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}