White Giant Bamboo Simulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# White Dwarf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cusfbamboo as bam\n",
    "import cantera as ct\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import pypropep as ppp\n",
    "from CoolProp.CoolProp import PropsSI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operating conditions:\n",
    "- Chamber pressure: 15 bar\n",
    "- Thrust: 7 kN\n",
    "- Ambient pressure: 1 atm\n",
    "- O/F ratio: 3.5 (by mass)\n",
    "- Oxidiser: 100% N2O\n",
    "- Fuel: 25% Water, 75% IPA (by mass)\n",
    "\n",
    "### Chamber geometry:\n",
    "- Inner wall thickness: 1 mm\n",
    "- Chamber diameter: 200 mm\n",
    "- L*: 1.2\n",
    "\n",
    "### Cooling jacket:\n",
    "- Inlet temperature = 298.15 K\n",
    "- Inlet stagnation pressure = 20 bar\n",
    "- Inner wall material: Copper C106\n",
    "- Channel height: 1.5 mm \n",
    "- Number of fins = 100\n",
    "- Blockage ratio: 0.4 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combustion operating conditions\n",
    "pc = 15e5                   # Chamber pressure (Pa)\n",
    "thrust = 7e3                # Desired thrust (N)\n",
    "p_amb = 1.01325e5           # Ambient pressure (Pa). 1.01325e5 is sea level atmospheric.\n",
    "OF_ratio = 3.5              # Oxidiser/fuel mass ratio\n",
    "water_mass_fraction = 0.25  # Fraction of the fuel that is water, by mass\n",
    "\n",
    "# Chamber geometry\n",
    "inner_wall_thickness = 1.5e-3\n",
    "Dc = 15e-2            \n",
    "L_star = 1.2           \n",
    "copper_material = bam.materials.CopperC106   \n",
    "\n",
    "# Coolant jacket - coolant mass flow rate is calculated after we find the total fuel mass flow rate\n",
    "inlet_T = 298.15                                      # Coolant inlet static temperature (K)\n",
    "inlet_p = 20e5                                        # Tank / inlet coolant static pressure (Pa)\n",
    "channel_height = 1.5e-3\n",
    "number_of_fins = 100\n",
    "blockage_ratio = 0.4                                  # Fraction of area that is blocked by 'fins' - ignoring copper-stainless gap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combustion using pypropep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 1921 thermo species\n",
      "Loaded 1031 propellants\n",
      "\n",
      "Tc = 2896.8324210017586 K\n",
      "gamma = 1.2349287918250849\n",
      "cp = 1776.8366311145642 J/kg/K\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Initialise both a frozen-flow and equilibrium-flow model\n",
    "ppp.init()\n",
    "p_froz = ppp.FrozenPerformance()\n",
    "p_shift = ppp.ShiftingPerformance()\n",
    "\n",
    "# Propellant set up\n",
    "ipa = ppp.PROPELLANTS['ISOPROPYL ALCOHOL']\n",
    "water = ppp.PROPELLANTS['WATER']\n",
    "n2o = ppp.PROPELLANTS['NITROUS OXIDE']\n",
    "\n",
    "# Add propellants\n",
    "ipa_mass = (1 - water_mass_fraction)\n",
    "water_mass = water_mass_fraction\n",
    "n2o_mass = OF_ratio\n",
    "\n",
    "p_froz.add_propellants_by_mass([(ipa, ipa_mass), (water, water_mass), (n2o, n2o_mass)])\n",
    "p_shift.add_propellants_by_mass([(ipa, ipa_mass), (water, water_mass), (n2o, n2o_mass)])\n",
    "                 \n",
    "# Set chamber pressure and exit pressure\n",
    "p_froz.set_state(P = pc/1e5, Pe = p_amb/1e5)                      \n",
    "p_shift.set_state(P = pc/1e5, Pe = p_amb/1e5)                      \n",
    "\n",
    "# Retrieve perfect gas properties (index 0 means chamber conditions, 1 means throat)\n",
    "gamma = p_froz.properties[1].Isex   # 'Isex' is the ratio of specific heats\n",
    "cp = 1000*p_froz.properties[1].Cp   # Cp is given in kJ/kg/K, we want J/kg/K\n",
    "Tc = p_froz.properties[0].T\n",
    "\n",
    "# Get specific impulse\n",
    "isp_froz = p_froz.performance.Isp\n",
    "isp_shift = p_shift.performance.Isp\n",
    "\n",
    "mdot_froz = thrust/isp_froz        \n",
    "mdot_shift = thrust/isp_shift\n",
    "\n",
    "print(f\"\"\"\n",
    "Tc = {Tc} K\n",
    "gamma = {gamma}\n",
    "cp = {cp} J/kg/K\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combustion chamber and nozzle geometry\n",
    "\n",
    "We will use the frozen flow results from pypropep as inputs into Bamboo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-2a7076be2ff4>:21: UserWarning: The area ratio provided (2.798183891554796) is outside of the range of available data. Maximum available is 54.623, minimum is 3.678. Will use a 15 degree cone instead.\n",
      "  xs, rs = bam.rao.get_rao_contour(r_c = Rc,\n"
     ]
    }
   ],
   "source": [
    "# Get throat area and area ratio from pypropep\n",
    "At_froz = p_froz._equil_structs[1].performance.a_dotm * mdot_froz / 101325  # pypropep uses units of atm, need to convert to Pa\n",
    "At_shift = p_shift._equil_structs[1].performance.a_dotm * mdot_shift / 101325\n",
    "\n",
    "area_ratio_froz = p_froz.performance.ae_at \n",
    "area_ratio_shift = p_shift.performance.ae_at \n",
    "\n",
    "Ae_froz = At_froz * area_ratio_froz\n",
    "Ae_shift = At_shift * area_ratio_shift\n",
    "\n",
    "# Useful intermediate values\n",
    "Rc = Dc / 2\n",
    "Dt = 2*(At_froz/np.pi)**0.5\n",
    "Ac = np.pi * Rc**2   \n",
    "Vc = L_star * At_froz         # From the definition of L*\n",
    "\n",
    "# Equation (1.34) from http://www.braeunig.us/space/propuls.htm \n",
    "Lc = ( 24*Vc/np.pi - (Dc**3 - Dt**3)/np.tan(45*np.pi/180) ) / (6*Dc**2)   \n",
    "\n",
    "# Rao bell nozzle geometry\n",
    "xs, rs = bam.rao.get_rao_contour(r_c = Rc, \n",
    "                                 r_t = Dt / 2, \n",
    "                                 area_ratio = area_ratio_froz, \n",
    "                                 L_c = Lc, \n",
    "                                 theta_conv = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cooling channels\n",
    "\n",
    "Calculate the actual dimensions we'll use (taking into account the copper-stainless gap), and set up the transport properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  gri30:\n",
      "\n",
      "       temperature   2896.8 K\n",
      "          pressure   1.5e+06 Pa\n",
      "           density   1.5316 kg/m^3\n",
      "  mean mol. weight   24.593 kg/kmol\n",
      "   phase of matter   gas\n",
      "\n",
      "                          1 kg             1 kmol     \n",
      "                     ---------------   ---------------\n",
      "          enthalpy       -3.1771e+05       -7.8137e+06  J\n",
      "   internal energy       -1.2971e+06       -3.1899e+07  J\n",
      "           entropy             10657        2.6208e+05  J/K\n",
      "    Gibbs function       -3.1188e+07       -7.6703e+08  J\n",
      " heat capacity c_p            1799.9             44266  J/K\n",
      " heat capacity c_v            1461.8             35951  J/K\n",
      "\n",
      "                      mass frac. Y      mole frac. X     chem. pot. / RT\n",
      "                     ---------------   ---------------   ---------------\n",
      "                H2         0.0050276          0.061333           -20.819\n",
      "                 H        0.00030033         0.0073276           -10.409\n",
      "                 O        0.00048948        0.00075242           -17.026\n",
      "                O2         0.0017727         0.0013625           -34.052\n",
      "                OH         0.0070237          0.010157           -27.436\n",
      "               H2O           0.20407           0.27859           -37.845\n",
      "               HO2         3.513e-06        2.6176e-06           -44.462\n",
      "              H2O2        8.1246e-07        5.8744e-07           -54.871\n",
      "                 C        2.5958e-12        5.3151e-12           -15.973\n",
      "                CH        1.4351e-12        2.7109e-12           -26.382\n",
      "               CH2        4.9851e-12        8.7403e-12           -36.792\n",
      "            CH2(S)        4.7944e-13         8.406e-13           -36.792\n",
      "               CH3        2.4962e-11        4.0831e-11           -47.201\n",
      "               CH4        1.3772e-11        2.1112e-11            -57.61\n",
      "                CO           0.14273           0.12532           -32.999\n",
      "               CO2           0.14189          0.079293           -50.025\n",
      "               HCO        1.3872e-06        1.1757e-06           -43.409\n",
      "              CH2O        5.6808e-08         4.653e-08           -53.818\n",
      "             CH2OH        9.5545e-12        7.5716e-12           -64.227\n",
      "              CH3O        2.2881e-13        1.8133e-13           -64.227\n",
      "             CH3OH        1.2227e-12         9.385e-13           -74.637\n",
      "              C2H2        5.4744e-14        5.1707e-14           -52.765\n",
      "              HCCO        3.3309e-13        1.9966e-13           -59.382\n",
      "             CH2CO        4.3614e-13        2.5516e-13           -69.791\n",
      "                 N        6.8455e-07        1.2019e-06           -13.192\n",
      "                NH        4.0815e-07        6.6851e-07           -23.601\n",
      "               NH2        3.3258e-07        5.1047e-07           -34.011\n",
      "               NH3        7.8203e-07        1.1293e-06            -44.42\n",
      "               NNH         1.958e-07        1.6592e-07           -36.793\n",
      "                NO         0.0030664         0.0025132           -30.218\n",
      "               NO2        9.1325e-07         4.882e-07           -47.244\n",
      "               N2O        7.2712e-07        4.0629e-07            -43.41\n",
      "               HNO        1.1309e-06         8.968e-07           -40.628\n",
      "                CN        1.4701e-09        1.3896e-09           -29.165\n",
      "               HCN        1.5286e-07        1.3911e-07           -39.574\n",
      "              H2CN        2.9463e-12        2.5847e-12           -49.984\n",
      "              HCNO        4.7334e-12        2.7057e-12           -56.601\n",
      "              HOCN        4.7024e-09        2.6879e-09           -56.601\n",
      "              HNCO        4.5963e-07        2.6273e-07           -56.601\n",
      "               NCO        2.6765e-08        1.5666e-08           -46.191\n",
      "                N2           0.49362           0.43334           -26.384\n",
      "     [  +12 minor]        6.6363e-15        4.3041e-15  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Setup the exhaust gas transport properties using Cantera\n",
    "gri30 = ct.Solution('gri30.yaml')\n",
    "\n",
    "ipa_ct = ct.Quantity(gri30, constant = \"TP\", mass = ipa_mass)\n",
    "ipa_ct.TPX = Tc, pc, \"C:3, H:8, O:1\"\n",
    "\n",
    "water_ct = ct.Quantity(gri30, constant = \"TP\", mass = water_mass)\n",
    "water_ct.TPX = Tc, pc, \"H:2, O:1\"\n",
    "\n",
    "water_ct = ct.Quantity(gri30, constant = \"TP\", mass = water_mass)\n",
    "water_ct.TPX = Tc, pc, \"H:2, O:1\"\n",
    "\n",
    "n2o_ct = ct.Quantity(gri30, constant = \"TP\", mass = n2o_mass)\n",
    "n2o_ct.TPX = Tc, pc, \"N:2, O:1\"\n",
    "\n",
    "# Mix and reach equilibrium at chamber conditions\n",
    "quantity = ipa_ct + water_ct + n2o_ct\n",
    "quantity.equilibrate(\"TP\")\n",
    "\n",
    "gas = ct.Solution('gri30.yaml')\n",
    "gas.TPY = quantity.TPY\n",
    "gas()\n",
    "\n",
    "def mu_exhaust(T, p):\n",
    "    gas.TP = T, p\n",
    "    return gas.viscosity\n",
    "\n",
    "def k_exhaust(T, p):\n",
    "    gas.TP = T, p\n",
    "    return gas.thermal_conductivity\n",
    "\n",
    "def Pr_exhaust(T, p):\n",
    "    gas.TP = T, p\n",
    "    return gas.cp * gas.viscosity / gas.thermal_conductivity # Definition of Prandtl number\n",
    "\n",
    "\n",
    "# Coolant is pure water - use CoolProp\n",
    "def Pr_coolant(T, p):\n",
    "    try:\n",
    "        return PropsSI(\"PRANDTL\", \"T\", T, \"P\", p, \"ETHANOL\")\n",
    "    except ValueError:\n",
    "        return PropsSI(\"PRANDTL\", \"Q\", 0, \"P\", p, \"ETHANOL\")\n",
    "\n",
    "def mu_coolant(T, p):\n",
    "    try:\n",
    "        return PropsSI(\"VISCOSITY\", \"T\", T, \"P\", p, \"ETHANOL\")\n",
    "    except ValueError:\n",
    "        return PropsSI(\"VISCOSITY\", \"Q\", 0, \"P\", p, \"ETHANOL\")\n",
    "\n",
    "def k_coolant(T, p):\n",
    "    try:\n",
    "        return PropsSI(\"CONDUCTIVITY\", \"T\", T, \"P\", p, \"ETHANOL\")\n",
    "    except ValueError:\n",
    "        return PropsSI(\"CONDUCTIVITY\", \"Q\", 0, \"P\", p, \"ETHANOL\")\n",
    "\n",
    "def rho_coolant(T, p):\n",
    "    try:\n",
    "        return PropsSI(\"DMASS\", \"T\", T, \"P\", p, \"ETHANOL\")\n",
    "    except ValueError:\n",
    "        return PropsSI(\"DMASS\", \"Q\", 0, \"P\", p, \"ETHANOL\")\n",
    "\n",
    "def cp_coolant(T, p):\n",
    "    try:\n",
    "        return PropsSI(\"CPMASS\", \"T\", T, \"P\", p, \"ETHANOL\")\n",
    "    except ValueError:\n",
    "        return PropsSI(\"CPMASS\", \"Q\", 0, \"P\", p, \"ETHANOL\")\n",
    "\n",
    "\n",
    "# Transport property objects\n",
    "exhaust_transport = bam.TransportProperties(Pr = Pr_exhaust,\n",
    "                                            mu = mu_exhaust,\n",
    "                                            k = k_exhaust)\n",
    "\n",
    "coolant_transport = bam.TransportProperties(Pr = Pr_coolant,\n",
    "                                            mu = mu_coolant,\n",
    "                                            k = k_coolant,\n",
    "                                            cp = cp_coolant,\n",
    "                                            rho = rho_coolant)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up Bamboo simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fuel mass flow rate = 0.7565947867776254 kg/s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Daniel\\anaconda3\\lib\\site-packages\\cusfbamboo\\hx.py:84: UserWarning: ReDh < 3500 in cooling channels: Flow is in between the laminar and turbulent regions - blending will be used. Constant wall temperature is assumed for laminar Nusselt number.\n",
      "  R = self.Rdx(self.state[i]))\n"
     ]
    }
   ],
   "source": [
    "# Set up the objects we need\n",
    "perfect_gas = bam.PerfectGas(gamma = gamma, cp = cp)                   # Approximate values for CO2\n",
    "chamber_conditions = bam.ChamberConditions(p0 = pc, T0 = Tc)\n",
    "geometry = bam.Geometry(xs = xs, rs = rs)\n",
    "\n",
    "copper_wall = bam.Wall(material = copper_material, thickness = inner_wall_thickness) \n",
    "\n",
    "# Main inputs\n",
    "engine = bam.Engine(perfect_gas = perfect_gas, \n",
    "                    chamber_conditions = chamber_conditions, \n",
    "                    geometry = geometry,\n",
    "                    exhaust_transport = exhaust_transport,\n",
    "                    walls = [copper_wall])\n",
    "\n",
    "# Use Bamboo's mass flow rate result, and the OF ratio, to get the coolant mass flow rate\n",
    "mdot_f = engine.mdot/(OF_ratio + 1)\n",
    "mdot_coolant = mdot_f                                \n",
    "print(f\"Fuel mass flow rate = {mdot_f} kg/s\")\n",
    "\n",
    "# Add a cooling jacket to the engine\n",
    "engine.cooling_jacket = bam.CoolingJacket(T_coolant_in = inlet_T, \n",
    "                                         p_coolant_in = inlet_p, \n",
    "                                         mdot_coolant = mdot_coolant, \n",
    "                                         channel_height = channel_height,\n",
    "                                         blockage_ratio = blockage_ratio,\n",
    "                                         number_of_channels = number_of_fins,\n",
    "                                         coolant_transport = coolant_transport,\n",
    "                                         configuration = 'vertical',\n",
    "                                         restrain_fins = False)\n",
    "\n",
    "engine.plot()\n",
    "plt.savefig(\"img/engine.svg\")\n",
    "plt.savefig(\"img/engine.pdf\")\n",
    "bam.show()\n",
    "\n",
    "cooling_data = engine.steady_heating_analysis(num_grid = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot simulation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hw56Cb8VTkPKKMyCz7Ndp84O/ilJKKV+iiUMppVSxaOJQSilVLJo4lFJKFYsmDqWUzwio0Zw2vQfTqucgbvq/W0lJ8YOzqgrw4lsflncIXqOJQynlM8JCQ1k1expr5/1EUHAQH34+Kcdyp7PsTkHNzDyzs5ZefPu/+ZYbY3C5XGfUd3nTxKGU8kldO3dmy/adxC/8k15Dh3P9HQ/SOu4ynE4nDz83lo79ruT8uMv4r51c9h04SM/BN2SPWOYvWY7T6WTEvaNo1XMQrS+6jDc/nABA3NDhLF+1BoDDR45Sv4M1QeGESd9z9b/u5bIb7+CSa24hOTmFW0Y+Rsd+V9K2zxCm/fx7njj37z+QZ72jRr/GqdRU2vQezA13PsiOXQk07z6Aux59lnYXD2X3nn28+t7H2Z/hmVfeASA5OYWBN9zOBb0up1XPQXz7g3XzqlGjX6NFj0s5P+4yHnp2rLc3fZHO7es4lFL5W/A6HN5Uun3GNoXuD3pUNTMzk99+n82gXhcCsPSvNayd+yMN6tVh/OffElmxIstmTSEtLZ1ulw3jkou68f3M3+gX150n7r8Tp9NJyqlTrFq7gT37DrB23k8AHD9xssh1L16+itVzplM5OorHx7xB7x6d+fTtlzh+4iSd+l/NxT27Eh5+egLF/02Zkme9PTp34N1Pv2LV7GkA7NiVwD9btvPZWy/x/thn+TV+AZu37WTpL5MxxnD58DuZt3gZh44cpWa1qsz4ajwAJ04mcvTYcab+/BsbF/6CiHj0GbxNE4dSymdk/UoHuLBLN269/ioWLfuLTm1b06CeNR3Kr3MXsnr9P0z+aRZgfblu3r6Tjm1ac8t9j5ORmcmQARfTplVzGtarw7adu/nPY6MZ2PciLonrXmQMfS/qRuXoKHtdC5g+azavvf8pAKlpaezas4/mTRtl12/Xti33/Oc/Odabn3q1a9K5Qxur3/iF/Dp3IW37DAEgKTmFzdt20KNzBx56diyPjn6VQX170aNzBzIzMwkNCeFf9z/BwL5xDOobV9zNWuo0cSil8vJwZFDaso5xACQGRhOceQzIOUW6MYZxLz5Jv1498rSfN+1LZvw2l+F3P8LDd9/KTdcM4e8505g1ZwHvffo13037mU/ffonAgABcLmuevtS09Bx9hFcIc1sXTPn0Hc5r3LDAmLt17ZLvenPL/Rkeu/d2/n3TsDz1Vvz2PTP/mMtjY17nkrhuPP3gPSz9ZTJ/zF/MpB9m8O4nXzL7+88LjKcs6DEOpZRf6RfXnQ8mfENGRgYAm7ZuJzk5hZ2791A1Nobbhl/DrddfycrV6zh85Cgul+HKQf0YPWokK9dY83TVr1OLFavXAjD5x18KXde4T74kazLYv9bknedr1+7dedYLEBQUmB1jnn57defTr6eQZE+OuGffAQ4eOsLe/QeoEBbGjVcN5qE7b2Xl6vXW9O0nE7n04ot4a/TjrFq3sYRbrvToiEMp5Vf+dePV7Ni9h3YXX4Exhiox0fww8X3iFy3l1fc+ISgokIjwCnw+bix79h/k/0Y+ln0W00tPWPcQf+iuW7jmtvv44n/T6d39wgLX9dQDd3HfUy9yftzlGGOoX6cWP32V82yp+QsWcu24cTnWC3D78Gs4v9fltGvdgjGP3Z+jzSVx3dmwaStdLrVGHBHhFfjy/VfZsn0nDz/3Cg6Hg6CgQD4Y+yyJSckMvukuUtPSMAbefP6xUtuWJXVuT6s+rgU46p5xHPFVryTu4JQz7qcs+Eus/hInnD2xbug4iub1a5ZxRAVLDIymor2rypeVW5wmHaq1KrKaN6ZV111VSimlisVriUNEQkVkqYj8LSLrROQ5u7yyiPwmIpvt52i3No+JyBYR+UdE+nkrNqWUUiXnzRFHGtDbGHMB0AboLyKdgVHAH8aYJsAf9ntEpAUwDGgJ9AfeF5EAL8anlFKqBLyWOIwl647tQfbDAIOBiXb5RGCI/XowMMkYk2aM2Q5sATp5Kz6llFIl49WD4/aIYQXQGHjPGPOoiBw3xkS51TlmjIkWkXeBJcaYL+3yT4CfjTGTc/V5O3A7QLVq1dpPmpRzLptiObgOJLjk7W1JgdFE+MFBPPCfWP0lTjh7Yo1scAGNGzYo44gK5pQAAkz53R7VU+UXp4GgsCJrbdmyhRMnTgCQlJREREQEvXr18t17jhtjnEAbEYkCpopIYacASH5d5NPneGA8WGdVxcXFlTzAcXfpWVU+yl/ihLMn1g31mvjUWUx6VlURTDpULvqsqtDQUNq2bQucPqvqTJXJWVXGmONAPNaxiwMiUgPAfj5oV0sA6rg1qw3sLYv4lFK+Qaqdx/C7H85+n5mZSZUWnRl0w79L1N/0X/7g5XfGl1Z4peKHmb+z/p8t5R3GGfHmWVVV7JEGIhIGXAxsBKYDN9vVbgam2a+nA8NEJEREGgBNgKXeik8p5XvCK1Rg7cbNnDqVCsBvcxdSq3q1Evd3ef8+jLr39tIKr1T88MvvrN+Uf+I406ncy4o3Rxw1gDkishpYBvxmjPkJeBnoKyKbgb72e4wx64DvgPXAL8Dd9q4updQ5ZEDvnsz4PR6Ab6bO4LqhA7OXLV25mq4Dh9G2zxC6DhzGP1u2AfDGh59xy0jriuo16/+hVc9BpKScYsKk77nnsecBGHHvKO585Bl6DR1Ow459mLtoKbeMfIzm3Qcw4t5R2euIaNA2+/XkH3/JXlZQ+w6du+Vo7y73dOiLlq1k+qzZPPzcK7TpPZitO3YRN3Q4j495g4uG3MjbH33Oir/XctGQG2nf9wr6XXsr+w5YO2Xe+ejz7L6G3W5diT537lzatGlDmzZtaNu2LYmJiaXxJyiS145xGGNWA23zKT8C9CmgzRhgjLdiUkp57v/+fDpPWb/qXRlWrz+nnGnctTzvf9XBteIYUrs3x9JP8sBfr+VY9tmFz3u03mFDLuX5N97nogFDWb3+H2657krmL1kBQLMmDZk37UsCAwP5fe4iHn/xTaZ8Oo77br+ZuKHDmTrzN8a89QH/fe05KlTIe+D42PGTzP7+c6b/8geXDb+DhT9+w8fNmtCx31WsWruhwJltC2v/ZqtO9O7TJ0/7/KZDj4qsxOX9ejOobxxXXdY/u+7xkyeZ+8OXZGRkcNGQ4Uyb+D5VYivz7Q8zeeLFN/n07Zd4edx4ti+bTUhIcPbU6q+99hrvvfce3bp1IykpidDQUI+28ZnSuaqUUj7l/JbN2LE7gclTpnJpn4tyLDtxMpGb//Mom7ftRETIyLQmEXQ4HEx4+2XO73U5/77pWrp1ap9v35dd0gsRoXXz86hWJZbWLc4DoOV5jdmxa0+RiSO/9okOR77tK1WM8Hg69GsHXwrAP1u2s3bjJvpe838AOJ0ualSrYm2XFudxw10PMWRAH4YMuBiAbt268cADD3DDDTdwxRVXULt27ULjLy2aOJRS+SpshBAWEFLo8ujgSh6PMPJzeb/ePPHMs8z9fiJHjh3PLn9q7Nv06nYhUye8x45dCcRdcVP2ss3bdxARXoG9+w/m06MlJMQ6/d7hEEKCT5+K73A4yHRaxxfE7fzO1LS0YrfPEhgY6PF06FlTuRtjaHleExbP/DZPnRlfjWfe4mVMnzWb0W+8z7q5Uxk1ahQDBw5k5syZdO7cmd9//51mzZoV+PlLi85VpZTyObdcdxWPPvxg9oggy4mTidSqYR0sn/Dt1BzlI58cw7xpX3Lk2PFCp0ovSrUqsWzYtBWXy8XUmXlvFeupgqZDrxgeTmJScr5tzmvcgENHjrJ42V8AZGRksG7jZlwuF7v37KNX98688vTDHD+RSFJyClu3bqV169Y8+uijdOjQgY0by2bKdR1xKKV8Tu2a1bnr37dDrusjHrn7X9x87yje+PAzenfvnF1+/1MvcteI62naqAGfvDmGXlfcRM/OHUu07peffJBBN/6bOrVq0KpZE5KSU0rUT0HToQ8beim3PfgU73z8BZM/eSdHm+DgYCZ/8g73PvECJ04mkul0ct9tN9O0UX1uvPthTpxMwhjD/f8eQVRkJZ564S3mzJlDQEAALVq0YMCAASWKtbh0WnW9ANAn+UuccPbEqtOql4xOq66UUkoVQROHUkqpYtHEoZRSqlg0cSillCoWTRxKKaWKpcjTcUWkA9ADqAmcAtYCvxtjjno5NqWUUj6owMQhIiOAe4HtWDdj+gcIBboDj4rIWuApY8yuMohTKVXWHn4adu8pvf7q1IJXC76a/MjRY/S5agQA+w8exhEQSNWYKACW/vI/goNLftO1rgOHsWjGJHbsSmDQjXewdt5PJe4L4K3/TuD24dfmOx9WadqxK4FFy/7i+isvA2DCpO9Z/vda3n0p7zxiZamwEUc40M0Ycyq/hSLSBmvqc00cSp2Ndu+B2rVKt79CxFSOZtVs6y4Lz746jqCKMTxxx/WlsupFM87gTqH5eOujz7nxqsvzTRxOp5OAgIBSWc+O3Xv4+vufshOHryjwGIcx5r2Ckoa9fJUx5g/vhKWUUhQ4xXjc0OE8OvpVOvW7iqZd+jF/iXUh8LqNm+nU7yra9B7M+XGXsXnbDiDnVOlZnE4nDz83lo79ruT8uMv47+d5k0tycgoDb7idC3pdTqueg/j2h5m889Hn7N1/kF5X3EyvocOz+3967Ntc2P9qFi//iy8nT8uO498PPY3T6cyu98SLb3JBr8vpPOAaDhw8DMDWHbvoPOAaOva7kqfHvp0d76gXXmf+n8tp03swb344AYC9+w/Sf9itNOkykEceeaT0NnYxFHlwXERCReRuEXlfRD7NepRFcEqpc5cxhv88/gKTP36HFb99zy3XXckTL76ZvTwz08nSWZN5a/TjPPfauwB8OHESI2+7iVWzp7H81ynUrlG9wP4/+WoykRUrsmzWFJbNmsJHX37H9p27c9T5Zc58alaryt9zprN23k/0792De2+7iZrVqzLn+4nMmfoFAMkpKbRq1oQ/f/kfMdHRfPvDzyz86RtWzZ5GQICDr6b8mF2vc/sL+HvOdHp26cBHX34HwMgnxjDytptYNmsKNatXzV7/y08+SI8LO7Bq9jTuv2MEAKvWbuDb8W+xZs73fPvtt+zenTPmsuDJXFVfYN25rx/wPHADsMGbQSmlVFpaeoFTjANcMbAvAO3Pb8kOezdYlw5tGPP2hyTs288VAy+hScP6Bfb/69yFrF7/D5N/mgVYEyVu3r6TBvVO38G6dfOmPPTsWB4d/SqD+vaiR+f8Z+kICAjgykH9APhj/mJWrF5Lx35XAXAqNZWqsTEABAcHMeiSXnbcrfht7kIAFq9YxQ8T3wPg+isu46FnXykw7j49uhBZqSKYdFq0aMHOnTupU6dOgfW9wZPE0dgYc7WIDDbGTBSRr4FZ3g5MKXVuMxQ8xTiQPa15QICDTHtX0PVXXsaF7S9gxm/x9Lv2Vj5+4wV69+iSf//GMO7FJ+nXq0eBMTRt1IAVv33PzD/m8tiY17kkrhtPP3hPnnqhISHZxzWMMdx8zVBeevLBPPWCAoMQe95297iLI2tqd6uPgHK53awn13Fk2M/HRaQVEAnU91pESimFlRjym2K8MNt27KZhvTrce9tNXN6vN6vX/1Ng3X5x3flgwjdkZFhfcZu2bic510y4e/cfoEJYGDdeNZiH7ryVlavXA4VPjd6nRxcm/zSLg4eOANadAHcWcWJA5/YXMOWnXwGYNHVGdnnFiHASk/NfT3nyZMQxXkSigaeA6UCE/VopdTarU6v0T8ctBofDke8U4y2bNSmwzbfTZvLllOkEBQZSvWosTz94d4F1/3Xj1ezYvYd2F1+BMYYqMdH8MPH9HHXWbNjEw8+9gsPhICgokA/GPgvA7cOvYcD1t1GjahWm/5jz1N4W5zXmhVH3ccm1t+ByuQgKCuK9l56mXiGf/63Rj3PjXQ/z+gefMrBvHJGVIgDrrn+BAQFc0OtyRlx7BdFRlYrabGWi0GnVRWQI0BhYY4zxud1TOq168flLrP4SJ5w9seq06iVTGnGmpJwiLCwUEWHS1Bl8M/Unpn3+QeGNynFa9cIuAHwfaAksAkaLSCdjzOiSrkgppVT+Vqxexz2PPY8xhqjISnz61ovlHVKhCttV1RO4wBjjFJEKwHxAE4dSSpWyHp078Pec6eUdhscKOzieboxxAhhjUgAppK5SSqlzRGEjjmYistp+LUAj+70AxhhzvtejU0op5XMKSxzNyywKpZRSfqPAxGGM2VmWgSillPIPhZ1VlQi4n6sr9vusXVW+cUKxUso7gmeB40jp9eeKgfR+BS42xtDj8ut54r47GNDnIgC+mzaTYf9+gBcff4BR995eYNvCphuPaNCWpO1/eRRiWU2X7u8K21X1B1Ad+B6YpPfdUOoc4zgCroInCSx+f/sLXSwifPjKc1x920h6detMsiTzxEtvsXnJrzSqf+bXW3mirKZL93eF7aoaIiKRwBXARyISCnyLlUT07n9KqVLXqnlTLrukF2Pf/YhjqS5uumYw85cs583/TuDdl57m0OGj3PHIM+zasxewrrju1ql9jj6279zN9Xc+RKYzk/4FzEOVnJzCNbffR8Le/TidLp564C4OHDqcPV16bOUo5kz9gogGbXngjhHMmrOA1597lB279/DOR1+QnpHBhe0u4P2xz+AUJyPuHcXyVWsREW657kruv2ME73z0OR9+PonAgABaNG3MpPFv5huLPyp0yhFjzAngMxGZCFwLjMO6C+AbZRCbUuoc9MyD99Cu71ACg8NYOes7vpl6ekqPkU+O4f5/30z3CzuwK2Ev/YbdyoYFP+doP/LJMdw54jpuumYI7336Vb7ryJoufcZX4wFrZtzIShV5478TmPP9RGJjKgOnp0t//tGRbNi0lbHjPmbhT98QFBTEXY8+y1dTfqR+i3bs2Xcg+66Cx0+cBODlcePZvmw2ISHB2WVni0ITh4h0Ba7Duuf4AmCoMWZ+WQSmlDo3hYdX4NrBlxJUMSbHTLAAv89bxPpNW7Lfn0xMIjEpKUedhcv+Ysqn4wAYfvVgHh39Wp51lOZ06XGXDmXbzt3857HRDOx7EZfEdQeseaZuuOshhgzow5ABF5dwa/imwg6O7wCOA5OA24FMu7wdgDFmpffDU0qdixwOBw5H3uuTXcbF4hnfEhYWWmj7rKnLC1Ka06UnBkbx95xpzJqzgPc+/Zrvpv3Mp2+/xIyvxjNv8TKmz5rN6DfeZ928GQQGejKvrO8r7MrxHcAxrBs4vQy87vbIm8KVUsrLLrmoO+9++mX2+1Vr895TrlvHtkz6wZqa/Ksp+U/jUZrTpR85cgSXy3DloH6MHjWSlWvW43K52L1nH726d+aVpx/m+IlEknJN2e7PCjs4HleGcSilfI0rpsgzoYrd3xl6Z8wT3D3qec6Pu4xMp5OenTvw4avP56jz9gtPcP2dD/H2R59z5cD8T//1ZLr0rNvCZilounRXMlxxzz24XC4AXnriAZxOJzfe/TAnTiZhjOH+f48gKvLsuYKhwGnVRaS7MWZBgQ1FKgF1jTFrvRVcUXRa9eLzl1j9JU44e2LVadVLptzi9MVp1YErReQV4BdgBXAI64yqxkAvoB6Q996ISimlzmqF7aq6377z31XA1UAN4BSwAfhvYaMRpZRSZ6+iruM4BnxkP4pFROoAn2Ndfe4Cxhtj3haRylgXEtbHOgB/jb0eROQx4FbACdzri3cdVOqsZQzGmCLPSFL+o7A7vJ6Jws6qOlOZwIPGmOZAZ+BuEWkBjAL+MMY0wZrWZBSAvWwY1l0H+wPvi4he369UGQlNOcCRpDSvfdmosmWM4ciRI4SGFn7qckl47aRiY8w+YJ/9OlFENgC1gMFAnF1tIhAPPGqXTzLGpAHbRWQL0AlY7K0YlVKn1d70PxK4mkMVqoEPjDpSHccJdfn+KazlFqdxwtHCf1uHhoZSu3btUl91gWdVlepKROoD84BWwC5jTJTbsmPGmGgReRdYYoz50i7/BPjZGDM5V1+3Y12QSLVq1dpPmjSp5IEd/QdSXSVvb0sKjSUi9fAZ91MW/CVWf4kTNFZv8ZdYyy3OUAdUPq9YTZKSkoiIiKBXr15eO6sKABG5GvjFHjU8CbQDXvD0ynERiQCmAPcZY04Wsv80vwV5spoxZjwwHqzTcePi4jwJowAbgDPPxvHxLuLiSj+re4O/xOovcYLG6i3+Emv5xZnA6Z03nsk6HfdMeXKM4yk7aXTHuop8IvCBJ52LSBBW0vjKGPO9XXxARGrYy2sAB+3yBKCOW/PawF5P1qOUUqrseJI4nPbzQOADY8w0ILiQ+gCINbT4BNhgjHGfTXc6cLP9+mZgmlv5MBEJEZEGQBNgqQfxKaWUKkOeHBzfIyL/BS4GxopICJ4lnG7AcGCNiKyyyx7HmvfqOxG5FdiFdY0Ixph1IvIdsB7rjKy7jTHOPL0qpZQqV54kjmuwTo99zRhz3N699HBRjewLBAs6oNGngDZjgDEexKSUUqqceJI4agAzjDFpIhIHnI91YZ9SSqlzkCe7nKYAThFpjHXMogHwtVejUkop5bM8SRwuY0wm1r3H3zLG3I81ClFKKXUO8iRxZIjIdcBNQNbNf4O8F5JSSilf5kni+D+gCzDGGLPdPlX2yyLaKKWUOksVeXDcGLNeRB4F6trvt2OdUquUUuocVOSIQ0QuA1Zh3dAJEWkjIvnfyFcppdRZz5NdVc9izVJ7HMAYswrrzCqllFLnIE8SR6Yx5kSuMp2wXymlzlGeXAC4VkSuBwJEpAlwL7DIu2EppZTyVZ6MOP6DdVe+NKwL/04A93kxJqWUUj6s0BGHfevW6caYi4EnyiYkpZRSvqzQEYc9O22KiESWUTxKKaV8nCfHOFKxpkb/DUjOKjTG3Ou1qJRSSvksTxLHDPvhTs+qUkqpc5QniSPKGPO2e4GIjPRSPEoppXycJ2dV3ZxP2YhSjkMppZSfKHDEYc+Iez3QINcUI5WAI94OTCmllG8qbFfVImAfEAu87laeCKz2ZlBKKaV8V4GJwxizE9gpIhcDp4wxLhFpCjQD1pRVgEoppXyLJ8c45gGhIlIL+APr/hwTvBmUUkop3+VJ4hBjTArWrWPHGWOGAi28G5ZSSilf5VHiEJEuwA2cvp7Dk9N4lVJKnYU8SRwjgceAqcaYdSLSEJjj3bCUUkr5Kk9uHTsP6zhH1vttWFOrK6WUOgd5MuJQSimlsmniUEopVSyaOJRSShVLYVOOjKOQWXB1WnWllDo3FXZwfHmZRaGUUspvFDblyMSyDEQppZR/KPJ0XBGpAjyKdbV4aFa5Maa3F+NSSinlozw5OP4VsAFoADwH7ACWeTEmpZRSPsyTxBFjjPkEyDDGzDXG3AJ09nJcSimlfJQnc05l2M/7RGQgsBeo7b2QlFJK+TJPEscLIhIJPAiMw7oD4P1ejUoppZTP8mSuqp/slyeAXt4NRymllK8r7ALAR4wxrxR0IWBRFwCKyKfAIOCgMaaVXVYZ+Baoj3WQ/RpjzDF72WPArYATuNcYM6skH0gppZR3FXZwfIP9vBxYkc+jKBOA/rnKRgF/GGOaYN1NcBSAiLQAhgEt7Tbvi0iAZx9BKaVUWSrsAsAf7Zcpxpj/uS8TkauL6tgYM09E6ucqHgzE2a8nAvFY14gMBiYZY9KA7SKyBegELC76IyillCpLYkyB01FZFURWGmPaFVVWQNv6wE9uu6qOG2Oi3JYfM8ZEi8i7wBJjzJd2+SfAz8aYyfn0eTtwO0C1atXaT5o0qagwCnEICD6D9pakJIiIOONuyoS/xOovcYLG6i3+Emv5xZkOVClWi6SkJCIiIujVq9cKY0yHkq65sGMcA4BLgVoi8o7bokpAZklXWNDq8inLN6MZY8YD4wE6dOhg4uLizmC1H1AaZxbHx7uIi/OPiYb9JVZ/iRM0Vm/xl1jLL87DQJE7f3KIj4/nzL4zLYWdVbUX6/jG5eQ8ppFIyU/HPSAiNYwx+0SkBnDQLk8A6rjVq22vXymllI8p7BjH38DfIvK1MSajoHrFNB24GXjZfp7mVv61iLwB1ASaAEtLaZ1KKaVKkScXAHYSkWeBenZ9AYwxpmFhjUTkG6wD4bEikgA8g5UwvhORW4Fd2OMsY8w6EfkOWI+1G+xuY4yzRJ9IKaWUV3mSOD7B2jW1AusaC48YY64rYFGfAuqPAcZ42r9SSqny4UniOGGM+dnrkSil1Fkq3ZnJibQUDBAeFEJYYBAO8f0D/wXxJHHMEZFXge+BtKxCY8xKr0WllFI+whhDhstJckYaKZnp1nNGOs0q1yA0MIhdaXv4ZM02TqSf4mRaCifSTnEiPYVxvW8iPCiEt1fO4uM1c3P0KQhLb3iW0MAgPl4zl9m71hEeFEpEUAjhQSFUDA7j0U4DAZi7eyPbTxwi0OEg0BFAoCOASsGhXFI/ujw2B+BZ4rjQfnY/59cAeiMnpZTXncpMZ/2RPaRmZpDpcpFpXDhdTlrG1qZWRDSHTyUyZ/cGnC4XTuMi037uXacF9SNj2X7iED9sWZG9PN2ZSbrLyYiW3WkUVY3l+7fz8Zp40l2ZZDidZLicpGZm8OpFw2gSXZ3Jm5fx/OIf8sQ1bch9NIysyta0nXy/chZBjgCiQioQGRJGpeAKpGZmEB4UQucajakQGEKl4FBEhJSMdJIz0wgJsL5+KwaFUCk4jKSMNA6fSiQ5Iw2DyU4cM7av4uftq3OsOzasIpfUH+7tTV8gTyY51IkNlVJlJik9laX7t7E3PRKoxfYThxjxy0d56r3Q7SpqNY5md+LRfL/Y61asTP3IWPYlHefL9YsIcDgIFAdBAQEEOQIZ3Mi6hjnTOElMTyXQEUBoYDAVHQGEBgYS6LBmPWodW5t7215ChaBgKgSGEB4UTIWgEKpXiASgW0QHRl3aidCAIETyXpJ2YY1GXFijUYGf99pmnbm2WcG3OBrT7Sqe6TKUTJeV1DJdTjKcTuBUIVvRuzy5dWw14EWgpjFmgD2vVBf75k5KKXVGjDH8fWgXi/dtYcneraw+tItM46JHxQu5kVo0iarG+31upmJwGIEOBwHiINDhoFq49cXdMqYWv1/1qL0bx1oe4HAQ7LC+3rrWasKK4c8XuP7ONRrTeWDjApc3q1yTZpVrFrg82BFMWKD3jlcEBQQSFJDfV3WC19ZZFE92VU0APgOesN9vwprhVhOHUqpEktJT2XnyMC1jrZkbHpz7DYdSEmlWuQY3t+xBt1pNOL7BWhYUEEiP2ucV2FdwQGB2ElFlw5PEEWuM+c6e9hxjTKaI6DUWSqliyXA5mbt7Az9uXcWCPZuIDAnjt6seJcDh4J1eN1K7YmUiQypk14/f6CrHaFVhPEkcySISgz13lIh0xrqpk1JKeeSXHasZu3QGh08lUjWsEtec14l+9VvjsI8JZI08lH/wJHE8gDUlSCMRWYg1HeNVXo1KKeX3Vh3cRbUKlagREUVMaAStYmpxVdNOdK/VlACH/17DoDw7q2qliFwEnIc13cg/pTh3lVLqLOIyLubs3siEtfNYdWgXw1t045GOA+lYvSEdqxc6S5HyI56cVXVFrqKmInICWGOMOZhfG6XUuefHrX8xfvUcdpw8TK2IaEZ1GsTQxiW+5YPyYZ7sqroV6ALMsd/HAUuwEsjzxpgvvBSbUsrHpTszCbZPFV22fxuhgUG80vNa+tZrlX0dhDr7eJI4XEBzY8wByL6u4wOsK8rnAZo4lDrHJKan8u0/S/hi/ULe7X0TravUYVSnywgLzP8iOHV28SRx1M9KGraDQFNjzFER0WMdSp1DjpxK4ssNC5m0cQlJGWl0q9U0e8RRIejMb8Os/IMniWO+iPwE/M9+fyUwT0TCgePeCkwp5VsyXU6u+fFdDp1KpG+9lvyrdRzNYwq+olqdvTxJHHdjJYtuWGdVfQ5MMcYYQOexUuostv3EIX7c+hf3tL2YQEcAj194GQ2jqtIgskqJ+kvPhE0HYNthSDgGB08KiamQnAYOBwQFQIVgqFbJEHBM6OGCAD1z1+d4cjquASbbD6XUOWDDkb18vCae33auIzgggAENzqdJdHX61GtZrH5SM+DP7TD3H2HlLli/D9IzTx8DqRhqqBRqJQuADCckpsKRZCtbTNpquLqD4eoOUCuqlD6cOmOeno47FqiKNeLIunVsJS/HVgaqUToThcUCh0uhn7LgL7H6S5xwNsV6+FQKD8/9leUH9hIRFMytrdtyY/PziQnLxNP/K6kZ8MeGMKatCmf+5lBOZTgICXRxQZ10RnRN5/za6TSumkHt6EwiQkyBfbwzpRprkl28/Ucob/8BPZukMqxjEn2anyLYk30lZaa8/v7VymGdFk82/yvAZcaYDd4OpuzlvkSlpOKxb5/uB+Lxj1jj8Y84wV9jPZZ6jFUHV7H8wHJiwmK4pdUtRIc4MaxhZLuruea8a6gU7Pnvw3V7T/Dlkp38tHofiamZVK0YwlXtq9OrWRW6NIwlLNjz03NDg6BT9XgeiYtj99EU/rd8N98tT+DOr8KIrhDE4Da1uLpDbVrW9IXJDePxn79/6fAkcRw4O5OGUme/5Ixk9ifvJzE9kTZV2wDw/OLnid8dz6FThwBrWvBLG14KQIAjgAn9J3jcf4bTxa/rDjBh0XaW7ThGWFAAA1pX54q2tenSKIYAx5mfmluncgUeuOQ8Rl7clHmbDjF5RQJf/7mLCYt20KJGJS5vU5OBrWtQp3KFojtTpcKTxLFcRL4FfiDnrWO/91ZQqvwt3ruYqZunYji9K6F9tfYMazYMgFHzR5HpyszRplvNbgxtMpRMVyaj5o/K02fvOr25tOGlpGSk8MyiZ/Is71+/P33q9eFo6lE+O/QZM+fOzLH88saX071Wd/Yn7+fNFW/maX9V06voWL0jO0/u5IO/P8iz/IZmN9C6Sms2HdvEZ2s/y7N8RMsRnFf5PNYdXsfXG7/Os/z282+nXqV6rDywkimbp2SX7z+8n98W/MZ/2v6H6uHVWbx3MTO2zcjT/oEOD1A5tDLzEubx287f8iwf1WkU4UHh/L7zd+bvmZ9n+ZOdnyTIEcTMbTP5c/+fuIwLl3GR6crEIQ5e6vESAK8vf52ft//MyfSTnMq0bvYTGxbLnGusa3gjQyLpUrMLTaOb0jKmJa2rtCYkICTP+gqTmuHkfysS+DB+K3uOn6Ju5Qo8ObA5V7evQ2SFoGL15akAh9CrWVV6NavKseR0pv+9lykrE3j55428/PNGWteK5NLWNbi0dXXqxYR7JQZl8SRxVAJSgEvcygzWPcjVWerjNR+z5vAaqlU4vR+1ZsTpUy83HduUJ3E0jrJuhmMwbDq2KU+frWNbA5BpMtl4dGOe5R2rdwQgw5lBQnoCR48ezbG8e2p3AFIzU1l7eG2e9n3q9gEgJSOF1YdW51k+sIF1K87E9ERWHVyVZ3lik0QAjqUdY8WBFXmWJ2UkAXD41GGW71+eXZ6amkrC/gRSMlIAOJhykKX7l+Zpn5Zp/e7ak7SHJfuW5FmetT13nNzBgj0L8iy3zlOBrSe2siBhASJCoCMQhzgIdpy+hqJOxTp0qdmFisEViQmNoWZETWqE18hePrLdyDx9eyolPZOv/9zF+HnbOJiYRvt60Tx3eUt6NataKqMLT0WHB3Nz1/rc3LU+u4+mMHPNPmau3c/YXzYy9peNNK4aQa/zqtDrvKp0qF+ZYC/eaOlcJFn/GP1Rhw4dzPLly4uu6GXx8fHExcWVdxgecY/VGEOqM5W0zDTr2ZlGamYqkSGRVAquxKFTh6hXqV65x+nrzoVYU9Iz+WLxTsbP28aR5HS6Norhnt6N6dIwxmtXipck1oRjKcxad4D4fw7y57ajpDtdhAcH0K1xLHHnVaV741jqVA4r1Zj98e8vIiuMMSWeSMyTs6pCsearagmEZpUbY24p6UpVXpmuzOwvboCYsBgANhzZwMn0k6Q507IflUMr07VmVwAmrpvIsdRjVls7CbSMbckNzW8A4LZfb+NE2onstomnErl86eU82ulRADp91SlPLMNbDOeRjo9QL6h8kobyHVkJ47/ztnE0OZ2Lmlbh3j6NaV+vcnmHlq/a0RW4tXsDbu3egOS0TBZtPUL8PweJ/+cQv663JsCoERlK54YxXNigMp0bxlAvpoJOk1JMnuyq+gLYCPQDngduAM7ag+XGGDJNJkEOaz/tkVNHOJ52PPuLNzUzFWMMXWtZX9wL9ywk/mQ8W9ZssepkphEWGMadbe4E4P1V77Pm8BrSnenZX+w1I2ryTu93ALh11q2sPLCSTHN6t0+bKm344lJrCrDH5j/G1hNbc8TYpUaX7MTx9YavOXjqIKEBoYQEhBAaGEpEcER23fCgcIIDgq1lAaEcOXiEFjEtABARHmz/IEEBQYQEhGS3L69RhvIduUcYFzWtwsiLm9CubnR5h+ax8JBA+raoRt8W1TDGsOVgEku2HWHJ9qPM33yIqX/tAaB6pVA6NahM27pRtKkTRYualQgJ1AkaC1Ng4hCRQGNMJtDYGHO1iAw2xkwUka+BWWUXoneNXz2eyZsmZyeFNKe1H3rVTasAeHvl20zdMjVHm4igCBZfvxiAqVumMuvYLDhmLQsJCKFWRK3sxHE09SjHUo8REhBCeGA4lUMrUyuiVnZfF9e7mAuqXJD9xR0SGJLjuMKzXZ8lw5WR44s9Iuh0Yph5xUwCCpmF9K1eb+V4Hx8fT1yjuOz3I1qN8Gg7qXNDSnomXy7ZyX/nWgmjZ9MqjOzThPb1/Cdh5EdEaFKtIk2qVWR4l/oYY9h6KNlKJNuO8Of2I0z/ey8AwQEOmtesRNs6ViJpUydKRyW5FDbiWAq0A7ImMjwuIq2A/UB9L8dVZmpF1KJj9Y7WL/bAkOxf7i7jwiEOrmx6JV1rdiU4IDi7TlhgWHb7p7s8Te/M3vSJ60OwIzjPP64nOz9Z6Pqva3ZdocuzTqEsSGFJQylPJRxLYfKKBL5YvPOsShgFEREaV42gcdUIbuxsjbD3nTjFql3HWbX7OH/tPs63y3YzYdEOACqGBNKsRkVa1KhEc/txXvWKhAadm///PNlVNV5EooEnsW4hGwE85dWoytDAhgMZ2HBggcsvqHIBF1S5oMDllYIrUSGgQrFPZ1SqvKWkZ/LHhoN8t3w3C7ZYVz5f1LQK/+l99iaMwtSIDKNG6zAGtLbOQMt0uth0IIlVu4+zft8JNuxLZPKKBJLTnQA4BBpWiSDGkco6s4XGVSNoVCWCejEVCDrLJ9gqLHFUFZEH7Nf/Zz+/Zz/rSdJK+aHkDMMPf+3h57X7mLvpEKkZLmpFhXFv7yZc1b62XkTnJjDAQYualWhR8/TV8y6XYfexFDbsO8n6vSdZvy+Rv7Yn8+esf063cwj1YirQqEpEdjJpVDWCRlXCqRjqnWtcylphiSMAa3SR3449/z2HV6lzSIbTxardx5m/+TALNh9i1e4UXGYV1SqFcE2HOvRvVZ0LG5TOFd7nAodDqBcTTr2YcPq3skYm8fHxdOjSna0Hk9h6KIkt9vPWQ8nM3niQTNfpr8sGseH0bBJLz6ZV6NwwhvAQn5p0y2OFRb3PGPN8mUWilDpjR5PT+WvXMf7adZyVu47x9+7jJKc7cQi0rh3FwAZBjOjXkbZ1onBosig1ESGBXFAnigvqROUoz3C62HU0hS0HrYSyfMdRvluewMTFOwkKEDrWr0zPplXo0SSW5tUr+c3fpLDE4R+foAROpTvZcSQZY6yrnLOugcx67zLWabnGLsOuk/XefZkxhvVHnARuPpzdl7XMnqwjq09XzvJ8+7Xfu+yA3MsK7Nfkqmc3zC9WlzFs2Z7BZse2XLHm3g4FDyiLe2ZJQdWlgH9eWfW3bUtno2x1q1+6/XuqoM/rXrplRwbbFmwvIp5i9l+Mfk6mZrJ+70nW7T3BjiPW1esBDqF5jYpc2b42XRrG0LVRLJEVgoiPjz8nj1+Ul6AAh7WrqkoE/ewZ6dMynSzfcYy5mw4xb9Mhe8oUiI0IplvjWHo0sRJJtUqhhXdejgpLHH3KLIoy9s+BRIa8t7B0O132Z+n2503/+MllOJvyTkviszauL9fV16kcRssakVzbsS7t6kZxfu2oYs1Gq8pOSKB1JXu3xrE8fmlzDpxMZcHmw8zffIgFWw4zbZV1WnDTahFc2CCGjg0q07F+NDUiw4rouewUmDiMMUcLWubvGsSE8+GN7ex3goh9oxERHIL93irMKreerXL3+iLw96pVtG3b1q08Z78OkRxtyd2X22uH5I0p97qz2ufu126aN0Y7BgQWLlhAjx7dC+w3q8/8FDQQcZ8I0ZP6BXGvP2/+PHr26Fmi/gtabUEjqYLrF7Qg59sFCxfQvVv3coszNCiACD/dV66gWqVQrmxfmyvb18blMmzcn5idRL5fmcAXS3YCUDs6jI71K9OxfmW6NIqhfjleW3JO/muLrBCUfWCrNKTuCqBTA9+cgiG3CkHiF2d2hASI3/xiDg8Sr80Iq84tDodkn8n174sakel0sWFfIst2HGXZjqPM33w4+4r3gefX4L3r2xXRo3eck4lDKaX8QWCAg9a1I2ldO5JbujfAGMP2w8ks2nqE2IjgojvwVlzltuYCiEh/4G2s04E/Nsa8XM4hKaWUTxARGlaJoGGViKIre5FPXd4oIgFYFxkOAFoA14lIi/KNSimllDufShxAJ2CLMWabMSYdmAQMLueYlFJKufG1xFEL2O32PsEuU0op5SN86g6AInI10M8Y8y/7/XCgkzHmP251bgduB6hWrVr7SZMmlUus7pKSkoiIKN99jp7yl1j9JU7QWL3FX2L1lzjhdKy9evU6ozsAWlcW+8gD6ALMcnv/GPBYQfXbt29vfMGcOXPKOwSP+Uus/hKnMRqrt/hLrP4SpzGnYwWWmzP4rva1XVXLgCYi0kBEgoFhWFO5K6WU8hE+dTquMSZTRO7BusNgAPCpMWZdOYellFLKjU8lDgBjzExgZnnHoZRSKn8+lzjKxIkEWPQuBIZAUJj1HBhqP+d6n2O5XRYQYpeFQED5Xb2plFLl4dxMHEkHYdVXkJkKzvQz7q6HIxgWh51OJlmJJiDYTjbB+SSdULfk414WnKttQe1DcpYFnJt/SuVlzgxIS4T0JEhPhsw0e2ZMByDgCISQihBaCYIjij9vvfJL5+a3Ta128Jh9uYjLBc40yDhl/afITHV72O8zUiHzFGSmW3Uz09zqprFn+xbq1qyas9yZfrqP9BRIOepW5rbMmQauzDP/TBJQRNKyylodT4JDE4tIWiEQFgUtr9CEdDbLSIUTu+HYDutxcg8kHoDEfZBkP5865nl/EmAlkIjqEFkLKtWESrWhcgOIbWo9gvXWtGcD/VZwOMARZu2SKqFt8fHUjYsreQwuZ45ElDM5ZSWi/MpyJ6L8ytz6Sz1BaOoR2H80//5yT9wdGAIt9MJ9v2WMNbrOSgzHdtBswxLYNtZ6n7iPHH9zR6D1pV+xGlRuCHW7QERVCKkEIRHWiCIwxJ4n3oBxnR6RpJ2E1JNWokk6YCWhfash+WDOmKLqQtUWUKu99QOuZjuo4B8zS6vTNHH4AkeA9UusDH6NLY+PJy6/JGeMNfLJTLVGX+Pawz8/Qy23a4Ry7IaQgsvOqK4lMCPJ7dduafTrxbrGlfOmG2W5uybjFBzbmSM55HhknspRPTo4BqqfBw3jILoeRNe3HlH1IKKa9UOqNGWmwdHtcPgfOGQ/9q+GTbPITlqVG0L97tDgImjQ00pWyqdp4lAWEQgIsh4hFa3/wH9/Yz3KQXeAUr5Jo7fEAcz1pKYXkldmas5VBIVbiaByQ2jU+3RiiK4PUXVZvHBJ/j8cvCUwBKo2sx7uUk/CvlWwZwXsXgrrpsHKz61lVZpDs4GEZJxXdnGqYtHEofLX/2Vo2s96nWNaGrfX2eX5lbmVF3gLvILrbt6ymSaNG5/hus40VpPvy9x1t+/YToP69cs+VrB2H0U3OJ0cwmP94wB1aCXrx0kD6y6PuJyw72/YPg+2zoYFb9BJgiDqCHS63RqVK5+hiUPlL6oOtLup3Fa/JzWeJp3jym39xbEzPp4GZfkr/mzkCLCOedRqB93vg+O7OP75CGJ+GQVrv4fLx+Udtahy42tTjiilFETVZU3rp2Dof+HIZvigK/w40roGS5U7TRxKKd8kAhcMg3uWQ6fb4K+v4K3z4X8jYNeSgneBKq/TXVVKKd8WHgsDxkKXe2Dpf2HF57BuKlRuZCWW86+xju+oMqMjDqWUf4iqA5e8AA+sh8HvWRcYzhkDb18An/aHJR/C8V3lHeU5QUccSin/EhIBbW+0Hsd3wervYM1k+OVR61H9fGg2CJoNhGot/eMsMz+jiUMp5b+i6kLPh6zHka2w8SfYOAPiX4L4F60LG5tcAo37QP0eVtJRZ0wTh1Lq7BDTCLqNtB6JB2DTz7BxpjWh6bKPwBEEdTtbF0Y27gPVWpf+lfLnCE0cSqmzT8Vq0H6E9chMs87C2voHbJkNfzxnPcKrWEmkQU9rNBJdr7yj9huaOJRSZ7fAEGh4kfXo+zwk7oetc+xE8ges/taqF1XXSiD1e1hzZ0XVKd+4fZgmDqXUuaVidWhznfUwBg5ugB0LYMc8a2LPVV9Z9aLrWwmkfk/rObJWuYbtSzRxKKXOXSJQrYX1uPB26/48B9fDjvlWMtnwE/z1pVW3ckOo29U6TlK3i3VM5Rw9Y0sTh1JKZXE4oHor69H5TiuRHFh7OpH8MxNW2YmkQizU7UztjCqQEGGdBhx4btxKWhOHUkoVxOGAGudbjy53W7u2Dm+GXYutA+67FtP42HbY+pl198xaHU6PSOp0hNDI8v4EXqGJQymlPCUCVZpaj/Y3A7Bo1lS61nFkJxIWvAnmNUCsCxCzE0kniKxzVuze0sShlFJnID0kGlrEnb7NcloS7FkOu/60Esnfk2DZx9ayiGpQu+PpR802EBxeXqGXmCYOpZQqTSER1q15G8ZZ752Z1nGShGWnHxt/spZJgDUqqdPpdDKp3NDnRyWaOJRSypsCAq2RRc021vTwAMmHIWH56UTiPioJq+w2KukAtdpbd0z0IZo4lFKqrIXHwnn9rQdYt849tPF0Itm9DDbPsisLVG1uJZHa9sgktmm5TpeiiUMppcqbw95lVa2lNU0KwKnjsGeFPTJZCuunwcrPrWUhkdBuOPQbUy7hauJQSilfFBZlTcbYuI/13uWCo1vtEclSiKxdbqFp4lBKKX/gcEBsE+vR5vryDaVc166UUsrvaOJQSilVLJo4lFJKFYsmDqWUUsWiiUMppVSxaOJQSilVLJo4lFJKFYsmDqWUUsUixpjyjqHEROQQsLO84wBigcPlHYSH/CVWf4kTNFZv8ZdY/SVOOB1rPWNMlZJ24teJw1eIyHJjTIfyjsMT/hKrv8QJGqu3+Eus/hInlF6suqtKKaVUsWjiUEopVSyaOErH+PIOoBj8JVZ/iRM0Vm/xl1j9JU4opVj1GIdSSqli0RGHUkqpYtHEoZRSqlg0cXhIRCqLyG8istl+js6nTh0RmSMiG0RknYiMdFv2rIjsEZFV9uNSH461yPZlFadd71MROSgia3OV+9Q2LSLWMtmmxYy1v4j8IyJbRGSUW7lXt2tB63VbLiLyjr18tYi087RtaTvDWHeIyBp7Gy73gVibichiEUkTkYeK0zYPY4w+PHgArwCj7NejgLH51KkBtLNfVwQ2AS3s988CD/lJrEW2L6s47WU9gXbA2lzlPrVNi4i1TLZpMf7+AcBWoCEQDPxdFv9WC1uvW51LgZ8BAToDf3ra1lditZftAGLL6N+nJ7FWBToCY9z/viXZrjri8NxgYKL9eiIwJHcFY8w+Y8xK+3UisAGoVVYBujnTWItsX1Zx2vHNA456KQZPnWmsZbVNPV1XJ2CLMWabMSYdmGS38zZP1jsY+NxYlgBRIlKjHGI+k1jLWpGxGmMOGmOWARnFbZubJg7PVTPG7APrSxcrexdIROoDbYE/3YrvsYezn3pzVwVnHmux2pdVnAXwyW3qhfalva5awG639wnk/JHjre1a1HoLq+NJ29J0JrECGOBXEVkhIrd7Lcqi4yj1toHFCu0sJyK/A9XzWfREMfuJAKYA9xljTtrFHwCjsf4xjQZeB27x0VhLTWnFWQCf3KZloRRilXzKss7NL9XtWoz1FlXHk7al6UxiBehmjNkrIlWB30Rkoz0i9YYz2TbFbquJw40x5uKClonIARGpYYzZZw9FDxZQLwjri/grY8z3bn0fcKvzEfCTr8YKeNS+rOIspG+f26aFKLVtWkqxJgB13N7XBvbafZfqdvV0vR7UCfagbWk6k1gxxmQ9HxSRqVi7hLyVODyJtdTa6q4qz00HbrZf3wxMy11BRAT4BNhgjHkj1zL3/Z5DgRxn3ZSyM4rVk/ZlFWdhfG2berl9aa9rGdBERBqISDAwzG7n7e1a4HpzxX+TfcZSZ+CEvcvNk7alqcSxiki4iFQEEJFw4BK8++/zTLZN8duWxRH/s+EBxAB/AJvt58p2eU1gpv26O9YQbzWwyn5cai/7AlhjL5sO1PDhWPNtXx5x2u+/AfZhHdRLAG71xW1aRKxlsk2LGeulWGfTbQWecCv36nbNb73AHcAd9msB3rOXrwE6FBWzF7dliWLFOkPpb/uxzkdirW7/mzwJHLdfVyrJdtUpR5RSShWL7qpSSilVLJo4lFJKFYsmDqWUUsWiiUMppVSxaOJQSilVLJo4lN8RkaEiYkSkmQd1O4jIO0XUiROR0rzILXf/H4tIC/v147mWLSrF9bwlIj2LUb+KiPxSWutX5w5NHMofXQcswLpQqVDGmOXGmHu9H1KhMfzLGLPefvt4rmVdS2MdIlIZ6GyKMaWFMeYQsE9EupVGDOrcoYlD+RV7bq1uwK24JQ57FPK7fQVvDRHZJCLV3UcTItJJRBaJyF/283lFrGuEiEwTkV/EulfBM27LHhCRtfbjPrssXERmiMjfdvm1dnm8PfJ5GQgT6/4MX9nLkuxnEZFX7XZr3NrG2e0ni8hGEfnKvuo/t6uA7NGDWPeCeFGs+y8sF5F2IjJLRLaKyB1u7X4AbvB0+ysFOleV8j9DgF+MMZtE5KiItDPGrDTGTBWRK4G7gf7AM8aY/bl2Z20EehpjMkXkYuBF4Moi1tcJaAWkAMtEZAbWFff/B1yIdeXwnyIyF+tq4b3GmIEAIhLp3pExZpSI3GOMaZPPeq4A2gAXALH2urJGD22BlljzBy3ESpwLcrXvBkzOVbbbGNNFRN4EJth1QrGuZP7QrrMceKGIbaBUDpo4lL+5DnjLfj3Jfr/Sfv8frPmAlhhjvsmnbSQwUUSaYH35B3mwvt+MMUcAROR7Tk/VMtUYk+xW3gPrF/9rIjIW+MkYM78Yn6s78I0xxok1IeJcrJvunASWGmMS7HWtAuqTN3HUAA7lKsuab2gNEGGs+64kikiqiEQZY45jTYBYsxhxKqWJQ/kPEYkBegOtRMRg3bnMiMgjxpo7pxbgAqqJiMMY48rVxWhgjjFmqFj3IIn3YLW55+QpaHpv7FFQe6x5f14SkV+NMc97+vEKWZbm9tpJ/v9vT2GNJvJr58rVh8utj1C7rVIe02Mcyp9chXW3tXrGmPrGmDrAdqC7iAQCnwHXY93N8IF82kcCe+zXIzxcZ1+x7uEdhrWbbCHW1NhDRKSCPfPpUGC+iNQEUowxXwKvYd1CNrcMsaazz20ecK2IBIhIFaxb0C71MEawPnPjYtTP0hTvztqqzkI64lD+5Drg5VxlU7CSRS9gvjFmvr07J+t4hLtXsHZVPQDM9nCdC7Bmi20MfG2MWQ4gIhM4/cX+sTHmLxHpB7wqIi6s2XHvzKe/8cBqEVlpjHE/KD0V6II1m6oBHsnnGE1hZgD/Bj72sH6WXnZbpTyms+MqVQARGYE1TfY95R2LJ0RkATDIPnbhaZt5wGBjzDGvBabOOrqrSqmzx4NAXU8r27vE3tCkoYpLRxxKKaWKRUccSimlikUTh1JKqWLRxKGUUqpYNHEopZQqFk0cSimliuX/AXFKGqMXa69xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot temperatures\n",
    "bam.plot.plot_temperatures(cooling_data)\n",
    "bam.show()\n",
    "\n",
    "bam.plot.plot_temperatures(cooling_data, only_indexes = [0,1,2])\n",
    "\n",
    "plt.hlines(y = PropsSI(\"T\", \"Q\", 0, \"P\", inlet_p, \"ETHANOL\"), \n",
    "           label =\"Boiling point\", \n",
    "           color = \"red\", \n",
    "           linestyle = \"--\", \n",
    "           xmin =xs[0], \n",
    "           xmax = xs[-1])\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(\"img/temperatures.svg\")\n",
    "plt.savefig(\"img/temperatures.pdf\")\n",
    "bam.show()\n",
    "\n",
    "# Plot coolant pressures\n",
    "bam.plot.plot_p_coolant(cooling_data)\n",
    "plt.savefig(\"img/p_coolant.svg\")\n",
    "plt.savefig(\"img/p_coolant.pdf\")\n",
    "bam.show()\n",
    "\n",
    "# Plot stresses\n",
    "bam.plot.plot_tangential_stress(cooling_data, wall_index = 0)\n",
    "plt.title(\"Stresses in the Copper Wall\")\n",
    "plt.legend()\n",
    "plt.savefig(\"img/stresses.svg\")\n",
    "plt.savefig(\"img/stresses.pdf\")\n",
    "bam.show()\n",
    "\n",
    "# Failure data for C106 in MPa - https://www.azom.com/article.aspx?ArticleID=2851\n",
    "yield_min = 50\n",
    "yield_max = 340\n",
    "yield_height = yield_max - yield_min\n",
    "\n",
    "tensile_strength_min = 200\n",
    "tensile_strength_max = 360\n",
    "tensile_strength_height = tensile_strength_max - tensile_strength_min\n",
    "\n",
    "xy_yield = (cooling_data[\"x\"][-1], yield_min)\n",
    "xy_tensile_strength = (cooling_data[\"x\"][-1], tensile_strength_min)\n",
    "width = cooling_data[\"x\"][0] - cooling_data[\"x\"][-1]\n",
    "\n",
    "bam.plot.plot_tangential_stress(cooling_data, wall_index = 0)\n",
    "plt.title(\"Stresses in the Copper Wall\")\n",
    "\n",
    "sigma_ts = patches.Rectangle(xy_tensile_strength, \n",
    "                             width, \n",
    "                             tensile_strength_height, \n",
    "                             label = \"Tensile strength\", \n",
    "                             alpha = 0.6, \n",
    "                             color = \"red\")\n",
    "\n",
    "sigma_y = patches.Rectangle(xy_yield, \n",
    "                            width, \n",
    "                            yield_height, \n",
    "                            label = \"Yield stress\", \n",
    "                            alpha = 0.3, \n",
    "                            color = \"yellow\")\n",
    "\n",
    "plt.gca().add_patch(sigma_ts)\n",
    "plt.gca().add_patch(sigma_y)\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(\"img/stresses_with_limits.svg\")\n",
    "plt.savefig(\"img/stresses_with_limits.pdf\")\n",
    "bam.show()\n",
    "\n",
    "\n",
    "# Plot heat fluxes\n",
    "bam.plot.plot_q_per_area(cooling_data)\n",
    "bam.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Useful engine data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "OPERATING CONDITIONS\n",
      "--------------------\n",
      "Mass flow rate = 3.448 kg/s (frozen flow - PyProPEP)\n",
      "Mass flow rate = 3.405 kg/s (Bamboo - perfect gas)\n",
      "\n",
      "Sea level thrust     = 6.931 kN      (Bamboo - perfect gas)\n",
      "Sea level thrust     = 7.000 kN      (frozen flow - PyProPEP)\n",
      "Sea level thrust     = 7.000 kN      (equilibrium flow - PyProPEP)\n",
      "\n",
      "Sea level Isp        = 207.587 s     (Bamboo - perfect gas)\n",
      "Sea level Isp        = 207.014 s     (frozen flow - PyProPEP)\n",
      "Sea level Isp        = 210.672 s     (equilibrium flow - PyProPEP)\n",
      "\n",
      "Chamber Temperature  = 2896.832 K\n",
      "Gamma                = 1.235\n",
      "cp                   = 1776.837 J/kg/K\n",
      "\n",
      "\n",
      "GEOMETRY\n",
      "--------\n",
      "Chamber length       = 209.9095 mm (injector face to start of converging section)   \n",
      "Nozzle length        = 147.0912 mm  (start of converging section to exit)\n",
      "Total engine length  = 357.0007 mm (injector face to nozzle exit)\n",
      "\n",
      "Throat area          = 0.00343 m^2  (Bamboo - perfect gas)\n",
      "Throat area          = 0.00343 m^2  (PyProPEP - frozen flow)\n",
      "Throat area          = 0.00342 m^2  (PyProPEP - equilibrium flow)\n",
      "\n",
      "Throat radius        = 33.0 mm  (Bamboo - perfect gas)\n",
      "Throat radius        = 33.0 mm  (PyProPEP - frozen flow)\n",
      "Throat radius        = 33.0 mm  (PyProPEP - equilibrium flow)\n",
      "\n",
      "Exit area            = 0.00959 m^2  (Bamboo - perfect gas)\n",
      "Exit area            = 0.00959 m^2  (PyProPEP - frozen flow)\n",
      "Exit area            = 0.00987 m^2  (PyProPEP - frozen flow)\n",
      "\n",
      "Exit radius          = 55.3 mm  (Bamboo - perfect gas)\n",
      "Exit radius          = 55.3 mm  (PyProPEP - frozen flow)\n",
      "Exit radius          = 56.1 mm  (PyProPEP - equilibrium flow)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "OPERATING CONDITIONS\n",
    "--------------------\n",
    "Mass flow rate = {mdot_froz:.3f} kg/s (frozen flow - PyProPEP)\n",
    "Mass flow rate = {engine.mdot:.3f} kg/s (Bamboo - perfect gas)\n",
    "\n",
    "Sea level thrust     = {engine.thrust(1e5)/1000:.3f} kN      (Bamboo - perfect gas)\n",
    "Sea level thrust     = {isp_froz * mdot_froz/1000:.3f} kN      (frozen flow - PyProPEP)\n",
    "Sea level thrust     = {isp_shift * mdot_shift/1000:.3f} kN      (equilibrium flow - PyProPEP)\n",
    "\n",
    "Sea level Isp        = {engine.isp(1e5)/9.80665:.3f} s     (Bamboo - perfect gas)\n",
    "Sea level Isp        = {isp_froz/9.80665:.3f} s     (frozen flow - PyProPEP)\n",
    "Sea level Isp        = {isp_shift/9.80665:.3f} s     (equilibrium flow - PyProPEP)\n",
    "\n",
    "Chamber Temperature  = {Tc:.3f} K\n",
    "Gamma                = {gamma:.3f}\n",
    "cp                   = {cp:.3f} J/kg/K\n",
    "\"\"\")\n",
    "\n",
    "print(f\"\"\"\n",
    "GEOMETRY\n",
    "--------\n",
    "Chamber length       = {1e3*Lc:.4f} mm (injector face to start of converging section)   \n",
    "Nozzle length        = {1e3*(xs[-1] - xs[0] - Lc):.4f} mm  (start of converging section to exit)\n",
    "Total engine length  = {1e3*(xs[-1] - xs[0]):.4f} mm (injector face to nozzle exit)\n",
    "\n",
    "Throat area          = {geometry.A_t:.5f} m^2  (Bamboo - perfect gas)\n",
    "Throat area          = {At_froz:.5f} m^2  (PyProPEP - frozen flow)\n",
    "Throat area          = {At_shift:.5f} m^2  (PyProPEP - equilibrium flow)\n",
    "\n",
    "Throat radius        = {1e3*(geometry.A_t/np.pi)**0.5:.1f} mm  (Bamboo - perfect gas)\n",
    "Throat radius        = {1e3*(At_froz/np.pi)**0.5:.1f} mm  (PyProPEP - frozen flow)\n",
    "Throat radius        = {1e3*(At_shift/np.pi)**0.5:.1f} mm  (PyProPEP - equilibrium flow)\n",
    "\n",
    "Exit area            = {geometry.A_e:.5f} m^2  (Bamboo - perfect gas)\n",
    "Exit area            = {At_froz * area_ratio_froz:.5f} m^2  (PyProPEP - frozen flow)\n",
    "Exit area            = {At_shift * area_ratio_shift:.5f} m^2  (PyProPEP - frozen flow)\n",
    "\n",
    "Exit radius          = {1e3*(geometry.A_e/np.pi)**0.5:.1f} mm  (Bamboo - perfect gas)\n",
    "Exit radius          = {1e3*(Ae_froz/np.pi)**0.5:.1f} mm  (PyProPEP - frozen flow)\n",
    "Exit radius          = {1e3*(Ae_shift/np.pi)**0.5:.1f} mm  (PyProPEP - equilibrium flow)\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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