White Giant Bamboo Simulation (Ox Rich).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# White Dwarf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "##### Note: Used CO2 properties as a placeholder for N2O, since CoolProp doesn't do N2O viscosity.\n",
    "\n",
    "### Operating conditions:\n",
    "- Chamber pressure: 15 bar\n",
    "- Thrust: 7 kN\n",
    "- Ambient pressure: 1 atm\n",
    "- O/F ratio: 7 (by mass)\n",
    "- Oxidiser: 100% N2O\n",
    "- Fuel: 100% IPA\n",
    "\n",
    "### Chamber geometry:\n",
    "- Inner wall thickness: 1 mm\n",
    "- Chamber diameter: 200 mm\n",
    "- L*: 1.2\n",
    "\n",
    "### Cooling jacket:\n",
    "- Coolant: N2O\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": 14,
   "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 = 7                # Oxidiser/fuel mass ratio\n",
    "water_mass_fraction = 0    # 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 = 273.15 - 25                                 # Coolant inlet static temperature (K)\n",
    "inlet_p = 30e5                                        # Coolant inlet 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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 1921 thermo species\n",
      "Loaded 1031 propellants\n",
      "\n",
      "Tc = 3133.2752742068815 K\n",
      "gamma = 1.2411815312801315\n",
      "cp = 1595.4209600763534 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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-2a7076be2ff4>:21: UserWarning: The area ratio provided (2.7847392573028564) 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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  gri30:\n",
      "\n",
      "       temperature   3133.3 K\n",
      "          pressure   1.5e+06 Pa\n",
      "           density   1.5434 kg/m^3\n",
      "  mean mol. weight   26.805 kg/kmol\n",
      "   phase of matter   gas\n",
      "\n",
      "                          1 kg             1 kmol     \n",
      "                     ---------------   ---------------\n",
      "          enthalpy        9.7265e+05        2.6072e+07  J\n",
      "   internal energy            752.48             20170  J\n",
      "           entropy             10005        2.6818e+05  J/K\n",
      "    Gibbs function       -3.0375e+07       -8.1421e+08  J\n",
      " heat capacity c_p            1612.1             43212  J/K\n",
      " heat capacity c_v            1301.9             34897  J/K\n",
      "\n",
      "                      mass frac. Y      mole frac. X     chem. pot. / RT\n",
      "                     ---------------   ---------------   ---------------\n",
      "                H2         0.0012799          0.017017            -22.38\n",
      "                 H         0.0002982         0.0079299            -11.19\n",
      "                 O         0.0053229          0.008918           -15.512\n",
      "                O2          0.045738          0.038315           -31.025\n",
      "                OH          0.020132           0.03173           -26.702\n",
      "               H2O            0.1251           0.18614           -37.893\n",
      "               HO2        4.9253e-05             4e-05           -42.215\n",
      "              H2O2        3.7601e-06        2.9632e-06           -53.405\n",
      "                 C        3.4259e-12        7.6454e-12           -18.033\n",
      "                CH        6.7807e-13        1.3961e-12           -29.223\n",
      "               CH2        6.3269e-13         1.209e-12           -40.414\n",
      "            CH2(S)        6.8314e-14        1.3055e-13           -40.414\n",
      "               CH3         7.794e-13        1.3895e-12           -51.604\n",
      "               CH4        1.1749e-13        1.9631e-13           -62.794\n",
      "                CO           0.07199          0.068893           -33.546\n",
      "               CO2           0.16151          0.098369           -49.058\n",
      "               HCO        5.9402e-07        5.4872e-07           -44.736\n",
      "              CH2O        8.0223e-09        7.1617e-09           -55.926\n",
      "             CH2OH        9.5819e-13        8.2761e-13           -67.116\n",
      "              CH3O        2.5566e-14        2.2082e-14           -67.116\n",
      "             CH3OH        3.5776e-14        2.9929e-14           -78.306\n",
      "              HCCO        3.3546e-14        2.1916e-14           -62.769\n",
      "                 N        3.1297e-06        5.9892e-06           -13.244\n",
      "                NH        6.6506e-07        1.1873e-06           -24.434\n",
      "               NH2         1.687e-07        2.8221e-07           -35.624\n",
      "               NH3        9.8418e-08         1.549e-07           -46.814\n",
      "               NNH         2.497e-07        2.3063e-07           -37.678\n",
      "                NO          0.021793          0.019468           -28.756\n",
      "               NO2        2.9184e-05        1.7004e-05           -44.269\n",
      "               N2O        5.7563e-06        3.5057e-06               -42\n",
      "               HNO        4.5501e-06        3.9326e-06           -39.946\n",
      "                CN        8.7643e-10        9.0294e-10           -31.277\n",
      "               HCN        1.8545e-08        1.8394e-08           -42.467\n",
      "              H2CN        2.6857e-13         2.568e-13           -53.657\n",
      "              HCNO        3.5128e-12        2.1885e-12            -57.98\n",
      "              HOCN         1.896e-09        1.1812e-09            -57.98\n",
      "              HNCO        1.3624e-07        8.4877e-08            -57.98\n",
      "               NCO        3.3209e-08        2.1186e-08            -46.79\n",
      "                N2           0.54674           0.52314           -26.488\n",
      "     [  +14 minor]        1.3647e-14        9.0059e-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, \"CARBONDIOXIDE\")\n",
    "    except ValueError:\n",
    "        return PropsSI(\"PRANDTL\", \"Q\", 0, \"P\", p, \"CARBONDIOXIDE\")\n",
    "\n",
    "def mu_coolant(T, p):\n",
    "    try:\n",
    "        return PropsSI(\"VISCOSITY\", \"T\", T, \"P\", p, \"CARBONDIOXIDE\")\n",
    "    except ValueError:\n",
    "        return PropsSI(\"VISCOSITY\", \"Q\", 0, \"P\", p, \"CARBONDIOXIDE\")\n",
    "\n",
    "def k_coolant(T, p):\n",
    "    try:\n",
    "        return PropsSI(\"CONDUCTIVITY\", \"T\", T, \"P\", p, \"CARBONDIOXIDE\")\n",
    "    except ValueError:\n",
    "        return PropsSI(\"CONDUCTIVITY\", \"Q\", 0, \"P\", p, \"CARBONDIOXIDE\")\n",
    "\n",
    "def rho_coolant(T, p):\n",
    "    try:\n",
    "        return PropsSI(\"DMASS\", \"T\", T, \"P\", p, \"CARBONDIOXIDE\")\n",
    "    except ValueError:\n",
    "        return PropsSI(\"DMASS\", \"Q\", 0, \"P\", p, \"CARBONDIOXIDE\")\n",
    "\n",
    "def cp_coolant(T, p):\n",
    "    try:\n",
    "        return PropsSI(\"CPMASS\", \"T\", T, \"P\", p, \"CARBONDIOXIDE\")\n",
    "    except ValueError:\n",
    "        return PropsSI(\"CPMASS\", \"Q\", 0, \"P\", p, \"CARBONDIOXIDE\")\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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fuel mass flow rate = 0.4282631139360066 kg/s\n",
      "Oxidiser mass flow rate = 2.997841797552046 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"
    }
   ],
   "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_ox = mdot_f * OF_ratio\n",
    "mdot_coolant = mdot_ox   \n",
    "print(f\"Fuel mass flow rate = {mdot_f} kg/s\")\n",
    "print(f\"Oxidiser mass flow rate = {mdot_ox} 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/ox_rich/engine.svg\")\n",
    "plt.savefig(\"img/ox_rich/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": 20,
   "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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g15vZfWY23MxOr5rykkxE0mXDBli0KNzELgZnnBGeiJ88OekkJSvuJaxzgYOA5nx1CcuBx3MZSkRSaMGCMKRssRSQwYOhZUsYPRqOOy7pNCUpbgHp5u4F7J9ZRFJjzpzwmsQT6LVp2zZ08f7443DHHWHsdMmpuEd0ipkdnJckIpJuc+eG7tT33TfpJF8580xYsiQ8mS45F7eA9ANmm9lbasYrItuYOxcOPTQMJFUshgyB5s3hsceSTlKS4l7CGpSXFCKSbu7hEtYZZySdZFsdOsCJJ4b7ILfcEh4ylJyJdQZSvemumvGKyJeWLoWVK4vnBnp1Z54J77//1VPykjO6qyQiDVc1gFOx3ECvbtiwcFlNl7FyTgVERBquqgXWYUXYSHOXXaBPH3jxxaSTlJxIBcTMrjCzXmYW956JiDQGc+fC3nuHew7FqH//0E/X+vVJJykpUc9AOgO/A5aZ2UQz+6WZfcPMEu7wRkSKwty5xXn5qkqfPrBli+6D5FikAuLu/+XufYDdgZ8CK4HzgPlmtiDqzszsATNbZma1dg1vwZ1m9nammXCCfUKLSCQbN8KbbxbnDfQqZWXhddWqZHOUmLj3QFoBOwLtM9NSYGqM9UdSd1PgU4D9M9MI4I8x84lIoS1cGDosLOYC0qZNeP3882RzlJhI9zTM7M/AIcA6QsF4Dfitu8cq5+7+ipl1qWORYcBD7u6Ep947mNke7v5xnP2ISAEVcwusKq1bh1fdA8mpqGcgewM7AJ8AS4DFwOo85OkEfFTt8+LMPBEpVnPmQKtWsN9+SSfZvqoCojOQnIp0BuLug8zMCGchfYCrgEPNbCXwurtfn6M8tT0m6rUuaDaCcJmLsrIyJk6cmKMI2auoqCiKHFGkJWtackLjzXr4K6/QbO+9mTlpUk62V1MusjbZuJFjgXfmzeOjPP2OGuXv391jTYQWWd8itMp6B1gdc/0uwPztfPcnYHi1z28Be9S3zZ49e3oxmDBhQtIRIktL1rTkdG/EWXff3f2cc3K3vRpyknXrVndwv+66hm9rO9L4+weme8waUH2K+hzIZWY2ysw+Al4BhmT+uJ8O5LIp71PA9zOtsXoDa1z3P0SK14oV8MknoRPFYtakSbjMpktYORX1wcAuwGPAjxryB93MHgGOBzqa2WLgesLgVLj7PcBYYDDwNvA5YQArESlW5eXhtdgLCISWWCogORW1gFyVOd3ZLjOz+pZx9+H1fO/AxREziUjS0lRAWrdWAcmxqK2wJpjZpWa2d/WZZtbCzE4wsweBH+Q+nogUtfnzoX172HPPpJPUr3VrNePNsahnIIMIT54/YmZdCU14WwJNgeeB2919dj4CikgRmz8/nH2kYZwNXcLKuajNeDcCdwN3m1lzoCOwwd1X5zGbiBQz91BA/v3fk04SjS5h5Vzs3nXdfTOgllEijd0nn4S+pQ45JOkk0bRurb6wckzjgYhIduZn+kRNww100CWsPFABEZHspLGArFoVLr1JTsQqIGZ2cC3zjs9VGBFJkfJy2G032HXXpJNEc8wxsGQJzJ6ddJKSEfcM5P/M7L8zT4q3MrPfA7/KRzARKXLz56fn/gfAt74FLVrAQw8lnaRkxC0gRwN7Ebpzn0YYD6RvrkOJSJGrrAxnIGm5fAWw884wdCj89a+weXPSaUpC3AKyGdhAGFiqJfCeu1fmPJWIFLcPP4SKinQVEIBzzoHly+HZZ5NOUhLiFpBphALSC+gHDDezx3KeSkSKW1UXJmm6hAUwaBDssQfcf3/SSUpC3AJyvrtf5+6b3f0Tdx8GPJmPYCJSxKpaYKWtgDRrFs5Cxo4NN9SlQeIWkMFmdl31Ceiaj2AiUsTmz4fOnaFDh6STxHfeeeEezoMPJp0k9eIWkPXVpq3AKYSu3kWkMUlbC6zq9tsPjj8+XMaq1C3chohVQNz9tmrT/xDG9tCY5SKNydatsHBh+m6gV3fBBfDuu/Dyy0knSbWGPoneGtg3F0FEJCXeeQe++CLdBeT000M39Pfdl3SSVIv7JPo8M5ubmcoJw9r+Lj/RRKQopa0Lk9q0agXf+x6MHg0rVyadJrXinoEMAYZmppOBPd39rpynEpHiVdWE9+tfTzZHQ/3Hf4Qzqb/8JekkqRX3HsgH1aYl7r4lX8FEpEjNnw/77hs6J0yzbt2gb1/44x91Mz1LkQqIma0zs7W1TOvMbG2+Q4pIEakahbAUXHJJuKfz/PNJJ0mlqGcgT7r7jsB17r5jtaldZr6INAabNsGiReltwlvT6adDWRn84Q9JJ0mlqAWkh5ntA5xrZjuZ2c7Vp3wGFJEismgRbNlSOmcgLVrAiBGhb6z33ks6TepELSB/Ap4DDgJm1Jim5yeaiBSdtPaBVZcLL4QmTeCee5JOkjqRCoi73+nuXwcecPd93b1rtUnPgYg0FvPnhz+2Bx6YdJLc6dQJTj01PJm+YUPSaVIlbiusH+YriIikQHl56AqkZcukk+TWJZfAZ5+FsUIkMo2JLiLRpW0QqaiOOw569IDf/lZNemNQARGRaDZuhLffLq37H1XM4MorQx9f48YlnSY1VEBEJJo33wz/d16KBQTCmOmdOsFttyWdJDXi9oX16yjzRKQEVbXAKsVLWADNm8Nll8GLL8Ls2UmnSYW4ZyAn1TLvlDgbMLNBZvaWmb1tZlfX8n17M3vazOaYWbmZnRszo4jkQ3l5GNFv//2TTpI/I0ZA27bhXojUK2pXJj80s3nAgdV6451rZu8B86LuzMyaAn8gFJ2DCWOqH1xjsYuBBe7ejTDeyG1m1iLqPkQkT+bPhwMOCA/flaoOHeD88+GRRzTkbQRRz0D+RuiB9ym+6o13KNDT3b8bY39HAW+7+7vuvgkYBQyrsYwD7czMgLbASkCdNookrVRbYNV0xRXgrnshEUR9kHCNu7/v7sOBtUAZsA9wqJkdG2N/nYCPqn1ezL+OaHgX8HVgKeHs5nJ3V7s6kSStXx+6+ijVG+jVdekC3/0u/OlPsGJF0mmKWrM4C5vZBcDlQGdgNtAbeB04IeomapnnNT4PzGz7BOBrwAtmNsndt+n118xGACMAysrKmDhxYsQI+VNRUVEUOaJIS9a05ITSztrurbfo6c58d1YU+GdM4ri2PuEEej38MB9ceSXvn3depHVK+fe/Xe4eeSKcEbQEZmc+HwQ8GmP9Y4Bx1T5fA1xTY5lngf7VPr8EHFXXdnv27OnFYMKECUlHiCwtWdOS073Es44c6Q7uCxfmJU9dEjuuZ5zh3r69++rVkRZP4+8fmO4xakDNKW4rrI3uvhHAzHZw9zeBOJ3iTAP2N7OumRvj3ybcV6nuQ+DfMvsoy2z/3Zg5RSSXysvDzfP99ks6SeFcey2sWQN33510kqIVt4AsNrMOwBOES0tPEu5VROJhBMNLgHHAQuD/3L3czC4ys4syi90E9Mm0+noR+G9314VIkSTNnw8HHRSa8TYWPXrAKafA7bfD558nnaYoxfrX4O6nZd7eYGYTgPaEbt7jbGMsMLbGvHuqvV9KGG9dRIpFeXkY/rWxufZa6NcvDHt71VVJpyk6cZ9ENzP7npld5+4vE252d89HMBEpEuvWwYcfNo4mvDX17Qsnnww33wwVFUmnKTpxL2HdTbgRPjzzeR3hwUARKVULFoTXxtCEtzY33RSa8955Z9JJik7cAnK0u18MbARw91VACT+WKiLMnx9eG2sBOeooGDoUbrkFVq9OOk1RiVtANme6I3EAM9sV0EN+IqWsvBxatYKuXZNOkpwbbwzFQ0+nbyNuAbkTGAOUmdn/AJOBX+Y8lYgUj/Jy+PrXoWnTpJMkp3t3OOssuOMOWL486TRFI+6Qtn8FfkIoGkuBU9397/kIJiJFYv78xnv5qrqf/zw05/3Nb5JOUjTitsLaATiC0Hx3F+AsM7suH8FEpAisXg1LlzbOFlg1ff3roY+su+6Cjz6qf/lGIO4lrCcJveduAdZXm0SkFFUNIqUzkODGG0NPvddem3SSohD3sdLO7j4oL0lEpPg09hZYNXXpApdfHi5jXXEFHHFE0okSFfcM5DUzOywvSUSk+JSXQ5s2sPfeSScpHj/9KXTsGJ5M95qdiTcuUUcknGdmc4F+wMzMkLRzq80XkVJUXh7OPprE/X/NEta+PdxwA0ycCM88k3SaREW9hDUkrylEpPi4w7x54SE62daIEfD738OPfwyDBkHz5kknSkTUEQk/qGvKd0gRScCSJeGZh0Z+nb9WzZuH+yBvvQV//nPSaRKj81IRqd3MmeFVBaR2Q4fCgAHws5812qFvVUBEpHYzZ4IZHH540kmKk1m4jLVuHVxzTdJpEqECIiK1mzkzDCLVpk3SSYrXIYeEZr3330+7ql6LG5GorbDWmdnaWqZ1ZrY23yFFJAEzZ+ryVRTXXw+7784Bv/sdbN2adJqCinoTvZ2771jL1M7dd8x3SBEpsGXLwk10FZD6tWsHt95Ku0WL4L77kk5TULEvYZnZTmZ2lJkdWzXlI5iIJGjWrPCqAhLN8OGs7tYtPGTYiG6ox+1M8QLgFWAc8PPM6w25jyUiiapqgdW9e6IxUsOMRZdfDmvXwpVXJp2mYOKegVwO9AI+cPcBQA9AneOLlJoZM2DffaFDh6STpMbnXbvC1VfDww/Dc88lHacg4haQje6+EULX7u7+JnBg7mOJSKKmTg1DuUo8/+//hZZrF14IFRVJp8m7uAVksZl1AJ4AXjCzJwkDS4lIqViyBBYvht69k06SPjvsEG6kf/RRo+jyPe6IhKe5+2p3vwH4GXA/cGoecolIUqZODa8qINnp2xf+8z/DQ4avv550mrzK+kFCd3/Z3Z9y9025DCQiCZsyBVq00A30hvjVr6BzZzj/fNi4Mek0eRP1QcLJmdd11R8g1IOEIiVo6lTo0SNcjpHstGsH994LCxeW9KWsqA8S9su8tqv+AKEeJBQpMVu2wLRpunyVCwMHwg9/CLffHsYOKUGRxgMxszobNrv7b3MTR0QSNW8ebNigApIrt9wC48fDD34Qju2OpfX/21HvgbTLTEcCPwQ6ZaaLgIPzE01ECq7qBvrRRyebo1S0aQMPPRRatV1+edJpci7qJayfu/vPgY7AEe5+lbtfBfQEOuczoIgU0OTJsPvu0KVL0klKR+/eoYuTkSNhzJik0+RU3FZYewPVW11tArrE2YCZDcqMqf62mV29nWWON7PZZlZuZi/HzCgi2Zo0Cfr3D2NdSO5cdx307BlaZX34YdJpciZuAXkYeMPMbjCz64GpwINRVzazpsAfgFMIl76Gm9nBNZbpANwNfNPdDwHOiplRRLLxwQfhj9ux6h8155o3h1GjQiOF4cNh8+akE+VE3AcJ/wc4F1gFrAbOdfdfxdjEUcDb7v5u5vmRUcCwGst8B3jc3T/M7HNZnIwikqVXXgmvKiD5sd9+Yfz0114LY4iUAHP3wu3M7ExgkLtfkPl8NnC0u19SbZk7gObAIYQb979z94dq2dYIYARAWVlZz1GjRuX/B6hHRUUFbdu2TTpGJGnJmpackP6sB9x6K7u+/DKvPvkkNCmewUrTclyj5jzg1lvZY+xY5v7mN6w68sgCJPtXVVkHDBgww92zD+HusSZgJ8KZxLFVU4x1zwLuq/b5bOD3NZa5C5gCtCHctP8ncEBd2+3Zs6cXgwkTJiQdIbK0ZE1LTvcSyHrgge5DhhQ8S33Sclwj51y/3v2QQ9x328196dK8ZtqeqqzAdI9ZA6pPhR4PZDGwV7XPnfnXzhgXA8+5+3p3X5HZX7c4OUUkpk8/hbfe0uWrQmjdGh59NPTWe9ZZsCm9vUEVejyQacD+ZtbVzFoA3waeqrHMk0B/M2tmZq2Bo4GFMXOKSByTJoXX/v2TzdFYHHIIPPAAvPoqXHVV0mmyFulJ9Go2uvtGM/tyPBAzizweiLtvMbNLCGcuTYEH3L3czC7KfH+Puy80s+eAuUAl4ZLX/Jg5RSSO8eND/009eyadpPH41rdCtzG33Qa9esH3v590otjiFpCa44GsApbE2YC7jwXG1ph3T43PtwC3xMwmItkaPx4GDAjNTaVwbr45DB984YVw6KGpG4M+F+OBJN/8SUSy99578M47cOKJSSdpfJo1C/dDdt0VTjst3ItKkajdue9oZteY2V1mdrKZGXA48HvgzLwmFJH8Gj8+vKqAJGPXXeGJJ2DFChg2LHRmmRJRz0AeJox9Pg+4AHieUDiGuXvNBwFFJE3Gj4dOncJY3pKMI46A//1feOMNOPdcqKxMOlEkUe+B7OvuhwGY2X3ACmBvd1+Xt2Qikn+VlfDiizBkiPq/Stppp4V7Iv/937D//nDTTUknqlfUAvJlxy3uvtXM3lPxECkBs2bBZ5/p8lWx+PGPYdEi+MUv4IAD4Oyzk05Up6gFpFu1oWsNaJX5bIC7RiUUSaennw5nHoMGJZ1EIPwu7r4b3n0XLrggjKs+YEDSqbYr6nggTT0MYVs1jG0z15C2Iun3zDNwzDHQsWPSSaRKixYwenS4jDVsWDhLLFLF02OaiBTW0qUwYwYMHZp0Eqlpp51g3LjwOmgQvP120olqpQIi0lg9+2x4HTIk2RxSu06d4PnnQ0OHk0+Gjz9OOtG/UAERaayefjoMXXvIIUknke058EAYOxaWLQtnIqtXJ51oGyogIo1Qky++CM9/qPlu8evVK4ylvnAhnHIKrF1b/zoFogIi0gh1mDkzPPGs+x/pcNJJocuTadNg8ODQFXwRUAERaYQ6vvYatGkDxx2XdBSJ6rTT4JFHYMoU+MY3YP36pBOpgIg0Olu20HHSpHD5aocdkk4jcZx1Fjz8MEyeDN/8Jnz+eaJxVEBEGpsJE2ixZk0Yj0LSZ/hwGDkSJkyASy9NNIoKiEhj8+ijbGnVKtyQlXQ6++wwANWYMbB1a2IxVEBEGpNNm+Dxx/msb19o2TLpNNIQAwfCqlXhYdCEqICINCYvvgirVrGsiPtXkohOPDF0wb9qVWIR4g5pKyJp9uij0L49K488Mukk0lC77hqeDUmQzkBEGouNG8PId6eeirdokXQaKQEqICKNxZNPwpo18J3vJJ1ESoQKiEhj8Ze/wF57wb/9W9JJpESogIg0Bh99FHp2PeccaNo06TRSIlRARBqDBx8E91BARHJEBUSk1FVWhstXxx8P++6bdBopISogIqVu0qQwxvZ55yWdREqMCohIqfvTn2DHHeH005NOIiVGBUSklC1dCn//ezj7aNMm6TRSYlRARErZn/4UOtu7+OKkk0gJKngBMbNBZvaWmb1tZlfXsVwvM9tqZmcWMp9Iydi0KRSQwYNhv/2STiMlqKAFxMyaAn8ATgEOBoab2cHbWe7XwLhC5hMpKX//O3z6aeJjRkjpKvQZyFHA2+7+rrtvAkYBw2pZ7lJgNLCskOFESoY73HknHHhgGE9bJA/M3Qu3s3A5apC7X5D5fDZwtLtfUm2ZTsDfgBOA+4Fn3P2xWrY1AhgBUFZW1nPUqFEF+AnqVlFRQdu2bZOOEUlasqYlJxRX1g6zZ9P9Rz9i0RVXsHTYv/4/WjFlrU9asqYlJ3yVdcCAATPcPfuumd29YBNwFnBftc9nA7+vsczfgd6Z9yOBM+vbbs+ePb0YTJgwIekIkaUla1pyuhdZ1pNOci8rc9+wodaviyprPdKSNS053b/KCkz3BvxNL/R4IIuBvap97gwsrbHMkcAoMwPoCAw2sy3u/kRBEoqk3bRp8MIL8JvfaNRByatCF5BpwP5m1hVYAnwb2KZvaXfvWvXezEYSLmE9UcCMIun2y19Chw5w0UVJJ5ESV9Cb6O6+BbiE0LpqIfB/7l5uZheZmf61izRUeXkYNOqyy6Bdu6TTSIkr+JC27j4WGFtj3j3bWfacQmQSKRk33ABt24YCIpJnehJdpFTMmAGPPQZXXQW77JJ0GmkEVEBESsW114bCceWVSSeRRqLgl7BEElVZ+dW0deu2rw2ct2N5OTRvHn/dXOx/2TIYNw5uuSX0vCtSACogueAOlZXYli3wxRc5/8OU9bw6vuv01lswZ05i+48678i1a6FVq9xlyqMj8rr1epjBmWfq3ocUVOMuIL/9Ldx7b8P/MGWe5j8u4R8njv2jLti0KTRpEqaq93HnRVm+efNal9uwahVtd9utYfsv0Ly55eUc3r174bM0aRIKiEiBNe4CUlYGhx+es/+Y3/vgA7rut1/R/EGra96rU6bQt3//upcrgj9K5RMncvzxxycdI5KVbdqEYWNFGonGXUC++90w5cgHEyfSNSV/QDa3bw8775x0DBFJMbXCEhGRrKiAiIhIVlRAREQkKyogIiKSFRUQERHJigqIiIhkRQVERESyogIiIiJZMc90w5FmZrYc+CDpHIQheFckHSKitGRNS05Q1nxJS9a05ISvsu7j7rtmu5GSKCDFwsymu/uRSeeIIi1Z05ITlDVf0pI1LTkhd1l1CUtERLKiAiIiIllRAcmtPycdIIa0ZE1LTlDWfElL1rTkhBxl1T0QERHJis5AREQkKyogIiKSFRWQmMxsZzN7wcz+mXndqZZl9jKzCWa20MzKzezyat/dYGZLzGx2ZhpcpDnrXb+QWTPLPWBmy8xsfo35BTmmOcpajMd1kJm9ZWZvm9nV1ebn9bhub7/VvjczuzPz/VwzOyLqurnWwKzvm9m8zDGcXgRZDzKz183sCzP7rzjr/gt31xRjAn4DXJ15fzXw61qW2QM4IvO+HbAIODjz+Qbgv1KQs971C5k1892xwBHA/BrzC3JMc5S1qI4r0BR4B9gXaAHMKcS/1br2W22ZwcA/AAN6A1OjrlssWTPfvQ90LNC/zyhZdwN6Af9T/febzXHVGUh8w4AHM+8fBE6tuYC7f+zuMzPv1wELgU6FCpjR0Jz1rl/IrADu/gqwMo85omho1mI7rkcBb7v7u+6+CRiVWS/foux3GPCQB1OADma2RwKZG5K10OrN6u7L3H0asDnuujWpgMRX5u4fQ/gDTKjm22VmXYAewNRqsy/JnOY+kMdLGA3NGWv9BsrFvgpxTKHhWYvtuHYCPqr2eTHb/s9Ovo5rffuta5ko6+ZSQ7ICOPC8mc0wsxF5S1l/jpyv2yxWtEbCzMYDu9fy1bUxt9MWGA1c4e5rM7P/CNxE+Ed1E3AbcF4R5sypXGXdjpwdU8h71pzKQVarZV5V2/6cHtcY+61vmSjr5lJDsgL0dfelZrYb8IKZvZk5Q82Hhhyb2OuqgNTC3U/c3ndm9qmZ7eHuH2dOUZdtZ7nmhD/Kf3X3x6tt+9Nqy9wLPFOMOYFI6xcyax3bztkxzWwvb1kpvuO6GNir2ufOwNLMtnN6XKPuN8IyLSKsm0sNyYq7V70uM7MxhEtF+SogUbLmbF1dworvKeAHmfc/AJ6suYCZGXA/sNDdf1vju+rXRU8DtmmlUyw5o6yfQw3aVwGPKTT8uBTbcZ0G7G9mXc2sBfDtzHr5Pq7b3W+N/N/PtHDqDazJXIqLsm4uZZ3VzNqYWTsAM2sDnEx+/3025NjEX7cQLQNKaQJ2AV4E/pl53Tkzf09gbOZ9P8Kp31xgdmYanPnuYWBe5rungD2KNGet6yeVNfP5EeBjws2/xcD5hTymOcpajMd1MKEF3jvAtdXm5/W41rZf4CLgosx7A/6Q+X4ecGR9mfN4LLPKSmjRNCczlRdJ1t0z/ybXAqsz73fM5riqKxMREcmKLmGJiEhWVEBERCQrKiAiIpIVFRAREcmKCoiIiGRFBURSy8xOMzM3s4MiLHukmd1ZzzLHm1kuH5aruf37zOzgzPuf1vjutRzu5w4zOzbG8rua2XO52r80HiogkmbDgcmEB57q5O7T3f2y/EeqM8MF7r4g8/GnNb7rk4t9mNnOQG+P0VWGuy8HPjazvrnIII2HCoikUqb/rr7A+VQrIJmzkvGZJ4L3MLNFZrZ79bMLMzvKzF4zs1mZ1wPr2dc5ZvakmT1nYayE66t9d6WZzc9MV2TmtTGzZ81sTmb+tzLzJ2bOhG4GWlkYH+Kvme8qMq9mZrdk1ptXbd3jM+s/ZmZvmtlfMz0J1HQm8OXZhIWxKH5pYfyH6WZ2hJmNM7N3zOyiaus9AXw36vEXAfWFJel1KvCcuy8ys5VmdoS7z3T3MWZ2BnAxMAi43t0/qXGZ603gWHffYmYnAr8Ezqhnf0cBhwKfA9PM7FnCU/znAkcTnkSeamYvE54+Xuru3wAws/bVN+TuV5vZJe7evZb9nA50B7oBHTP7qjqb6AEcQuif6FVCAZ1cY/2+wGM15n3k7seY2e3AyMwyLQlPRt+TWWY68It6joHINlRAJK2GA3dk3o/KfJ6Z+Xwpob+hKe7+SC3rtgceNLP9CUWgeYT9veDunwGY2eN81Q3MGHdfX21+f8IZwK1m9mvgGXefFOPn6gc84u5bCR0vvkwY/Gct8Ia7L87sazbQhX8tIHsAy2vMq+rPaB7Q1sPYL+vMbKOZdXD31YSOFveMkVNEBUTSx8x2AU4ADjUzJ4yk5mb2Ew9983QCKoEyM2vi7pU1NnETMMHdT7MwDsrECLut2efP9roVJ3NW1JPQr9CvzOx5d78x6o9Xx3dfVHu/ldr/+91AOLuobb3KGtuorLaNlpl1RSLTPRBJozMJo7/t4+5d3H0v4D2gn5k1A/4CfIcwwuKVtazfHliSeX9OxH2eZGGM8VaEy2evErrkPtXMWmd6Wj0NmGRmewKfu/v/ArcShratabOFrvRregX4lpk1NbNdCUPjvhExI4Sfeb8Yy1c5gPz2EislSGcgkkbDgZtrzBtNKBoDgEnuPilzmafqfkV1vyFcwroSeCniPicTeqfdD/ibu08HMLORfPUH/j53n2VmA4FbzKyS0BvvD2vZ3p+BuWY2092r37weAxxD6L3VgZ/Ucg+nLs8CFwL3RVy+yoDMuiKRqTdekXqY2TmE7rkvSTpLFGY2GRiSubcRdZ1XgGHuvipvwaTk6BKWSOm5Ctg76sKZS2W/VfGQuHQGIiIiWdEZiIiIZEUFREREsqICIiIiWVEBERGRrKiAiIhIVv4/m7EsDJxbg+4AAAAASUVORK5CYII=\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, \"CARBONDIOXIDE\"), \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/ox_rich/temperatures.svg\")\n",
    "plt.savefig(\"img/ox_rich/temperatures.pdf\")\n",
    "bam.show()\n",
    "\n",
    "# Plot coolant pressures\n",
    "bam.plot.plot_p_coolant(cooling_data)\n",
    "plt.savefig(\"img/ox_rich/p_coolant.svg\")\n",
    "plt.savefig(\"img/ox_rich/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/ox_rich/stresses.svg\")\n",
    "plt.savefig(\"img/ox_rich/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/ox_rich/stresses_with_limits.svg\")\n",
    "plt.savefig(\"img/ox_rich/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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "OPERATING CONDITIONS\n",
      "--------------------\n",
      "Mass flow rate = 3.470 kg/s (frozen flow - PyProPEP)\n",
      "Mass flow rate = 3.426 kg/s (Bamboo - perfect gas)\n",
      "\n",
      "Sea level thrust     = 6.929 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        = 206.237 s     (Bamboo - perfect gas)\n",
      "Sea level Isp        = 205.703 s     (frozen flow - PyProPEP)\n",
      "Sea level Isp        = 215.501 s     (equilibrium flow - PyProPEP)\n",
      "\n",
      "Chamber Temperature  = 3133.275 K\n",
      "Gamma                = 1.241\n",
      "cp                   = 1595.421 J/kg/K\n",
      "\n",
      "\n",
      "GEOMETRY\n",
      "--------\n",
      "Chamber length       = 210.0191 mm (injector face to start of converging section)   \n",
      "Nozzle length        = 146.6118 mm  (start of converging section to exit)\n",
      "Total engine length  = 356.6308 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          = 0.00338 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        = 32.8 mm  (PyProPEP - equilibrium flow)\n",
      "\n",
      "Exit area            = 0.00955 m^2  (Bamboo - perfect gas)\n",
      "Exit area            = 0.00955 m^2  (PyProPEP - frozen flow)\n",
      "Exit area            = 0.01106 m^2  (PyProPEP - frozen flow)\n",
      "\n",
      "Exit radius          = 55.1 mm  (Bamboo - perfect gas)\n",
      "Exit radius          = 55.1 mm  (PyProPEP - frozen flow)\n",
      "Exit radius          = 59.3 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",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}