Launcher_Design_Problem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <h1><center> <font color='red'> Two-Stage-To-Orbit design problem</font> </h1></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> L. BREVAULT (ONERA/DTIS), M. BALESDENT (ONERA/DTIS), G. SIRE (ONERA/DTIS)</center>\n",
    "<center> loic.brevault@onera.fr, mathieu.balesdent@onera.fr, glen.sire@onera.fr </center>\n",
    "<center> Nov 2021  </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> Problem description:</font>\n",
    "\n",
    "- <font color='blue'> Simulation</font> of a **Two-Stage-To-Orbit expendable launch vehicle** using four coupled disciplines:\n",
    "    - Propulsion,\n",
    "    - Aerodynamics,\n",
    "    - Structure and sizing,\n",
    "    - Trajectory.\n",
    "- <font color='blue'> Mission</font>: injection of a **5t** payload into a **700km x 700km** circular orbit (equatorial)\n",
    "- <font color='blue'> Trajectory simulation </font> in the local geographic frame, 3D, non-rotating Earth:\n",
    "<center>\n",
    "$\\dot{r} = V \\sin(\\gamma)$\n",
    "</center> \n",
    "<center>\n",
    "$\\dot{V} = -\\frac{1}{2m} \\rho S_{ref} C_X V^2 -g(r) \\sin(\\gamma) + \\frac{T}{m} \\cos(\\theta-\\gamma) +\\omega_E^2 r \\sin(\\gamma)$ \n",
    "</center>\n",
    "<center>   \n",
    "$\\dot{\\gamma} = \\left(\\frac{V}{r} - \\frac{g(r)}{V}\\right) \\cos(\\gamma) + \\frac{T}{mV} \\sin(\\theta - \\gamma)+ 2\\omega_E + \\frac{\\omega_E^2 r \\cos(\\gamma)}{V} + \\frac{L}{mV}$\n",
    "</center>    \n",
    "<center>\n",
    "$\\dot{l} = \\frac{V}{r} \\cos(\\gamma) $\n",
    "</center> \n",
    "<center>       \n",
    "$\\dot{m} = - q $\n",
    "</center>    \n",
    "where:\n",
    "$r$: altitude (m),\n",
    "$V$: norm of velocity (m/s),\n",
    "$\\gamma$: flight path angle (rad),\n",
    "$l$: longitude (rad),\n",
    "$m$: mass (kg),\n",
    "$\\theta$: pitch angle (rad),\n",
    "$S_{ref}$: surface of reference ($m^2$),\n",
    "$T$: thrust (N),\n",
    "$\\rho$: air density ($kg/m^3$),\n",
    "$C_X$: drag coefficient,\n",
    "$g(r)$: Earth gravity ($m/s^2$),\n",
    "$q$: mass flow rate ($kg/m^3$) and $\\alpha$: angle of attack (rad).\n",
    "\n",
    "<img src=\"angles_definition.png\" alt=\"Drawing\" style=\"width: 300px;\"/>\n",
    "\n",
    "- <font color='blue'> Trajectory phases and control </font>:\n",
    "    - Lift-off: $\\theta = \\pi/2$\n",
    "    - Pitch-over maneuver: $\\theta(t) = \\gamma - \\frac{\\Delta_{\\theta} * t}{\\Delta_t}$\n",
    "        - where $\\Delta_{\\theta}$ is the variation (in rad) of $\\theta$ during the maneuver and $\\Delta_t$ is the duration of the maneuver (in s)\n",
    "    - Gravity turn: return to angle of attack of zero and then $\\theta = \\gamma$ ($\\alpha = 0.$)\n",
    "    - Exo-atmospheric command for first stage: $\\theta$ is optimized by a linear interpolation control law (parameters are waypoints of $\\theta$ as a function of time, 2 parameters are used: $\\theta$ at the time at which the exo-atmospheric phase begins and $\\theta$ at the end of the first stage flight)\n",
    "    - Separation between the first and second stages\n",
    "    - Second stage flight (bi-linear tangent command): $\\tan(\\theta) = \\frac{a^\\xi \\tan(\\theta_i)+(\\tan(\\theta_f)-a^\\xi \\tan(\\theta_i))t}{a^\\xi+(1-a^\\xi)t}$\n",
    "        - where $a$ is a constant, $\\xi$ a parameter defining the shape of the bi-linear tangeant law, $\\theta_i$ and $\\theta_f$ the pitch angles at the beginning and at the end of the phase, see [F. Castellini PhD thesis](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=2ahUKEwiYq9yN9urhAhWaAmMBHeMHDmQQFjABegQIARAC&url=https%3A%2F%2Fwww.politesi.polimi.it%2Fbitstream%2F10589%2F56841%2F1%2F201203_PhD_Castellini.pdf&usg=AOvVaw14PiJUsq8IUSFMbhVEfBef) page 69\n",
    "    - Coast phase (balistic flight to reach the final orbit -- not simulated)\n",
    "    - Orbit circularization boost (-- not simulated)\n",
    "\n",
    "<img src=\"trajectory_phase.png\" alt=\"Drawing\" style=\"width: 600px;\"/>\n",
    "\n",
    "\n",
    "- <font color='blue'> Propulsion</font>: simplified model based on [M. Balesdent PhD thesis](https://tel.archives-ouvertes.fr/tel-00659362/document) pages 100-101\n",
    "- <font color='blue'> Structure and sizing</font>: Mass Estimating Relations (MER) based on [F. Castellini PhD thesis](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=2ahUKEwiYq9yN9urhAhWaAmMBHeMHDmQQFjABegQIARAC&url=https%3A%2F%2Fwww.politesi.polimi.it%2Fbitstream%2F10589%2F56841%2F1%2F201203_PhD_Castellini.pdf&usg=AOvVaw14PiJUsq8IUSFMbhVEfBef) pages 79-85\n",
    "- <font color='blue'> Aerodynamics </font>: fixed drag table $C_X(\\text{Mach},\\alpha)$ as a function of Mach and angle of attack $\\alpha$ (rad) obtained for a representative TSTO launcher geometry using low fidelity aerodynamics code (e.g. MissileDATCOM).\n",
    "\n",
    "\n",
    "- Use of [OpenMDAO](http://openmdao.org/) to connect the different disciplines as a group\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> Problem simulation:</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import of general python librairy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "np.set_printoptions(threshold=sys.maxsize)\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import OpenMDAO + CMA-ES optimization algorithm\n",
    "\n",
    "-Required packages : \n",
    "    - pip install openmdao\n",
    "    - pip install cma\n",
    "    - pip install geopy\n",
    "    - pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openmdao.api as om\n",
    "import cma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import of specific Python functions for launch vehicle design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Launch_vehicle_Group\n",
    "import post_traitement\n",
    "import result_vizualization\n",
    "import constants as Cst\n",
    "import specifications as Spec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creation of an OpenMDAO problem using the launch vehicle group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<openmdao.core.problem.Problem at 0x2c35fb6fc40>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P_obj = om.Problem()\n",
    "P_obj.model=Launch_vehicle_Group.Launcher_vehicle()\n",
    "P_obj.setup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the N2 chart to visualize the connections between the disciplines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"700\"\n",
       "            src=\"n2.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2c35d15ad00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "om.n2(P_obj,outfile='n2.html',show_browser=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fixed Point Iteration (FPI)\n",
    "\n",
    "Enable the solving of interdisciplinary couplings between the 'trajectory' and the 'sizing and structure (struct_1)' disciplines. The feedback coupling \"Pdyn_max_dim\" is an input of the 'sizing and structure' discipline and \"max_pdyn_load_ascent_stage_1\" is an output of the trajectory discipline and these variables have to be equal to satisfy the interdisciplinary couplings. \n",
    "\n",
    "The variable \"Pdyn_max_dim\" is initialized to run the propulsion, the aerodynamics, the structure and then the trajectory disciplines and the output of the trajectory \"max_pdyn_load_ascent_stage_1\" is compared to the input of the structure \"Pdyn_max_dim\". If the difference is too large, the input of the structure \"Pdyn_max_dim\" is updated with the corresponding output of the trajectory discipline and a new evaluation of the disciplines is carried out.\n",
    "\n",
    "For the stage fallout: use variable 'is_fallout': if is_fallout=0: no simulation of stage 1 fallout else if is_fallout=1: simulation of stage 1 fallout (not running for the moment, see Project 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FPI(tuple_input):\n",
    "#    x = tuple_input[0]\n",
    "#    lowerbnd_exp = tuple_input[1]\n",
    "#    upperbnd_exp = tuple_input[2]\n",
    "    Pb = tuple_input\n",
    "#    \n",
    "    Pb.setup(check = False)\n",
    "#    XX = lowerbnd_exp + (upperbnd_exp - lowerbnd_exp)*x\n",
    "    Pb['Diameter_stage_1'] = 5.0\n",
    "    Pb['Diameter_stage_2'] = 5.0\n",
    "    Pb['Mass_flow_rate_stage_1'] = 250.\n",
    "    Pb['Mass_flow_rate_stage_2'] = 250.\n",
    "    Pb['Thrust_stage_1'] = 1000.\n",
    "    Pb['Thrust_stage_2'] = 800.\n",
    "    Pb['OF_stage_1'] = 5.0\n",
    "    Pb['OF_stage_2'] = 5.0\n",
    "    Pb['Pc_stage_1'] = 100.0\n",
    "    Pb['Pc_stage_2'] = 100.0\n",
    "    Pb['Pe_stage_1'] = 1.0\n",
    "    Pb['Pe_stage_2'] = 1.0\n",
    "    Pb['N_eng_stage_1'] = 8.\n",
    "    Pb['N_eng_stage_2'] = 1.\n",
    "    Pb['Prop_mass_stage_1'] = 320000.\n",
    "    Pb['Prop_mass_stage_2'] = 75000.\n",
    "    Pb['thetacmd_i'] = 2.72\n",
    "    Pb['thetacmd_f'] = 10.\n",
    "    Pb['ksi'] = 0.293\n",
    "    Pb['Pitch_over_duration'] = 5.\n",
    "    Pb['Exit_nozzle_area_stage_1'] = 0.79\n",
    "    Pb['Exit_nozzle_area_stage_2'] = 3.6305\n",
    "    Pb['Delta_vertical_phase'] = 10.\n",
    "    Pb['Delta_theta_pitch_over'] = 1.        \n",
    "    Pb['command_stage_1_exo'] = np.array([30.,-20.])\n",
    "    Pb['is_fallout']=0. #0: no simulation of stage 1 fallout, 1: simulation of stage 1 fallout\n",
    "\n",
    "    error = 100.\n",
    "    Pb['Pdyn_max_dim'] = 40.\n",
    "    k=0\n",
    "    while error>1. and k<20:\n",
    "        Pb.run_model()\n",
    "        error = abs(Pb['Pdyn_max_dim']-Pb['max_pdyn_load_ascent_stage_1']/1e3)\n",
    "        Pb['Pdyn_max_dim'] = Pb['max_pdyn_load_ascent_stage_1']/1e3\n",
    "        k = k+1\n",
    "        print('Fixed Point Iteration: ',k ,', error: ', np.round(error[0],2), ', Input pdyn max (kPa): ', np.round(Pb['Pdyn_max_dim'][0],2))\n",
    "    return Pb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixed Point Iteration:  1 , error:  3.83 , Input pdyn max (kPa):  36.17\n",
      "Fixed Point Iteration:  2 , error:  0.01 , Input pdyn max (kPa):  36.18\n"
     ]
    }
   ],
   "source": [
    "P_out = FPI(P_obj)\n",
    "GLOW, contraintes = post_traitement.post_traitement(P_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot outputs of trajectory simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_vizualization.plots_output(P_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output data from simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GLOW (t)</th>\n",
       "      <th>Dry_mass_stage_1  (t)</th>\n",
       "      <th>Dry_mass_stage_2  (t)</th>\n",
       "      <th>Prop_mass_stage_1 (t)</th>\n",
       "      <th>Prop_mass_stage_2 (t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>441.198</td>\n",
       "      <td>32.27</td>\n",
       "      <td>6.928</td>\n",
       "      <td>320.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GLOW (t)  Dry_mass_stage_1  (t)  Dry_mass_stage_2  (t)  \\\n",
       "0   441.198                  32.27                  6.928   \n",
       "\n",
       "   Prop_mass_stage_1 (t)  Prop_mass_stage_2 (t)  \n",
       "0                  320.0                   75.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [[P_out['GLOW'][0]/1e3,\n",
    "         P_out['Dry_mass_stage_1'][0]/1e3,\n",
    "         P_out['Dry_mass_stage_2'][0]/1e3,\n",
    "        P_out['Prop_mass_stage_1'][0]/1e3,\n",
    "        P_out['Prop_mass_stage_2'][0]/1e3]]\n",
    "df = pd.DataFrame(data, columns=[\"GLOW (t)\", \"Dry_mass_stage_1  (t)\", \n",
    "                                     \"Dry_mass_stage_2  (t)\", \"Prop_mass_stage_1 (t)\", \n",
    "                                     \"Prop_mass_stage_2 (t)\"])\n",
    "df.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> Optimization problem:</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{eqnarray}\n",
    "\\text{min} & & \\text{GLOW}(\\mathbf{z}) \\\\\n",
    "\\text{w.r.t} & & \\mathbf{z}=[\\text{M}_{prop1},\\text{M}_{prop2},\\theta_i, \\theta_f, \\xi, \\Delta_t, \\Delta_\\theta, t_{vertical}, \\theta_{cmd} ] \\\\\n",
    "\\text{s.t.} & & \\alpha_{ascent}(\\mathbf{z}) \\leq 15 \\\\\n",
    " & & NX_{ascent}(\\mathbf{z}) \\leq 4.5 \\\\\n",
    " & & Pdyn_{ascent}(\\mathbf{z}) \\leq 40. \\\\\n",
    " & & HeatFlux_{ascent}(\\mathbf{z}) \\leq 100. \\\\\n",
    " & & \\text{Apogee}(\\mathbf{z}) = 700. \\\\\n",
    " & & \\text{Perigee}_{CoastPhase}(\\mathbf{z}) \\geq 140. \\\\\n",
    " & & M_{Circularization}(\\mathbf{z}) \\geq 0. \\\\\n",
    " &&\\mathbf{z}_\\text{min} \\leq \\mathbf{z} \\leq \\mathbf{z}_\\text{max}\n",
    "\\end{eqnarray}\n",
    "\n",
    "with:\n",
    "- GLOW: Gross-Lift-Off Weight\n",
    "- $\\mathbf{z} \\in \\mathbb{R}^{10}$, in practice the input variables are normalized into $[0. ,1.]^{10}$\n",
    "- $\\text{M}_{prop1}$: Prop_mass_stage_1\n",
    "- $\\text{M}_{prop2}$: Prop_mass_stage_2\n",
    "- $\\theta_i$: thetacmd_i, pitch angle begining of bilinear phase \n",
    "- $\\theta_f$: thetacmd_f, pitch angle end of bilinear phase \n",
    "- $\\xi$: ksi, shape parameter of bilinear phase \n",
    "- $\\Delta_t$: Pitch_over_duration\n",
    "- $\\Delta_\\theta$: Delta_theta_pitch_over\n",
    "- $t_{vertical}$: Delta_vertical_phase\n",
    "- $\\theta_{cmd}$: command_stage_1_exo (vector of dimension 2)\n",
    "\n",
    "The constraints are defined in the specification.py file:\n",
    "- $\\alpha_{ascent}$: angle of attack during the ascent phase that must be below 15 degrees\n",
    "- $NX_{ascent}$: axial load factor during the ascent phase that must be below 4.5g\n",
    "- $Pdyn_{ascent}$: dynamic pressure during the ascent phase that must be below 40kPa\n",
    "- $HeatFlux_{ascent}$: heat flux during the ascent phase that must be below 100 $W/m^2$\n",
    "- $\\text{Apogee}$: Apogee altitude of the coast phase transition orbit must be equal to 700km\n",
    "- $\\text{Perigee}_{CoastPhase}$: Perigee altitude of the coast phase transition orbit must be above 140km\n",
    "- $M_{Circularization}$: remaining propellant mass before the circularization burn must be sufficient to circularize the final orbit 700 x 700km "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FPI for optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FPI_optim(x,lowerbnd_exp,upperbnd_exp,Pb):\n",
    "\n",
    "    Pb.setup(check = False)\n",
    "    XX = lowerbnd_exp + (upperbnd_exp - lowerbnd_exp)*x\n",
    "    Pb['Diameter_stage_1'] = 5.0\n",
    "    Pb['Diameter_stage_2'] = 5.0\n",
    "    Pb['Mass_flow_rate_stage_1'] = 300.\n",
    "    Pb['Mass_flow_rate_stage_2'] = 200.\n",
    "    Pb['Thrust_stage_1'] = 1000.\n",
    "    Pb['Thrust_stage_2'] = 1000. # not used\n",
    "    Pb['OF_stage_1'] = 5.\n",
    "    Pb['OF_stage_2'] = 5.5\n",
    "    Pb['Pc_stage_1'] = 80.0\n",
    "    Pb['Pc_stage_2'] = 60.0\n",
    "    Pb['Pe_stage_1'] = 1.0\n",
    "    Pb['Pe_stage_2'] = 1.0\n",
    "    Pb['N_eng_stage_1'] = 6.\n",
    "    Pb['N_eng_stage_2'] = 1.\n",
    "    Pb['Prop_mass_stage_1'] = XX[0]*1e3\n",
    "    Pb['Prop_mass_stage_2'] = XX[1]*1e3\n",
    "    Pb['thetacmd_i'] = XX[2]\n",
    "    Pb['thetacmd_f'] = XX[3] \n",
    "    Pb['ksi'] = XX[4]\n",
    "    Pb['Pitch_over_duration'] = XX[5]\n",
    "    Pb['Exit_nozzle_area_stage_1'] = 0.79\n",
    "    Pb['Exit_nozzle_area_stage_2'] = 3.6305\n",
    "    Pb['Delta_vertical_phase'] = XX[6]\n",
    "    Pb['Delta_theta_pitch_over'] = XX[7]        \n",
    "    Pb['command_stage_1_exo'] = XX[8:10]\n",
    "    Pb['is_fallout']=0. #0: no simulation of stage 1 fallout, 1: simulation of stage 1 fallout\n",
    "\n",
    "    error = 100.\n",
    "    Pb['Pdyn_max_dim'] = 40.\n",
    "    k=0\n",
    "    while error>1. and k<20: # Solving of Fixed Point Iteration via Gauss-Seidel\n",
    "        Pb.run_model()\n",
    "        error = abs(Pb['Pdyn_max_dim']-Pb['max_pdyn_load_ascent_stage_1']/1e3)\n",
    "        Pb['Pdyn_max_dim'] = Pb['max_pdyn_load_ascent_stage_1']/1e3\n",
    "        k = k+1\n",
    "    return Pb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definition of objective function\n",
    "Definition the objective function for evolutionary algorithm Covariance Matrix Adaptation (CMA-ES) more details on CMA-ES [here](http://cma.gforge.inria.fr/) and [here](https://en.wikipedia.org/wiki/CMA-ES). Penalization strategy is used to handle the different constraints in this example.\n",
    "If the constraints are not satisfied, the \"penalized objective function\" corresponds to the minimization of the constraint violation, and if the constraints are satisfied, $-(1e6 - GLOW)/1e5$ is minimized: \n",
    "when the constraints are not satisfied, the objective function takes as value the sum of non satisfied constraints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Objective_function(x,lowerbnd_exp,upperbnd_exp,Pb,simu):\n",
    "    P_out = FPI_optim(x,lowerbnd_exp,upperbnd_exp,Pb)\n",
    "    GLOW, contraintes = post_traitement.post_traitement(P_out)\n",
    "    if simu==0: \n",
    "        if len(np.where(contraintes>1e-2)[0])==0:            \n",
    "            return -(1e6 - GLOW)/1e5\n",
    "        else:\n",
    "\n",
    "            cont = np.sum(contraintes[np.where(contraintes>0)[0]])\n",
    "            return cont\n",
    "    else :\n",
    "        if len(np.where(contraintes>1e-2)[0])==0:\n",
    "            return GLOW/1e5,-5 \n",
    "        else:\n",
    "            return GLOW/1e5,contraintes    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definition of lower and upper bounds for the normalization during optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lowerbnd_exp = np.array([150.,#Prop_mass_stage_1\n",
    "                         20., #Prop_mass_stage_2\n",
    "                          0.,#theta_cmd_i\n",
    "                         -10.,#theta_cmd_f\n",
    "                         -1.,#ksi\n",
    "                         5., #Pitch_over_duration\n",
    "                         5., #Delta_vertical_phase\n",
    "                         1., #Delta_theta_pitch_over\n",
    "                         10.,10.]) #command_stage_1_exo\n",
    "\n",
    "upperbnd_exp = np.array([600.,#Prop_mass_stage_1\n",
    "                         200., #Prop_mass_stage_2\n",
    "                         50.,#theta_cmd_i\n",
    "                         20.,#theta_cmd_f\n",
    "                         1.,#ksi\n",
    "                         20., #Pitch_over_duration\n",
    "                         20., #Delta_vertical_phase\n",
    "                         10., #Delta_theta_pitch_over\n",
    "                         70.,70.]) #command_stage_1_exo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definition of CMA-ES options and bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower = np.zeros([1,len(lowerbnd_exp)])\n",
    "upper = np.ones([1,len(upperbnd_exp)])  \n",
    "options = cma.CMAOptions()\n",
    "bound_cma = np.array([lower[0],upper[0]])\n",
    "options = {'ftarget':-9e7,'tolfun':1e-5,'tolx':1e-6,'seed':2,\n",
    "           'bounds':[bound_cma[0],bound_cma[1]],'popsize':12,'maxiter':100,'verb_disp': 1}\n",
    "simu = 0\n",
    "init = np.ones(len(lowerbnd_exp))*0.5\n",
    "critere_cma = lambda x: Objective_function(x,lowerbnd_exp,upperbnd_exp,P_obj,simu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters of CMA are regrouped into a dictionnary \"options\", of which the keys are the following: \n",
    "- ftarget: stopping value of objective function,\n",
    "- tolfun: tolerance value on objective functon,\n",
    "- tolx: tolerance value on design variable variations,\n",
    "- seed: seed of random generator of CMA-ES\n",
    "- bounds: (normalized) bounds on the design variables,\n",
    "- popsize: population size of CMA\n",
    "- maxiter: maximum number of iterations,\n",
    "- verdisp: display option"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running of CMA-ES\n",
    "Parameter $\\sigma$ is set at 0.2 in the following example, see CMA documentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6_w,12)-aCMA-ES (mu_w=3.7,w_1=40%) in dimension 10 (seed=2, Mon Nov 15 16:27:30 2021)\n",
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "    1     12 7.009361667898098e+03 1.0e+00 1.89e-01  2e-01  2e-01 0:02.3\n",
      "    2     24 2.626912895821953e+01 1.2e+00 1.83e-01  2e-01  2e-01 0:04.5\n",
      "    3     36 3.774428581593899e+01 1.3e+00 1.72e-01  2e-01  2e-01 0:06.2\n",
      "    4     48 2.187920646314429e+01 1.3e+00 1.72e-01  2e-01  2e-01 0:10.5\n",
      "    5     60 3.522218637582093e+00 1.4e+00 1.66e-01  2e-01  2e-01 0:12.8\n",
      "    6     72 -5.882444538691285e+00 1.4e+00 1.50e-01  1e-01  2e-01 0:15.5\n",
      "    7     84 1.467139639505596e+01 1.4e+00 1.42e-01  1e-01  1e-01 0:18.4\n",
      "    8     96 7.271240440154187e+00 1.4e+00 1.58e-01  1e-01  2e-01 0:21.0\n",
      "    9    108 9.664573976404686e-01 1.5e+00 1.55e-01  1e-01  2e-01 0:24.4\n",
      "   10    120 2.783258868994599e+00 1.6e+00 1.45e-01  1e-01  2e-01 0:26.9\n",
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "   11    132 3.282417792624794e-01 1.7e+00 1.30e-01  1e-01  1e-01 0:30.0\n",
      "   12    144 1.483348658473307e+00 1.6e+00 1.22e-01  1e-01  1e-01 0:32.8\n",
      "   13    156 7.605351019207794e+00 1.7e+00 1.17e-01  9e-02  1e-01 0:35.5\n",
      "   14    168 4.957031544260555e+00 1.7e+00 1.05e-01  8e-02  1e-01 0:38.5\n",
      "   15    180 2.983921426587004e+00 1.7e+00 9.41e-02  7e-02  1e-01 0:41.7\n",
      "   16    192 2.383137112691033e+01 1.6e+00 9.17e-02  7e-02  1e-01 0:45.2\n",
      "   17    204 5.749524597268366e+00 1.7e+00 8.44e-02  7e-02  9e-02 0:48.9\n",
      "   18    216 7.832564240414293e-01 1.7e+00 7.88e-02  6e-02  8e-02 0:51.9\n",
      "   19    228 1.094661256337156e+01 1.8e+00 8.12e-02  6e-02  9e-02 0:55.7\n",
      "   20    240 9.406694226866383e+00 1.8e+00 8.22e-02  6e-02  9e-02 0:58.2\n",
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "   21    252 5.701834276088809e+00 1.9e+00 8.16e-02  6e-02  9e-02 1:01.3\n",
      "   22    264 4.784153372385136e+00 2.0e+00 8.12e-02  6e-02  9e-02 1:03.9\n",
      "   23    276 2.408010580950873e+00 2.1e+00 7.38e-02  6e-02  8e-02 1:06.8\n",
      "   24    288 4.175844310281526e+00 2.3e+00 6.65e-02  5e-02  7e-02 1:09.3\n",
      "   25    300 1.625716284072091e+00 2.4e+00 6.23e-02  5e-02  7e-02 1:11.5\n",
      "   26    312 7.459002949450632e-01 2.3e+00 6.35e-02  5e-02  7e-02 1:14.1\n",
      "   27    324 5.038824583056160e+00 2.4e+00 6.81e-02  5e-02  8e-02 1:17.3\n",
      "   28    336 -5.829555074371280e+00 2.6e+00 6.92e-02  5e-02  8e-02 1:20.3\n",
      "   29    348 -5.871029675931941e+00 2.7e+00 7.09e-02  5e-02  8e-02 1:23.5\n",
      "   30    360 -5.876284642703077e+00 2.7e+00 6.72e-02  5e-02  8e-02 1:26.3\n",
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "   31    372 8.246784948244691e-02 2.8e+00 6.79e-02  5e-02  8e-02 1:29.9\n",
      "   32    384 3.306819238433842e-01 3.0e+00 7.04e-02  5e-02  8e-02 1:32.8\n",
      "   33    396 1.640308973045247e+00 3.2e+00 6.70e-02  5e-02  8e-02 1:36.5\n",
      "   34    408 4.610541926587746e-01 3.3e+00 6.34e-02  4e-02  7e-02 1:39.4\n",
      "   35    420 7.697609946591659e-01 3.3e+00 5.94e-02  4e-02  7e-02 1:42.2\n",
      "   36    432 4.898460329861567e-01 3.4e+00 5.61e-02  4e-02  6e-02 1:45.6\n",
      "   37    444 4.760694835588801e-01 3.4e+00 4.82e-02  3e-02  6e-02 1:48.5\n",
      "   38    456 4.732914490894764e-01 3.4e+00 4.44e-02  3e-02  5e-02 1:51.6\n",
      "   39    468 -5.771374770118396e+00 3.6e+00 4.29e-02  3e-02  5e-02 1:54.4\n",
      "   40    480 9.029405259653274e-01 3.6e+00 4.25e-02  2e-02  5e-02 1:57.4\n",
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "   41    492 -5.701853456598228e+00 3.7e+00 4.24e-02  2e-02  5e-02 2:00.3\n",
      "   42    504 -5.830677639064562e+00 3.8e+00 3.92e-02  2e-02  5e-02 2:03.2\n",
      "   43    516 -5.860457309447637e+00 4.1e+00 4.02e-02  2e-02  5e-02 2:06.0\n",
      "   44    528 -5.815257424617235e+00 4.4e+00 4.45e-02  3e-02  5e-02 2:09.1\n",
      "   45    540 7.188601847912715e-01 4.6e+00 4.98e-02  3e-02  6e-02 2:12.1\n",
      "   46    552 -5.932417872613841e+00 4.7e+00 4.98e-02  3e-02  6e-02 2:15.0\n",
      "   47    564 -5.896623915695689e+00 4.8e+00 4.64e-02  3e-02  6e-02 2:17.7\n",
      "   48    576 -5.842562165877842e+00 4.9e+00 4.34e-02  2e-02  5e-02 2:20.5\n",
      "   49    588 -5.928531451569063e+00 5.3e+00 3.94e-02  2e-02  5e-02 2:23.4\n",
      "   50    600 -5.848674394297724e+00 5.6e+00 3.77e-02  2e-02  5e-02 2:26.0\n",
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "   51    612 -5.960121612453726e+00 5.8e+00 3.50e-02  2e-02  5e-02 2:29.1\n",
      "   52    624 -5.929903704385240e+00 6.2e+00 3.35e-02  2e-02  5e-02 2:31.8\n",
      "   53    636 -5.885962465010013e+00 6.3e+00 3.25e-02  2e-02  4e-02 2:34.7\n",
      "   54    648 -5.905084488078524e+00 6.2e+00 3.25e-02  2e-02  4e-02 2:37.6\n",
      "   55    660 -5.792003490833816e+00 6.3e+00 3.48e-02  2e-02  5e-02 2:40.6\n",
      "   56    672 -5.904804595931438e+00 6.8e+00 3.33e-02  2e-02  4e-02 2:43.4\n",
      "   57    684 -5.929959652694303e+00 6.8e+00 3.11e-02  1e-02  4e-02 2:46.2\n",
      "   58    696 -5.954907251043590e+00 6.6e+00 2.82e-02  1e-02  4e-02 2:49.0\n",
      "   59    708 -5.956862338132394e+00 6.8e+00 2.75e-02  1e-02  4e-02 2:52.1\n",
      "   60    720 -5.971237920156605e+00 6.9e+00 2.45e-02  1e-02  3e-02 2:54.9\n",
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "   61    732 -5.978622091116873e+00 6.9e+00 2.13e-02  9e-03  3e-02 2:57.8\n",
      "   62    744 -6.006770178688746e+00 6.7e+00 2.11e-02  9e-03  3e-02 3:00.6\n",
      "   63    756 -6.033803432712691e+00 7.0e+00 2.12e-02  1e-02  3e-02 3:03.8\n",
      "   64    768 -6.011414962523559e+00 7.2e+00 2.04e-02  9e-03  2e-02 3:06.6\n",
      "   65    780 -6.000503082438315e+00 7.4e+00 1.88e-02  8e-03  2e-02 3:09.5\n",
      "   66    792 -6.037160363105249e+00 7.7e+00 1.88e-02  9e-03  2e-02 3:12.3\n",
      "   67    804 -6.051484453330933e+00 7.8e+00 1.93e-02  9e-03  2e-02 3:15.3\n",
      "   68    816 -6.036422748578076e+00 8.2e+00 1.99e-02  9e-03  2e-02 3:17.9\n",
      "   69    828 -6.059667250532065e+00 8.5e+00 2.06e-02  9e-03  3e-02 3:20.8\n",
      "   70    840 -6.118692148047313e+00 9.1e+00 2.15e-02  1e-02  3e-02 3:23.3\n",
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "   71    852 -6.132245979307429e+00 1.0e+01 2.27e-02  1e-02  3e-02 3:25.6\n",
      "   72    864 -6.097295384795585e+00 1.0e+01 2.41e-02  1e-02  4e-02 3:27.6\n",
      "   73    876 -6.104658861615034e+00 1.1e+01 2.56e-02  1e-02  4e-02 3:29.8\n",
      "   74    888 -6.080967510003606e+00 1.0e+01 2.68e-02  1e-02  4e-02 3:31.8\n",
      "   75    900 -6.104044719604413e+00 1.1e+01 2.90e-02  1e-02  4e-02 3:33.8\n",
      "   76    912 -6.115304970708976e+00 1.0e+01 3.20e-02  1e-02  5e-02 3:35.6\n",
      "   77    924 -6.094862010769262e+00 1.1e+01 3.34e-02  1e-02  5e-02 3:37.6\n",
      "   78    936 -6.075390681784940e+00 1.1e+01 3.19e-02  1e-02  5e-02 3:39.6\n",
      "   79    948 -6.079594502036219e+00 1.1e+01 3.15e-02  1e-02  4e-02 3:42.0\n",
      "   80    960 -6.106645584029134e+00 1.0e+01 3.28e-02  1e-02  4e-02 3:44.4\n",
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "   81    972 -6.100270186809398e+00 1.0e+01 3.41e-02  1e-02  4e-02 3:46.7\n",
      "   82    984 -6.110733790758725e+00 1.0e+01 3.54e-02  1e-02  5e-02 3:49.1\n",
      "   83    996 -6.137090666215251e+00 1.0e+01 3.58e-02  1e-02  5e-02 3:51.6\n",
      "   84   1008 -6.123049061784083e+00 1.1e+01 3.41e-02  1e-02  4e-02 3:53.7\n",
      "   85   1020 -6.136624702328723e+00 1.1e+01 3.19e-02  1e-02  4e-02 3:55.9\n",
      "   86   1032 -6.129463097655070e+00 1.1e+01 3.03e-02  1e-02  4e-02 3:57.6\n",
      "   87   1044 -6.085154584141793e+00 1.1e+01 2.74e-02  9e-03  3e-02 3:59.8\n",
      "   88   1056 -6.116040180195609e+00 1.1e+01 2.52e-02  8e-03  3e-02 4:01.9\n",
      "   89   1068 -6.151865107034156e+00 1.1e+01 2.52e-02  7e-03  3e-02 4:04.0\n",
      "   90   1080 -6.156725877970052e+00 1.1e+01 2.39e-02  7e-03  3e-02 4:05.9\n",
      "Iterat #Fevals   function value  axis ratio  sigma  min&max std  t[m:s]\n",
      "   91   1092 -6.160347700241937e+00 1.0e+01 2.34e-02  7e-03  3e-02 4:08.2\n",
      "   92   1104 -6.181231873802223e+00 1.0e+01 2.51e-02  7e-03  3e-02 4:10.1\n",
      "   93   1116 -6.139833676661165e+00 9.8e+00 2.51e-02  7e-03  3e-02 4:12.1\n",
      "   94   1128 -6.143208644911235e+00 1.0e+01 2.56e-02  7e-03  3e-02 4:14.1\n",
      "   95   1140 -6.130393898531850e+00 1.0e+01 2.35e-02  6e-03  3e-02 4:16.1\n",
      "   96   1152 -6.147047068644147e+00 1.0e+01 2.20e-02  6e-03  3e-02 4:18.2\n",
      "   97   1164 -6.146287442288647e+00 1.0e+01 2.13e-02  5e-03  2e-02 4:20.1\n",
      "   98   1176 -6.166404980106016e+00 1.0e+01 1.98e-02  5e-03  2e-02 4:22.0\n",
      "   99   1188 -6.169870522888853e+00 1.1e+01 1.97e-02  5e-03  2e-02 4:23.8\n",
      "  100   1200 -6.185403581278391e+00 1.1e+01 2.21e-02  5e-03  3e-02 4:25.4\n",
      "termination on maxiter=100 (Mon Nov 15 16:31:57 2021)\n",
      "final/bestever f-value = -6.172423e+00 -6.185404e+00\n",
      "incumbent solution: [0.23453973 0.37025646 0.40115063 0.54564402 0.23763885 0.9747077\n",
      " 0.74553659 0.8734499  ...]\n",
      "std deviations: [0.00518179 0.00864859 0.01560658 0.02140996 0.01733043 0.02669278\n",
      " 0.01765425 0.02343751 ...]\n"
     ]
    }
   ],
   "source": [
    "res = cma.fmin(critere_cma,init,.2,options)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results of optimization\n",
    "#### Optimal value of the design variables, of the objective function and penalized constraints and of the GLOW\n",
    "Be careful that the optimal value of the design variables do not reach the bounds defined in lowerbnd_exp and upperbnd_exp especially for the propellant masses. If it is the case, modify the bounds and rerun the optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> Optimal design variable values [0.23527546 0.36153667 0.39784285 0.53903249 0.22424615 0.96230176\n",
      " 0.75167229 0.8693451  0.44461484 0.17382506]\n",
      ">> Objective, Penalized Constraint (3.8145964187216097, -5)\n",
      ">> Optimal GLOW (t) [381.45964187]\n"
     ]
    }
   ],
   "source": [
    "print('>> Optimal design variable values',res[0])\n",
    "print('>> Objective, Penalized Constraint',Objective_function(res[0],lowerbnd_exp,upperbnd_exp,P_obj,1))\n",
    "print('>> Optimal GLOW (t)', P_obj['GLOW']/1e3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots of the results of optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "P_out = FPI_optim(res[0],lowerbnd_exp,upperbnd_exp,P_obj)\n",
    "result_vizualization.plots_output(P_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GLOW (t)</th>\n",
       "      <th>Dry_mass_stage_1  (t)</th>\n",
       "      <th>Dry_mass_stage_2  (t)</th>\n",
       "      <th>Prop_mass_stage_1 (t)</th>\n",
       "      <th>Prop_mass_stage_2 (t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>381.46</td>\n",
       "      <td>26.13</td>\n",
       "      <td>7.379</td>\n",
       "      <td>255.874</td>\n",
       "      <td>85.077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GLOW (t)  Dry_mass_stage_1  (t)  Dry_mass_stage_2  (t)  \\\n",
       "0    381.46                  26.13                  7.379   \n",
       "\n",
       "   Prop_mass_stage_1 (t)  Prop_mass_stage_2 (t)  \n",
       "0                255.874                 85.077  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [[P_out['GLOW'][0]/1e3,\n",
    "         P_out['Dry_mass_stage_1'][0]/1e3,\n",
    "         P_out['Dry_mass_stage_2'][0]/1e3,\n",
    "        P_out['Prop_mass_stage_1'][0]/1e3,\n",
    "        P_out['Prop_mass_stage_2'][0]/1e3]]\n",
    "df = pd.DataFrame(data, columns=[\"GLOW (t)\", \"Dry_mass_stage_1  (t)\", \n",
    "                                     \"Dry_mass_stage_2  (t)\", \"Prop_mass_stage_1 (t)\", \n",
    "                                     \"Prop_mass_stage_2 (t)\"])\n",
    "df.round(3)"
   ]
  },
  {
   "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": 2
}