{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 13 - Mass-Benefit Analysis - Mars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "\n",
    "from AMAT.planet import Planet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet = Planet(\"MARS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ISP  = 320.0\n",
    "g0   = 9.80665\n",
    "\n",
    "peri_alt_km    = 400.0\n",
    "apoa_alt_km_pp = 400.0   \n",
    "apoa_alt_km_ab = 60000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "energy_pp      = -planet.GM / ((planet.RP+apoa_alt_km_pp*1E3 + peri_alt_km*1E3 + planet.RP))\n",
    "energy_ab      = -planet.GM / ((planet.RP+apoa_alt_km_ab*1E3 + peri_alt_km*1E3 + planet.RP))\n",
    "\n",
    "v_peri_orb_pp  =  np.sqrt(2*energy_pp + 2*planet.GM/(peri_alt_km*1E3+planet.RP))\n",
    "v_peri_orb_ab  =  np.sqrt(2*energy_ab + 2*planet.GM/(peri_alt_km*1E3+planet.RP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_v_arrival(vinf):\n",
    "    return np.sqrt(vinf**2 + (2.0*planet.GM)/(peri_alt_km*1E3+planet.RP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_DV(vinf):\n",
    "    v_arrival = compute_v_arr(vinf)\n",
    "    DV_pp     = v_arr - v_peri_orb_pp\n",
    "    DV_ab     = v_arr - v_peri_orb_ab\n",
    "\n",
    "    return DV_pp, DV_ab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_fP_prop_pp(vinf_array):\n",
    "    v_arrival_arr = compute_v_arrival(vinf_array)\n",
    "    DV_arr        = v_arrival_arr - v_peri_orb_pp\n",
    "    \n",
    "    return 1 - 1.12*(1.0 - np.exp(-DV_arr/(ISP*g0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_fP_prop_ab(vinf_array):\n",
    "    v_arrival_arr = compute_v_arrival(vinf_array)\n",
    "    DV_arr        = v_arrival_arr - v_peri_orb_ab\n",
    "\n",
    "    return 1 - 1.12*(1.0 - np.exp(-DV_arr/(ISP*g0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "vinf_array = np.linspace(0.0, 10.0E3, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fP_prop_pp    = compute_fP_prop_pp(vinf_array)\n",
    "fP_prop_ab    = compute_fP_prop_ab(vinf_array)\n",
    "\n",
    "Q_array    = np.array([5.00E3, 5.25E3, 5.95E3, 7.07E3, 8.6E3,\n",
    "\t                   10.4E3, 12.7E3, 15.0E3, 18.0E3, 21.0E3, 25.0E3]) \n",
    "\n",
    "tps_mf_array = 0.091*np.power(Q_array, 0.51575)/100.0\n",
    "ess_mf       = 0.23\n",
    "\n",
    "fP_aero      = 1.0 - ess_mf - tps_mf_array\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.25,6.25])\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\t\n",
    "\n",
    "plt.plot(vinf_array/1.0E3, fP_prop_pp, linestyle='-', linewidth=1.00, marker='s',ms=12, markerfacecolor=\"None\", markeredgecolor='red', markeredgewidth=0.75, color='red', label='Propulsive')\n",
    "plt.plot(vinf_array/1.0E3, fP_prop_ab, linestyle='-', linewidth=1.00, marker='o',ms=12, markerfacecolor=\"None\", markeredgecolor='magenta', markeredgewidth=0.75, color='magenta',  label='Aerobraking')\n",
    "plt.plot(vinf_array/1.0E3, fP_aero,    linestyle='-', linewidth=1.00, marker='^',ms=12, markerfacecolor=\"None\", markeredgecolor='blue', markeredgewidth=0.75, color='blue', label='Aerocapture')\n",
    "\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=16)\n",
    "plt.ylabel('Payload mass fraction, '+r'$f_P$' ,fontsize=16)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "plt.legend(loc='upper right', fontsize=16, frameon=False)\n",
    "ax.set_ylim(0,1.03)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "ax.set_ylim(0,1.03)\n",
    "\n",
    "ax.annotate('',\n",
    "            xy=(vinf_array[6]/1.0E3, fP_prop_ab[6]) ,\n",
    "            xytext=(vinf_array[6]/1.0E3, fP_aero[6]),\n",
    "            arrowprops=dict(arrowstyle=\"<-\"),  va=\"center\", ha=\"center\", fontsize=16)\n",
    "\n",
    "\n",
    "\n",
    "plt.text(vinf_array[6]/1.0E3, 0.5*(fP_prop_ab[6]+fP_aero[6]), \n",
    "\t    str(int((fP_aero[6] - fP_prop_ab[6])*100/fP_prop_ab[6]))+'%' , \n",
    "\t    fontsize=16, ha='center', va='center',\n",
    "\t    bbox=dict(facecolor='white', edgecolor='white', pad=0.0)) \n",
    "\n",
    "ax.annotate('',\n",
    "            xy=(vinf_array[8]/1.0E3, fP_prop_ab[8]) ,\n",
    "            xytext=(vinf_array[8]/1.0E3, fP_aero[8]),\n",
    "            arrowprops=dict(arrowstyle=\"<-\"),  va=\"center\", ha=\"center\", fontsize=16)\n",
    "\n",
    "plt.text(vinf_array[8]/1.0E3, 0.5*(fP_prop_ab[8]+fP_aero[8]), \n",
    "\t    str(int((fP_aero[8] - fP_prop_ab[8])*100/fP_prop_ab[8]))+'%' , \n",
    "\t    fontsize=16, ha='center', va='center',\n",
    "\t    bbox=dict(facecolor='white', edgecolor='white', pad=0.0)) \n",
    "\n",
    "ax.annotate('',\n",
    "            xy=(vinf_array[10]/1.0E3, 0.0) ,\n",
    "            xytext=(vinf_array[10]/1.0E3, fP_aero[10]),\n",
    "            arrowprops=dict(arrowstyle=\"<-\"),  va=\"center\", ha=\"center\", fontsize=16)\n",
    "\n",
    "plt.text(vinf_array[10]/1.0E3, 0.5*(0.0+fP_aero[10]), \n",
    "\t    r'$\\infty$' , \n",
    "\t    fontsize=16, ha='center', va='center',\n",
    "\t    bbox=dict(facecolor='white', edgecolor='white', pad=0.0)) \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('../data/jsr-paper/mars/mars-mb.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/mars/mars-mb.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/mars/mars-mb.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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