{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12 - Mass-Benefit Analysis - Earth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet = Planet(\"EARTH\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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": 5,
   "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": 6,
   "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": 7,
   "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": 8,
   "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": 9,
   "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([29.2E3, 29.6E3, 30.8E3, 32.8E3, 35.7E3,\n",
    "\t                   39.7E3, 45.1E3, 51.0E3, 60.0E3, 71.0E3, 84.0E3]) \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[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, fP_prop_ab[10]) ,\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*(fP_prop_ab[10]+fP_aero[10]), \n",
    "\t    str(int((fP_aero[10] - fP_prop_ab[10])*100/fP_prop_ab[10]))+'%' , \n",
    "\t    fontsize=16, ha='center', va='center',\n",
    "\t    bbox=dict(facecolor='white', edgecolor='white', pad=0.0)) \n",
    "\n",
    "plt.savefig('../data/jsr-paper/earth/earth-mb.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/earth/earth-mb.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/earth/earth-mb.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}