{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14 - Mass-Benefit Analysis - Titan"
   ]
  },
  {
   "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(\"TITAN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ISP  = 320.0\n",
    "g0   = 9.80665\n",
    "\n",
    "peri_alt_km    = 1700.0\n",
    "apoa_alt_km_pp = 1700.0   \n",
    "apoa_alt_km_ab = 300000.0"
   ]
  },
  {
   "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": 10,
   "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([1.53E3, 1.88E3, 2.77E3, 4.09E3, 5.82E3,\n",
    "\t                   7.96E3, 10.5E3, 13.5E3, 16.8E3, 20.7E3, \n",
    "\t                   24.9E3])\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",
    "plt.savefig('../data/jsr-paper/titan/titan-mb.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/titan/titan-mb.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/titan/titan-mb.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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
}