docs/source/examples/example-25-pv-large-venus.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example - 25 - PV Large - Venus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook simulates the atmospheric entry of the Pioneer Venus Large probe.\n",
    "https://en.wikipedia.org/wiki/Pioneer_Venus_Multiprobe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the planet and atmosphere model.\n",
    "planet=Planet(\"VENUS\")    \n",
    "planet.loadAtmosphereModel('../atmdata/Venus/venus-gram-avg.dat', 0 , 1 , 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the vehicle\n",
    "vehicle=Vehicle('PV-Large', 316 , 188, 0.00, 1.59, 0.0, 0.36, planet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up entry parameters\n",
    "vehicle.setInitialState(180.0,0.0,0.0,11.54,0.0,-32.4,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up solver\n",
    "vehicle.setSolverParams(1E-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Propogate vehicle entry trajectory\n",
    "vehicle.propogateEntry (2400.0,0.1,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import rcParams to set figure font type\n",
    "from matplotlib import rcParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(vehicle.v_kmsc, vehicle.h_kmc, 'k-', linewidth=2.0)\n",
    "plt.xlabel('Speed, km/s',fontsize=14)\n",
    "plt.ylabel('Altitude, km', fontsize=14)\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",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(vehicle.acc_net_g, vehicle.h_kmc, 'b-', linewidth=2.0)\n",
    "plt.xlabel('Deceleration, Earth g',fontsize=14)\n",
    "plt.ylabel('Altitude, km', fontsize=14)\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",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.plot(vehicle.q_stag_total, vehicle.h_kmc,'r-', linewidth=2.0)\n",
    "plt.xlabel('Stagnation point heat-rate, '+r'$W/cm^2$',fontsize=14)\n",
    "plt.ylabel('Altitude, km', fontsize=14)\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",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.plot(vehicle.heatload/1.0E3, vehicle.h_kmc, 'm-', linewidth=2.0)\n",
    "plt.xlabel('Stagnation point heat-load, '+r'$kJ/cm^2$',fontsize=14)\n",
    "plt.ylabel('Altitude, km', fontsize=14)\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",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "plt.savefig('../plots/pv-large-venus.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/pv-large-venus.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/pv-large-venus.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}