{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一维Lax激波管\n",
    "\n",
    "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_notebook.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindflow/zh_cn/cfd_solver/mindspore_lax_tube.ipynb) [![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_download_code.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindflow/zh_cn/cfd_solver/mindspore_lax_tube.py) [![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindflow/docs/source_zh_cn/cfd_solver/lax_tube.ipynb)\n",
    "\n",
    "本案例要求**MindSpore版本 >= 2.0.0**调用如下接口: *mindspore.jit，mindspore.jit_class*。\n",
    "\n",
    "激波管问题是检验计算流体代码准确性的常见问题。这个案例为一个一维黎曼问题，即理想气体在左右端不同条件下的发展问题。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问题描述\n",
    "\n",
    "Lax激波管问题的定义为:\n",
    "\n",
    "$$\n",
    "\\frac{\\partial}{\\partial t} \\left(\\begin{matrix} \\rho \\\\ \\rho u \\\\ E \\\\\\end{matrix} \\right) + \\frac{\\partial}{\\partial x} \\left(\\begin{matrix} \\rho u \\\\ \\rho u^2 + p \\\\ u(E + p) \\\\\\end{matrix} \\right) = 0\n",
    "$$\n",
    "\n",
    "$$\n",
    "E = \\frac{\\rho}{\\gamma - 1} + \\frac{1}{2}\\rho u^2\n",
    "$$\n",
    "\n",
    "其中，对理想气体， $\\gamma = 1.4$ ，初始条件为：\n",
    "\n",
    "$$\n",
    "\\left(\\begin{matrix} \\rho \\\\ u \\\\ p \\\\\\end{matrix}\\right)_{x<0.5} = \\left(\\begin{matrix} 0.445 \\\\ 0.698 \\\\ 3.528 \\\\\\end{matrix}\\right), \\quad\n",
    "\\left(\\begin{matrix} \\rho \\\\ u \\\\ p \\\\\\end{matrix}\\right)_{x>0.5} = \\left(\\begin{matrix} 0.5 \\\\ 0.0 \\\\ 0.571 \\\\\\end{matrix}\\right)\n",
    "$$\n",
    "\n",
    "在激波管两端，施加第二类边界条件。\n",
    "\n",
    "本案例中`src`包可以在[src](https://gitee.com/mindspore/mindscience/tree/master/MindFlow/applications/cfd/lax/src)下载。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mindspore as ms\n",
    "from mindflow import load_yaml_config, vis_1d\n",
    "from mindflow import cfd\n",
    "from mindflow.cfd.runtime import RunTime\n",
    "from mindflow.cfd.simulator import Simulator\n",
    "\n",
    "from src.ic import lax_ic_1d\n",
    "\n",
    "ms.set_context(device_target=\"GPU\", device_id=3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义Simulator和RunTime\n",
    "\n",
    "网格、材料、仿真时间、边界条件和数值方法的设置在文件[numeric.yaml](https://gitee.com/mindspore/mindscience/blob/master/MindFlow/applications/cfd/lax/numeric.yaml)中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = load_yaml_config('numeric.yaml')\n",
    "simulator = Simulator(config)\n",
    "runtime = RunTime(config['runtime'], simulator.mesh_info, simulator.material)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始条件\n",
    "\n",
    "根据网格坐标确定初始条件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_x, _, _ = simulator.mesh_info.mesh_xyz()\n",
    "pri_var = lax_ic_1d(mesh_x)\n",
    "con_var = cfd.cal_con_var(pri_var, simulator.material)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 执行仿真\n",
    "\n",
    "随时间推进执行仿真。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current time = 0.000000, time step = 0.001117\n",
      "current time = 0.001117, time step = 0.001107\n",
      "current time = 0.002224, time step = 0.001072\n",
      "current time = 0.003296, time step = 0.001035\n",
      "current time = 0.004332, time step = 0.001016\n",
      "current time = 0.005348, time step = 0.001008\n",
      "current time = 0.006356, time step = 0.000991\n",
      "current time = 0.007347, time step = 0.000976\n",
      "current time = 0.008324, time step = 0.000966\n",
      "current time = 0.009290, time step = 0.000960\n",
      "current time = 0.010250, time step = 0.000957\n",
      "current time = 0.011207, time step = 0.000954\n",
      "current time = 0.012161, time step = 0.000953\n",
      "current time = 0.013113, time step = 0.000952\n",
      "current time = 0.014066, time step = 0.000952\n",
      "current time = 0.015017, time step = 0.000951\n",
      "current time = 0.015969, time step = 0.000951\n",
      "current time = 0.016920, time step = 0.000952\n",
      "current time = 0.017872, time step = 0.000951\n",
      "current time = 0.018823, time step = 0.000951\n",
      "current time = 0.019775, time step = 0.000952\n",
      "current time = 0.020726, time step = 0.000953\n",
      "current time = 0.021679, time step = 0.000952\n",
      "current time = 0.022631, time step = 0.000952\n",
      "current time = 0.023583, time step = 0.000952\n",
      "current time = 0.024535, time step = 0.000952\n",
      "current time = 0.025488, time step = 0.000952\n",
      "current time = 0.026440, time step = 0.000952\n",
      "current time = 0.027392, time step = 0.000953\n",
      "current time = 0.028345, time step = 0.000952\n",
      "...\n",
      "current time = 0.136983, time step = 0.000953\n",
      "current time = 0.137936, time step = 0.000953\n",
      "current time = 0.138889, time step = 0.000953\n",
      "current time = 0.139843, time step = 0.000953\n"
     ]
    }
   ],
   "source": [
    "while runtime.time_loop(pri_var):\n",
    "    pri_var = cfd.cal_pri_var(con_var, simulator.material)\n",
    "    runtime.compute_timestep(pri_var)\n",
    "    con_var = simulator.integration_step(con_var, runtime.timestep)\n",
    "    runtime.advance()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post Processing\n",
    "\n",
    "您可以对密度、压力、速度进行可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x750 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pri_var = cfd.cal_pri_var(con_var, simulator.material)\n",
    "vis_1d(pri_var, 'lax.jpg')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.0 ('py39')",
   "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": "3.9.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "57ace93c29d9374277a79956c3f1b916d7d9a05468d906842f9921d0d494a29f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}