{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一维Sod激波管\n",
    "\n",
    "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0.0-alpha/resource/_static/logo_notebook.png)](https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/r2.0.0-alpha/mindflow/zh_cn/cfd/mindspore_sod_tube.ipynb) [![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0.0-alpha/resource/_static/logo_download_code.png)](https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/r2.0.0-alpha/mindflow/zh_cn/cfd/mindspore_sod_tube.py) [![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0.0-alpha/resource/_static/logo_source.png)](https://gitee.com/mindspore/docs/blob/r2.0.0-alpha/docs/mindflow/docs/source_zh_cn/cfd/sod_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",
    "Sod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",
    "$$\n",
    "\\left(\\begin{matrix} \\rho \\\\ u \\\\ p \\\\\\end{matrix}\\right)_{x<0.5} = \\left(\\begin{matrix} 1.0 \\\\ 0.0 \\\\ 1.0 \\\\\\end{matrix}\\right), \\quad\n",
    "\\left(\\begin{matrix} \\rho \\\\ u \\\\ p \\\\\\end{matrix}\\right)_{x>0.5} = \\left(\\begin{matrix} 0.125 \\\\ 0.0 \\\\ 0.1 \\\\\\end{matrix}\\right)\n",
    "$$\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/r0.2.0-alpha/MindFlow/applications/cfd/sod/src)下载。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mindspore import context\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 sod_ic_1d\n",
    "\n",
    "context.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/r0.2.0-alpha/MindFlow/applications/cfd/sod/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 = sod_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 = 0.007606\n",
      "current time = 0.007606, time step = 0.005488\n",
      "current time = 0.013094, time step = 0.004744\n",
      "current time = 0.017838, time step = 0.004501\n",
      "current time = 0.022339, time step = 0.004338\n",
      "current time = 0.026678, time step = 0.004293\n",
      "current time = 0.030971, time step = 0.004268\n",
      "current time = 0.035239, time step = 0.004198\n",
      "current time = 0.039436, time step = 0.004157\n",
      "current time = 0.043593, time step = 0.004150\n",
      "current time = 0.047742, time step = 0.004075\n",
      "current time = 0.051818, time step = 0.004087\n",
      "current time = 0.055905, time step = 0.004056\n",
      "current time = 0.059962, time step = 0.004031\n",
      "current time = 0.063993, time step = 0.004021\n",
      "current time = 0.068014, time step = 0.004048\n",
      "current time = 0.072062, time step = 0.004039\n",
      "current time = 0.076101, time step = 0.004016\n",
      "current time = 0.080117, time step = 0.004049\n",
      "current time = 0.084166, time step = 0.004053\n",
      "current time = 0.088218, time step = 0.004045\n",
      "current time = 0.092264, time step = 0.004053\n",
      "current time = 0.096317, time step = 0.004062\n",
      "current time = 0.100378, time step = 0.004065\n",
      "current time = 0.104443, time step = 0.004068\n",
      "current time = 0.108511, time step = 0.004072\n",
      "current time = 0.112583, time step = 0.004075\n",
      "current time = 0.116658, time step = 0.004077\n",
      "current time = 0.120735, time step = 0.004080\n",
      "current time = 0.124815, time step = 0.004081\n",
      "...\n",
      "current time = 0.186054, time step = 0.004084\n",
      "current time = 0.190138, time step = 0.004084\n",
      "current time = 0.194222, time step = 0.004084\n",
      "current time = 0.198306, time step = 0.004085\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, 'sod.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
}