DiffusionExample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "combined-forest",
   "metadata": {},
   "source": [
    "# Example 1. Difussion \n",
    "\n",
    "This example aim to set up a simple diffussion problem for demostrating the workflow of running the problem on CPU and also ploting the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "sophisticated-harmony",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: FourierFlows will use 8 threads\n",
      "└ @ FourierFlows /mnt/d/julialib/FourierFlows.jl_myfork/src/FourierFlows.jl:116\n"
     ]
    }
   ],
   "source": [
    "using MHDFlows,PyPlot\n",
    "using LinearAlgebra: mul!, ldiv!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "straight-official",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MHDFlows Problem\n",
       "  │    Funtions\n",
       "  │     ├──────── B-field: OFF\n",
       "  ├─────├────── VP Method: OFF\n",
       "  │     ├──────────── Dye: OFF\n",
       "  │     └── user function: OFF\n",
       "  │                        \n",
       "  │     Features           \n",
       "  │     ├─────────── grid: grid (on CPU)\n",
       "  │     ├───── parameters: params\n",
       "  │     ├────── variables: vars\n",
       "  └─────├─── state vector: sol\n",
       "        ├─────── equation: eqn\n",
       "        ├────────── clock: clock\n",
       "        └──── timestepper: RK4TimeStepper"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Declare the problem on CPU\n",
    "CPUprob  = Problem(CPU();nx = 32,Lx = 2π,\n",
    "                                  ν = 1/10,\n",
    "                                 nν = 1,\n",
    "                                  η = 1/10, \n",
    "                   # Timestepper and equation options\n",
    "                                 dt = 1/50,\n",
    "                            stepper = \"RK4\",\n",
    "                   # Float type and dealiasing\n",
    "                                  T = Float64);\n",
    "CPUprob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "executive-canvas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProblemGenerator3D! (generic function with 1 method)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function of setting up the initial condition\n",
    "function ProblemGenerator3D!(prob,L;N = prob.grid.nx)\n",
    "\n",
    "  xx,yy,zz = fill(0.0,N,N,N),fill(0.0,N,N,N),fill(0.0,N,N,N);\n",
    "  \n",
    "  for k ∈ 1:N, j ∈ 1:N, i ∈ 1:N\n",
    "    xx[i,j,k] = prob.grid.x[i];\n",
    "    yy[i,j,k] = prob.grid.y[j];\n",
    "    zz[i,j,k] = prob.grid.z[k];\n",
    "  end\n",
    "    \n",
    "  sl=1; sk=1; sm=1; lamlkm=sqrt(sl.^2+sk.^2+sm.^2);\n",
    "\n",
    "  ux = @. -0.5*(lamlkm*sl*cos(sk*xx).*sin(sl*yy).*sin(sm.*zz) +sm*sk*sin(sk*xx).*cos(sl*yy).*cos(sm.*zz));\n",
    "  uy=  @.  0.5*(lamlkm*sk*sin(sk*xx).*cos(sl*yy).*sin(sm.*zz)-sm*sl*cos(sk*xx).*sin(sl*yy).*cos(sm.*zz));\n",
    "  uz=  @.                 cos(sk*xx).*cos(sl*yy).*sin(sm.*zz);\n",
    "\n",
    "  #Update V + B Conponment to Problem\n",
    "  SetUpProblemIC!(prob; ux = ux, uy = uy, uz = uz);\n",
    "\n",
    "  return nothing\n",
    "    \n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cultural-ordinary",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#function for monitoring the energy\n",
    "function KEfoo(prob)\n",
    "    vx,vy,vz = prob.vars.ux,prob.vars.uy,prob.vars.uz;\n",
    "    return sum(vx.^2+vy.^2 + vz.^2)\n",
    "end\n",
    "\n",
    "KE = MHDFlows.Diagnostic(KEfoo, CPUprob);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "threaded-review",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Warning: ProgressMeter by default refresh meters with additional information in IJulia via `IJulia.clear_output`, which clears all outputs in the cell. \n",
      "│  - To prevent this behaviour, do `ProgressMeter.ijulia_behavior(:append)`. \n",
      "│  - To disable this warning message, do `ProgressMeter.ijulia_behavior(:clear)`.\n",
      "└ @ ProgressMeter /home/doraho/.julia/packages/ProgressMeter/sN2xr/src/ProgressMeter.jl:618\n",
      "\u001b[32mSimulation in rogress :  63%|██████▂   |  ETA: 0:00:07 ( 0.20  s/it)\u001b[39m\n",
      "\u001b[34m  Progress:    iter/Nₒ = 63/100, t/t₀ = 5.08/5.0\u001b[39m\n",
      "\u001b[34m  Statistics:  KE = 4.42\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total CPU/GPU time run = 12.614 s, zone update per second = 163657.3 \n"
     ]
    }
   ],
   "source": [
    "# Set up the initial condition\n",
    "ProblemGenerator3D!(CPUprob,2π);\n",
    "\n",
    "# Actaul computation\n",
    "TimeIntegrator!(CPUprob,5.0,100;\n",
    "                diags = [KE],\n",
    "          loop_number = 100,\n",
    "\t             save = false);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confirmed-istanbul",
   "metadata": {},
   "source": [
    "## Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "environmental-cambridge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(30.000000000000007, 0.5, 'KE [code unit]')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = KE.i\n",
    "t  = KE.t[2:n];\n",
    "u0 = KE.data[2];\n",
    "plot(KE.t[2:n],KE.data[2:n],\"r\",label=L\"U^2\")\n",
    "semilogy(KE.t[2:n],exp.(-2*3*(t.-t[1])/10)*u0*0.8,\"k--\",label=L\"OffSet\\:predicted\\:KE\");\n",
    "legend()\n",
    "xlabel(\"t  [code unit]\",size=16)\n",
    "ylabel(\"KE [code unit]\",size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "israeli-gothic",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia (8 threads) 1.7.3",
   "language": "julia",
   "name": "julia-(8-threads)-1.7"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}