{
 "cells": [
  {
   "cell_type": "code",
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   "source": [
    "\n",
    "# Document Author: Dr. Vishal Sharma\n",
    "# Author email: sharma_vishal14@hotmail.com\n",
    "# License: MIT\n",
    "# This tutorial is applicable for NAnPack version 1.0.0-alpha4 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 3: Solving a 1D diffusion equation using all methods\n",
    "\n",
    "### I. Objectives\n",
    "\n",
    "The objectives of this tutorial are two-fold:\n",
    "Firstly, inform users about the various available numerical methods for solving 1D diffusion equation and comparing the numerical solutions obtained from those methods, and\n",
    "Secondly, creating an automation script- that can run simulations using all available numerical method for 1D diffusion model so as to reduce user efforts that will go into writing multiple scripts. This automation example can be used as a reference for creating other automation scripts.\n",
    "\n",
    "### II. Case Description\n",
    "\n",
    "We will use the same example which was presented in [Tutorial 2](./tutorial-02-diffusion-1D-solvers-FTCS.ipynb).\n",
    "\n",
    "### III. Numerical Methods\n",
    "\n",
    "1. **Forward Time Central Spacing (FTCS) method**\n",
    "\n",
    "Brief description of this method is given in [Tutorial 2](./tutorial-02-diffusion-1D-solvers-FTCS.ipynb).  \n",
    "\n",
    "Function call:  \n",
    "    \n",
    "    nanpack.parabolicsolvers.FTCS(Uo, diffX, diffY)  \n",
    "\n",
    "2. **DuFort-Frankel method**\n",
    "\n",
    "This is an explicit method in which the time and space derivatives are discretized by second-order central differencing which is the same as in Richardson method, however, to make the scheme stable, the term $u_{i}^n$ in the diffusion term is approximated by averaging over two time steps such that\n",
    "\n",
    "$$u_{i}^n = \\frac{u_{i}^{n+1}+u_{i}^{n-1}}{2}$$\n",
    "\n",
    "Such modification makes the scheme unconditionally stable. After some modifications, the discretized equation is expressed as\n",
    "\n",
    "$$[1+2d_x]u_{i}^{n+1} = [1-2d_x]u_{i}^{n-1} + 2d_x[u_{i+1}^{n}+u_{i-1}^{n}]$$\n",
    "\n",
    "where $$d_x=\\frac{\\nu(\\Delta t)}{(\\Delta x)^2}$$\n",
    "\n",
    "As observed in the equation, values of the dependent variable at two time steps *n* and (*n*-1) is required, hence the storage requirements are increased. Also, the accuracy of the DuFort Frankel depends on the starter solution which depends on two sets of initial conditions. This method is second-order accurate in both space and time.\n",
    "\n",
    "Function call:\n",
    "\n",
    "    nanpack.parabolicsolvers.DuFortFrankel(Uo, Uold2, diffX, diffY)\n",
    "\n",
    "3. **Laasonen method**\n",
    "\n",
    "This is an implicit formulation which is expressed as\n",
    "\n",
    "$$Au_{i+1}^{n+1}+Bu_{i}^{n+1}+Cu_{i-1}^{n+1}=D$$\n",
    "\n",
    "where,\n",
    "$$A = -d_{x}$$\n",
    "$$B = 1+2d_{x}$$\n",
    "$$C = -d_{x}$$\n",
    "$$D = u_{i}^{n}$$\n",
    "$$d_x=\\frac{\\nu(\\Delta t)}{(\\Delta x)^2}$$\n",
    "\n",
    "The implicit schemes are unconditionally stable and therefore a larger time step can be used to minimize the simulation steps. The time step is, however, restricted due to other numerical errors such as truncation error. \n",
    "\n",
    "The discretized equation results in the set of linear algebraic equations. Subsequently, the algebraic equations are written in the matrix form that consists of a tridiagonal coefficient matrix. This formulation leads to larger computational time which can be somewhat compensated by using a larger time step.\n",
    "\n",
    "Function call:\n",
    "\n",
    "    nanpack.parabolicsolvers.Laasonen(Uo, diffX) \n",
    "\n",
    "4. **Crank-Nicolson method**\n",
    "\n",
    "The Crank-Nicolson is also an implicit formulation in which the diffusion term is approximated by averaging the central difference at time levels *n* and *n*+1. The discretized equation is expressed as:\n",
    "\n",
    "$$Au_{i+1}^{n+1}+Bu_{i}^{n+1}+Cu_{i-1}^{n+1}=D$$\n",
    "\n",
    "where,\n",
    "$$A = -\\frac{1}{2}d_{x}$$\n",
    "$$B = 1+d_{x}$$\n",
    "$$C = -\\frac{1}{2}d_{x}$$\n",
    "$$D = \\frac{1}{2}d_{x}u_{i+1}^{n}+(1-d_{x})u_{i}^{n}+\\frac{1}{2}d_{x}u_{i-1}^{n}$$\n",
    "$$d_x=\\frac{\\nu(\\Delta t)}{(\\Delta x)^2}$$\n",
    "\n",
    "The Crank-Nicolson method is second-order accurate in both space and time.  \n",
    "\n",
    "Function call: \n",
    "\n",
    "    nanpack.parabolicsolvers.CrankNicolson(Uo, diffX)  \n",
    "\n",
    "-----\n",
    "**Important**: It is to be noted that both Laasonen and Crank-Nicolson methods are inefficient for 2D applications because the coefficient matrix is pentadiagonal, the solution of which is very time-consuming.\n",
    "-----\n",
    "\n",
    "-----\n",
    "**Additional resources**  \n",
    "1. Link to my [blogs](https://www.linkedin.com/in/vishalsharmaofficial/detail/recent-activity/posts/).  \n",
    "2. Computational Fluid Dynamics, Vol. 1 by Dr. Klaus Hoffmann- This book is very clear and informative.\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IV. Script Development\n",
    "\n",
    "*This code script is provided in file* `./examples/tutorial-03-diffusion-1D-solvers-all.py`\n",
    "\n",
    "Most of the script remains the same as in [Tutorial 2](./tutorial-02-diffusion-1D-solvers-FTCS.ipynb) and therefore explanation is only provided on how you can automate to run all numerical methods in a single program without the need to write the same code multiple times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*******************************************************\n",
      "*******************************************************\n",
      "Starting configuration.\n",
      "\n",
      "Searching for simulation configuration file in path:\n",
      "\"D:/MyProjects/projectroot/nanpack/input/config.ini\"\n",
      "SUCCESS: Configuration file parsing.\n",
      "Checking whether all sections are included in config file.\n",
      "Checking section SETUP: Completed.\n",
      "Checking section DOMAIN: Completed.\n",
      "Checking section MESH: Completed.\n",
      "Checking section IC: Completed.\n",
      "Checking section BC: Completed.\n",
      "Checking section CONST: Completed.\n",
      "Checking section STOP: Completed.\n",
      "Checking section OUTPUT: Completed.\n",
      "Checking numerical setup.\n",
      "User inputs in SETUP section check: Completed.\n",
      "Accessing domain geometry configuration: Completed\n",
      "Accessing meshing configuration: Completed.\n",
      "Calculating grid size: Completed.\n",
      "Assigning COLD-START initial conditions to the dependent term.\n",
      "Initialization: Completed.\n",
      "Accessing boundary condition settings: Completed\n",
      "Accessing constant data: Completed.\n",
      "Calculating time step size for the simulation: Completed.\n",
      "Calculating maximum iterations/steps for the simulation: Completed.\n",
      "Accessing simulation stop settings: Completed.\n",
      "Accessing settings for storing outputs: Completed.\n",
      "\n",
      "**********************************************************\n",
      "CASE DESCRIPTION                SUDDENLY ACC. PLATE\n",
      "SOLVER STATE                    TRANSIENT\n",
      "MODEL EQUATION                  DIFFUSION\n",
      "DOMAIN DIMENSION                1D\n",
      "    LENGTH                      0.04\n",
      "GRID STEP SIZE\n",
      "    dX                          0.001\n",
      "TIME STEP                       0.002\n",
      "GRID POINTS\n",
      "    along X                     41\n",
      "DIFFUSION CONST.                2.1700e-04\n",
      "DIFFUSION NUMBER                0.5\n",
      "TOTAL SIMULATION TIME           1.08\n",
      "NUMBER OF TIME STEPS            468\n",
      "START CONDITION                 COLD-START\n",
      "**********************************************************\n",
      "SUCEESS: Configuration completed.\n",
      "\n",
      "Uniform rectangular grid generation in cartesian coordinate system: Completed.\n",
      "Calculating diffusion numbers: Completed.\n"
     ]
    }
   ],
   "source": [
    "# Import modules\n",
    "import nanpack.preprocess as pre\n",
    "from nanpack.grid import RectangularGrid\n",
    "import nanpack.parabolicsolvers as pb\n",
    "import nanpack.postprocess as post\n",
    "from nanpack.benchmark import ParallelPlateFlow\n",
    "\n",
    "cfg = pre.RunConfig(\"path/to/project/input/config.ini\")\n",
    "\n",
    "X, _ = RectangularGrid(cfg.dX, cfg.iMax)\n",
    "diffX,_ = pre.DiffusionNumbers(cfg.Dimension, cfg.diff, cfg.dT, cfg.dX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a list `func` that contains the reference to the the numerical methods. Also, since in our configuration file we can provide only one file name as the input that will lead to overwriting of results in that one file, we have to provide 4 file names to the program to save the results from different numerical methods in their respective files. Let's create a list `files` as shown in code cell 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "func = [pb.FTCS, pb.DuFortFrankel, pb.Laasonen, pb.CrankNicolson]\n",
    "files = [\"FTCS\", \"DuFortFrankel\", \"Laasonen\", \"CrankNicolson\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a `for` loop to iterate over the functions provided in the list `func`. In this way, one numerical solution is obtained using one method at the required time step and after completion, the next numerical method will be executed and so on, until all the numerical methods in `func` list have been executed.\n",
    "\n",
    "Since DuFort-Frankel method requires two sets of initial solution, one of which will be obtained using the FTCS method as can be seen in the codes. The accurcy of the DuFort-Frankel method depends on this starter solution and often this starter solution is provided by the analytical solution, if available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "STATUS: SOLUTION OBTAINED AT\n",
      "TIME LEVEL= 1.08 s.\n",
      "TIME STEPS= 468\n",
      "\n",
      "Writing convergence log file: Completed.\n",
      "Files saved:\n",
      "\"D:/MyProjects/projectroot/nanpack/output/histFTCS1D.dat\".\n",
      "\n",
      "\n",
      "STATUS: SOLUTION OBTAINED AT\n",
      "TIME LEVEL= 1.08 s.\n",
      "TIME STEPS= 468\n",
      "\n",
      "Writing convergence log file: Completed.\n",
      "Files saved:\n",
      "\"D:/MyProjects/projectroot/nanpack/output/histDuFortFrankel1D.dat\".\n",
      "\n",
      "\n",
      "STATUS: SOLUTION OBTAINED AT\n",
      "TIME LEVEL= 1.08 s.\n",
      "TIME STEPS= 468\n",
      "\n",
      "Writing convergence log file: Completed.\n",
      "Files saved:\n",
      "\"D:/MyProjects/projectroot/nanpack/output/histLaasonen1D.dat\".\n",
      "\n",
      "\n",
      "STATUS: SOLUTION OBTAINED AT\n",
      "TIME LEVEL= 1.08 s.\n",
      "TIME STEPS= 468\n",
      "\n",
      "Writing convergence log file: Completed.\n",
      "Files saved:\n",
      "\"D:/MyProjects/projectroot/nanpack/output/histCrankNicolson1D.dat\".\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Start a loop for 4 solver functions\n",
    "for f in range(len(func)):\n",
    "    # Define initial conditions\n",
    "    cfg.U[0] = 40.0\n",
    "    cfg.U[1:] = 0.0\n",
    "    # Define boundary conditions\n",
    "    U = BC(cfg.U)\n",
    "    # Start iterations\n",
    "    Error = 1.0\n",
    "    n = 0\n",
    "\n",
    "    while n <= cfg.nMax and Error > cfg.ConvCrit:\n",
    "        Error = 0.0\n",
    "        n += 1\n",
    "        # DuFort Frankel will be executed in this block\n",
    "        if f == 1:\n",
    "            if n == 1: # at first-time step, obtain starter solutions\n",
    "                Uold = U.copy() # initial condition for n= -1 time level\n",
    "                U = pb.FTCS(Uold, diffX) # FTCS solution for n=0 time level  \n",
    "            Uold2 = Uold.copy() # Store solution at (n-1)th time step\n",
    "            Uold = U.copy() # Store solution at (n)th time step\n",
    "            U = func[f](Uold, Uold2, diffX)\n",
    "        # All other numerical methods will be executed in this block\n",
    "        else: \n",
    "            Uold = U.copy()\n",
    "            U = func[f](Uold, diffX)\n",
    "        Error = post.AbsoluteError(U, Uold)\n",
    "        # Update BC\n",
    "        U = BC(U)\n",
    "        # Write output to file\n",
    "        # Provide a complete path where the files will be stored\n",
    "        fname = f\"path/to/project/output/{files[f]}1D.dat\"\n",
    "        convfname = f\"path/to/project/output/hist{files[f]}1D.dat\"\n",
    "        post.WriteSolutionToFile(U, n, cfg.nWrite, cfg.nMax, fname, cfg.dX)\n",
    "        # Write convergence history log to a file\n",
    "        post.WriteConvHistToFile(cfg, n, Error, convfname)\n",
    "\n",
    "    # Write output to file\n",
    "    post.WriteSolutionToFile(U, n, cfg.nWrite, cfg.nMax, fname, cfg.dX)\n",
    "    # Write convergence history log to a file\n",
    "    post.WriteConvHistToFile(cfg, n, Error, convfname)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain analytical solution\n",
    "Uana = ParallelPlateFlow(40.0, X, cfg.diff, cfg.totTime, 20)\n",
    "post.WriteSolutionToFile(Uana, 10, cfg.nWrite, cfg.nMax,\n",
    "                         \"path/to/project/output/analytical1D.dat\",\n",
    "                         cfg.dX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results using the `Plot1DResults` function included in the package. Use `help(Plot1DResults)` command to see the allowed input arguments. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing data to plot results...\n",
      "Plotting 1D results\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fn = [] # empty list to store full path to the files\n",
    "files.append(\"analytical\") # add another file name to the files list\n",
    "for f in range(len(files)): # add complete path to files from which the plotting function will read data to plot\n",
    "    fn.append(f\"path/to/project/output/{files[f]}1D.dat\")\n",
    "# Call the plotting function and provide arguments to customize plot\n",
    "post.Plot1DResults(dataFiles=fn, uAxis=\"X\", Markers=\"default\", Legend=files,\\\n",
    "                   Title=f\"Comparison of Numerical Methods\\nat t={cfg.totTime} s.\",\n",
    "                  xLabel=\"Velocity (m/s)\", yLabel=\"Plate distance (m)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def BC(U):\n",
    "    \"\"\"Return the dependent variable with the updated values at the boundaries.\"\"\"\n",
    "    U[0] = 40.0\n",
    "    U[-1] = 0.0\n",
    "\n",
    "    return U"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulation, you have created a script to run all the available numerical solvers for 1D diffusion model and compared the numerical results using plotting tools."
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}