Applied_CFD_01_CFD_Intro.ipynb

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    "![Imagen](http://www.eafit.edu.co/SiteCollectionImages/logo.png?raw=true \"\")\n",
    "\n",
    "\n",
    "<h1 align=\"center\">Dinámica de Fluidos Computacional Aplicada - IM0642</h1>\n",
    "\n",
    "<h1 align=\"center\">Introducción a la CFD, Proceso de simulación</h1>\n",
    "\n",
    "<h1 align=\"center\">ESCUELA DE INGENIERÍA</h1>\n",
    "\n",
    "<h1 align=\"center\">Especialización en Turbomáquinas</h1>\n",
    "\n",
    "<h1 align=\"center\">MEDELLÍN - COLOMBIA </h1>\n",
    "\n",
    "<h1 align=\"center\">2021/01 </h1>"
   ]
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    "<table>\n",
    " <tr align=left><td><img align=left src=\"https://github.com/carlosalvarezh/CFD_Applied/blob/master/figs/CC-BY.png?raw=true\">\n",
    " <td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license.(c) Carlos Alberto Alvarez Henao</td>\n",
    "</table>\n"
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   "source": [
    "*** \n",
    "\n",
    "***Docente:*** Carlos Alberto Álvarez Henao, I.C. D.Sc.\n",
    "\n",
    "***e-mail:*** calvar52@eafit.edu.co\n",
    "\n",
    "***skype:*** carlos.alberto.alvarez.henao\n",
    "\n",
    "***Herramienta:*** [Jupyter](http://jupyter.org/)\n",
    "\n",
    "***Kernel:*** Python 3.8\n",
    "\n",
    "\n",
    "***"
   ]
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   "source": [
    "<a id='TOC'></a>"
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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introductión-to-CFD\" data-toc-modified-id=\"Introductión-to-CFD-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introductión to CFD</a></span><ul class=\"toc-item\"><li><span><a href=\"#Mathematical-and-Computational-Model\" data-toc-modified-id=\"Mathematical-and-Computational-Model-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Mathematical and Computational Model</a></span></li><li><span><a href=\"#Example-01:\" data-toc-modified-id=\"Example-01:-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Example 01:</a></span></li><li><span><a href=\"#General-Strategy:\" data-toc-modified-id=\"General-Strategy:-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>General Strategy:</a></span></li><li><span><a href=\"#Experiment-vs-Simulations\" data-toc-modified-id=\"Experiment-vs-Simulations-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Experiment vs Simulations</a></span></li><li><span><a href=\"#Fluids-Characteristics\" data-toc-modified-id=\"Fluids-Characteristics-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Fluids Characteristics</a></span><ul class=\"toc-item\"><li><span><a href=\"#Macroscopic-Properties\" data-toc-modified-id=\"Macroscopic-Properties-1.5.1\"><span class=\"toc-item-num\">1.5.1&nbsp;&nbsp;</span>Macroscopic Properties</a></span></li><li><span><a href=\"#Classsification-of-fluid-flows\" data-toc-modified-id=\"Classsification-of-fluid-flows-1.5.2\"><span class=\"toc-item-num\">1.5.2&nbsp;&nbsp;</span>Classsification of fluid flows</a></span></li></ul></li></ul></li><li><span><a href=\"#How-does-CFD-make-predictions?\" data-toc-modified-id=\"How-does-CFD-make-predictions?-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>How does CFD make predictions?</a></span><ul class=\"toc-item\"><li><span><a href=\"#CFD-analysis-process\" data-toc-modified-id=\"CFD-analysis-process-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>CFD analysis process</a></span></li><li><span><a href=\"#Problem-statement\" data-toc-modified-id=\"Problem-statement-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Problem statement</a></span></li><li><span><a href=\"#Mathematical-model\" data-toc-modified-id=\"Mathematical-model-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Mathematical model</a></span></li><li><span><a href=\"#Discretization-process\" data-toc-modified-id=\"Discretization-process-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Discretization process</a></span></li><li><span><a href=\"#Iterative-solution-strategy\" data-toc-modified-id=\"Iterative-solution-strategy-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Iterative solution strategy</a></span></li></ul></li><li><span><a href=\"#CFD-simulations\" data-toc-modified-id=\"CFD-simulations-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span><em>CFD</em> simulations</a></span><ul class=\"toc-item\"><li><span><a href=\"#Postprocessing-and-analysis\" data-toc-modified-id=\"Postprocessing-and-analysis-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Postprocessing and analysis</a></span></li><li><span><a href=\"#Uncertainty-and-error\" data-toc-modified-id=\"Uncertainty-and-error-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Uncertainty and error</a></span></li><li><span><a href=\"#Classification-of-errors\" data-toc-modified-id=\"Classification-of-errors-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Classification of errors</a></span></li><li><span><a href=\"#Verification-of-CFD-codes\" data-toc-modified-id=\"Verification-of-CFD-codes-3.4\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>Verification of <em>CFD</em> codes</a></span></li><li><span><a href=\"#Validation-of-CFD-models\" data-toc-modified-id=\"Validation-of-CFD-models-3.5\"><span class=\"toc-item-num\">3.5&nbsp;&nbsp;</span>Validation of <em>CFD</em> models</a></span></li><li><span><a href=\"#Available-CFD-software\" data-toc-modified-id=\"Available-CFD-software-3.6\"><span class=\"toc-item-num\">3.6&nbsp;&nbsp;</span>Available CFD software</a></span></li></ul></li></ul></div>"
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    "<p float=\"center\">\n",
    "  <img src=\"https://github.com/carlosalvarezh/CFD_Applied/blob/master/figs/C01_Img01.gif?raw=true\" width=\"350\" />\n",
    "</p>\n"
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   "source": [
    "## Introductión to CFD"
   ]
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    "- Fluid (gas and liquid) flows are governed by Partial Differential Equations ($PDE$) which represent conservation laws for the $mass$, $momentum$, and $energy$.\n",
    "\n",
    "\n",
    "- Computational Fluid Dynamics ($CFD$) is the art of replacing such $PDE$ systems by a set of algebraic equations which can be solved using digital computers\n",
    "\n",
    "\n",
    "- Provides a qualitative (and sometimes even quantitative) prediction of fluid flows by means of:\n",
    "  - Mathematical modeling (partial differential equations)\n",
    "  - Numerical methods (discretization and solution techniques)\n",
    "  - Software tools (solvers, pre- and postprocessing utilities)\n",
    "  - $CFD$ enables scientists and engineers to perform ‘numerical experiments’ (i.e. computer simulations) in a ‘virtual flow laboratory’\n"
   ]
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   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
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   "source": [
    "### Mathematical and Computational Model"
   ]
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    "<strong>- Mathematical Model:</strong> Collection of mathematical constructions that provide abstractions of a physical event consistent with a scientific theory proposed to cover that event.\n",
    "\n",
    "\n",
    "<strong>- Computational Model:</strong> Discrete version of a mathematical model that has been designed to be implemented in a machine."
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![ProcesoSimulacion.PNG](attachment:ProcesoSimulacion.PNG)"
   ]
  },
  {
   "attachments": {
    "ProcesoSimulacion2.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![ProcesoSimulacion2.PNG](attachment:ProcesoSimulacion2.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Example 01: \n",
    "\n",
    "\n",
    "From Newton's Second Law:\n",
    "\n",
    "$$\\vec{F}  = m  \\vec{a}$$\n",
    "\n",
    "Clearing $\\vec{a}$ and representing it as the rate of change of velocity, $\\vec{v}$, with respect to time, $t$.\n",
    "\n",
    "\n",
    "$$\\frac{d\\vec{v}}{dt} = \\frac{\\vec{F}}{m}$$\n",
    "\n",
    "$$\\vec{F} = \\vec{FD} + \\vec{FU}$$\n",
    "\n",
    "$$\\vec{FD} = m  \\vec{g}$$\n",
    "\n",
    "$$\\vec{FU} = -c  \\vec{v}$$\n",
    "\n",
    "$$\\frac{d\\vec{v}}{dt} = \\frac{(m\\vec{g} - c\\vec{v})}{m}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Simplifying\n",
    "\n",
    "$$\\frac{d\\vec{v}}{dt} = \\vec{g} - c \\frac{\\vec{v}}{m}$$\n",
    "\n",
    "$EDO$ that relates the acceleration of a falling body, with the forces acting on it.\n",
    "\n",
    "- If the object is initially at rest $\\left(t = 0, v(t=0) = 0\\right)$:\n",
    "\n",
    "$$v(t) = \\frac{m\\vec{g}}{c}\\left(1 - e^{-\\frac{c}{m}t}\\right)$$\n",
    "\n",
    "\n",
    "> <strong>Statement:</strong> A parachutist with mass, $m = 68.1 kg$ jumps from a fixed balloon. Calculate the speed before opening the parachute. The coefficient of resistance is approximately $c = 12.5 kg / s$. Acceleration due to gravity $\\vec{g} = 9.8 m/s2$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<strong>- Analitic Solution (exact solution):</strong>\n",
    "\n",
    "Substituting the values in the previous equation:\n",
    "\n",
    "$$v(t) = \\frac{9.8 \\times 68.1}{12.5}\\left(1 - e^{-\\frac{12.5}{68.1}t}\\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# <-- comments in python are denoted by the pound sign, like this one\n",
    "\n",
    "import numpy as np               # import the array library\n",
    "import matplotlib.pyplot as plt  # import plotting library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Constants\n",
    "\n",
    "m = 68.1\n",
    "c = 12.5\n",
    "g = 9.78\n",
    "dt = 1.0\n",
    "n = 50\n",
    "\n",
    "cte1 = m / c\n",
    "cte2 = 1.0 / cte1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Exact Solution Plot:\n",
    "\n",
    "def f(t,cte1,cte2):\n",
    "    return g*cte1*(1-np.exp(-cte2*t))\n",
    "\n",
    "t = np.arange(0.0, n+1, dt)\n",
    "plt.xlabel (r\"t\")\n",
    "plt.ylabel (r'$v_{ex}(t)$')\n",
    "plt.title (r'$t$ vs $v_{ex}(t)$')\n",
    "plt.plot(t, f(t,cte1,cte2))\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<strong>- Numerical Solution (Discrete Solution):</strong>\n",
    "\n",
    "Approximating the rate of change of speed versus time:\n",
    "\n",
    "$$\\frac{dv}{dt} \\approx \\frac{\\Delta v}{\\Delta t}=\\frac{v(t_{i+1})-v(t_i)}{t_{i+1}-t_i}$$\n",
    "\n",
    "from calculus:\n",
    "\n",
    "$$\\frac{dv}{dt} = \\lim_{\\Delta t \\rightarrow  0} \\frac{\\Delta v}{\\Delta t}$$\n",
    "\n",
    "This equation is called $\\textbf{Divided Finite Difference}$, and is an approximation of the derivative in time $t_i$.\n",
    "\n",
    "Substituting in the approximate equation:\n",
    "\n",
    "$$\\frac{v(t_{i+1})-v(t_i)}{t_{i+1}-t_i}=g-\\frac{c}{m}v(t_i)$$\n",
    "\n",
    "rearranging:\n",
    "\n",
    "$$v(t_{i+1})=v(t_{i})+\\left(g-\\frac{c}{m}v(t_{i})\\right)\\left(t_{i+1}-t_{i}\\right)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The meaning of the above equation can be summarized as follows:\n",
    "\n",
    "\n",
    "$$\\text{New value = Previous value + slope} \\times \\text{Step size}$$\n",
    "\n",
    "At the beginning of the calculations ($t = 0$), the parachute velocity is equal to zero ($0$). With this information and the given parameter values, the last equation can be used to calculate the velocity at each of the following times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Approximate Solution (Discrete Solution)\n",
    "\n",
    "t = np.arange(0.0, n+1, dt)\n",
    "vap = np.zeros(n+1)\n",
    "dt = t[1]-t[0]\n",
    "\n",
    "for i in range(1,n+1):\n",
    "    vap[i] = vap[i-1] + (g-cte2*vap[i-1])*dt\n",
    "\n",
    "plt.xlabel (r\"t\")\n",
    "plt.ylabel (r\"$v_{approx}$(t)\")\n",
    "plt.title (r\"$t$ vs $v_{approx}$(t)\")\n",
    "plt.plot (t, vap)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "plt.xlabel (r\"$t$\")\n",
    "plt.ylabel (r\"$v$\")\n",
    "plt.title (r\"$v_{Exact}$ vs $v_{approx}$\")\n",
    "plt.plot(t, f(t,cte1,cte2),'b', label='$v_{Exact}$')\n",
    "plt.plot(t, vap,'r', label='$v_{approx}$')\n",
    "plt.legend(loc='center right')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General Strategy:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "***Replace a difficult problem with an easier one that has the same solution***\n",
    "\n",
    "\n",
    "- Infinite processes by finite processes:\n",
    "\n",
    "  - Integrals by series;\n",
    "\n",
    "  - Derivative by finite differences;\n",
    "\n",
    "- Complex functions by simple functions: e.g. Polynomials\n",
    "\n",
    "- Nonlinear Problems by Linear one\n",
    "\n",
    "- Differential by Algebraic Equations\n",
    "\n",
    "- High order by low order systems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment vs Simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "$CFD$ gives an insight into flow patterns that are difficult, expensive or impossible to study using traditional (experimental) techniques: \n",
    "\n",
    "\n",
    "| Experiments                        | Simulations                                  |\n",
    "|:-----------------------------------|:---------------------------------------------|\n",
    "| Quantitative description of flow phenomena using measurements:| Quantitative prediction of flow phenomena using CFD software:|\n",
    "| - For one quantity at a time                                  | - For all desired quantities                                 |\n",
    "| - At a limited number of points and time instants             | - With high resolution in space and time                     |\n",
    "| - For a laboratory-scale model                                | - For the actual flow domain                                 |\n",
    "| - For a limited range of problems and operating conditions  | - For virtually any problem and realistic operating conditions.|\n",
    "|                                                               |                                                              |\n",
    "| <strong>Error sources:</strong> measurement errors, flow disturbances by the probes | <strong>Error sources:</strong> modeling, discretization, iteration, implementation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "As a rule, $CFD$ does not replace the measurements completely but the amount of experimentation and the overall cost can be significantly reduced.\n",
    "\n",
    "| Experiments                        | Simulations                                  |\n",
    "|:-----------------------------------|:---------------------------------------------|\n",
    "| - Expensive                        | - Cheap(er)                                  |\n",
    "| - Slow                             | - Fast(er)                                   |\n",
    "| - Sequential                       | - Parallel                                   |\n",
    "| - Single - Purpose                 | - Multiple-Porpose                           |\n",
    "\n",
    "\n",
    "- Equipment and personnel are difficult to transport\n",
    "\n",
    "\n",
    "- $CFD$ software is portable, easy to use and modify"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The results of a $CFD$ simulation are never 100% reliable because:\n",
    "\n",
    "• Input data may involve too much guessing or imprecision;\n",
    "\n",
    "\n",
    "• Mathematical model of the problem at hand may be inadequate;\n",
    "\n",
    "\n",
    "• Accuracy of the results is limited by the available computing power."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fluids Characteristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Macroscopic Properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- $\\rho$: Density\n",
    "\n",
    "- $\\mu$: Viscosity\n",
    "\n",
    "- $p$: Pressure\n",
    "\n",
    "- $T$: Temperature\n",
    "\n",
    "- $v$: Velocity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classsification of fluid flows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "| Clasificacion            |         fluidos     |\n",
    "|:-------------------------|:--------------------|\n",
    "| - Viscous                | - Inviscid          |\n",
    "| - Compressible           | - Incompressible    |\n",
    "| - Steady                 | - Unsteady          |\n",
    "| - Laminar                | - Turbulent         |\n",
    "| - Single - Phase         | - Multiphase        |\n",
    "\n",
    "\n",
    "The reliability of $CFD$ simulations is greater:\n",
    "\n",
    "• for laminar/slow flows than for turbulent/fast ones;\n",
    "\n",
    "• for single-phase flows than for multi-phase flows;\n",
    "\n",
    "• for chemically inert systems than for reactive flows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How does CFD make predictions?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- $CFD$ uses a computer to solve the mathematical equations for the problem at hand. \n",
    "\n",
    "\n",
    "- The main components of a $CFD$ design cycle are as follows:\n",
    "\n",
    "  - The $\\text{human being}$ (analyst) who states the problem to be solved;\n",
    "\n",
    "  - $\\text{Scientific knowledge}$ (models, methods) expressed mathematically;\n",
    "\n",
    "  - The $\\text{computer code}$ (software) which embodies this knowledge and provides detailed instructions (algorithms)\n",
    "\n",
    "  - The $\\text{computer hardware}$ which performs the actual calculations\n",
    "\n",
    "  - The $\\text{human being}$ who inspects and interprets the simulation results\n",
    "\n",
    "\n",
    "- $CFD$ is a highly interdisciplinary research area which lies at the interface of physics, applied mathematics, and computer science\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CFD analysis process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<strong>- Problem statement:</strong> information about the flow;\n",
    "\n",
    "\n",
    "<strong>- Mathematical model:</strong> $IBVP = PDE + IC + BC$;\n",
    "\n",
    "\n",
    "<strong>- Mesh generation:</strong> nodes/cells, time instants;\n",
    "\n",
    "\n",
    "<strong>- Space discretization:</strong> coupled $ODE/DAE$ systems;\n",
    "\n",
    "\n",
    "<strong>- Time discretization:</strong> algebraic system $[A]{x} = {b}$;\n",
    "\n",
    "\n",
    "<strong>- Iterative solver:</strong> discrete function values;\n",
    "\n",
    "\n",
    "<strong>- $CFD$ software:</strong> implementation, debugging;\n",
    "\n",
    "\n",
    "<strong>- Simulation run:</strong> parameters, stopping criteria;\n",
    "\n",
    "\n",
    "<strong>- Postprocessing:</strong> visualization, analysis of data;\n",
    "\n",
    "\n",
    "<strong>- Verification:</strong> model validation / adjustment.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- What is $known$ $about$ $the$ $flow$ $problem$ to be dealt with?\n",
    "\n",
    "\n",
    "- What $physical$ $phenomena$ $need$ to be taken into account?\n",
    "\n",
    "\n",
    "- What is the $geometry$ $of$ $the$ $domain$ and $operating$ $conditions$?\n",
    "\n",
    "\n",
    "- Are there $any$ $internal$ $obstacles$ or free surfaces/interfaces?\n",
    "\n",
    "\n",
    "- What is the $type$ $of$ $flow$ (laminar/turbulent, steady/unsteady)?\n",
    "\n",
    "\n",
    "- What is the $objective$ $of$ $the$ $CFD$ $analysis$ to be performed?\n",
    "\n",
    "  - computation of integral quantities (lift, drag, yield)\n",
    "\n",
    "  - snapshots of field data for velocities, concentrations etc.\n",
    "\n",
    "  - shape optimization aimed at an improved performance\n",
    "\n",
    "\n",
    "- What is the easiest/cheapest/fastest way to achieve the goal?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mathematical model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Choose a suitable flow model (viewpoint) and reference frame;\n",
    "\n",
    "\n",
    "- Identify the forces which cause and influence the fluid motion;\n",
    "\n",
    "\n",
    "- Define the computational domain in which to solve the problem,\n",
    "\n",
    "\n",
    "- Formulate conservation laws for the Mass, Momentum, and Energy;\n",
    "\n",
    "\n",
    "- Simplify the governing equations to reduce the computational effort:\n",
    " \n",
    "  - use available information about the prevailing flow regime\n",
    "  \n",
    "  - check for symmetries and predominant flow directions (1D/2D)\n",
    "  \n",
    "  - neglect the terms which have little or no influence on the results\n",
    "  \n",
    "  - model the effect of small-scale fluctuations that cannot be captured\n",
    "  \n",
    "  - incorporate a priori knowledge (measurement data, CFD results)\n",
    "\n",
    "\n",
    "- Add constitutive relations and specify initial/boundary conditions.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discretization process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The $PDE$ system is transformed into a set of *Differential Algebraic Equations (DAE):\n",
    "\n",
    "- ***Mesh generation*** (decomposition into cells/elements)\n",
    "\n",
    "  - structured or unstructured, triangular or quadrilateral?\n",
    "\n",
    "  - CAD tools + grid generators (Delaunay, advancing front)\n",
    "\n",
    "  - mesh size, adaptive refinement in ‘interesting’ flow regions\n",
    "\n",
    "\n",
    "- ***Space discretization*** (approximation of spatial derivatives)\n",
    "\n",
    "  - finite differences/volumes/elements\n",
    "\n",
    "  - high- vs. low-order approximations\n",
    "\n",
    "\n",
    "- ***Time discretization*** (approximation of temporal derivatives)\n",
    "\n",
    "  - explicit vs. implicit schemes, stability constraints\n",
    "\n",
    "  - local time-stepping, adaptive time step control\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterative solution strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The coupled nonlinear algebraic equations must be solved iteratively\n",
    "\n",
    "- ***Outer iterations:*** the coefficients of the discrete problem are updated using the solution values from the previous iteration so as to\n",
    "\n",
    "  - get rid of the nonlinearities by a Newton-like method\n",
    "  \n",
    "  - solve the governing equations in a segregated fashion\n",
    "\n",
    "\n",
    "- ***Inner iterations:*** the resulting sequence of linear subproblems is typically solved by an iterative method (conjugate gradients, multigrid) because direct solvers (Gaussian elimination) are prohibitively expensive\n",
    "\n",
    "\n",
    "- ***Convergence criteria:*** it is necessary to check the residuals, relative solution changes and other indicators to make sure that the iterations converge.\n",
    "\n",
    "\n",
    "As a rule, the algebraic systems to be solved are very large (millions of unknowns) but sparse, i.e., most of the matrix coefficients are equal to zero.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *CFD* simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The *computing times* for a flow simulation depend on:\n",
    "\n",
    "- the choice of numerical algorithms and data structures;\n",
    "\n",
    "- linear algebra tools, stopping criteria for iterative solvers;\n",
    "\n",
    "- discretization parameters (mesh quality, mesh size, time step);\n",
    "\n",
    "- cost per time step and convergence rates for outer iterations;\n",
    "\n",
    "- programming language (most $CFD$ codes are written in `Fortran`);\n",
    "\n",
    "- many other things (hardware, vectorization, parallelization etc.);\n",
    "\n",
    "\n",
    "The quality of simulation results depends on:\n",
    "\n",
    "- the mathematical model and underlying assumptions;\n",
    "\n",
    "- approximation type, stability of the numerical scheme;\n",
    "\n",
    "- mesh, time step, error indicators, stopping criteria . . ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Postprocessing and analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Postprocessing of the simulation results is performed in order to extract the desired information from the computed flow field:\n",
    "\n",
    "- calculation of derived quantities (streamfunction, vorticity);\n",
    "\n",
    "\n",
    "- calculation of integral parameters (lift, drag, total mass);\n",
    "\n",
    "\n",
    "- visualization (representation of numbers as images):\n",
    "\n",
    "  - $1D$ data: function values connected by straight lines;\n",
    "  - $2D$ data: streamlines, contour levels, color diagrams;\n",
    "  - $3D$ data: cutlines, cutplanes, isosurfaces, isovolumes;\n",
    "  - arrow plots, particle tracing, animations $\\ldots$\n",
    "\n",
    "\n",
    "- Systematic data analysis by means of statistical tools;\n",
    "\n",
    "\n",
    "- Debugging, verification, and validation of the $CFD$ model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
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   "source": [
    "### Uncertainty and error"
   ]
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    "Whether or not the results of a $CFD$ simulation can be trusted depends on the degree of uncertainty and on the cumulative effect of various errors.\n",
    "\n",
    "\n",
    "- Uncertainty is defined as a potential deficiency due to the lack of knowledge (turbulence modeling is a classical example)\n",
    "\n",
    "\n",
    "- Error is defined as a recognizable deficiency due to other reasons\n",
    "\n",
    "\n",
    "  - Acknowledged errors have certain mechanisms for identifying, estimating and possibly eliminating or at least alleviating them;\n",
    "  \n",
    "  \n",
    "  - Unacknowledged errors have no standard procedures for detecting them and may remain undiscovered causing a lot of harm;\n",
    "\n",
    "\n",
    "  - Local errors refer to solution errors at a single grid point or cell;\n",
    "\n",
    "\n",
    "  - Global errors refer to solution errors over the entire flow domain.\n",
    "\n",
    "\n",
    "Local errors contribute to the global error and may move throughout the grid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification of errors"
   ]
  },
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   "metadata": {
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     "slide_type": "slide"
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   "source": [
    "***Acknowledged errors***\n",
    "\n",
    "- Physical modeling error due to uncertainty and deliberate simplifications;\n",
    "\n",
    "\n",
    "- Discretization error   approximation of $PDEs$ by algebraic equations\n",
    "\n",
    "  - spatial discretization error due to a finite grid resolution;\n",
    "\n",
    "  - temporal discretization error due to a finite time step size;\n",
    "\n",
    "\n",
    "- Iterative convergence error which depends on the stopping criteria;\n",
    "\n",
    "\n",
    "- Round-off errors due to the finite precision of computer arithmetic\n",
    "\n",
    "\n",
    "***Unacknowledged errors***\n",
    "\n",
    "\n",
    "- Computer programming error: “bugs” in coding and logical mistakes;\n",
    "\n",
    "\n",
    "- Usage error: wrong parameter values, models or boundary conditions\n",
    "\n",
    "*Awareness of these error sources and an ability to control or preclude the error are important prerequisites for developing and using $CFD$ software*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verification of *CFD* codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
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   "source": [
    "Verification amounts to looking for errors in the implementation of the models (loosely speaking, the question is: “***are we solving the equations right***”?)\n",
    "\n",
    "- <strong>Examine the computer programming</strong> by visually checking the source code, documenting it and testing the underlying subprograms individually;\n",
    "\n",
    "\n",
    "- <strong>Examine iterative convergence</strong> by monitoring the residuals, relative changes of integral quantities and checking if the prescribed tolerance is attained;\n",
    "\n",
    "\n",
    "- <strong>Examine consistency</strong> (check if relevant conservation principles are satisfied);\n",
    "\n",
    "\n",
    "- <strong>Examine grid convergence:</strong> as the mesh and/or and the time step are refined, the spatial and temporal discretization errors, respectively, should asymptotically approach zero (in the absence of round-off errors);\n",
    "\n",
    "\n",
    "- <strong>Compare the computational results with analytical and numerical solutions</strong> for standard benchmark configurations (representative test cases).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation of *CFD* models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Validation amounts to checking if the model itself is adequate for practical purposes (loosely speaking, the question is: “***are we solving the right equations***”?)\n",
    "\n",
    "\n",
    "- <strong>Verify the code</strong> to make sure that the numerical solutions are correct;\n",
    "\n",
    "\n",
    "- <strong>Compare the results</strong> with available experimental data (making a provision for measurement errors) to check if the reality is represented accurately enough;\n",
    "\n",
    "\n",
    "- <strong>Perform sensitivity analysis</strong> and a parametric study to assess the inherent uncertainty due to the insufficient understanding of physical processes;\n",
    "\n",
    "\n",
    "- <strong>Try using different models</strong>, geometry, and initial/boundary conditions;\n",
    "\n",
    "\n",
    "- <strong>Report the findings</strong>, document model limitations and parameter settings.\n",
    "\n",
    "\n",
    "*The goal of verification and validation is to ensure that the $CFD$ code produces reasonable results for a certain range of flow problems*.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Available CFD software"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- [ANSYS: CFX/Fluent:](https://www.ansys.com)\n",
    "\n",
    "- [Flow3D](https://www.flow3d.com/)\n",
    "\n",
    "- [OpenFoam: ](https://www.openfoam.com)\n",
    "\n",
    "- [Star-CCM: ](https://www.cd-adapco.com)\n",
    "\n",
    "- [Comsol: ](https://www.comsol.com)\n",
    "\n",
    "- [FEATFLOW: ](https://www.featflow.de)\n",
    "\n",
    "$\\vdots$\n",
    "\n",
    "- [CFD-OnLine](https://www.cfd-online.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volver a la Tabla de Contenido](#TOC)"
   ]
  }
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