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   "cells": [
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Texto y c\u00f3digo sujeto bajo Creative Commons Attribution license, CC-BY-SA. (c) Original por Lorena A. Barba y Gilbert Forsyth en 2013, traducido por F.J. Navarro-Brull para CAChemE.org"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[@LorenaABarba](https://twitter.com/LorenaABarba) - \n",
      "[@CAChemEorg](https://twitter.com/cachemeorg)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "12 pasos para Navier-Stokes\n",
      "=====\n",
      "***"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "En el paso previo resolvimos la [Ecuaci\u00f3n 2D de Burgers](http://nbviewer.ipython.org/urls/bitbucket.org/franktoffel/cfd-python-class-es/raw/master/lecciones/10%2520-%2520Paso%25208.ipynb): una ecuaci\u00f3n importante en la mec\u00e1nica de fluidos, debido a que contiene la convecci\u00f3n no lineal completa de las ecuaciones de flujo y al mismo tiempo posee muchas soluciones anal\u00edticas conocidas. Con aquel ejercicio, tambi\u00e9n ganamos experiencia para codificar gradualmente un solucionador de las ecuaciones de Navier-Stokes. \u00bfPoca cosa no? ;)\n",
      "\n",
      "En los dos pasos siguientes, vamos a resolver Laplace y la ecuaci\u00f3n de Poisson, para finalmente ponerlo todo unido."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Paso 9: La ecuaci\u00f3n de Laplace en 2D.\n",
      "----\n",
      "***"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Aqu\u00ed esta la ecuaci\u00f3n de Laplace en 2D:\n",
      "\n",
      "$$\\frac{\\partial ^2 p}{\\partial x^2} + \\frac{\\partial ^2 p}{\\partial y^2} = 0$$\n",
      "\n",
      "Sabemos c\u00f3mo discretizar una derivada de segunda de orden. Pero piensa en esto por un minuto, la ecuaci\u00f3n de Laplace tiene las caracter\u00edsticas t\u00edpicas de los fen\u00f3menos de difusi\u00f3n. Por esta raz\u00f3n, tiene que ser discretizado con *diferencias centradas*, de modo que la discretizaci\u00f3n es consistente con la f\u00edsica que se desea simular.\n",
      "\n",
      "La ecuaci\u00f3n discretizada es:\n",
      "\n",
      "$$\\frac{p_{i+1, j}^n - 2p_{i,j}^n + p_{i-1,j}^n}{\\Delta x^2} + \\frac{p_{i,j+1}^n - 2p_{i,j}^n + p_{i, j-1}^n}{\\Delta y^2} = 0$$\n",
      "\n",
      "Ten en cuenta que la ecuaci\u00f3n de Laplace no tiene una dependencia del tiempo - no hay $p^{n+1}$. En lugar del seguimiento de una onda a trav\u00e9s del tiempo (como en los pasos anteriores), la ecuaci\u00f3n de Laplace calcula el estado de equilibrio de un sistema bajo las condiciones de contorno suministradas.\n",
      "\n",
      "Si has estudiado el fen\u00f3meno de transferencia de calor, reconocer\u00e1s la Ecuaci\u00f3n de Laplace como la ecuaci\u00f3n del calor en estado estacionario.\n",
      "\n",
      "En lugar de calcular como estar\u00e1 el sistema en alg\u00fan tiempo $t$, vamos a resolver iterativamente por $p_{i,j}^n$ hasta que se llegue a una condici\u00f3n que se especifica. El sistema alcanza el equilibrio s\u00f3lo cuando el n\u00famero de iteraciones tiende a $\\infty$, pero podemos aproximar el estado de equilibrio iterando hasta que el cambio entre una iteraci\u00f3n y la siguiente sea *muy* peque\u00f1o.\n",
      "\n",
      "\n",
      "Vamos a reorganizar la ecuaci\u00f3n discretizada, resolviendo para $p_{i,j}^n$:\n",
      "\n",
      "$$p_{i,j}^n = \\frac{\\Delta y^2(p_{i+1,j}^n+p_{i-1,j}^n)+\\Delta x^2(p_{i,j+1}^n + p_{i,j-1}^n)}{2(\\Delta x^2 + \\Delta y^2)}$$\n",
      "\n",
      "Utilizar esquemas de diferencias centradas de segundo orden en ambas direcciones, corresponde con el m\u00e9todo m\u00e1s ampliamente aplicado para el operador de Laplace. Tambi\u00e9n se conoce como el **five-point difference operator**, aludiendo a su plantilla de cinco puntos."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vamos a resolver la ecuaci\u00f3n de Laplace num\u00e9ricamente asumiendo un estado inicial de $p=0$ en todas partes. Entonces a\u00f1adiremos las condiciones de contorno de la siguiente manera:\n",
      "\n",
      "$p=0$ en $x=0$\n",
      "\n",
      "$p=y$ en $x=2$\n",
      "\n",
      "$\\frac{\\partial p}{\\partial y}=0$ en $y=0, \\ 1$\n",
      "\n",
      "En estas condiciones, hay una soluci\u00f3n anal\u00edtica para la ecuaci\u00f3n de Laplace:\n",
      "\n",
      "$$p(x,y)=\\frac{x}{4}-4\\sum_{n=1,odd}^{\\infty}\\frac{1}{(n\\pi)^2\\sinh2n\\pi}\\sinh n\\pi x\\cos n\\pi y$$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Ejercicio"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Escribe tu propio c\u00f3digo para resolver la ecuaci\u00f3n de Poisson con bucles, al estilo de codificaci\u00f3n utilizado en nuestras primeras lecciones. Entonces, considera la demostraci\u00f3n de c\u00f3mo se escribe usando funciones (abajo) y modifica el c\u00f3digo con ese estilo. \u00bfPuedes pensar en razones para abandonar el viejo estilo y adoptar c\u00f3digos modulares?\n",
      "\n",
      "Otros consejos:\n",
      "\n",
      "+ Visualiza cada paso del proceso iterativo\n",
      "+ Piensa en lo que est\u00e1n haciendo las condiciones de contorno\n",
      "+ Piensa en lo que est\u00e1s haciendo el PDE"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Usando funciones"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00bfRecuerdas la lecci\u00f3n de [escribiendo funciones en Python](http://nbviewer.ipython.org/urls/bitbucket.org/franktoffel/cfd-python-class-es/raw/master/lecciones/11%2520-%2520Definiendo%2520funciones.ipynb)? Vamos a utilizar ese estilo de programar para este ejercicio.\n",
      "\n",
      "Vamos a definir dos funciones: una que dibuje los datos en un gr\u00e1fico en 3D y el otro que itere para resolver por $p$ hasta que el cambio en la [Norma L1](http://es.wikipedia.org/wiki/Geometr%C3%ADa_del_taxista) de $p$ sea menor que un valor especificado.   \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "from matplotlib import cm\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot2D(x, y, p):\n",
      "    fig = plt.figure(figsize=(11,7), dpi=100)\n",
      "    ax = fig.gca(projection='3d')\n",
      "    X,Y = np.meshgrid(x,y)\n",
      "    surf = ax.plot_surface(X,Y,p[:], rstride=1, cstride=1, cmap=cm.coolwarm,\n",
      "            linewidth=0, antialiased=False)\n",
      "    ax.set_xlim(0,2)\n",
      "    ax.set_ylim(0,1)\n",
      "    ax.view_init(30,225)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La funci\u00f3n `plot2D` tiene tres argumentos, un vector-x, un vector-y y nuestra matriz p. Teniendo en cuenta estos tres valores, se produce un gr\u00e1fico en 3D, establece los l\u00edmites de la malla y nos da un buen \u00e1ngulo de visi\u00f3n."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def laplace2d(p, y, dx, dy, l1norm_target):\n",
      "    l1norm = 1\n",
      "    pn = np.empty_like(p)\n",
      "\n",
      "    while l1norm > l1norm_target:\n",
      "        pn[:] = p[:]\n",
      "        p[1:-1,1:-1] = (dy**2*(pn[2:,1:-1]+pn[0:-2,1:-1])+dx**2*(pn[1:-1,2:]+pn[1:-1,0:-2]))/(2*(dx**2+dy**2)) \n",
      "        p[0,0] = (dy**2*(pn[1,0]+pn[-1,0])+dx**2*(pn[0,1]+pn[0,-1]))/(2*(dx**2+dy**2))\n",
      "        p[-1,-1] = (dy**2*(pn[0,-1]+pn[-2,-1])+dx**2*(pn[-1,0]+pn[-1,-2]))/(2*(dx**2+dy**2)) \n",
      "    \n",
      "        p[:,0] = 0\t\t##p = 0 @ x = 0\n",
      "        p[:,-1] = y\t\t##p = y @ x = 2\n",
      "        p[0,:] = p[1,:]\t\t##dp/dy = 0 @ y = 0\n",
      "        p[-1,:] = p[-2,:]\t##dp/dy = 0 @ y = 1\n",
      "        l1norm = (np.sum(np.abs(p[:])-np.abs(pn[:])))/np.sum(np.abs(pn[:]))\n",
      "     \n",
      "    return p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`laplace2d` toma un total de cinco argumentos de entrada: la matriz `p`, el `vector-y`, `dx`, `dy` y `l1norm_target valor`. Este \u00faltimo valor define c\u00f3mo de pr\u00f3ximo debe estar  la matriz `p` en dos iteraciones consecutivas antes de finalizar el bucle `while` y devuelve el valor calculado `p`.\n",
      "\n",
      "Ten en cuenta que cuando ejecutes las celdas de arriba en tu ordenador, no dar\u00e1 ninguna soluci\u00f3n. S\u00f3lo se ha *definido* la funci\u00f3n, pero a\u00fan no la has *llamado*. Ahora est\u00e1 disponible para utilizarla, al igual que `np.linspace` o cualquier otra funci\u00f3n en nuestro espacio de nombres (namespace)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Creaci\u00f3n de variables\n",
      "nx = 31\n",
      "ny = 31\n",
      "c = 1\n",
      "dx = 2.0/(nx-1)\n",
      "dy = 2.0/(ny-1)\n",
      "\n",
      "\n",
      "## Condiciones iniciales\n",
      "p = np.zeros((ny,nx)) ##create a XxY vector of 0's\n",
      "\n",
      "\n",
      "## Gu\u00edas del gr\u00e1fico\n",
      "x = np.linspace(0,2,nx)\n",
      "y = np.linspace(0,1,ny)\n",
      "\n",
      "## Condiciones de contorno\n",
      "p[:,0] = 0\t\t##p = 0 @ x = 0\n",
      "p[:,-1] = y\t\t##p = y @ x = 2\n",
      "p[0,:] = p[1,:]\t\t##dp/dy = 0 @ y = 0\n",
      "p[-1,:] = p[-2,:]\t##dp/dy = 0 @ y = 1\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ahora vamos a tratar de usar nuestra funci\u00f3n `plot2d` para echar un vistazo a las condiciones iniciales. Si la funci\u00f3n se ha definido correctamente, ahora deber\u00eda ser capaz de empezar a escribir `plot2d` y pulsa la tecla **Tab** para que automaticamente se termine de escribir (funci\u00f3n de autocompletado)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot2D(x, y, p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Un69VFzQYwW2PLZlH2vEUcNS0iu4rht6CwaDy+hMLK/r7+0tWbhrFqkGoWnY5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\nB8dAR0REROTgGOiIiIiIHNz/Aevt4IE71WmYAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f14d5e6fc10>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00a1Ha funcionado! Este es el estado inicial de nuestro problema, donde el valor de `p` es cero en todas partes excepto por lo largo de $x=2$ donde $p=y$. Ahora vamos a tratar de ejecutar nuestra funci\u00f3n `laplace2d` con L1 especificado de 0,01\n",
      "\n",
      "(Sugerencia: si tienes problemas para recordar el orden en que se env\u00edan las variables a una funci\u00f3n, puedes escribir `laplace2d(` y Notebook IPython mostrar\u00e1 un cuadro emergente para mostr\u00e1rtelo)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = laplace2d(p, y, dx, dy, .01)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ahora intenta representar este nuevo valor de `p` en nuestra funci\u00f3n `plot2D`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot2D(x, y, p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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N48rC/qYyyEoKJwpL65Kwfs6O1Q+/E7v+2+fMLklFUTAYDNBqtQDAccYZIZ+krAEMO/sO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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f14d27e9dd0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "***"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Aprende m\u00e1s"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "En el [siguiente paso](http://nbviewer.ipython.org/urls/bitbucket.org/franktoffel/cfd-python-class-es/raw/master/lecciones/13%2520-%2520Paso%252010.ipynb) resolveremos la ecuaci\u00f3n de Poisson. Mira la **Video Lesson 11** en YouTube para comprender por qu\u00e9 necesitamos la la ecuaci\u00f3n de Poisson CFD."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "YouTubeVideo('ZjfxA3qq2Lg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "            <iframe\n",
        "                width=\"400\"\n",
        "                height=\"300\"\n",
        "                src=\"http://www.youtube.com/embed/ZjfxA3qq2Lg\"\n",
        "                frameborder=\"0\"\n",
        "                allowfullscreen\n",
        "            ></iframe>\n",
        "        "
       ],
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x10ba2fe50>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Y para un an\u00e1lsis detallado de la discretizaci\u00f3n de las ecuaci\u00f3nes de Laplace y Poisson  (pasos 9 y 10), mira la **Video Lesson 12**:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "YouTubeVideo('iwL8ashXhWU')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "            <iframe\n",
        "                width=\"400\"\n",
        "                height=\"300\"\n",
        "                src=\"http://www.youtube.com/embed/iwL8ashXhWU\"\n",
        "                frameborder=\"0\"\n",
        "                allowfullscreen\n",
        "            ></iframe>\n",
        "        "
       ],
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x10ba41c50>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "def css_styling():\n",
      "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
      "    return HTML(styles)\n",
      "css_styling()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
        "    }\n",
        "    div.cell{\n",
        "        width:800px;\n",
        "        margin-left:16% !important;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    h1 {\n",
        "        font-family: Helvetica, serif;\n",
        "    }\n",
        "    h2 {\n",
        "        font-family: Helvetica, serif;\n",
        "    }\n",
        "    h4{\n",
        "        margin-top:12px;\n",
        "        margin-bottom: 3px;\n",
        "       }\n",
        "    div.text_cell_render{\n",
        "        font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
        "        line-height: 135%;\n",
        "        font-size: 120%;\n",
        "        width:600px;\n",
        "        margin-left:auto;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    .CodeMirror{\n",
        "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
        "    }\n",
        "/*    .prompt{\n",
        "        display: None;\n",
        "    }*/\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 300;\n",
        "        font-size: 16pt;\n",
        "        color: #4057A1;\n",
        "        font-style: italic;\n",
        "        margin-bottom: .5em;\n",
        "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "    }\n",
        "    \n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
        "        }  \n",
        "</style>\n",
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
        "                           extensions: [\"AMSmath.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
        "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
        "                }\n",
        "        });\n",
        "</script>"
       ],
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<IPython.core.display.HTML at 0x2b66690>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> (La celda de arriba establece el formato de este notebook)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}