03_tutorial_lbm_formulation.ipynb 406 KB
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lbmpy.session import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 03: Defining LB methods in *lbmpy*\n",
    "\n",
    "\n",
    "## A) General Form"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lattice Boltzmann equation in its most general form is:\n",
    "\n",
    "$$f_q(\\mathbf{x} + \\mathbf{c}_q \\delta t, t+\\delta t) = K\\left( f_q(\\mathbf{x}, t) \\right)$$\n",
    "\n",
    "with a discrete velocity set $\\mathbf{c}_q$ (stencil) and a generic collision operator $K$.\n",
    "\n",
    "So a lattice Boltzmann method can be fully defined by picking a stencil and a collision operator. \n",
    "The collision operator $K$ has the following structure:\n",
    "- Transformation of particle distribution function $f$ into collision space. This transformation has to be invertible and may be nonlinear.\n",
    "- The collision operation is an convex combination of the pdf representation in collision space $c$ and some equilibrium vector $c^{(eq)}$. This equilibrium can also be defined in physical space, then $c^{(eq)} = C( f^{(eq)} ) $. The convex combination is done elementwise using a diagonal matrix $S$ where the diagonal entries are the relaxation rates.\n",
    "- After collision, the collided state $c'$ is transformed back into physical space\n",
    "\n",
    "![](../img/collision.svg)\n",
    "\n",
    "\n",
    "The full collision operator is:\n",
    "\n",
    "$$K(f) = C^{-1}\\left( (I-S)C(f) + SC(f^{(eq}) \\right)$$\n",
    "\n",
    "or\n",
    "\n",
    "$$K(f) = C^{-1}\\left( C(f) - S (C(f) - C(f^{(eq})) \\right)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B) Moment-based relaxation\n",
    "\n",
    "The most commonly used LBM collision operator is the multi relaxation time (MRT) collision.\n",
    "In MRT methods the collision space is spanned by moments of the distribution function. This is a very natural approach, since the pdf moments are the quantities that go into the Chapman Enskog analysis that is used to show that LB methods can solve the Navier Stokes equations. Also the lower order moments correspond to the macroscopic quantities of interest (density/pressure, velocity, shear rates, heat flux). Furthermore the transformation to collision space is linear in this case, simplifying the collision equations:\n",
    "\n",
    "$$K(f) = C^{-1}\\left( C(f) - S (C(f) - C(f^{(eq})) \\right)$$\n",
    "\n",
    "$$K(f) = f - \\underbrace{ C^{-1}SC}_{A}(f - f^{(eq)})$$\n",
    "\n",
    "in *lbmpy* the following formulation is used, since it is more natural to define the equilibrium in moment-space instead of physical space:\n",
    "\n",
    "$$K(f) = f -  C^{-1}S(Cf - c^{(eq)})$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use a pre-defined method\n",
    "\n",
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    "Lets create a moment-based method in *lbmpy* and see how the moment transformation $C$ and the relaxation rates that comprise the diagonal matrix $S$ can be defined. We start with a function that creates a basic MRT model.\n",
    "Don't use this for real simulations, there orthogonalized MRT methods should be used, as discussed in the next section."
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table style=\"border:none; width: 100%\">\n",
       "            <tr style=\"border:none\">\n",
       "                <th style=\"border:none\" >Moment</th>\n",
       "                <th style=\"border:none\" >Eq. Value </th>\n",
       "                <th style=\"border:none\" >Relaxation Rate</th>\n",
       "            </tr>\n",
       "            <tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$1$</td>\n",
       "                            <td style=\"border:none\">$\\rho$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x$</td>\n",
       "                            <td style=\"border:none\">$u_{0}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{1}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$y$</td>\n",
       "                            <td style=\"border:none\">$u_{1}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{2}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\frac{\\rho}{3} + u_{0}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{3}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$y^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\frac{\\rho}{3} + u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{4}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x y$</td>\n",
       "                            <td style=\"border:none\">$u_{0} u_{1}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{5}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} y$</td>\n",
       "                            <td style=\"border:none\">$\\frac{u_{1}}{3}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{6}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x y^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\frac{u_{0}}{3}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{7}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} y^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\frac{\\rho}{9} + \\frac{u_{0}^{2}}{3} + \\frac{u_{1}^{2}}{3}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{8}$</td>\n",
       "                         </tr>\n",
       "\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
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       "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7fc00786c3d0>"
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      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lbmpy.creationfunctions import create_lb_method\n",
    "method = create_lb_method(stencil='D2Q9', method='mrt_raw')\n",
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    "# check also method='srt', 'trt', 'mrt'\n",
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    "method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first column labeled \"Moment\" defines the collision space and thus the transformation matrix $C$.\n",
    "The remaining columns specify the equilibrium vector in moment space $c^{(eq)}$ and the corresponding relaxation rate.\n",
    "\n",
    "Each row of the \"Moment\" column defines one row of $C$. In the next cells this matrix and the discrete velocity set (stencil) of our method are shown. Check for example the second last row of the table $x^2 y$: In the corresponding second last row of the moment matrix $C$ where each column stands for a lattice velocity (for ordering visualized stencil below) and each entry is the expression $x^2 y$ where $x$ and $y$ are the components of the lattice velocity.\n",
    "\n",
    "In general the transformation matrix $C_{iq}$ is defined as;\n",
    "\n",
    "$$c_i = C_{iq} f_q = \\sum_q  m_i(c_q)$$\n",
    "\n",
    "where $m_i(c_q)$ is the $i$'th moment polynomial where $x$ and $y$ are substituted with the components of the $q$'th lattice velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/latex": [
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       "$\\displaystyle \\left[\\begin{matrix}1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1\\\\0 & 0 & 0 & -1 & 1 & -1 & 1 & -1 & 1\\\\0 & 1 & -1 & 0 & 0 & 1 & 1 & -1 & -1\\\\0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1\\\\0 & 1 & 1 & 0 & 0 & 1 & 1 & 1 & 1\\\\0 & 0 & 0 & 0 & 0 & -1 & 1 & 1 & -1\\\\0 & 0 & 0 & 0 & 0 & 1 & 1 & -1 & -1\\\\0 & 0 & 0 & 0 & 0 & -1 & 1 & -1 & 1\\\\0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1\\end{matrix}\\right]$"
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      ],
      "text/plain": [
       "⎡1  1  1   1   1  1   1  1   1 ⎤\n",
       "⎢                              ⎥\n",
       "⎢0  0  0   -1  1  -1  1  -1  1 ⎥\n",
       "⎢                              ⎥\n",
       "⎢0  1  -1  0   0  1   1  -1  -1⎥\n",
       "⎢                              ⎥\n",
       "⎢0  0  0   1   1  1   1  1   1 ⎥\n",
       "⎢                              ⎥\n",
       "⎢0  1  1   0   0  1   1  1   1 ⎥\n",
       "⎢                              ⎥\n",
       "⎢0  0  0   0   0  -1  1  1   -1⎥\n",
       "⎢                              ⎥\n",
       "⎢0  0  0   0   0  1   1  -1  -1⎥\n",
       "⎢                              ⎥\n",
       "⎢0  0  0   0   0  -1  1  -1  1 ⎥\n",
       "⎢                              ⎥\n",
       "⎣0  0  0   0   0  1   1  1   1 ⎦"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformation matrix C\n",
    "method.moment_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAAVCAYAAADlwkDzAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAIzUlEQVR4Ae2d63EcNwyAZY8LUJIOLh1IUQW2OpDcQewO4p/Sv4zSgeMKMnYHiSvIRB04HdhRBxdgb1fH45EEH+ADWuzMekkCJIAP5Iq3XulOttvtiXne3NxszLpdpuS2fuv6CP6N4IOP+wi+jeCDjw9H+wjxjeADB0vfGCPEN4IPPj7YPoJ/I/gQYlQiGyG2EXwIMRzBvxF8CDHqLRuBzwg+1MwDFZ9L/vzEOG5vb3+B6pnRdFCk5AfK/Sqb2c8uHghgpHzqzwxlrIzrE6At6DykGZVoKF+anjKiGfXW0BzVzwDF+Ej+DD8t4AGbyiu4XMD13dRg/ROSg+xuVv8K1x/hvIO2f60hWKoxtkAHP2Q8wPV3FqORg4A9L8MYvyPNkGpgCz9gfYTzHMoPdgdoWzUfm0dOXSLjnDjtPlTctn5uHeyQ95QR5zHGq4zonye582Lp15CxuHtpyzkYY2vUdbrMpdxrqzmY65/Zj/J11BxRfpsx9i5TvlKMbfm0OYfGUwjsM1zPXQGG5CD7B/r8CtdP2HfWxbZLKLNu0GG8aFuz7ku4Hm1O0U/uA+x4Gab4nevXbP8D9P8G509w4g+V73zxzz6thg+wKD4kMy4JPjXuElvYd56bUfeUWbf7PFZG+6zPLLw/T/aaaaVWjFPtgD7+XFrVHJTKKG3GHWunxn08QruWVF/XOI9Ls8HN2MzBi9k5fEr1PuCoUw4DvYE+p3CdNubYH8r4xBrrON4ltnEcMGaqLbSPfr/lsB8xRldGyB18vEY/oRx8PWmOZVV85piLLlIZFwUNnTPizjYJtqSu89T1tzpG2QHPHVvNwww7o9xLW87BVFtDMJIyB0v9xP46jzkohseowPhxnSzvnL8GI6FXQHxy3AzeO9z/G9pewZinDlluU5KtOR70m9OHkO8jMAr5dyBTPgc4qlQGYlwlvkqDSl3nlXA4h1VGTix9GnWd09yVEc2ot4bmqH4GKMam/DlU8D1p7+snhPwV9MXXKOxjGQ/lXEeOLfTjNZcDvnEGYuRz0deufHxk+NpHYMwXTf2RpK7z+mT2FpTRnsUoJV3ndCaUEc2ot4bmqH4GKMaTHJ+c46snfwX8ccphQxrzRPr7wLjRogJbGBfbqzUBh7szCvgWEimfEB0eWVfGPCG0GUXqOm9DZ2dFGbWknWRL1zmNSxnRjHpraI7qZ4BiPMlxc46/PPgl4I9Pvmy88d0z3xGzgff1NdtzbWFcG3OgSuURGOWEpnxyqKX16c04zdu+2lLXeUtqyqgl7Xhbus5pVsqIZtRbQ3NUPwMU40mOm3PcQLteTVlcpOSLnuv6g6uxUpvLFsbVYnMuhZGNXvnYRPjrEhjzR11vRKnrvB6R45GV0TGT2i26zmnCyohm1FtDc1Q/AxTjSf4C/MAnMQ8Bf3xyHMB3LE93vvoUEttzbeG7O7hxrn2MwCgnxlXxmV8J+AygUubENfS7z4E79+nN+KRT3DnIpK7znFhz+4hlJGge5uSm+zrPcbpxn+6MJM3BTr52z1HLOTko4ykHuDnPOiAo/JOJ2Ne10Vna0EjxUWDLt2ku9ilmgAK/Y4bn0FkVH8wHQDvnAJcwRlfG6GenuBMQ7VQL1kt3xsnBZnaQzEjKPMxMzWrmYCYf7NadkaQ52MnX7jkqmF/JXQdlPOUAX2vBJzHLZtoVXEiOL667XhvBwfFAOdeRYwvjCj1p4vJtFEap8SifVGLp+iMwTve6Xw+p67wlMWXUknacLV3nNCdlRDPqraE5qp8BivEkx805Pt12bbAXF0Ny/Ip4/GVI+8Cnk/fzp5JJBmU0WHJE2zKM4IcE8uk9g2+jMDJCjyoqnyhMRUojMC4KIKUzw1qSus6jMY3KiMEvZBC6F0YzKlVkiiXFDVHrvAMfZCmKUUryXbqdGLtcSWkTlaMnynjKAW7O7+G8CGTPKwcw+MVF3+B6tfSfYeHfFv/ZavsPZPg1x1lHrC1r8OlDgtV2UJ39LfINBhyCkRHY8gthmOTQsVY+ISaxMjGMYwOK1PPGzbGWpK5zi504Rhy5mxl474UWo9JqVcaGc147hg4Wz+HE2L0HE+NYvl6/mfww4/TaMpWgPBojy73kqjfuCoyTnbM6eH219EbLkdfvJ8x4ygFuzv+A88xKkFml5DjQJYC6gxO/Nv4DnC+h/HijgvIDtOETlVMolzxBJ22BDfPAL+v402ywy0y+DcEIYvmIJ8SIX4GOx1SHtqW+a93/uyo++7DzS4IZ5wcNPWPiBp1Vr3PJjBhzR90LRczDmFxagQxxL43xmyvXMbZGZGT5lFyNiZuLcbJzVocYX60uq5vHVvzJ1QqMdznYbrcnNzc3X+A8w7LrpOSuPq42GOcKzlOXjLsN7GzQ79hxS31DW3B6GbbyI8GO8vHM91iGlB7MB5GMqbgoeelaosY35VIZj8qIwy8Yg+VeaOY5p8wRS4xdsCNynbfigwylMorJf0inJeOQHzEyqTl6SozNHOCTczzu4Hw7ldz/UHJ3r+PWi/kT5bGEv+UdDIl+xx6lvkljpHxiZ0a+nlTG+RHvepaupRT7UhmPyojDL657Yco8cOlyxOIa127TOWgTOa5LZXQcSVpLqzmY5pVbW2qOnhLjxxxMm3PYMOO74xu4blw5o+SuPnYbjIGvs3D93XN7+IP6HAfGg3GRB4dvsy0vQ9IJUODwI9IO5ln5xMDK1IFcimScGe5jt1ZzGA1KZTwqIy6/YJzgz5PHyVKxwBUL5SLYEbnOW/FBflIZUbmn5C0ZU75Qcqk5ekqM7Rw8N5J2DeX3Rt0uUnJb366/AeO/2Y2V6hhH6H8CbLNcvklhpHzsGcBfl8q4lATXWorxQyrjURlx+lV6L4zJf0iHM5aQHZ2DITo7mVRGdGRhjVZzMOxFnFRqjp4S44McPMN3kZZj3rlf+TbRlHwZp+cVfMRfSv0EV/JPKNbwc3RGyqdG1g/HVMaHPGrUlDFNVRnRjEo0lC9NTxnRjHpraI7qZ4Bi7JL/D0hX4JBsdFfbAAAAAElFTkSuQmCC\n",
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      "text/latex": [
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       "$\\displaystyle \\left( \\left( 0, \\  0\\right), \\  \\left( 0, \\  1\\right), \\  \\left( 0, \\  -1\\right), \\  \\left( -1, \\  0\\right), \\  \\left( 1, \\  0\\right), \\  \\left( -1, \\  1\\right), \\  \\left( 1, \\  1\\right), \\  \\left( -1, \\  -1\\right), \\  \\left( 1, \\  -1\\right)\\right)$"
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      ],
      "text/plain": [
       "((0, 0), (0, 1), (0, -1), (-1, 0), (1, 0), (-1, 1), (1, 1), (-1, -1), (1, -1))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
Frederik Hennig's avatar
Frederik Hennig committed
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      "image/png": 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\n",
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      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "ps.stencil.plot(method.stencil)\n",
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    "method.stencil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "### Orthogonal MRTs\n",
    "\n",
    "For a real MRT method, the moments should be orthogonalized.\n",
    "One can either orthogonalize using the standard scalar product or a scalar product that is weighted with the lattice weights. If unsure, use the weighted version.\n",
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    "\n",
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    "The next cell shows how to get both orthogonalized MRT versions in lbmpy.\n"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table style=\"border:none; width: 100%\">\n",
       "            <tr style=\"border:none\">\n",
       "                <th style=\"border:none\" >Moment</th>\n",
       "                <th style=\"border:none\" >Eq. Value </th>\n",
       "                <th style=\"border:none\" >Relaxation Rate</th>\n",
       "            </tr>\n",
       "            <tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$1$</td>\n",
       "                            <td style=\"border:none\">$\\rho$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x$</td>\n",
       "                            <td style=\"border:none\">$u_{0}$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$y$</td>\n",
       "                            <td style=\"border:none\">$u_{1}$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
       "                            <td style=\"border:none\">$u_{0}^{2} - u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x y$</td>\n",
       "                            <td style=\"border:none\">$u_{0} u_{1}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$3 x^{2} + 3 y^{2} - 2$</td>\n",
       "                            <td style=\"border:none\">$3 u_{0}^{2} + 3 u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{1}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$3 x^{2} y - y$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{2}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$3 x y^{2} - x$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{2}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$9 x^{2} y^{2} - 3 x^{2} - 3 y^{2} + 1$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{3}$</td>\n",
       "                         </tr>\n",
       "\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
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       "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7fc00774f640>"
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weighted_ortho_mrt = create_lb_method(stencil=\"D2Q9\", method=\"mrt\", weighted=True)\n",
    "weighted_ortho_mrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table style=\"border:none; width: 100%\">\n",
       "            <tr style=\"border:none\">\n",
       "                <th style=\"border:none\" >Moment</th>\n",
       "                <th style=\"border:none\" >Eq. Value </th>\n",
       "                <th style=\"border:none\" >Relaxation Rate</th>\n",
       "            </tr>\n",
       "            <tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$1$</td>\n",
       "                            <td style=\"border:none\">$\\rho$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x$</td>\n",
       "                            <td style=\"border:none\">$u_{0}$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$y$</td>\n",
       "                            <td style=\"border:none\">$u_{1}$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
       "                            <td style=\"border:none\">$u_{0}^{2} - u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x y$</td>\n",
       "                            <td style=\"border:none\">$u_{0} u_{1}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$3 x^{2} + 3 y^{2} - 4$</td>\n",
       "                            <td style=\"border:none\">$- 2 \\rho + 3 u_{0}^{2} + 3 u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{1}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$3 x^{2} y - 2 y$</td>\n",
       "                            <td style=\"border:none\">$- u_{1}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{2}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$3 x y^{2} - 2 x$</td>\n",
       "                            <td style=\"border:none\">$- u_{0}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{2}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$9 x^{2} y^{2} - 6 x^{2} - 6 y^{2} + 4$</td>\n",
       "                            <td style=\"border:none\">$\\rho - 3 u_{0}^{2} - 3 u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{3}$</td>\n",
       "                         </tr>\n",
       "\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
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       "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7fc0075d7b50>"
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      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ortho_mrt = create_lb_method(stencil=\"D2Q9\", method=\"mrt\", weighted=False)\n",
    "ortho_mrt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can check if a method is orthogonalized:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ortho_mrt.is_orthogonal, weighted_ortho_mrt.is_weighted_orthogonal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define custom MRT method\n",
    "\n",
    "To choose custom values for the left moment column one can pass a nested list of moments.\n",
    "Moments that should be relaxed with the same paramter are grouped together.\n",
    "\n",
    "*lbmpy* also comes with a few templates for this list taken from literature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/latex": [
       "$\\displaystyle \\left[ \\left[ 1\\right], \\  \\left[ x, \\  y\\right], \\  \\left[ 3 x^{2} + 3 y^{2} - 2, \\  x^{2} - y^{2}, \\  x y\\right], \\  \\left[ x \\left(3 x^{2} + 3 y^{2} - 4\\right), \\  y \\left(3 x^{2} + 3 y^{2} - 4\\right)\\right], \\  \\left[ - 15 x^{2} - 15 y^{2} + 9 \\left(x^{2} + y^{2}\\right)^{2} + 2\\right]\\right]$"
      ],
      "text/plain": [
       "⎡                                                                             \n",
       "⎢             ⎡   2      2       2    2     ⎤  ⎡  ⎛   2      2    ⎞    ⎛   2  \n",
       "⎣[1], [x, y], ⎣3⋅x  + 3⋅y  - 2, x  - y , x⋅y⎦, ⎣x⋅⎝3⋅x  + 3⋅y  - 4⎠, y⋅⎝3⋅x  +\n",
       "\n",
       "             ⎡                             2    ⎤⎤\n",
       "    2    ⎞⎤  ⎢      2       2     ⎛ 2    2⎞     ⎥⎥\n",
       " 3⋅y  - 4⎠⎦, ⎣- 15⋅x  - 15⋅y  + 9⋅⎝x  + y ⎠  + 2⎦⎦"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lbmpy.methods import mrt_orthogonal_modes_literature\n",
    "from lbmpy.stencils import get_stencil\n",
    "from lbmpy.moments import MOMENT_SYMBOLS\n",
    "\n",
    "x, y, z = MOMENT_SYMBOLS\n",
    "\n",
    "moments = mrt_orthogonal_modes_literature(get_stencil(\"D2Q9\"), is_weighted=True, is_cumulant=False)\n",
    "moments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This nested moment list can be passed to `create_lb_method`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table style=\"border:none; width: 100%\">\n",
       "            <tr style=\"border:none\">\n",
       "                <th style=\"border:none\" >Moment</th>\n",
       "                <th style=\"border:none\" >Eq. Value </th>\n",
       "                <th style=\"border:none\" >Relaxation Rate</th>\n",
       "            </tr>\n",
       "            <tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$1$</td>\n",
       "                            <td style=\"border:none\">$\\rho$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x$</td>\n",
       "                            <td style=\"border:none\">$u_{0}$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$y$</td>\n",
       "                            <td style=\"border:none\">$u_{1}$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$3 x^{2} + 3 y^{2} - 2$</td>\n",
       "                            <td style=\"border:none\">$3 u_{0}^{2} + 3 u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
       "                            <td style=\"border:none\">$u_{0}^{2} - u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x y$</td>\n",
       "                            <td style=\"border:none\">$u_{0} u_{1}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x \\left(3 x^{2} + 3 y^{2} - 4\\right)$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{1}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$y \\left(3 x^{2} + 3 y^{2} - 4\\right)$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{1}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$- 15 x^{2} - 15 y^{2} + 9 \\left(x^{2} + y^{2}\\right)^{2} + 2$</td>\n",
       "                            <td style=\"border:none\">$9 u_{0}^{2} + 9 u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{2}$</td>\n",
       "                         </tr>\n",
       "\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
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       "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7fc04815aa30>"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "create_lb_method(stencil=\"D2Q9\", method=\"mrt\", nested_moments=moments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If one needs to also specify custom equilibrium moments the following approach can be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
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   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table style=\"border:none; width: 100%\">\n",
       "            <tr style=\"border:none\">\n",
       "                <th style=\"border:none\" >Moment</th>\n",
       "                <th style=\"border:none\" >Eq. Value </th>\n",
       "                <th style=\"border:none\" >Relaxation Rate</th>\n",
       "            </tr>\n",
       "            <tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$1$</td>\n",
       "                            <td style=\"border:none\">$\\rho$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x$</td>\n",
       "                            <td style=\"border:none\">$u_{0}$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$y$</td>\n",
       "                            <td style=\"border:none\">$u_{1}$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x y$</td>\n",
       "                            <td style=\"border:none\">$u_{0} u_{1}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
       "                            <td style=\"border:none\">$u_{0}^{2} - u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} + y^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\frac{2 \\rho}{3} + u_{0}^{2} + u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{1}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x y^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\frac{u_{0}}{3}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{2}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} y$</td>\n",
       "                            <td style=\"border:none\">$\\frac{u_{1}}{3}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{2}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} y^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\frac{\\rho}{9} + \\frac{u_{0}^{2}}{3} + \\frac{u_{1}^{2}}{3}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{3}$</td>\n",
       "                         </tr>\n",
       "\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
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       "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7fc0075b2e50>"
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      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rho = sp.symbols(\"rho\")\n",
    "u = sp.symbols(\"u_:3\")\n",
    "omega = sp.symbols(\"omega_:4\")\n",
    "\n",
    "method_table = [\n",
    "  # Conserved moments     \n",
    "  (1,    rho,  0 ),\n",
    "  (x,    u[0], 0 ),\n",
    "  (y,    u[1], 0 ),\n",
    "  \n",
    "  # Shear moments    \n",
    "  (x*y,       u[0]*u[1],                   omega[0]),\n",
    "  (x**2-y**2, u[0]**2 - u[1]**2,           omega[0]),\n",
    "  (x**2+y**2, 2*rho/3 + u[0]**2 + u[1]**2, omega[1]),\n",
    "  \n",
    "  # Higher order\n",
    "  (x * y**2,    u[0]/3,                        omega[2]),\n",
    "  (x**2 * y,    u[1]/3,                        omega[2]),\n",
    "  (x**2 * y**2, rho/9 + u[0]**2/3 + u[1]**2/3, omega[3]),  \n",
    "]\n",
    "method = create_generic_mrt(get_stencil(\"D2Q9\"), method_table)\n",
    "method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of manually defining all entries in the method table, *lbmpy* has functions to fill the table according to a specific pattern. For example:\n",
    "- for a full stencil (D2Q9, D3Q27) there exist exactly 9 or 27 linearly independent moments. These can either be taken as they are, or orthogonalized using Gram-Schmidt, weighted Gram-Schmidt or a Hermite approach\n",
    "- equilibrium values can be computed from the standard discrete equilibrium of order 1,2 or 3. Alternatively they can also be computed as continuous moments of a Maxwellian distribution\n",
    "\n",
    "One option is to start with one of *lbmpy*'s built-in methods and modify it with `create_lb_method_from_existing`.\n",
    "In the next cell we fix the fourth order relaxation rate to a constant, by writing a function that defines how to alter each row of the collision table. This is for demonstration only, of course we could have done it right away when passing in the collision table."
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 11,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table style=\"border:none; width: 100%\">\n",
       "            <tr style=\"border:none\">\n",
       "                <th style=\"border:none\" >Moment</th>\n",
       "                <th style=\"border:none\" >Eq. Value </th>\n",
       "                <th style=\"border:none\" >Relaxation Rate</th>\n",
       "            </tr>\n",
       "            <tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$1$</td>\n",
       "                            <td style=\"border:none\">$\\rho$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x$</td>\n",
       "                            <td style=\"border:none\">$u_{0}$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$y$</td>\n",
       "                            <td style=\"border:none\">$u_{1}$</td>\n",
       "                            <td style=\"border:none\">$0$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x y$</td>\n",
       "                            <td style=\"border:none\">$u_{0} u_{1}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
       "                            <td style=\"border:none\">$u_{0}^{2} - u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{0}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} + y^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\frac{2 \\rho}{3} + u_{0}^{2} + u_{1}^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{1}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x y^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\frac{u_{0}}{3}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{2}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} y$</td>\n",
       "                            <td style=\"border:none\">$\\frac{u_{1}}{3}$</td>\n",
       "                            <td style=\"border:none\">$\\omega_{2}$</td>\n",
       "                         </tr>\n",
       "<tr style=\"border:none\">\n",
       "                            <td style=\"border:none\">$x^{2} y^{2}$</td>\n",
       "                            <td style=\"border:none\">$\\frac{\\rho}{9} + \\frac{u_{0}^{2}}{3} + \\frac{u_{1}^{2}}{3}$</td>\n",
       "                            <td style=\"border:none\">$1.0$</td>\n",
       "                         </tr>\n",
       "\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
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       "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7fc007825f70>"
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     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def modification_func(moment, eq, rate):\n",
    "    if rate == omega[3]:\n",
    "        return moment, eq, 1.0\n",
    "    return moment, eq, rate\n",
    "\n",
    "\n",
    "method = create_lb_method_from_existing(method, modification_func)\n",
    "method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our customized method can be directly passed into one of the scenarios. We can for example set up a channel flow with it. Since we used symbols as relaxation rates, we have to pass them in as `kernel_params`."
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 12,
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   "metadata": {},
   "outputs": [
    {
     "data": {
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ax48fb0oRZmhoqFt507YiRYpor169dM6cOV6991aqVKkc5RYR9ff31/bt2+sHH3zgtW2nZMmSOc7t3Nf1/PPP6w8//HDTz82qV6+e47xBQUHapUsX/fzzz33imuNWQeHiv7QJhYu3hMmTJ7u9s23btq1OmzbN1MISeyeCJweJ6dOnm1bE+OGHH7qVOV++fNqtWzedPn26qReP9s49V1vZsmX1iSee0CVLlph6Q9Xemerquo6Pj9fx48fr4cOHTcus+r/Oa1da/fr19fXXX9e1a9ea/hTzxIkTMyweSdtCQkL0/vvv19mzZ1viqZPp06en6rTJrDVu3FjffPNN/f333y1xQT5r1qxsT/zz5s2rMTExlip0nTt3rpYpUybL3Pnz59fExESdMGGCZYpGv/rqq2wvEJ3Xt1U6zebPn68hISFZ5vbz89NWrVpZajSvBQsWuNTxUa9ePUuNortw4ULNnz9/trmrVKmiL7zwglcutF2xZMmSTIv8nVu5cuX0mWee0W+//dYSN8u+/vprzZs3r8/l/u677zQ4ODjb3JUqVbLUKKkrVqzQIkWKZPkUuIhonTp1tG/fvrpmzRpLfC5XrVqlJUuWzDK3fT9opRHr1qxZo+XKlcsyt33EzokTJ5p+/mq3fv16rVatWrafyaeeekoXL15smaKpTZs2ab169TLNbBiGNm/eXN9++23dtGmTJbYR1esjM9hHhM6oFSpUSLt37265kS///PNPbd26daa5K1eurM8//7yuWLHCEvs/u927d2vHjh2z3I8MGzbMMg982u3fv18jIyMzPe/u0qWLTpkyxXKFRwcOHNDY2NgMcxcvXlwfeughS458efjwYU1ISMh0//d///d/+sMPP1jugb6jR49q165dM8xdrVo1ffXVVy1ZfHn8+HHHyMoZXSsMHDhQt2zZYrncJ0+e1O7du2eYu3nz5jp8+HBLFl+ePn1ae/bsmS6zv7+/durUST/66CPLXMM7O3fuXIYP7efNm1e7dOmiU6dO1VOnTpkdM50LFy7o/fffn+Hx/d5779W5c+dasjjt0qVL+tBDD6XLHRISoo888ohliy8vX77sGBk/7THHyrmvXr2a4chR+fPn1zvvvFO/+OILS94wt49QnNEDlA0aNNDBgwfrX3/9ZXbMdGw2m0ZERGTY92PlIkabzaa1atXKtDjKykWMBQoU0ICAgCyvMa1YxBgYGJhlZnsrWrSoPvTQQ7p48WJL7GNc6WdzblWqVNFXXnnF9AFNXOlny+x8cdCgQaaNlO5qwWJmrUmTJjp06FCv7y/dLVjMqPn5+Wnbtm31vffe89pnt2jRojnObW/2BxmnTJmiJ0+evKm5s7u/40lr3Lixvv322zd1289poWhmrVixYnr//ffrl19+eVPOccqXL5+refPly6fdu3fX2bNnW/Lc/VZC4eK/tAmFi7eEYcOGebyjLVq0qPbp00c3b97s9dwjRozI0UEiKChIExMTddq0aV4tnLI/bepJCwwM1JiYGJ06darXO7jdLVx0boUKFdIRI0aYcsPJ3UJRezMMQ3v37m3azT13ChedW9OmTXX9+vWmZFZNPTqDq61IkSI6evRoUwt3PCng7ty5s+7YscO0zKqqn376qVuZAwIC9MUXXzR99Nlp06a5lTs4OFgHDRpk+iiMn3/+udv77Jdeesn0zuFZs2a5vX336NHD9HO7tKNHudIaNWqkv/zyi6m5582b53busLAwnT17tqmdexmNgppdK168uH7yySemFqZlNOpsdi0kJETHjx9vau6MRm/NroWHh5u+nSxbtswnP5cZjaqcVTMMQ++++279559/TM39ww8/uJW7YMGC+s4775h+oyOjUX6zajVr1tRly5aZmllV9eeff3Yrd/fu3S3xUMjatWtdzpw/f359++23LTFS5/r1613OXb58eZ03b54lio42bNjgcu6WLVvq77//bnZkVc141NmMmr+/v3bv3t0yN3y3bNniUu7g4GB94oknLFN46TwKU1YtNDRUX331VdOvF+x27tzpUu5y5crp4MGDLbEvUVXdtWuXS7mrVaumY8aMscRDQ6rXR2FyJXf9+vV1+vTpltgHql6fScaVc6mWLVta4vhu988//2SbO0+ePBoTE6MbN240O67DkSNHXDq+33PPPbp3716z4zocP34829yFCxfW3r17W+bhG1XVU6dOuZT74YcfNr3PxNmZM2eyzW2flWrNmjVmx3W4cOGCy/vBYcOGWeZG/+XLl13KbZ8ucubMmZZ4oO/q1asu5Rb5XxHj9OnTTT9uJicnu5zb3po2barvvvuuqfuXlJQUt3Pb9zH333+/Lly40LSHXNLOsuFOq1Chgr744ou6du1ar+fOrrjVlVajRg3t16+fV+95u1somlWrU6eODhgwwCv3rVx5cN/d1rhxYx08ePBNnc0lp4WimTV/f3/t0KGDjhkz5qaMxOjKwCs5adWqVdNXXnkl14sYixcvflNzi1yv90hISMjVz+3NKrgUud5/cffdd+tXX31lmWvqW8nNKFzMIwC84tixYx7/7enTp+WDDz6QDz74QJo1aya9e/eWHj16SHBwcC4mzNiVK1dy9PfFixeXIkWKOJZVqFChXEiVvaSkJI/+rmLFihIZGSmRkZHSoUMH8ff3z+VkWdPrxcouK1y4sERFRUlsbKxERUVJaGjoTUqWe4KCgqRjx44SFxcnsbGxUq5cObMjZcswDGnatKnExcVJfHy81KlTRwzDMC1PYGCgFC5cWM6ePZvl75UtW1bi4+MlLi5OOnToIPny5fNSwozlz59fypYtK//880+mv5MnTx5p06aNxMfHS3x8vFSuXNmLCTNWqFAhqVKliuzatSvT3ylWrJjExMRIfHy8REZGSuHChb2YMGOFChWS2267Tf78889Mf6d06dKOdd2xY0fTtxERkYIFC0rVqlXlr7/+yvR3ihcv7vg8du7cWQoWLOjFhBkLDg6W8uXLy/79+zP9nbx580qnTp0kPj5eYmNjpWzZsl5MmHmmMmXKyKFDh7L8vcaNGzvWeYMGDUzdB4pc3w+GhobKiRMnsjx2li9f3rG+27VrJ3nz5vViyvQCAgKkWLFicvr06SxzlyhRQmJiYiQuLk4iIiK8dv6UmYCAAClevLicOnUqy9wlS5ZMldvsz2ZgYKCUKFFCjh8/nmXuihUrOo6Xbdq0kaCgIC+mTC8oKEjCwsLk0KFDYrPZMv29Jk2aOD6X9erVM/1zmS9fPqlcubLs3bs309z58+eXzp07Oz6XJUuW9HLK9AoUKCC1a9eWbdu2ZZo7PDxc4uPjJSEhQdq1a2f6NiJy/XjZuHFj+e233zLdvps2bSoJCQkSHx8vtWvXNn0bEbl+ftKqVStZtWpVhq/ny5cv1TZSqlQpLyfMWKFChaRDhw6yfPnyDF8vUaKExMXFOfZ/BQoU8HLCjBUqVEgiIyPlm2++yfD1ypUrO/Z/rVu3lsDAQC8nzFihQoUkNjZWFi9enOHrderUkdjYWImNjZVmzZpJnjzW6N4sVKiQxMfHy8KFCzN8/fbbb5fo6GiJiYmRxo0be72/ITMFCxaUhIQEWbBgQbrX7NfC0dHREhUVJY0aNbJM7gIFCkiXLl3kq6++Svean5+fNG/eXKKioiQqKkoaNmwofn5+3g+ZgQIFCki3bt1k7ty56V7LkyePtGjRQqKioiQyMlLq169vmdzBwcFy5513ypdffpnutcDAQGnTpo2jP80qxxyR67nvuusumTVrVoavtW/fXiIjIyUqKkqqVKliQsKM5c+fX+6++275/PPP071WrFgxiYiIkMjISOncubMlrivt8uXLJ7169ZJp06aley0sLMyxrjt27ChFixY1IWHG8ubNKw888IB8+umn6V6rVq2aY9tu27atV/rjXRUUFCQPPvigTJkyJd1rtWrVcuRu3bq1Jfp77AICAuShhx6SyZMnp3utatWq0rlzZ+ncubO0a9fO9GthZ/7+/pluJ0WKFJGOHTtKRESERERESKVKlUxImLnM9iciItWrV3fkbteuneTPn9/L6TKWkpIi0dHRsmzZMklOTs7wd+rVqyedOnWSiIgIadWqlSWOmcnJyVK3bl35888/s7y3VqFCBcd6b9++venns0lJSVKgQAG5dOlSlv0QItf3Pa1bt5ZOnTpJ+/btpUSJEl5KmZ4n9wFLlCjhyF6/fn1Tzm2Tk5OzXc+ZKV++vHTu3FmaNm3q9XsnqirXrl3L0TKqVq0qHTt2lCZNmkj58uVzKVn2PL1nnFatWrUkIiJCmjdv7pV7mzld384Mw5AmTZpIVFSUtGvX7qaeQ2a2386JwoULS0REhOPa9Gack6WkpOT6Mu0aN24sUVFREh0dLVWrVs3VZXu6P3FFSEiIREdHS2xsrHTu3DlXz+EvX76ca8tK6+LFi/L555/LzJkzpV27djJq1CipV6/eTXs/5ILcqH6kMeIisjdr1ix96KGHsp22M6tWuHBh7dixo7766qv6/fffeyX3+vXrdcyYMVq1alWXMhYoUEDj4+P1/fff123btpn2pPL+/fv122+/zXZY5+DgYI2Li9PRo0frn3/+afqT1adPn9aNGzdm+TRKjRo19MUXX9QffvjBMtN5Xb58Wbdv357p9JdlypTRxx57TBcsWHBTnoLJiZ07d2b4dFuBAgW0W7duOnnyZD1y5IjZMdP55ZdfMlzXt99+uw4cONAy0yyn9c0336TLXKxYMe3Vq5d+8cUXlpsiw+6rr77K8OmoF198UX/66SfTn4rNzNy5c9Plrl+/vr7xxhu6bt06SzyJnJGMRnerUaOGvvzyy7pq1SrLTfdml9FoY6VLl9bevXvrggULLPPUelpr1qxJl9s+jfj48eNNHw0tMxs3bkyX23kaWitOVaequnXr1gynpG3YsKG+8cYblpmOO60dO3ZkeLxs1KiR9u/fX9euXWvJ3Nu3b0+3vv39/bVNmzY6bNgwU89Xs7J58+Z0ue3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\n",
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      "text/plain": [
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       "<Figure size 3200x1200 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "ch = create_channel(domain_size=(100, 30), lb_method=method, u_max=0.05,\n",
    "                    kernel_params={'omega_0': 1.8, 'omega_1': 1.4, 'omega_2': 1.5})\n",
    "ch.run(500)\n",
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    "plt.figure(dpi=200)\n",
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    "plt.vector_field(ch.velocity[:, :]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bonus: Automatic analysis\n",
    "Above we have created a non-orthogonal MRT, where the shear viscosity and bulk viscosity can be independently controlled. For moment-based methods, *lbmpy* also offers an automatic Chapman Enskog analysis that can find the relation between viscosity and relaxation rate(s):"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 13,
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   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": "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\n",
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      "text/latex": [
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       "$\\displaystyle - \\frac{\\omega_{0} - 2}{6 \\omega_{0}}$"
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      ],
      "text/plain": [
       "-(ω₀ - 2) \n",
       "──────────\n",
       "   6⋅ω₀   "
      ]
     },
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     "execution_count": 13,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lbmpy.chapman_enskog import ChapmanEnskogAnalysis\n",
    "analysis = ChapmanEnskogAnalysis(method)\n",
    "analysis.get_dynamic_viscosity()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 14,
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   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": "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\n",
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      "text/latex": [
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       "$\\displaystyle - \\frac{1}{9} - \\frac{1}{3 \\omega_{1}} + \\frac{5}{9 \\omega_{0}}$"
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      ],
      "text/plain": [
       "  1    1      5  \n",
       "- ─ - ──── + ────\n",
       "  9   3⋅ω₁   9⋅ω₀"
      ]
     },
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     "execution_count": 14,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis.get_bulk_viscosity()"
   ]
  }
 ],
 "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",
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   "version": "3.8.2"
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  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}