2D-Flow-Cylinder.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO3Kj0C7JALwrD5+SNEjWtr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/costpetrides/Fluid-Dynamics-Navier-Stokes/blob/main/2D-Flow-Cylinder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "7va-Uzy7XPPX",
        "outputId": "c67b84df-f79a-417e-b172-d1def944bcac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "================================================\n",
            "Lattice Boltzmann Method\n",
            "\n",
            "Solving 2D Heat flow around square/circular cylinder\n",
            "\n",
            "BKG Model\n",
            "------------------------------------------------\n",
            "\n",
            "Number of iterations:  10000\n",
            "Flow Reynolds Number:  5 \n",
            "\n",
            "Select the geometry: SQUARE CYLINDER or CIRCULAR CYLINDER: SQUARE CYLINDER\n",
            "\n",
            "\n",
            "================================================\n",
            " Solving ...\n",
            "------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#2D Flow around cylinder\n",
        "\n",
        "#Lattice Boltzmann BKG Method\n",
        "\n",
        "from numpy import *\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import cm\n",
        "\n",
        "lineSingle = '------------------------------------------------'\n",
        "lineDouble = '================================================'\n",
        "\n",
        "print(\"\\n\")\n",
        "print(lineDouble)\n",
        "print(\"Lattice Boltzmann Method\\n\")\n",
        "print(\"Solving 2D Heat flow around square/circular cylinder\\n\")\n",
        "print(\"BKG Model\")\n",
        "print(lineSingle)\n",
        "\n",
        "#flow defination\n",
        "\n",
        "iterations = 10000       #total number of time iteration\n",
        "print(\"\\nNumber of iterations: \",iterations)\n",
        "\n",
        "Re         = 5          #flow reynolds number\n",
        "print(\"Flow Reynolds Number: \",Re,\"\\n\")\n",
        "\n",
        "nx, ny     = 420, 180    #number of lattice node\n",
        "ly         = ny-1        #height of the domain\n",
        "\n",
        "#creating shape of the obstacle: cylinder\n",
        "\n",
        "cx, cy = nx//4, ny//2             #cylinder coordinates\n",
        "\n",
        "geometry = \"\"\n",
        "while not (geometry == 'SQUARE CYLINDER' or geometry == 'CIRCULAR CYLINDER'):\n",
        "\n",
        "    geometry = input(\"Select the geometry: SQUARE CYLINDER or CIRCULAR CYLINDER: \").upper()\n",
        "\n",
        "def obstacle_cylinder(x,y):\n",
        "\n",
        "        if geometry == \"CIRCULAR CYLINDER\":\n",
        "            r = ny//9                                #circle radius\n",
        "            return (x-cx)**2 + (y-cy)**2<r**2\n",
        "        elif geometry == \"SQUARE CYLINDER\":\n",
        "            r = ny//6                                #side of square\n",
        "            return abs((x-cx) + (y-cy)) + abs((x-cx) - (y-cy)) < r\n",
        "\n",
        "obstacle = fromfunction(obstacle_cylinder, (nx,ny))        #forming the obstacle\n",
        "\n",
        "if geometry == \"CIRCULAR CYLINDER\":\n",
        "    r = ny//9                                #circle radius\n",
        "elif geometry == \"SQUARE CYLINDER\":\n",
        "    r = ny//6                                #side of square\n",
        "\n",
        "in_velocity = 0.04                #inflow velocity\n",
        "nu          = in_velocity*r/Re;   #fluid viscoisty\n",
        "omega       = 1 / (3*nu + 0.5);   #relaxatation parameter\n",
        "\n",
        "\n",
        "#defining lattice constants\n",
        "\n",
        "v = array([ [1,1], [1,0], [1,-1], [0,1], [0,0], [0,-1], [-1,1], [-1,0], [-1,-1] ])\n",
        "\n",
        "t = array([ 1/36, 1/9, 1/36, 1/9, 4/9, 1/9, 1/36, 1/9, 1/36])\n",
        "\n",
        "col1 = array([0, 1, 2])\n",
        "col2 = array([3, 4, 5])\n",
        "col3 = array([6, 7, 8])\n",
        "\n",
        "#defining macroscopic flow variable\n",
        "\n",
        "def macroscopic(fin):\n",
        "    rho = sum(fin, axis = 0)                     #calculating density\n",
        "    u = zeros((2,nx,ny))                         #calculating velocities\n",
        "    for i in range(9):\n",
        "        u[0,:,:] += v[i,0]*fin[i,:,:]\n",
        "        u[1,:,:] += v[i,1]*fin[i,:,:]\n",
        "    u /= rho\n",
        "    return rho, u\n",
        "\n",
        "# Equilibrium distribution function.\n",
        "\n",
        "def equilibrium(rho,u):\n",
        "    usqr = 3/2 * (u[0]**2 + u[1]**2)\n",
        "    eq = zeros((9,nx,ny))\n",
        "    for i in range(9):\n",
        "        cu = 3*(v[i,0]*u[0,:,:] + v[i,1]*u[1,:,:])\n",
        "        eq[i:,:] = rho*t[i]*(1 + cu + 0.5*cu**2 - usqr)\n",
        "    return eq\n",
        "\n",
        "# Initial velocity field: almost zero, with a slight perturbation to trigger the instability.\n",
        "\n",
        "def init_vel(d,x,y):\n",
        "    return (1-d) * in_velocity * (1 + 1e-4*sin(y/ly*2*pi))\n",
        "\n",
        "vel = fromfunction(init_vel, (2,nx,ny))\n",
        "\n",
        "# Initialization of the populations at equilibrium with the given velocity.\n",
        "\n",
        "fin = equilibrium(1, vel)\n",
        "\n",
        "print(\"\\n\")\n",
        "print(lineDouble)\n",
        "print(' Solving ...')\n",
        "print(lineSingle)\n",
        "\n",
        "#Time Loop\n",
        "\n",
        "for time in range(iterations):\n",
        "\n",
        "    fin[col3,-1,:] = fin[col3,-2,:]        #outflow BC\n",
        "\n",
        "    rho, u = macroscopic(fin)              #compute velocity & density\n",
        "\n",
        "    u[:,0,:] = vel[:,0,:]                  #inflow BC\n",
        "    rho[0,:] = 1/(1-u[0,0,:])*(sum(fin[col2,0,:],axis=0) + 2*(sum(fin[col3,0,:],axis=0)))\n",
        "\n",
        "    #compute the population & equilibrium\n",
        "    feq = equilibrium(rho,u)\n",
        "    fin[[0,1,2],0,:] = feq[[0,1,2],0,:] + fin[[8,7,6],0,:] - feq[[8,7,6],0,:]\n",
        "\n",
        "    #collision Step\n",
        "\n",
        "    fout = fin - omega * (fin - feq)\n",
        "\n",
        "    #No slip/Bounce Back Condition for the cylinder wall\n",
        "\n",
        "    for i in range(9):\n",
        "        fout[i,obstacle] = fin[8-i,obstacle]\n",
        "\n",
        "    #Streaming Step\n",
        "\n",
        "    for i in range(9):\n",
        "        fin[i,:,:] = roll(roll(fout[i,:,:], v[i,0], axis=0), v[i,1], axis=1)\n",
        "\n",
        "    #Visualization of the velocity field\n",
        "\n",
        "    if (time%100==0):\n",
        "        plt.clf()\n",
        "        plt.imshow(sqrt(u[0]**2+u[1]**2).transpose(), cmap=cm.viridis)\n",
        "        plt.savefig(\"vel.{0:04d}.png\".format(time//100),dpi=600)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install imageio"
      ],
      "metadata": {
        "id": "4sCpd3csWgDF",
        "outputId": "56fcc522-f12e-44e8-b930-19279905fa6d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imageio in /usr/local/lib/python3.10/dist-packages (2.36.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from imageio) (1.26.4)\n",
            "Requirement already satisfied: pillow>=8.3.2 in /usr/local/lib/python3.10/dist-packages (from imageio) (11.0.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import *\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import cm\n",
        "import imageio\n",
        "import io\n",
        "\n",
        "lineSingle = '------------------------------------------------'\n",
        "lineDouble = '================================================'\n",
        "\n",
        "print(\"\\n\")\n",
        "print(lineDouble)\n",
        "print(\"Lattice Boltzmann Method\\n\")\n",
        "print(\"Solving 2D Heat flow around square/circular cylinder\\n\")\n",
        "print(\"BKG Model\")\n",
        "print(lineSingle)\n",
        "\n",
        "# Flow definition\n",
        "iterations = 10000       # Total number of time iterations\n",
        "print(\"\\nNumber of iterations: \", iterations)\n",
        "\n",
        "Re = 5          # Flow Reynolds Number\n",
        "print(\"Flow Reynolds Number: \", Re, \"\\n\")\n",
        "\n",
        "nx, ny = 420, 180    # Number of lattice nodes\n",
        "ly = ny-1        # Height of the domain\n",
        "\n",
        "# Creating shape of the obstacle: cylinder\n",
        "cx, cy = nx//4, ny//2             # Cylinder coordinates\n",
        "\n",
        "geometry = \"\"\n",
        "while not (geometry == 'SQUARE CYLINDER' or geometry == 'CIRCULAR CYLINDER'):\n",
        "    geometry = input(\"Select the geometry: SQUARE CYLINDER or CIRCULAR CYLINDER: \").upper()\n",
        "\n",
        "def obstacle_cylinder(x, y):\n",
        "    if geometry == \"CIRCULAR CYLINDER\":\n",
        "        r = ny//9                                # Circle radius\n",
        "        return (x-cx)**2 + (y-cy)**2 < r**2\n",
        "    elif geometry == \"SQUARE CYLINDER\":\n",
        "        r = ny//6                                # Side of square\n",
        "        return abs((x-cx) + (y-cy)) + abs((x-cx) - (y-cy)) < r\n",
        "\n",
        "obstacle = fromfunction(obstacle_cylinder, (nx, ny))        # Forming the obstacle\n",
        "\n",
        "if geometry == \"CIRCULAR CYLINDER\":\n",
        "    r = ny//9                                # Circle radius\n",
        "elif geometry == \"SQUARE CYLINDER\":\n",
        "    r = ny//6                                # Side of square\n",
        "\n",
        "in_velocity = 0.04                # Inflow velocity\n",
        "nu = in_velocity * r / Re;   # Fluid viscosity\n",
        "omega = 1 / (3 * nu + 0.5);   # Relaxation parameter\n",
        "\n",
        "# Defining lattice constants\n",
        "v = array([[1, 1], [1, 0], [1, -1], [0, 1], [0, 0], [0, -1], [-1, 1], [-1, 0], [-1, -1]])\n",
        "t = array([1/36, 1/9, 1/36, 1/9, 4/9, 1/9, 1/36, 1/9, 1/36])\n",
        "col1 = array([0, 1, 2])\n",
        "col2 = array([3, 4, 5])\n",
        "col3 = array([6, 7, 8])\n",
        "\n",
        "# Defining macroscopic flow variable\n",
        "def macroscopic(fin):\n",
        "    rho = sum(fin, axis=0)                     # Calculating density\n",
        "    u = zeros((2, nx, ny))                         # Calculating velocities\n",
        "    for i in range(9):\n",
        "        u[0, :, :] += v[i, 0] * fin[i, :, :]\n",
        "        u[1, :, :] += v[i, 1] * fin[i, :, :]\n",
        "    u /= rho\n",
        "    return rho, u\n",
        "\n",
        "# Equilibrium distribution function\n",
        "def equilibrium(rho, u):\n",
        "    usqr = 3/2 * (u[0]**2 + u[1]**2)\n",
        "    eq = zeros((9, nx, ny))\n",
        "    for i in range(9):\n",
        "        cu = 3 * (v[i, 0] * u[0, :, :] + v[i, 1] * u[1, :, :])\n",
        "        eq[i:, :] = rho * t[i] * (1 + cu + 0.5 * cu**2 - usqr)\n",
        "    return eq\n",
        "\n",
        "# Initial velocity field: almost zero, with a slight perturbation to trigger the instability\n",
        "def init_vel(d, x, y):\n",
        "    return (1-d) * in_velocity * (1 + 1e-4 * sin(y/ly * 2 * pi))\n",
        "\n",
        "vel = fromfunction(init_vel, (2, nx, ny))\n",
        "\n",
        "# Initialization of the populations at equilibrium with the given velocity\n",
        "fin = equilibrium(1, vel)\n",
        "\n",
        "print(\"\\n\")\n",
        "print(lineDouble)\n",
        "print(' Solving ...')\n",
        "print(lineSingle)\n",
        "\n",
        "# Prepare to create GIF in memory\n",
        "images = []\n",
        "\n",
        "# Time Loop\n",
        "for time in range(iterations):\n",
        "    fin[col3, -1, :] = fin[col3, -2, :]        # Outflow BC\n",
        "\n",
        "    rho, u = macroscopic(fin)              # Compute velocity & density\n",
        "\n",
        "    u[:, 0, :] = vel[:, 0, :]                  # Inflow BC\n",
        "    rho[0, :] = 1/(1-u[0, 0, :]) * (sum(fin[col2, 0, :], axis=0) + 2 * (sum(fin[col3, 0, :], axis=0)))\n",
        "\n",
        "    # Compute the population & equilibrium\n",
        "    feq = equilibrium(rho, u)\n",
        "    fin[[0, 1, 2], 0, :] = feq[[0, 1, 2], 0, :] + fin[[8, 7, 6], 0, :] - feq[[8, 7, 6], 0, :]\n",
        "\n",
        "    # Collision Step\n",
        "    fout = fin - omega * (fin - feq)\n",
        "\n",
        "    # No slip/Bounce Back Condition for the cylinder wall\n",
        "    for i in range(9):\n",
        "        fout[i, obstacle] = fin[8-i, obstacle]\n",
        "\n",
        "    # Streaming Step\n",
        "    for i in range(9):\n",
        "        fin[i, :, :] = roll(roll(fout[i, :, :], v[i, 0], axis=0), v[i, 1], axis=1)\n",
        "\n",
        "    # Visualization of the velocity field\n",
        "    if (time % 100 == 0):\n",
        "        plt.clf()\n",
        "        plt.imshow(sqrt(u[0]**2 + u[1]**2).transpose(), cmap=cm.viridis)\n",
        "\n",
        "        # Save to a buffer instead of a file\n",
        "        buf = io.BytesIO()\n",
        "        plt.savefig(buf, format='png', dpi=600)\n",
        "        buf.seek(0)\n",
        "        images.append(imageio.imread(buf))\n",
        "        buf.close()\n",
        "\n",
        "# Create GIF from saved images\n",
        "imageio.mimsave('flow_evolution.gif', images, duration=0.1)\n",
        "\n",
        "print(\"GIF created: flow_evolution.gif\")"
      ],
      "metadata": {
        "id": "yRdsFTomWoFH",
        "outputId": "275a9424-bb1a-4610-eb18-42ea53fc9fc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "================================================\n",
            "Lattice Boltzmann Method\n",
            "\n",
            "Solving 2D Heat flow around square/circular cylinder\n",
            "\n",
            "BKG Model\n",
            "------------------------------------------------\n",
            "\n",
            "Number of iterations:  10000\n",
            "Flow Reynolds Number:  5 \n",
            "\n",
            "Select the geometry: SQUARE CYLINDER or CIRCULAR CYLINDER: SQUARE CYLINDER\n",
            "\n",
            "\n",
            "================================================\n",
            " Solving ...\n",
            "------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-37956ff158d7>:129: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning disappear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n",
            "  images.append(imageio.imread(buf))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GIF created: flow_evolution.gif\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}