{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Shan-Chen Two-Phase Single-Component Lattice Boltzmann" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from lbmpy.session import *\n", "from lbmpy.updatekernels import create_stream_pull_with_output_kernel\n", "from lbmpy.macroscopic_value_kernels import macroscopic_values_getter, macroscopic_values_setter\n", "from lbmpy.maxwellian_equilibrium import get_weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is based on section 9.3.2 of Krüger et al.'s \"The Lattice Boltzmann Method\", Springer 2017 (http://www.lbmbook.com).\n", "Sample code is available at [https://github.com/lbm-principles-practice/code/](https://github.com/lbm-principles-practice/code/blob/master/chapter9/shanchen.cpp)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parameters" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "N = 64\n", "omega_a = 1.\n", "g_aa = -4.7\n", "rho0 = 1.\n", "\n", "stencil = LBStencil(Stencil.D2Q9)\n", "weights = get_weights(stencil, c_s_sq=sp.Rational(1,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data structures" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "dh = ps.create_data_handling((N,) * stencil.D, periodicity=True, default_target=ps.Target.CPU)\n", "\n", "src = dh.add_array('src', values_per_cell=stencil.Q)\n", "dst = dh.add_array_like('dst', 'src')\n", "\n", "ρ = dh.add_array('rho')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Force & combined velocity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The force on the fluid is\n", "$\\vec{F}_A(\\vec{x})=-\\psi(\\rho_A(\\vec{x}))g_{AA}\\sum\\limits_{i=1}^{q}w_i\\psi(\\rho_A(\\vec{x}+\\vec{c}_i))\\vec{c}_i$\n", "with \n", "$\\psi(\\rho)=\\rho_0\\left[1-\\exp(-\\rho/\\rho_0)\\right]$." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def psi(dens):\n", " return rho0 * (1. - sp.exp(-dens / rho0));" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "zero_vec = sp.Matrix([0] * stencil.D) \n", "\n", "force = sum((psi(ρ[d]) * w_d * sp.Matrix(d)\n", " for d, w_d in zip(stencil, weights)), zero_vec) * psi(ρ.center) * -1 * g_aa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Kernels" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "lbm_config = LBMConfig(stencil=stencil, relaxation_rate=omega_a, compressible=True,\n", " force_model=ForceModel.GUO, force=force, kernel_type='collide_only')\n", "\n", "collision = create_lb_update_rule(lbm_config=lbm_config,\n", " optimization={'symbolic_field': src})\n", "\n", "stream = create_stream_pull_with_output_kernel(collision.method, src, dst, {'density': ρ})\n", "\n", "\n", "config = ps.CreateKernelConfig(target=dh.default_target, cpu_openmp=False)\n", "\n", "stream_kernel = ps.create_kernel(stream, config=config).compile()\n", "collision_kernel = ps.create_kernel(collision, config=config).compile()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialization" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "method_without_force = create_lb_method(LBMConfig(stencil=stencil, relaxation_rate=omega_a, compressible=True))\n", "init_assignments = macroscopic_values_setter(method_without_force, velocity=(0, 0), \n", " pdfs=src.center_vector, density=ρ.center)\n", "\n", "\n", "init_kernel = ps.create_kernel(init_assignments, ghost_layers=0, config=config).compile()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def init():\n", " for x in range(N):\n", " for y in range(N):\n", " if (x-N/2)**2 + (y-N/2)**2 <= 15**2:\n", " dh.fill(ρ.name, 2.1, slice_obj=[x,y])\n", " else:\n", " dh.fill(ρ.name, 0.15, slice_obj=[x,y])\n", "\n", " dh.run_kernel(init_kernel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Timeloop" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sync_pdfs = dh.synchronization_function([src.name])\n", "sync_ρs = dh.synchronization_function([ρ.name])\n", "\n", "def time_loop(steps):\n", " dh.all_to_gpu()\n", " for i in range(steps):\n", " sync_ρs()\n", " dh.run_kernel(collision_kernel)\n", " \n", " sync_pdfs()\n", " dh.run_kernel(stream_kernel)\n", " \n", " dh.swap(src.name, dst.name)\n", " dh.all_to_cpu()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def plot_ρs():\n", " plt.figure(dpi=200)\n", " plt.title(\"$\\\\rho$\")\n", " plt.scalar_field(dh.gather_array(ρ.name), vmin=0, vmax=2.5)\n", " plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the simulation\n", "### Initial state" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "