{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lees Edwards boundary conditions with lbmpy\n", "\n", "This example shows how to implement Lees Edwards boundary conditions following the principles discussed in Wagner, A.J., Pagonabarraga, I. Leesâ€“Edwards Boundary Conditions for Lattice Boltzmann. Journal of Statistical Physics 107, 521â€“537 (2002). https://doi.org/10.1023/A:1014595628808" ] }, { "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_setter\n", "from lbmpy.maxwellian_equilibrium import get_weights\n", "from lbmpy.relaxationrates import lattice_viscosity_from_relaxation_rate\n", "from pystencils.astnodes import LoopOverCoordinate\n", "from pystencils.slicing import get_periodic_boundary_functor\n", "from functools import partial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parameters" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "N = 64 # domain size\n", "omega = 1.0 # relaxation rate of first component\n", "U_x = 0.1 # shear velocity\n", "shear_dir = 0 # direction of shear flow\n", "shear_dir_normal = 1 # direction normal to shear plane, for interpolation\n", "\n", "stencil = get_stencil(\"D2Q9\")\n", "weights = get_weights(stencil, c_s_sq=sp.Rational(1, 3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data structures\n", "\n", "We allocate a set of PDFs src and dst, the density field rho and the velocity field u.\n", "For later testing, we also need a force field F. This will be allocated as well. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "dim = len(stencil[0])\n", "dh = ps.create_data_handling((N, ) * dim,\n", " periodicity=True)\n", "\n", "src = dh.add_array('src', values_per_cell=len(stencil))\n", "dst = dh.add_array_like('dst', 'src')\n", "F = dh.add_array('F', values_per_cell=dh.dim)\n", "\n", "rho = dh.add_array('rho', values_per_cell=1)\n", "u = dh.add_array('u', values_per_cell=dh.dim)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Kernels\n", "\n", "Following Wagner et al., we need to find all the populations that will cross the boundary in the direction normal to the shearing direction and alter their equilibrium distribution.\n", "Hence, we construct a piecewise function that fulfils this." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "counters = [LoopOverCoordinate.get_loop_counter_symbol(i) for i in range(dim)]\n", "points_up = sp.Symbol('points_up')\n", "points_down = sp.Symbol('points_down')\n", "\n", "U_p = sp.Piecewise((1, sp.And(ps.data_types.type_all_numbers(counters[1] <= 1, 'int'), points_down)),\n", " (-1, sp.And(ps.data_types.type_all_numbers(counters[1] >= src.shape[1] - 2, 'int'),\n", " points_up)), (0, True)) * U_x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the LB update, we will use a velocity input in the shearing direction with the magnitude U_x that is defined further up." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "collision = create_lb_update_rule(stencil=stencil,\n", " relaxation_rate=omega,\n", " compressible=True,\n", " velocity_input=u.center_vector+sp.Matrix([U_p, 0]),\n", " density_input=rho,\n", " force_model='luo',\n", " force=F.center_vector,\n", " kernel_type='collide_only',\n", " optimization={'symbolic_field': src})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to get the populations that cross the upper and lower boundary, respectively." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "to_insert = [s.lhs for s in collision.subexpressions\n", " if collision.method.first_order_equilibrium_moment_symbols[shear_dir]\n", " in s.free_symbols]\n", "for s in to_insert:\n", " collision = collision.new_with_inserted_subexpression(s)\n", "ma = []\n", "for a, c in zip(collision.main_assignments, collision.method.stencil):\n", " if c[shear_dir_normal] == -1:\n", " b = (True, False)\n", " elif c[shear_dir_normal] == 1:\n", " b = (False, True)\n", " else:\n", " b = (False, False)\n", " a = ps.Assignment(a.lhs, a.rhs.replace(points_down, b[0]))\n", " a = ps.Assignment(a.lhs, a.rhs.replace(points_up, b[1]))\n", " ma.append(a)\n", "collision.main_assignments = ma" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "stream = create_stream_pull_with_output_kernel(collision.method, src, dst,\n", " {'density': rho, 'velocity': u})\n", "\n", "stream_kernel = ps.create_kernel(stream, target=dh.default_target).compile()\n", "collision_kernel = ps.create_kernel(collision, target=dh.default_target).compile()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialization" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "init = macroscopic_values_setter(collision.method, velocity=(0, 0),\n", " pdfs=src.center_vector, density=rho.center)\n", "init_kernel = ps.create_kernel(init, ghost_layers=0).compile()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def init():\n", " dh.fill(rho.name, 1.0, ghost_layers=True)\n", " dh.fill(src.name, 0.0, ghost_layers=True)\n", " dh.fill(dst.name, 0.0, ghost_layers=True)\n", " dh.run_kernel(init_kernel)\n", " dh.fill(u.name, 0.0, ghost_layers=True)\n", " dh.fill(F.name, 0.0, ghost_layers=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpolation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After applying the normal periodic boundary conditions, we interpolate back in the original cells by using a linear interpolation scheme. In this step, the corners are not special anymore so that we can use the entire upper and lower slab." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def get_le_boundary_functor(neighbor_stencil, shear_offset, ghost_layers=1, thickness=None):\n", " functor_2 = get_periodic_boundary_functor(neighbor_stencil, ghost_layers, thickness)\n", "\n", " def functor(pdfs, **_):\n", "\n", " functor_2(pdfs)\n", " weight = np.fmod(shear_offset[0] + N, 1.)\n", "\n", " # First, we interpolate the upper pdfs\n", " for i in range(len(pdfs[:, ghost_layers, :])):\n", " ind1 = int(np.floor(i - shear_offset[0]) % N)\n", " ind2 = int(np.ceil(i - shear_offset[0]) % N)\n", "\n", " res = (1 - weight) * pdfs[ind1, ghost_layers, :] + weight * pdfs[ind2, ghost_layers, :]\n", " pdfs[i, -ghost_layers, :] = res\n", "\n", " # Second, we interpolate the lower pdfs\n", " for i in range(len(pdfs[:, -ghost_layers, :])):\n", " ind1 = int(np.floor(i + shear_offset[0]) % N)\n", " ind2 = int(np.ceil(i + shear_offset[0]) % N)\n", "\n", " res = (1 - weight) * pdfs[ind1, -ghost_layers - 1, :] + weight * pdfs[ind2, -ghost_layers - 1, :]\n", " pdfs[i, ghost_layers - 1, :] = res\n", "\n", " return functor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Timeloop" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "offset = [0.0]\n", "\n", "sync_pdfs = dh.synchronization_function([src.name],\n", " functor=partial(get_le_boundary_functor, shear_offset=offset))\n", "\n", "def time_loop(steps, shift):\n", " dh.all_to_gpu()\n", " for i in range(steps):\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", " shift[0] += U_x\n", " dh.all_to_cpu()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def plot_v():\n", " plt.subplot(121)\n", " plt.title(\"$v_A$\")\n", " plt.vector_field(dh.gather_array(u.name), step=2)\n", " plt.subplot(122)\n", " plt.vector_field_magnitude(dh.gather_array(u.name))\n", " plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the simulation\n", "### Initialize all velocities with zero" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "init()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run the simulation to show the flow profile" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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