{ "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. \n", "\n", "To run the simulation on GPU, change the default_target to gpu" ] }, { "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", " default_target='cpu')\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(u.name, 0.0, ghost_layers=True)\n", " dh.fill(F.name, 0.0, ghost_layers=True)\n", " dh.run_kernel(init_kernel)" ] }, { "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", "\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", "#\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, :] + \\\n", "# 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", "#\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, :] + \\\n", "# 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", "\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", "text/plain": [ "