{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# BEM-BEM Coupling using a simple multitrace formulation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, we demonstrate the solution of a Helmholtz transmission problem using a simple multitrace formulation.\n", "The basic multitrace operator has the form\n", "$$\n", "\\mathsf{A}:=\\begin{bmatrix}\n", "-\\mathsf{K} & \\mathsf{V}\\\\\n", "\\mathsf{W} & \\mathsf{K}'\\end{bmatrix},\n", "$$\n", "where $\\mathsf{K}$, $\\mathsf{V}$, $\\mathsf{K}'$ and $\\mathsf{W}$ are the double layer, single layer, adjoint double layer and hypersingular boundary operators.\n", "\n", "This multitrace operator has important properties. If $V = [u,u_\\nu]$ is a pair of Dirichlet/Neumann data of an interior Helmholtz solution then\n", "$$\n", "\\left[\\tfrac{1}{2}\\mathsf{Id} + \\mathsf{A}\\right]V=V.\n", "$$\n", "Similarly, if $V$ is a boundary data pair of an exterior radiating Helmholtz solution then\n", "$$\n", "\\left[\\tfrac{1}{2}\\mathsf{Id} - \\mathsf{A}\\right]V=V.\n", "$$\n", "Furthermore, from the above properties it follows that $(2\\mathsf{A})^2 = \\mathsf{Id}$. Hence, this operator is self-regularising. This property is heavily used in Calderón preconditioning.\n", "\n", "Now let $\\mathsf{A}^\\text{--}$ be the multitrace operator for an interior Helmholtz problem with wavenumber $k^\\text{--}$ and $A^\\text{+}$ be the multitrace operator for an exterior Helmholtz problem with wavenumber $k^\\text{+}$. Let $V^\\text{inc}:= [u^\\text{inc}, u_\\nu^\\text{inc}]$ be the boundary data of an incident wave (eg a plane wave). It follows from the properties of the multitrace operator that the solution pair of the Helmholtz scattering problem $V$ satisfies\n", "$$\n", "\\left[\\tfrac{1}{2}\\mathsf{Id} + \\mathsf{A}^\\text{--}\\right]V = \\left[\\tfrac{1}{2}\\mathsf{Id} - \\mathsf{A}^\\text{+}\\right]V + V^\\text{inc}\n", "$$\n", "giving\n", "$$\n", "(\\mathsf{A}^\\text{--} + \\mathsf{A}^\\text{--})V = V^\\text{inc}.\n", "$$\n", "To regularize this equation we multiply with $(\\mathsf{A}^\\text{--} + \\mathsf{A}^\\text{+})$ to obtain\n", "$$\n", "(\\mathsf{A}^\\text{--} + \\mathsf{A}^\\text{+})^2 = (\\mathsf{A}^\\text{--} + \\mathsf{A}^\\text{+})V^\\text{inc}.\n", "$$\n", "More details can be found in Claeys & Hiptmair (2013).\n", "\n", "The implementation of multitrace operators requires care. If we want to use a typical dual space pairing of piecewise constant functions for the Neumann data and continuous, piecewise linear functions for the Dirichlet data, we need to define the constant functions on the dual grid to ensure that both have the same number of unknowns and a stable dual-space pairing. Bempp implements constant basis functions on dual grids. However, the computational effort grows significantly as a dual grid approach requires barycentrically refined grids that have six times as many elements as the original grid.\n", "\n", "The following code automates all these steps. But because of the dual grid approach it requires the assembly of operators on quite large grids even though the original problem is relatively small." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start with the ususal imports." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import bempp.api\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following defines the wavenumber and the grid. We use roughly 10 elements per wavelength. The wavenumber in the interior domain is $n * k$, where $n$ is a refractive index." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "k = 2\n", "n = .5\n", "h = 2 * np.pi/(10 * k)\n", "grid = bempp.api.shapes.ellipsoid(1.5, 1, 1, h=h)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now define the Dirichlet and Neumann data of the incident wave." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "@bempp.api.complex_callable\n", "def dirichlet_fun(x, n, domain_index, result):\n", " result[0] = np.exp(1j * k * x[0])\n", " \n", "@bempp.api.complex_callable\n", "def neumann_fun(x, n, domain_index, result):\n", " result[0] = 1j * k * n[0] * np.exp(1j *k * x[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code defines the interior and exterior multitrace operators. In particular, the operator product is interesting. Bempp handles all occuring mass matrices automatically. The assembly of a multitrace operator is efficient in the sense that only one single layer and one double layer operator need to be assembled. All others are derived from those two. The ``multitrace_operator`` method uses the correct spaces for the Dirichlet and Neumann data. To access these spaces we just access the spaces of the component operators." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "Ai = bempp.api.operators.boundary.helmholtz.multitrace_operator(grid, n * k)\n", "Ae = bempp.api.operators.boundary.helmholtz.multitrace_operator(grid, k)\n", "\n", "op = (Ai + Ae)\n", "op_squared = op * op\n", "\n", "dirichlet_space = Ai[0, 0].domain\n", "neumann_space = Ai[0, 1].domain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to discretise the incident field into grid functions." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dirichlet_grid_fun = bempp.api.GridFunction(dirichlet_space, fun=dirichlet_fun)\n", "neumann_grid_fun = bempp.api.GridFunction(neumann_space, fun=neumann_fun)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following discretises the left-hand side operator and the right-hand side vector." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/sparse/linalg/dsolve/linsolve.py:296: SparseEfficiencyWarning: splu requires CSC matrix format\n", " warn('splu requires CSC matrix format', SparseEfficiencyWarning)\n" ] } ], "source": [ "op_discrete = op.strong_form()\n", "op_discrete_squared = op_discrete * op_discrete\n", "rhs = op_discrete * np.concatenate([dirichlet_grid_fun.coefficients,\n", " neumann_grid_fun.coefficients])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now solve the discretised linear system and recover the Dirichlet and Neumann boundary data of the solution." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The linear system was solved in 6 iterations\n" ] } ], "source": [ "from scipy.sparse.linalg import gmres\n", "\n", "it_count = 0\n", "def iteration_counter(x):\n", " global it_count\n", " it_count += 1\n", " \n", "x, info = gmres(op_discrete_squared, rhs, callback=iteration_counter)\n", "print(\"The linear system was solved in {0} iterations\".format(it_count))\n", "\n", "total_field_dirichlet = bempp.api.GridFunction(\n", " dirichlet_space, coefficients=x[:dirichlet_space.global_dof_count])\n", "total_field_neumann = bempp.api.GridFunction(\n", " neumann_space, coefficients=x[dirichlet_space.global_dof_count:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now generate the points for the plot of a slice of the solution in the $z=0$ plane." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "Nx = 300\n", "Ny = 300\n", "xmin, xmax, ymin, ymax = [-3, 3, -3, 3]\n", "plot_grid = np.mgrid[xmin:xmax:Nx * 1j, ymin:ymax:Ny * 1j]\n", "points = np.vstack((plot_grid[0].ravel(),\n", " plot_grid[1].ravel(),\n", " np.zeros(plot_grid[0].size)))\n", "u_evaluated = np.zeros(points.shape[1], dtype=np.complex128)\n", "\n", "x, y = points[:2]\n", "idx_ext = np.sqrt((x/1.5)**2 + y**2) > 1.0\n", "idx_int = np.sqrt((x/1.5)**2 + y**2) <= 1.0\n", "\n", "points_exterior = points[:, idx_ext]\n", "points_interior = points[:, idx_int]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compute the solution in the interior and exterior of the sphere we need to assemble the corresponding potential operators and then compute the field data using Greens' representation formula." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "slp_pot_int = bempp.api.operators.potential.helmholtz.single_layer(\n", " dirichlet_space, points_interior, n * k)\n", "slp_pot_ext = bempp.api.operators.potential.helmholtz.single_layer(\n", " dirichlet_space, points_exterior, k)\n", "dlp_pot_int = bempp.api.operators.potential.helmholtz.double_layer(\n", " dirichlet_space, points_interior, n * k)\n", "dlp_pot_ext = bempp.api.operators.potential.helmholtz.double_layer(\n", " dirichlet_space, points_exterior, k)\n", "\n", "total_field_int = (slp_pot_int * total_field_neumann \n", " - dlp_pot_int * total_field_dirichlet).ravel()\n", "total_field_ext = (dlp_pot_ext * total_field_dirichlet \n", " - slp_pot_ext * total_field_neumann).ravel() \\\n", " + np.exp(1j * k * points_exterior[0])\n", "\n", "total_field = np.zeros(points.shape[1], dtype='complex128')\n", "total_field[idx_ext] = total_field_ext\n", "total_field[idx_int] = total_field_int\n", "total_field = total_field.reshape([Nx, Ny])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can plot the solution of the scattering problem in the $z=0$ plane." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib import pylab as plt\n", "fig = plt.figure(figsize=(10, 8))\n", "plt.imshow(np.real(total_field.T), extent=[-3, 3, -3, 3])\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "plt.colorbar()" ] } ], "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", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 1 }