{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bistatic RCS generated by dielectric spheres" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the following notebook is very compute intensive. To speed it up disable the near-field computations.\n", "\n", "In this notebook, we consider the bistatic radar cross section generated from plane wave scattering by an array of dielectric spheres $\\Omega_j$, each with its individual permittivity $\\epsilon_j$ and permeability $\\mu_j$.\n", "\n", "We denote by $\\epsilon_{0}$ and $\\mu_0$ the electric permittivity and magnetic permeability in vacuum, and by $\\epsilon_{r, j} = \\frac{\\epsilon_j}{\\epsilon_0}$ and $\\mu_{r, j} = \\frac{\\mu_j}{\\mu_0}$ the relative permittivity and relative permeability for each dielectric object. Denote by $\\mathbf{E}^\\text{s}$, $\\mathbf{H}^\\text{s}$ the scattered electric and magnetic field in the exterior of the scatterers. Moreover, let $\\mathbf{E}$ and $\\mathbf{H}$ be the total exterior fields. Correspondingly, we denote the interior fields in the $j$th obstacle by $\\mathbf{E}_j$ and $\\mathbf{H}_j$.\n", "\n", "For a given medium, we normalize the Maxwell equations by setting $\\hat{\\mathbf{H}} = \\sqrt{\\mu}\\mathbf{H}$ and $\\hat{\\mathbf{E}} = \\sqrt{\\epsilon}\\mathbf{E}$ and obtain\n", "\n", "\\begin{align}\n", "\\nabla\\times \\hat{\\mathbf{E}} &= \\mathrm{i}k \\hat{\\mathbf{H}},\\nonumber\\\\\n", "\\nabla\\times \\mathbf{H} &= -\\mathrm{i}k \\mathbf{E}.\\nonumber\n", "\\end{align}\n", "with $k=\\omega\\sqrt{\\mu\\epsilon}$.\n", "\n", "The electric field equations for the dielectric scattering problem now take the form\n", "\\begin{align}\n", "\\nabla\\times\\nabla\\times \\hat{\\mathbf{E}}^{s}(x) - k_0^2\\hat{\\mathbf{E}}^{s}(x) &= 0,~x\\in\\Omega^{+}\\nonumber\\\\\n", "\\nabla\\times\\nabla \\times \\hat{\\mathbf{E}}_{j}(x) - k_j^2\\hat{\\mathbf{E}}_{j}(x) &= 0,~x\\in\\Omega_j\\nonumber\\\\\n", "\\end{align}\n", "with $k_0 = \\omega\\sqrt{\\epsilon_0\\mu_0}$ and $k_j = k_0\\sqrt{\\epsilon_{r,j}\\mu_{r,j}}$.\n", "\n", "We still have to fix the correct boundary conditions between the media.\n", "\n", "We denote the interior tangential trace of the electric field $\\mathbf{E}_{j}$ at the $j$th obstacle as $\\gamma_\\text{t}^{j, -} \\mathbf{E}_{j} = \\mathbf{E}_{j}\\times \\nu$ with $\\nu$ the exterior normal direction on the boundary of $\\Omega_j$. Correspondingly, we define the exterior tangential trace $\\gamma_\\text{t}^{j, +}\\mathbf{E} = \\mathbf{E}\\times \\nu$. Moreover, we define the interior Neumann trace as $\\gamma_\\text{N}^{j,-} \\mathbf{E}_{j} = \\frac{1}{\\mathrm{i}k_j}\\gamma_\\text{t}^{j,-}\\left(\\nabla\\times \\mathbf{E}_{j}\\right)$ and the exterior Neumann trace as $\\gamma_\\text{N}^{j,+} \\mathbf{E} = \\frac{1}{\\mathrm{i}k_0}\\gamma_\\text{t}^{j,+}\\left(\\nabla\\times \\mathbf{E}\\right)$.\n", "\n", "The boundary conditions on the $j$th obstacles are that the tangential component of the electric and magnetic field is continuous across the boundary. Taking the rescaling into account this implies the conditions\n", "\\begin{align}\n", "\\gamma_\\text{t}^{j, -}\\hat{\\mathbf{E}}_j &= \\sqrt{\\epsilon_{r,j}}\\gamma_\\text{t}^{j,+}\\hat{\\mathbf{E}}\\nonumber\\\\\n", "\\gamma_\\text{N}^{j, -}\\hat{\\mathbf{E}}_j &= \\sqrt{\\mu_{r,j}}\\gamma_\\text{N}^{j,+}\\hat{\\mathbf{E}}.\n", "\\end{align}\n", "Towards infinity we need to satisfy the Silver-Müller radiation conditions, which are given as\n", "$$\n", "\\lim_{|\\mathbf{x}|\\rightarrow\\infty}|\\mathbf{x}|\\left(\\hat{\\mathbf{H}}^\\text{s}(\\mathbf{x})\\times \\frac{x}{|x|} - \\hat{\\mathbf{E}}^\\text{s}(\\mathbf{x})\\right) = 0\n", "$$\n", "uniformly in all directions.\n", "\n", "In the following we describe a formulation based on the multitrace operator $\\mathsf{A}:=\\begin{bmatrix}\\mathsf{H} & \\mathsf{E}\\\\ - \\mathsf{E} & \\mathsf{H}\\end{bmatrix}$ whose implementation in Bempp is described in more detail in [Scroggs, Betcke, Smigaj (2017)](https://bempp.com/publications/#scroggs). Here, the operator $\\mathsf{H}$ is the magnetic boundary operator and $\\mathsf{E}$ is the electric boundary operator. The operator $\\mathsf{A}_{j,+}$ associated with the exterior solution on the boundary of $\\Omega_j$ satisfies\n", "$$\n", "\\left[\\frac{1}{2}\\mathsf{Id} - \\mathsf{A}_{j,+}\\right]\\begin{bmatrix}\\gamma_\\text{t}^{j,+}{\\hat{\\mathbf{E}}^\\text{s}}\\\\ \\gamma_\\text{N}^{j,+}{\\hat{\\mathbf{E}}^\\text{s}}\\end{bmatrix} = \n", "\\begin{bmatrix}\\gamma_\\text{t}^{j,+}{\\hat{\\mathbf{E}}^\\text{s}}\\\\ \\gamma_\\text{N}^{j,+}{\\hat{\\mathbf{E}}^\\text{s}}\\end{bmatrix},\n", "$$\n", "where $\\mathsf{A}_{j,+}$ is the multitrace operator on the boundary of $\\Omega_j$ with wavenumber $k_0$. The interior solutions $\\mathbf{E}^{j}$ satisfy\n", "$$\n", "\\left[\\frac{1}{2}\\mathsf{Id} + \\mathsf{A}_{j,-}\\right]\\begin{bmatrix}\\gamma_\\text{t}^{j,-}{\\hat{\\mathbf{E}}_j}\\\\ \\gamma_\\text{N}^{j,-}{\\hat{\\mathbf{E}}_j}\\end{bmatrix} = \n", "\\begin{bmatrix}\\gamma_\\text{t}^{j,-}{\\hat{\\mathbf{E}}_j}\\\\ \\gamma_\\text{N}^{j,-}\\hat{\\mathbf{E}}_j\\end{bmatrix},\n", "$$\n", "where $\\mathsf{A}_{j,-}$ is the multitrace operator across the boundary of $\\Omega_j$ with wavenumber $k_j$. \n", "\n", "Now denote by $\\hat{V}_j^\\text{s}:=\\begin{bmatrix}\\gamma_\\text{t}^{j,+}\\hat{\\mathbf{E}}^{s}\\\\ \\gamma_\\text{N}^{j,+}\\hat{\\mathbf{E}}^\\text{s}\\end{bmatrix}$ the vector of trace data associated with the scattered field on the boundary of $\\Omega_j$. Correspondingly, we denote by $\\hat{V}_j$ the vector of the trace data of the interior solution in $\\Omega_j$ and by $\\hat{V}^\\text{inc}$ the vector of trace data of the incident field on the boundary of $\\Omega_j$.\n", "\n", "We first consider the case of a single scatterer $\\Omega_1$. The boundary conditions are given by\n", "$$\n", "\\hat{V}_1 = \\begin{bmatrix}\\sqrt{\\epsilon_{r, 1}} & \\\\ & \\sqrt{\\mu_{r, 1}}\\end{bmatrix}\\left(\\hat{V}_1^\\text{s} + \\hat{V}_1^\\text{inc}\\right)=: D_1\\left(\\hat{V}_1^\\text{s} + \\hat{V}_1^\\text{inc}\\right).\n", "$$\n", "From $\\left(\\frac{1}{2}\\mathsf{Id} + \\mathsf{A}_{1,-}\\right)\\hat{V}_1 = \\hat{V}_1$ together with the above relationship for the exterior multitrace operator, and the boundary condition, we obtain that\n", "$$\n", "\\left(\\frac{1}{2}\\mathsf{Id} + \\mathsf{A}_{1,-}\\right)D_1\\left(\\hat{V}_1^\\text{s} + \\hat{V}_1^\\text{inc}\\right) = D_1\\left(\\frac{1}{2}\\mathsf{Id} - \\mathsf{A}_{1,+}\\right)\\hat{V}_1^\\text{s} + D_1\\hat{V}_1^\\text{inc}.\n", "$$\n", "Simplifying the above equation leads to\n", "$$\n", "\\left(D_1^{-1}\\mathsf{A}_{1,-}\\mathsf{D}_1 + \\mathsf{A}_{1, +}\\right)\\hat{V}_1^\\text{s} = \\left(\\frac{1}{2}\\mathsf{Id} - D_1^{-1}\\mathsf{A}_{1, -}D_1\\right)\\hat{V}^\\text{inc}.\n", "$$\n", "Now assume that we have a whole array of scatterers. Then the equation for each scatterer becomes\n", "$$\n", "\\left(D_j^{-1}\\mathsf{A}_{j,-}D_j + \\mathsf{A}_{j, +}\\right)\\hat{V}_j^\\text{s} = \\left(\\frac{1}{2}\\mathsf{Id} - D_j^{-1}\\mathsf{A}_{j, -}D_j\\right)\\hat{V}^\\text{inc} - \\sum_{i\\neq j}\\mathsf{A}_{i,j}\\hat{V}_i^\\text{s}\n", "$$\n", "for each scatterer $j$. The operator $-\\mathsf{A}_{i, j}$ maps scattered trace data on the $j$th scatterer to exterior trace data on the $i$th scatterer. It is just the exterior Stratton-Chu formula, hence the minus sign. To implement $\\mathsf{A}_{i, j}$ one just implements the multitrace operator with domain space on one obstacle and test space on the other obstacle.\n", "\n", "If there are $N$ obstacles then the above formula leads to a block operator system with $2N$ equations in $2N$ unknowns, whch is fully determined. In the following we demonstrate the implementation of this system in Bempp." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start with the usual imports and enable console logging." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "bempp:HOST:INFO: Creating pool for Platform: AMD Accelerated Parallel Processing\n", "bempp:HOST:INFO: Created pool with 2 workers.\n" ] } ], "source": [ "import bempp.api\n", "import numpy as np\n", "\n", "bempp.api.enable_console_logging()\n", "# bempp.api.pool.create_device_pool(\"AMD\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this notebook we will use three spheres of radius 0.4, centered at $-1$, $0$, and $1$ on the x-axis." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "bempp:HOST:INFO: Created grid with id f77f25a6-74f7-4424-9904-8615c88b367c. Elements: 638. Edges: 957. Vertices: 321\n", "bempp:HOST:INFO: Created grid with id a953552d-c0d3-4559-aaf5-6ce41ad2af98. Elements: 652. Edges: 978. Vertices: 328\n", "bempp:HOST:INFO: Created grid with id 6fee21ee-62cf-44c5-a3a6-1a047c5375f7. Elements: 640. Edges: 960. Vertices: 322\n" ] } ], "source": [ "centers = [-1, 0, 1]\n", "\n", "radius = .4\n", "\n", "number_of_scatterers = len(centers)\n", "\n", "grids = [bempp.api.shapes.sphere(r=radius, origin=(c, 0, 0), h=0.1)\n", " for c in centers]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now define the associated constants and material parameters. We have chosen the material parameters for Teflon. The overall simulation will be running at a frequency of 300Mhz." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The exterior wavenumber is: 6.287535065634769\n", "The interior wavenumbers are:\n", "[9.111503944099038, 9.111503944099038, 9.111503944099038]\n" ] } ], "source": [ "frequency = 300E6 # 300Mhz\n", "\n", "vacuum_permittivity = 8.854187817E-12\n", "vacuum_permeability = 4 * np.pi * 1E-7\n", "\n", "rel_permittivities = number_of_scatterers * [2.1]\n", "rel_permeabilities = number_of_scatterers * [1.0]\n", "\n", "k0 = 2 * np.pi * frequency * np.sqrt(vacuum_permittivity * vacuum_permeability)\n", "wavenumbers = [k0 * np.sqrt(er * mr) for er, mr in zip(rel_permittivities, rel_permeabilities)]\n", "print(\"The exterior wavenumber is: {0}\".format(k0))\n", "print(\"The interior wavenumbers are:\")\n", "print(wavenumbers)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now assemble the incident wave field. To that affect we choose a z-polarized plane wave travelling at an inciden angle theta in the (x,y) plane. Note that we already scale it with $\\sqrt{\\epsilon_0}$ to obtain the field $\\hat{\\mathbf{E}}^\\text{inc}$." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "theta = np.pi / 4 # Incident wave travelling at a 45 degree angle\n", "direction = np.array([np.cos(theta), np.sin(theta), 0])\n", "polarization = np.array([0, 0, 1.0])\n", "\n", "def plane_wave(point):\n", " return polarization * np.exp(1j * k0 * np.dot(point, direction))\n", "\n", "def scaled_plane_wave(point):\n", " return np.sqrt(vacuum_permittivity) * plane_wave(point)\n", "\n", "@bempp.api.complex_callable\n", "def tangential_trace(point, n, domain_index, result):\n", " value = np.sqrt(vacuum_permittivity) * polarization * np.exp(1j * k0 * np.dot(point, direction))\n", " result[:] = np.cross(value, n)\n", "\n", "def scaled_plane_wave_curl(point):\n", " return np.cross(direction, polarization) * 1j * k0 * np.sqrt(vacuum_permittivity) * np.exp(1j * k0 * np.dot(point, direction))\n", "\n", "@bempp.api.complex_callable\n", "def neumann_trace(point, n, domain_index, result):\n", " value = np.cross(direction, polarization) * 1j * k0 * np.sqrt(vacuum_permittivity) * np.exp(1j * k0 * np.dot(point, direction))\n", " result[:] = 1./ (1j * k0) * np.cross(value, n)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we setup the left-hand side block operator matrix we write a small routine that rescales a given $2\\times 2$ block operator matrix $A$ to $D^{-1}AD$, where $D$ is a diagonal matrix defined by its diagonal elements $d_1$ and $d_2$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def rescale(A, d1, d2):\n", " \"\"\"Rescale the 2x2 block operator matrix A\"\"\"\n", " \n", " A[0, 1] = A[0, 1] * (d2 / d1)\n", " A[1, 0] = A[1, 0] * (d1 / d2)\n", " \n", " return A" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now create all the scaled interior multitrace operators and the non-scaled exterior multitrace operators." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "bempp:HOST:INFO: Created grid with id 0634a55a-42b3-4a90-97ef-c1b4f534ca2d. Elements: 3828. Edges: 5742. Vertices: 1916\n", "bempp:HOST:INFO: OpenCL Device set to: pthread-Intel(R) Xeon(R) W-2155 CPU @ 3.30GHz\n", "bempp:HOST:INFO: Created grid with id 0ff6bd7b-9a70-4fe4-a3ef-d8622a939a42. Elements: 3912. Edges: 5868. Vertices: 1958\n", "bempp:HOST:INFO: Created grid with id 4f2f7d91-d56b-4762-b8cb-33fb85d6e6ad. Elements: 3840. Edges: 5760. Vertices: 1922\n" ] } ], "source": [ "scaled_interior_operators = [\n", " rescale(bempp.api.operators.boundary.maxwell.multitrace_operator(\n", " grid, wavenumber, space_type='electric_dual', assembler='dense_evaluator', precision='single'), \n", " np.sqrt(epsr), np.sqrt(mur)) for grid, wavenumber, epsr, mur in\n", " zip(grids, wavenumbers, rel_permittivities, rel_permeabilities)\n", "]\n", "\n", "identity_operators = [\n", " bempp.api.operators.boundary.sparse.multitrace_identity(op)\n", " for op in scaled_interior_operators\n", "]\n", "\n", "exterior_operators = [\n", " bempp.api.operators.boundary.maxwell.multitrace_operator(\n", " grid, k0, space_type='electric_dual', assembler='dense_evaluator', precision='single') for grid in grids\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now loop through the rows of the left-hand side block system to create all block operator entries. We will store the filter operators $\\frac{1}{2}\\mathsf{Id} - D_j^{-1}\\mathsf{A}_{j, -}D_j$ and the transfer operators $\\mathsf{A}_{i,j}$. The filter operators will be needed for the right-hand side, and the transfer operators will be needed to compute the interior near-field in each obstacle." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from bempp.api.assembly.blocked_operator import GeneralizedBlockedOperator\n", "\n", "filter_operators = number_of_scatterers * [None]\n", "transfer_operators = np.empty((number_of_scatterers, number_of_scatterers), dtype=np.object)\n", "\n", "#The following will contain the left-hand side block operator\n", "op = np.empty((number_of_scatterers, number_of_scatterers), dtype=np.object)\n", "\n", "for i in range(number_of_scatterers):\n", " filter_operators[i] = .5 * identity_operators[i]- scaled_interior_operators[i]\n", " for j in range(number_of_scatterers):\n", " if i == j:\n", " # Create the diagonal elements\n", " op[i, j] = scaled_interior_operators[j] + exterior_operators[j]\n", " else:\n", " # Do the off-diagonal elements\n", " transfer_operators[i, j] = bempp.api.operators.boundary.maxwell.multitrace_operator(\n", " grids[j], k0, target=grids[i], space_type=\"electric_dual\", assembler=\"dense_evaluator\", precision='single')\n", " op[i, j] = transfer_operators[i, j]\n", "blocked_operator = GeneralizedBlockedOperator(op)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code assembles the right-hand sides." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "rhs = 2 * number_of_scatterers * [None]\n", "incident_trace_data = number_of_scatterers * [None]\n", "\n", "for i in range(number_of_scatterers):\n", " incident_trace_data[i] = (\n", " bempp.api.GridFunction(blocked_operator.domain_spaces[2 * i], fun=tangential_trace, dual_space=blocked_operator.dual_to_range_spaces[2 * i]),\n", " bempp.api.GridFunction(blocked_operator.domain_spaces[2 * i + 1], fun=neumann_trace, \n", " dual_space=blocked_operator.dual_to_range_spaces[2 * i + 1]))\n", " rhs[2 * i], rhs[2 * i + 1] = filter_operators[i] * incident_trace_data[i]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have everything in place to solve the resulting scattering problem. We use mass matrix preconditioning to precondition the system." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "bempp:HOST:INFO: Starting GMRES iteration\n", "bempp:HOST:INFO: GMRES Iteration 1 with residual 0.43583039705163845\n", "bempp:HOST:INFO: GMRES Iteration 2 with residual 0.2755373752267419\n", "bempp:HOST:INFO: GMRES Iteration 3 with residual 0.1383105356552037\n", "bempp:HOST:INFO: GMRES Iteration 4 with residual 0.09826500580926656\n", "bempp:HOST:INFO: GMRES Iteration 5 with residual 0.06240219348917185\n", "bempp:HOST:INFO: GMRES Iteration 6 with residual 0.05606766660903622\n", "bempp:HOST:INFO: GMRES Iteration 7 with residual 0.032865749172841976\n", "bempp:HOST:INFO: GMRES Iteration 8 with residual 0.02152089331466054\n", "bempp:HOST:INFO: GMRES Iteration 9 with residual 0.011508024941734456\n", "bempp:HOST:INFO: GMRES Iteration 10 with residual 0.008568157553557483\n", "bempp:HOST:INFO: GMRES Iteration 11 with residual 0.004277455935141667\n", "bempp:HOST:INFO: GMRES Iteration 12 with residual 0.0026102296490043008\n", "bempp:HOST:INFO: GMRES Iteration 13 with residual 0.0011855811398403151\n", "bempp:HOST:INFO: GMRES Iteration 14 with residual 0.0008821372223869267\n", "bempp:HOST:INFO: GMRES Iteration 15 with residual 0.0005540376069927982\n", "bempp:HOST:INFO: GMRES Iteration 16 with residual 0.0004091951471039425\n", "bempp:HOST:INFO: GMRES Iteration 17 with residual 0.00020440861847224016\n", "bempp:HOST:INFO: GMRES Iteration 18 with residual 0.00012199668491780794\n", "bempp:HOST:INFO: GMRES Iteration 19 with residual 6.50654188922274e-05\n", "bempp:HOST:INFO: GMRES Iteration 20 with residual 3.600735510934312e-05\n", "bempp:HOST:INFO: GMRES Iteration 21 with residual 3.567966757313431e-05\n", "bempp:HOST:INFO: GMRES Iteration 22 with residual 2.6732316503010026e-05\n", "bempp:HOST:INFO: GMRES Iteration 23 with residual 2.6393172169991727e-05\n", "bempp:HOST:INFO: GMRES Iteration 24 with residual 1.577727573482283e-05\n", "bempp:HOST:INFO: GMRES Iteration 25 with residual 1.564863428560281e-05\n", "bempp:HOST:INFO: GMRES Iteration 26 with residual 9.599005427967441e-06\n", "bempp:HOST:INFO: GMRES finished in 26 iterations and took 9.88E+01 sec.\n" ] } ], "source": [ "bempp.api.enable_console_logging()\n", "sol, info, _ = bempp.api.linalg.gmres(blocked_operator, rhs, use_strong_form=True, return_residuals=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following we visualize the near-field. The above code has computed the exterior trace data. But we also want to visualize the interior solutions. So we have to compute the interior trace data as well, which is done in the following code." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "interior_trace_data = number_of_scatterers * [None]\n", "\n", "for i in range(number_of_scatterers):\n", " interior_trace_data[i] = [np.sqrt(rel_permittivities[i]) * incident_trace_data[i][0],\n", " np.sqrt(rel_permeabilities[i]) * incident_trace_data[i][1]]\n", " interior_trace_data[i][0] += np.sqrt(rel_permittivities[i]) * sol[2 * i]\n", " interior_trace_data[i][1] += np.sqrt(rel_permeabilities[i]) * sol[2 * i + 1] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now create a grid of plotting points in the $(\\mathbf{x},\\mathbf{y})$ plane and assign the indices in the sets associated with one of the obstacles or the exterior." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Number of points in the x-direction\n", "nx = 200\n", "\n", "# Number of points in the y-direction\n", "ny = 200\n", "\n", "# Generate the evaluation points with numpy\n", "x, y, z = np.mgrid[-2:2:nx * 1j, -2:2:ny * 1j, 0:0:1j]\n", "points = np.vstack((x.ravel(), y.ravel(), z.ravel()))\n", "\n", "\n", "# Compute interior and exterior indices\n", "all_indices = np.ones(points.shape[1], dtype='uint32')\n", "index_sets = number_of_scatterers * [None]\n", "\n", "index = 0\n", "for c in centers:\n", " shifted_points = points - np.array([[c, 0, 0]]).T\n", " found_indices = np.arange(points.shape[1], dtype='uint32')[\n", " np.sum(shifted_points**2, axis=0) < radius**2]\n", " all_indices[found_indices] = 0\n", " index_sets[index] = found_indices\n", " index += 1\n", "ext_indices = np.arange(points.shape[1], dtype='uint32')[all_indices == 1]\n", "int_indices = np.arange(points.shape[1], dtype='uint32')[all_indices == 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each scatterer we now evaluate the interior and exterior potentials on the associated points." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "exterior_values = np.zeros((3, len(ext_indices)), dtype='complex128')\n", "interior_values = number_of_scatterers * [None]\n", "\n", "ext_points = points[:, ext_indices]\n", "\n", "for i in range(number_of_scatterers):\n", " int_points = points[:, index_sets[i]]\n", " epot_int = bempp.api.operators.potential.maxwell.electric_field(interior_trace_data[i][1].space, int_points,\n", " wavenumbers[i])\n", " mpot_int = bempp.api.operators.potential.maxwell.magnetic_field(interior_trace_data[i][0].space, int_points,\n", " wavenumbers[i])\n", " epot_ext = bempp.api.operators.potential.maxwell.electric_field(sol[2 * i + 1].space, ext_points, k0)\n", " mpot_ext = bempp.api.operators.potential.maxwell.magnetic_field(sol[2 * i].space, ext_points, k0)\n", " \n", " exterior_values += -epot_ext * sol[2 * i + 1] - mpot_ext * sol[2 * i] \n", " interior_values[i] = (epot_int * interior_trace_data[i][1] + mpot_int * interior_trace_data[i][0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code now plots the squared absolute value of the electric near field." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "from matplotlib.patches import Circle\n", "plt.rcParams['figure.figsize'] = (20, 16) # Increase the figure size in the notebook\n", "\n", "# First compute the scattered field\n", "scattered_field = np.empty((3, points.shape[1]), dtype='complex128')\n", "scattered_field[:, :] = np.nan\n", "scattered_field[:, ext_indices] = 1./np.sqrt(vacuum_permittivity) * exterior_values\n", "\n", "# Now compute the total field\n", "total_field = np.empty((3, points.shape[1]), dtype='complex128')\n", "\n", "for i in range(exterior_values.shape[1]):\n", " total_field[:, ext_indices[i]] = scattered_field[:, ext_indices[i]] + plane_wave(points[:, ext_indices[i]])\n", " \n", "for i in range(number_of_scatterers):\n", " # Add interior contributions\n", " total_field[:, index_sets[i]] = 1. / np.sqrt(rel_permittivities[i] * vacuum_permittivity) * interior_values[i]\n", " \n", "# Compute the squared field density\n", "squared_scattered_field = np.sum(np.abs(scattered_field)**2, axis=0)\n", "squared_total_field = np.sum(np.abs(total_field)**2, axis=0)\n", "\n", "# Show the resulting images\n", "scattered_image = squared_scattered_field.reshape(nx, ny).T\n", "total_image = squared_total_field.reshape(nx, ny).T\n", "fig, axes = plt.subplots(1, 2, sharex=True, sharey=True)\n", "\n", "f0 = axes[0].imshow(scattered_image, origin='lower', cmap='magma',\n", " extent=[-2, 2, -2, 2])\n", "for c in centers:\n", " axes[0].add_patch(\n", " Circle((c, 0), radius, facecolor='None', edgecolor='k', lw=3))\n", " \n", "axes[0].set_title(\"Squared scattered field strength\")\n", "divider = make_axes_locatable(axes[0])\n", "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", "fig.colorbar(f0, cax=cax)\n", "\n", "f1 = axes[1].imshow(total_image, origin='lower', cmap='magma',\n", " extent=[-2, 2, -2, 2])\n", "for c in centers:\n", " axes[1].add_patch(\n", " Circle((c, 0), radius, facecolor='None', edgecolor='k', lw=3))\n", "\n", "axes[1].set_title(\"Squared total field strength\")\n", "divider = make_axes_locatable(axes[1])\n", "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", "fig.colorbar(f1, cax=cax)\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now evaluate the bistatic radar cross section with the given incident angle. Here, we are only interested in backscattering into negative y plane. First, we define the evaluation points on the lower half of the unit circle. For the RCS we define the 0 degree angle to point into the negative x-direction, and correspondingly the 180 degree angle to be pointing into the positive x-direction." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "number_of_angles = 400\n", "angles = np.pi * np.linspace(0, 1, number_of_angles)\n", "unit_points = np.array([-np.cos(angles), -np.sin(angles), np.zeros(number_of_angles)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now assemble for each scatterer the electric and magnetic far-field operators and evaluate the scattered field contribution for each of them. The assembly of the far-field operators will be done in dense mode as they are sufficiently small." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "far_field = np.zeros((3, number_of_angles), dtype='complex128')\n", "\n", "for i in range(number_of_scatterers):\n", " electric_far = bempp.api.operators.far_field.maxwell.electric_field(sol[2 * i + 1].space, unit_points, k0)\n", " magnetic_far = bempp.api.operators.far_field.maxwell.magnetic_field(sol[2 * i].space, unit_points, k0) \n", " far_field += -electric_far * sol[2 * i + 1] - magnetic_far * sol[2 * i]\n", "far_field *= 1./ np.sqrt(vacuum_permittivity)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have everything together to plot the bistatic RCS." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = (10, 8) # Resize the figure\n", "\n", "bistatic_rcs= 10 * np.log10(4 * np.pi * np.sum(np.abs(far_field)**2, axis=0))\n", "plt.plot(angles * 180 / np.pi, bistatic_rcs)\n", "plt.title(\"Bistatic RCS [dB]\")\n", "_ = plt.xlabel('Angle (Degrees)')" ] } ], "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": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 4 }