{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from netgen.occ import *\n", "from ngsolve import *\n", "from netgen.meshing import IdentificationType\n", "#from MS_helper_functions import *\n", "from netgen.webgui import Draw as DrawGeo\n", "from ngsolve.webgui import Draw\n", "#Draw = lambda *args, **kwargs : None\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib widget\n", "plt.ioff()\n", "\n", "from myPackage import evalOnLine, L2Draw, colorprint, TextColor\n", "L2Draw.drawFunc = Draw\n", "from meshGen import mesh2DLaminates\n", "\n", "import numpy as np\n", "\n", "modelHalfAir = False\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "mu0 = 4e-7*np.pi\n", "\n", "muAir = 1 * mu0\n", "muFe = 10 * mu0\n", "\n", "sigmaFe = 2e6\n", "sigmaAir = 0\n", "omega = 50 * 2*np.pi\n", "\n", "\n", "\n", "\n", "order0 = 2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "D = 0.06\n", "d = 0.02\n", "\n", "ff = 0.7\n", "\n", "\n", "numSheets = 5\n", "\n", "maxh_edges = d/numSheets*1/2 * 0.1\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. All together with Eddy currents\n", "### 4.1. Reference Solution " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "maxh_edges [0.0002, 0.0002]\n", "Boundaries {'top', 'bottom', 'left', 'right', 'iright', 'itop', 'ileft', 'ibottom'}\n", "Materials {'insulation', 'outer', 'inner'}\n", "0.0028\n", "penetration depth = 0.015915494309189534\n" ] } ], "source": [ "import importlib\n", "\n", "import meshGen as mg\n", "mg = importlib.reload(mg)\n", "mesh2DLaminates = mg.mesh2DLaminates\n", "cMeshRef = mesh2DLaminates(D, d, ff, numSheets, multiscale=False, maxh_edges=[maxh_edges, maxh_edges], fullProblemX=True, onlySmooth = False, onlyRough = False, rotated=True, modelHalfAir=modelHalfAir, quad_dominated=False)\n", "meshRef = cMeshRef.mesh\n", "print(\"Boundaries\", set(meshRef.GetBoundaries()))\n", "print(\"Materials\", set(meshRef.GetMaterials()))\n", "\n", "print(cMeshRef.dFe)\n", "print(\"penetration depth = \", sqrt(2/(muFe*omega*sigmaFe)))\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "58964ef770074685a2493b55a5d8089b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {}, 'ngsolve_version': '6.2.2…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "BaseWebGuiScene" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Draw(meshRef)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'bottom', 'ibottom', 'ileft', 'iright', 'itop', 'left', 'right', 'top'}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set(meshRef.GetBoundaries())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2KCG iteration 1, residual = 0.06394207939667906 \n", "\u001b[2KCG iteration 2, residual = 1.203016854926081e-09 \n", "\u001b[2KCG iteration 3, residual = 7.969982366332003e-14 \n" ] } ], "source": [ "mu = meshRef.MaterialCF({\"inner\":muFe, \"outer\":muAir, \"insulation\":muAir, \"gap\":muAir, \"multiscale\":muFe})\n", "def calcRef():\n", " fesPhi = H1(meshRef, order=order0+1, dirichlet=\"left|right\" , complex=True)\n", " fesT = HCurl(meshRef, order=order0, dirichlet=\"itop|ibottom|ileft|iright\", complex=True, definedon=meshRef.Materials(\"inner\"), nograds=False)\n", " fes = FESpace([fesPhi, fesT])\n", "\n", " trials, tests = fes.TnT()\n", " sol = GridFunction(fes)\n", "\n", " a = BilinearForm(fes, symmetric=True)\n", " f = LinearForm(fes)\n", "\n", " a += 1j * omega * mu * (trials[1]-grad(trials[0])) * (tests[1]-grad(tests[0])) * dx\n", " a += 1/sigmaFe * curl(trials[1]) * curl(tests[1]) * dx(\"inner\")\n", " a += 1j * omega * 1e-3* trials[0] * tests[0] * dx(\"inner\")\n", "\n", "\n", " prec = Preconditioner(a, \"direct\")\n", "\n", " Phi = sol.components[0]\n", " T = sol.components[1]\n", "\n", " Phi.Set((1 * x/Norm(x)), BND)\n", " solvers.BVP(bf = a, lf= f, pre=prec, gf=sol, maxsteps=30, tol = 1e-10, print=True)\n", "\n", " \n", " \n", " energy = Integrate( InnerProduct(mu * (T - grad(Phi)), (T - grad(Phi))), meshRef, definedon=meshRef.Materials(\"inner|insulation\")).real\n", " eddyLosses = Integrate( InnerProduct(1/sigmaFe * curl(T), curl(T)), meshRef, definedon=meshRef.Materials(\"inner\")).real\n", " \n", " return sol, energy, eddyLosses\n", "\n", "sol_ref, energy_ref, eddyLosses_ref = calcRef()\n", "\n", "\n", "Phi = sol_ref.components[0]\n", "T = sol_ref.components[1]\n", "H_ref = T-grad(Phi)\n", "J_ref = curl(T)\n", "B_ref = mu * H_ref\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "energy:\t3.100541351020442e-07\n", "eddy current losses:\t5.041910562854848e-07\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c8d9dc7e80234af1ba357b6b2483c8a9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#energy:\t3.10067858198899e-07\n", "#eddy current losses:\t5.042166093099941e-07\n", "\n", "\n", "# stab \n", "# energy:\t3.100512177819719e-07\n", "# eddy current losses:\t5.041911557192412e-07\n", "\n", "print(f\"energy:\\t{energy_ref}\")\n", "print(f\"eddy current losses:\\t{eddyLosses_ref}\")\n", "L2Draw(J_ref.imag, meshRef, settings={\"Objects\":{\"Wireframe\":False}})\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "761b9ab83b114f068d98be0498808689", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bcb7494f955e4353a4c126d6b95c91af", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L2Draw( H_ref[0].real, meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False}, min = -200, max = 200)\n", "L2Draw( H_ref[1].real, meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False}, min = -200, max = 200)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "651d161e9fb8435b83547714837ca0f5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7fc05f53ecef437eb4bf55165ae8abad", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L2Draw(B_ref.real[0], meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False})\n", "L2Draw(B_ref.real[1], meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False})" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2fca67e3f9a942798ead7a0015cd6df6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L2Draw(B_ref.Norm(), meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False}, min = 0, max = 3e-4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4.1.1 Multiscale with REf Mesh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Multiscale " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "maxh_edges (0.002, 0.0006000000000000001)\n", "('outer', 'outer', 'multiscale', 'gap', 'gap')\n", "{'top', 'bottom', 'left', 'itop', 'right', 'iright', 'ileft', 'ibottom'}\n" ] } ], "source": [ "mg = importlib.reload(mg)\n", "mesh2DLaminates = mg.mesh2DLaminates\n", "cMeshMS = mesh2DLaminates(D, d, ff, numSheets, multiscale=True, modelHalfAir=modelHalfAir, onlySmooth=False, \n", " onlyRough=False, domainNameHalfAir=\"multiscale\", maxh_edges= (maxh_edges*10, maxh_edges*3), rotated=True, modelGap=True, maxh = d/2)\n", "# cMeshMS = mesh2DLaminates(D, d, ff, numSheets, multiscale=True, modelHalfAir=modelHalfAir, domainNameHalfAir=\"smoothFrame\", onlySmooth=onlySmooth)\n", "meshMS = cMeshMS.mesh\n", "print(meshMS.GetMaterials())\n", "print(set(meshMS.GetBoundaries()))\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8e91a91cb2e2425f9ba6839ccbd32869", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {}, 'ngsolve_version': '6.2.2…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ngsolve.webgui import Draw\n", "L2Draw.drawFunc=Draw\n", "L2Draw(CF([1, 2, 3, 4, 5, 6]), meshMS)\n", "from myPackage import drawBndAll\n", "\n", "# drawBndAll(meshMS, drawFunc=Draw, block=False)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "import importlib\n", "import MS_helper_functions as ms\n", "ms = importlib.reload(ms)\n", "cl_Phi = ms.cl_Phi\n", "\n", "getIntegrand4BFI = ms.getIntegrand4BFI\n", "cl_gradgradMS = ms.cl_gradgradMS\n", "cl_curlcurlMS = ms.cl_curlcurlMS\n", "pyLobatto = ms.pyLobatto\n", "pydxLobatto = ms.pydxLobatto\n", "getPhiPhiValue = ms.getPhiPhiValue\n", "pyPhiFunction = ms.pyPhiFunction\n", "pyPhiZero = ms.pyPhiZero\n", "pyPhiConst = ms.pyPhiConst\n", "\n", "\n", "cl_Phi.numSheets = numSheets\n", "cl_Phi.dFe = cMeshMS.dFe\n", "cl_Phi.d0 = cMeshMS.d0\n", "cl_Phi.mesh = meshMS\n", "\n", "cl_Phi.modelHalfAir = modelHalfAir\n", "cl_Phi.orientation = 1\n", "\n", "if False:\n", " import cempy as cp\n", " importlib.reload(cp)\n", " \n", " \n", " cl_Phi.phiFunction = cp.phiFunctions.Lobatto\n", " cl_Phi.dzPhiFunction = cp.phiFunctions.dxLobatto\n", "else:\n", " cl_Phi.phiFunction = pyLobatto\n", " cl_Phi.dzPhiFunction = pydxLobatto\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Eddy currents\n", "\\begin{align*}\n", "\\mathbf{J} &= \\nabla\\times\\mathbf{H} \\\\ \n", " &= \\nabla\\times\\Big(\\sum_i\\big(\\nabla(\\phi_i u_i)\\big) + \\sum_j \\phi_jT_j\\Big)\\\\\n", " &= \\nabla\\times\\Big(\\sum_i \\phi_iT_i\\Big)\n", "\\end{align*}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{align*}\n", " \\mathbf{J} = \\nabla\\times(\\phi_i\\mathbf{T}_i) &= \\phi_i\\nabla\\times\\mathbf{T}_i + \\nabla\\phi_i\\times\\mathbf{T}_i\\\\\n", " &=\\phi_i\\begin{pmatrix}\\partial_y T_z-\\partial_z T_y\\\\\\partial_z T_x-\\partial_x T_z \\\\ \\partial_x T_y-\\partial_y T_x\\end{pmatrix} + \\begin{pmatrix}\\partial_x \\phi_i\\\\\\partial_y \\phi_i \\\\ \\partial_z \\phi_i\\end{pmatrix} \\times \\begin{pmatrix} T_x\\\\ T_y \\\\ T_z\\end{pmatrix}\\\\\n", " &= \\phi_i\\begin{pmatrix}\\partial_y T_z-\\partial_z T_y\\\\\\partial_z T_x-\\partial_x T_z \\\\ \\partial_x T_y-\\partial_y T_x\\end{pmatrix} + \n", " \\begin{pmatrix}\\partial_y\\phi_i T_z - \\partial_z\\phi_iT_y\\\\\\partial_z\\phi_i T_x - \\partial_x\\phi_iT_z \\\\\\partial_x\\phi_i T_y - \\partial_y\\phi_iT_x \\end{pmatrix}\n", "\\end{align*}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "for $\\mathbf{T} = T_x\\mathbf{e}_x + T_y\\mathbf{e}_y $ and $\\phi_i(\\mathbf{x}) = \\phi_i(x)$\n", "$\\rightarrow \\mathbf{J} = J\\mathbf{e}_z$ \n", "\n", "\\begin{align*}\n", "J &= \\phi_i ( \\partial_x T_y - \\partial_y T_x) + \\partial_x\\phi_i T_y \\\\\n", "&= \\phi_i \\textup{curl}_{\\textup{2D}}(\\mathbf{T}) + \\partial_x\\phi_i T_y \n", "\\end{align*}" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "force_full_Phi = True\n", "\n", "def calcMultiscale(orderPhi, orderT, mesh, drawPhis=True, couple_fluxes=False):\n", "\n", "\n", " if drawPhis:\n", " # cl_Phi.plotEvaluated(orderPhi, nFig=1)\n", " cl_Phi.plotEvaluated(orderT, nFig=1)\n", " cl_Phi.plotDirectEvaluated(orderT, nFig=1)\n", "\n", " VSpace = []\n", " # # u0 \n", " VSpace.append(H1(mesh, order=order0+1, dirichlet=\"left|right\", complex=True)) \n", " \n", " # ui * phi i\n", " for phi_i in orderPhi: \n", " VSpace.append(H1(mesh, order=phi_i.fes_oder+1, definedon=phi_i.material, dirichlet=phi_i.dirichlet, complex=True))\n", "\n", " for phi_i in orderT: \n", " VSpace.append(HCurl(mesh, order=phi_i.fes_oder, definedon=phi_i.material, dirichlet=phi_i.dirichlet, complex=True, nograds=phi_i.nograds))\n", "\n", " VSpace = FESpace(VSpace)\n", " sol = GridFunction(VSpace, \"sol\")\n", " \n", " # multiscale container\n", " gradgradMS = cl_gradgradMS(orderPhi, sol, addPhi0Outer=True, secondOrder=False)\n", " curlcurlMS = cl_curlcurlMS(orderT, sol, eddy_inplane=False, istart = len(gradgradMS.orderPhi) + 1)\n", " gradgradMS.addCurlCurlMS(curlcurlMS)\n", "\n", "\n", "\n", " a = BilinearForm(VSpace, symmetric=True)\n", " f = LinearForm(VSpace)\n", "\n", " a += 1j * omega * muAir * grad(gradgradMS.trials[0]) * grad(gradgradMS.tests[0]) * dx(\"outer\")\n", " # a += 1j * omega * gradgradMS.trials[0] * gradgradMS.tests[0] * dx(\"multiscale\")\n", " a += 1j * omega * gradgradMS.getIntegrand4BFI(gradgradMS.gradu_pack, gradgradMS.gradv_pack, muAir, muAir, force_full_Phi=force_full_Phi, checkDimensions=False) * dx(\"gap\")\n", " a += 1j * omega * gradgradMS.getIntegrand4BFI(gradgradMS.gradu_pack, gradgradMS.gradv_pack, muFe, muAir, force_full_Phi=force_full_Phi, checkDimensions=False) * dx(\"multiscale\")\n", "\n", " a += curlcurlMS.getIntegrand4BFI(curlcurlMS.curlu_pack, curlcurlMS.curlv_pack, 1/sigmaFe, 0, force_full_Phi=force_full_Phi, checkDimensions=False) * dx(\"multiscale\")\n", " \n", "\n", " # couple fluxes\n", " if couple_fluxes:\n", " # [dn u] [ dn v]\n", "\n", " alpha = 2000\n", " h = specialcf.mesh_size\n", " \n", "\n", " a += alpha*order0**2/h * gradgradMS.getIntegrand4BFI(gradgradMS.gradu_trace_n_pack, gradgradMS.gradv_trace_n_pack, muFe, muAir, force_full_Phi=force_full_Phi) *ds(smoothbnd)\n", " a += -alpha*order0**2/h * gradgradMS.getIntegrand4BFI(gradgradMS.gradu_trace_n_pack, gradgradMS.gradv_trace_n_pack[:1], muFe, muAir, force_full_Phi=force_full_Phi) *ds(smoothbnd)\n", " a += alpha*order0**2/h * gradgradMS.getIntegrand4BFI(gradgradMS.gradu_trace_n_pack[:1], gradgradMS.gradv_trace_n_pack[:1], muFe, muAir, force_full_Phi=force_full_Phi) *ds(smoothbnd)\n", " a += -alpha*order0**2/h * gradgradMS.getIntegrand4BFI(gradgradMS.gradu_trace_n_pack[:1], gradgradMS.gradv_trace_n_pack, muFe, muAir, force_full_Phi=force_full_Phi) *ds(smoothbnd)\n", "\n", " prec = Preconditioner(a,type=\"direct\") \n", "\n", "\n", " # dirichlet boundary values\n", " sol.components[0].Set((x/Norm(x)), BND)\n", " \n", " solvers.BVP(bf = a, lf= f, pre=prec, gf=sol, maxsteps=30, tol = 1e-20, print=True)\n", " \n", " H_MS = sum(gradgradMS.gradsol_comp)\n", " J_MS = sum(curlcurlMS.curlsol_comp)\n", "\n", "\n", " energy = Integrate(gradgradMS.getIntegrand4BFI(gradgradMS.gradsol_pack, gradgradMS.gradsol_pack, muFe, muAir, force_full_Phi=force_full_Phi, checkDimensions=False), mesh, \n", " definedon=mesh.Materials(\"multiscale\")).real\n", "\n", " losses = Integrate(curlcurlMS.getIntegrand4BFI(curlcurlMS.curlsol_pack, curlcurlMS.curlsol_pack, 1/sigmaFe, 0, force_full_Phi=force_full_Phi, checkDimensions=False), mesh, \n", " definedon=mesh.Materials(\"multiscale\")).real\n", " \n", " \n", " # print(\"a norm\", a.mat.AsVector().Norm())\n", "\n", " # print(\"ansatz\", gradgradMS.ansatz)\n", " return sol, energy, losses, gradgradMS, curlcurlMS, H_MS, J_MS\n", "\n", " \n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2KCG iteration 1, residual = 0.10699360755977388 \n", "\u001b[2KCG iteration 2, residual = 8.413921110025442e-16 \n", "\u001b[2KCG iteration 3, residual = 2.234894740020606e-29 \n", "-grad(u0_outer) - grad(u1 * pyLobatto(1)) - grad(u2 * pyLobatto(2)_Fe) - grad(u3 * pyLobatto(2)_ins) + T4 * pyLobatto(2)_Fe\n", "diff energy 3.106694761529257e-07 3.100541351020442e-07 6.153410508814829e-10 0.19846245581564767 %\n", "diff eddylosses 5.21458097867662e-07 5.041910562854848e-07 1.7267041582177188e-08 3.4247020780948136 %\n", "[pyLobatto(1), pyLobatto(2)_Fe, pyLobatto(2)_ins]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e2ee6fb34fa64d36840aec15301ccd7b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "\n", "\n", "if \"curlcurlMS\" in locals():\n", " del curlcurlMS\n", "if \"gradgradMS\" in locals():\n", " del gradgradMS\n", "\n", "\n", "\n", "orderPhi = [\n", " \n", " cl_Phi(1, fes_order=1, material=\"multiscale|gap\", dirichlet=\"itop|ibottom|left|right\", modelHalfAir=modelHalfAir), \n", " cl_Phi(2, fes_order=1, material=\"multiscale|gap\", dirichlet=\"itop|ibottom|left|right\", inAir=False, modelHalfAir=modelHalfAir), \n", " cl_Phi(2, fes_order=1, material=\"multiscale|gap\", dirichlet=\"itop|ibottom|left|right\", inIron=False, modelHalfAir=modelHalfAir), \n", "\n", " ]\n", "\n", "orderT = [\n", " cl_Phi(2, fes_order=1, material=\"multiscale\", dirichlet=\"iright|ileft\" , inAir=False, modelHalfAir=modelHalfAir, nograds=False), \n", " \n", "]\n", "sol_MS, energy_MS, eddyLosses_MS, gradgradMS, curlcurlMS, H_MS, J_MS = calcMultiscale(orderPhi, orderT, meshMS, drawPhis=False, couple_fluxes=False)\n", "\n", "\n", "print(gradgradMS.ansatz)\n", "print(\"diff energy\", energy_MS, energy_ref, energy_MS - energy_ref, (energy_MS - energy_ref)/energy_ref * 100, \"%\")\n", "print(\"diff eddylosses\", eddyLosses_MS, eddyLosses_ref, eddyLosses_MS - eddyLosses_ref, (eddyLosses_MS - eddyLosses_ref)/eddyLosses_ref * 100, \"%\")\n", "u_MS = sum(gradgradMS.sol_comp[:len(gradgradMS.orderPhi)])\n", "print(orderPhi)\n", "\n", "# J_MS = - (curlcurlMS.curlsol_comp[0] - curlcurlMS.curlsol_comp[1])\n", "# L2Draw((J_ref.imag, J_MS.imag), meshRef, settings = {\"Objects\":{\"Wireframe\":False}}, min = -20, max=20, diff=False)\n", "L2Draw((J_ref.imag, J_MS.imag), meshRef, settings = {\"Objects\":{\"Wireframe\":False}})" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "227bed26daa647abbeb775f6e6d73f39", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "diff energy 3.106694761529257e-07 3.100541351020442e-07 6.153410508814829e-10 0.19846245581564767 %\n", "diff eddylosses 5.21458097867662e-07 5.041910562854848e-07 1.7267041582177188e-08 3.4247020780948136 %\n" ] } ], "source": [ "\n", "from ngsolve.webgui import Draw\n", "\n", "from myPackage import drawBnd\n", "\n", "# drawBnd(meshMS, \"ibottom\", drawFunc=Draw)\n", "# J_MS = curlcurlMS.curlsol_comp[1] #+ curlcurlMS.curlsol_comp[1]\n", "J_MS = sum(curlcurlMS.curlsol_comp)\n", "print(len(curlcurlMS.curlsol_comp))\n", "L2Draw((J_ref.imag.Norm(), J_MS.imag.Norm()), meshRef, settings = {\"Objects\":{\"Wireframe\":False}}, min = 0, max=300)\n", "\n", "print(\"diff energy\", energy_MS, energy_ref, energy_MS - energy_ref, (energy_MS - energy_ref)/energy_ref * 100, \"%\")\n", "print(\"diff eddylosses\", eddyLosses_MS, eddyLosses_ref, eddyLosses_MS - eddyLosses_ref, (eddyLosses_MS - eddyLosses_ref)/eddyLosses_ref * 100, \"%\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.041910562854848e-07\n", "6.372211637642196e-07\n", "(-2.448498127965917e-08+6.794116722315447e-07j)\n" ] } ], "source": [ "print(Integrate(InnerProduct(J_ref, 1/sigmaFe * J_ref), meshRef, definedon=meshRef.Materials(\"inner\")).real)\n", "print(Integrate(InnerProduct(J_MS, 1/sigmaFe * J_MS), meshRef, definedon=meshRef.Materials(\"inner\")).real)\n", "print(Integrate(J_MS, meshRef))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 1\n", "-- ----------------- --------------- -----------------\n", " pyLobatto(2)_Fe pydxLobatto(2)_Fe\n", "0 pyLobatto(2)_Fe x\n", "1 pydxLobatto(2)_Fe x\n" ] } ], "source": [ "\n", "curlcurlMS.printCouplingMatrix(sparsity=True);\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 1 2 3 4 5 6 7\n", "-- ------------------ ------------- -------------- ------------ ----------------- --------------- ------------------ ---------------- ---------------\n", " pyPhiConstant pydxLobatto(1) pyLobatto(1) pydxLobatto(2)_Fe pyLobatto(2)_Fe pydxLobatto(2)_ins pyLobatto(2)_ins pyLobatto(2)_Fe\n", "0 pyPhiConstant x x x x x\n", "1 pydxLobatto(1) x x x x x\n", "2 pyLobatto(1) x x x\n", "3 pydxLobatto(2)_Fe x x\n", "4 pyLobatto(2)_Fe x x x x\n", "5 pydxLobatto(2)_ins x x\n", "6 pyLobatto(2)_ins x x x\n", "7 pyLobatto(2)_Fe x x x x\n" ] } ], "source": [ "#gradgradMS.generateCouplingMatrix(muFe, muAir, force_full_Phi=force_full_Phi)\n", "gradgradMS.printCouplingMatrix(sparsity=True);\n", "#assert gradgradMS.checkCouplingMatrxiSymmetric(1e-3) == True" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "energy MS :\t3.106694761529257e-07, energy ref \t3.100541351020442e-07\n", "eddyLosses MS :\t5.21458097867662e-07, eddyLosses ref \t5.041910562854848e-07\n", "diff energy 3.106694761529257e-07 3.100541351020442e-07 6.153410508814829e-10 0.19846245581564767 %\n", "diff eddylosses 5.21458097867662e-07 5.041910562854848e-07 1.7267041582177188e-08 3.4247020780948136 %\n", "('bottom', 'left', 'ibottom', 'ibottom', 'ibottom', 'right', 'itop', 'itop', 'itop', 'left', 'top', 'right', 'iright', 'ileft', 'right', 'left')\n" ] } ], "source": [ "print(f\"energy MS :\\t{energy_MS}, energy ref \\t{energy_ref}\")\n", "print(f\"eddyLosses MS :\\t{eddyLosses_MS}, eddyLosses ref \\t{eddyLosses_ref}\")\n", "print(\"diff energy\", energy_MS, energy_ref, energy_MS - energy_ref, (energy_MS - energy_ref)/energy_ref * 100, \"%\")\n", "print(\"diff eddylosses\", eddyLosses_MS, eddyLosses_ref, eddyLosses_MS - eddyLosses_ref, (eddyLosses_MS - eddyLosses_ref)/eddyLosses_ref * 100, \"%\")\n", "print(meshMS.GetBoundaries())\n", "#Draw(u_MS, meshRef, settings={\"Objects\":{\"Wireframe\":True}, \"deformation\": 0.01}, deformation=False)\n", "# Draw( Norm(H_MS), meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False}, max = 200)\n", "# Draw( lam * Norm(H_MS), meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False}, max=400)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3 Comparison " ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "\n", "start = -D/2*1.01\n", "end = -D/2*0.9\n", "\n", "start = -d/2\n", "end = d/2\n", "\n", "\n", "plt.figure(2)\n", "start = -d/2\n", "end = d/2\n", "\n", "pnt1 = [-d/2*0.99, start, 0] \n", "pnt2 = [-d/2*0.99, end, 0] \n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f9c5c57d3277489598c6aae501b603fe", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from ngsolve.webgui import Draw\n", "L2Draw.drawFunc = Draw\n", "\n", "J_MS = (curlcurlMS.curlsol_comp[0] + curlcurlMS.curlsol_comp[1])\n", "L2Draw((J_ref.imag, J_MS.imag),meshRef, orientation=0, settings={\"Objects\":{\"Wireframe\":False}}, min = -250, max= 250)\n", "\n", "\n", "# L2Draw((curlcurlMS.orderPhi[0].phi, curlcurlMS.orderPhi[0].phi),meshRef, orientation=0 if rotated else 1, settings={\"Objects\":{\"Wireframe\":False}})\n", "# L2Draw((curlcurlMS.orderPhi[0].dzphi, curlcurlMS.orderPhi[0].dzphi),meshRef, orientation=0 if rotated else 1, settings={\"Objects\":{\"Wireframe\":False}})\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b39f261147aa45c29771eb54e6e5c8fe", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start = -d/2\n", "end = d/2\n", "\n", "\n", "i = 0\n", "plt.figure(3)\n", "evalOnLine(J_MS.imag , meshRef, pnt1, pnt2, plot=True, ls='-', marker=\"x\", clear=True, label=\"MS\", show=False, N=1000);\n", "evalOnLine(curlcurlMS.curlsol_comp[i].imag , meshRef, pnt1, pnt2, plot=True, ls='-', marker=\"\", show=False, clear=False, label=\"test\", N = 1000, title=r\"$J$\");\n", "evalOnLine(J_ref.imag , meshRef, pnt1, pnt2, plot=True, ls='-', marker=\"\", show=False, clear=False, label=\"ref\", N = 1000, title=r\"$J$\");\n", "\n", "\n", "\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2f8d937b022d43338a44fe40baa2c39a", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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", 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start = -d/2\n", "end = d/2\n", "\n", "\n", "plt.figure(4)\n", "\n", "evalOnLine(H_MS[1] , meshRef, pnt1, pnt2, plot=True, ls='-', marker=\"x\", clear=True, label=\"MS\", show=False, N=1000);\n", "# [evalOnLine((H_comp[i])[1] , meshRef, pnt1, pnt2, plot=True, ls='-', marker=\"x\", clear=False, label=\"MS_\" + gradgradMS.gradsol_pack[i][1].name, show=False, N=1000) for i in range(len(H_comp))];\n", "\n", "evalOnLine(H_ref[1] , meshRef, pnt1, pnt2, plot=True, ls='-', marker=\"\", show=False, clear=False, label=\"ref\", N = 1000, title=r\"$H_y$\");\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c81985bcc6f440b1a313ccb1c0156faa", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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"metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pyPhiConstant\n", "pydxLobatto(1)\n", "pyLobatto(1)\n", "pydxLobatto(2)_Fe\n", "pyLobatto(2)_Fe\n", "pydxLobatto(2)_ins\n", "pyLobatto(2)_ins\n", "pyLobatto(2)_Fe\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a877dd9af8664434aba3fd477cb4aff9", "version_major": 2, "version_minor": 0 }, "image/png": 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", "text/html": [ "\n", "
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\n", " Figure\n", "
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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start = -d/2\n", "end = d/2\n", "\n", "[print(o[1].name) for o in gradgradMS.gradsol_pack]\n", "\n", "plt.figure(6)\n", "H_MS = sum(gradgradMS.gradsol_comp) \n", "evalOnLine(mu * H_MS[1] , meshRef, pnt1, pnt2, plot=True, ls='-', marker=\"x\", clear=True, label=\"MS\", show=False, N=1000);\n", "\n", "# [evalOnLine(mu * (gradgradMS.gradsol_comp[i])[1] , meshRef, pnt1, pnt2, plot=True, ls='-', marker=\"x\", clear=False, label=\"MS_\" + gradgradMS.gradsol_pack[i][1].name, show=False, N=1000) for i in range(len(gradgradMS.gradsol_comp))];\n", "\n", "evalOnLine(mu * H_ref[1] , meshRef, pnt1, pnt2, plot=True, ls='-', marker=\"\", show=False, clear=False, label=\"ref\", N = 1000, title=r\"$B_y$\");\n", "\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Drawings" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0fb70900b84447aeb191e6793500719e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from ngsolve.webgui import Draw\n", "L2Draw((J_ref.imag, J_MS.imag) ,meshRef, settings={\"Objects\":{\"Wireframe\":False}})" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "energy:\t3.106694761529257e-07 3.100541351020442e-07\n", "losses:\t5.21458097867662e-07 5.041910562854848e-07\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "67cbf5e3da354bbc91bbdeb743de38ac", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'\\n\\n\\n \\n NGSolve WebGUI\\n \\n \\n \\n \\n \\n \\n \\n\\n'" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(f\"energy:\\t{energy_MS}\", energy_ref)\n", "print(f\"losses:\\t{eddyLosses_MS}\", eddyLosses_ref)\n", "a = L2Draw(sol_ref.components[0], meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False})\n", "a.scene.GenerateHTML()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "True\n", "pyPhiConstant\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a04188a74de34cc3b4296a5dc86f65a8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fecd3a3b6d88409a9db0f3ce0fdb5988", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(H_ref[1].dim)\n", "print(gradgradMS.gradsol_comp[1].is_complex)\n", "\n", "H_MS = sum(gradgradMS.gradsol_comp)\n", "# H_MS = gradgradMS.gradsol_pack[1][1]\n", "\n", "print(gradgradMS.gradsol_pack[0][1].name)\n", "\n", "L2Draw((H_ref[0], H_MS[0]), meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False}, diff=False)\n", "L2Draw((H_ref[1], H_MS[1]), meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False}, diff=False, min = -10, max=10)\n", "\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "00a8dc6c5fb14f6ab2f4bf7d24ec9f48", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "af026065cdf04ff0b1432e0033f0513b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L2Draw((H_ref[1], H_MS[1]), meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False}, min = -50, max=50)\n", "L2Draw((H_ref[0], H_MS[0]), meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False}, min = -50, max=50)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6b5953bac56942bd949cb58661af8a24", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d48485459d7a49b781a1b2815ed65d4e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "WebGuiWidget(layout=Layout(height='500px', width='100%'), value={'gui_settings': {'Objects': {'Wireframe': Fal…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L2Draw((mu *H_ref[0], mu*H_MS[0]), meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False})\n", "L2Draw((mu *H_ref[1], mu*H_MS[1]), meshRef, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False})\n", "# lamMS = IfPos(cl_Phi(0, 0, inIron=False, material=\"multiscale\").phi, lambda1, lambda2)\n", "\n", "# Draw(lamMS * IfPos(y, -grad(sol_ref), H_MS)[0], meshMS, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False},min = -200, max=200)\n", "# Draw(lamMS * IfPos(y, -grad(sol_ref), H_MS)[1], meshMS, settings={\"Objects\":{\"Wireframe\":False}, \"deformation\": False},min = -20, max=20)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "diff energy 3.106694761529257e-07 3.100541351020442e-07 6.153410508814829e-10 0.19846245581564767 %\n", "diff eddylosses 5.21458097867662e-07 5.041910562854848e-07 1.7267041582177188e-08 3.4247020780948136 %\n" ] } ], "source": [ "# print(\"Norm soll diff\", Integrate((sum(sol_comp_MS) - sol_ref)* (sum(sol_comp_MS) - sol_ref)/( (sol_ref)**2) , meshRef)*100, \"%\")\n", "print(\"diff energy\", energy_MS, energy_ref, energy_MS - energy_ref, (energy_MS - energy_ref)/energy_ref * 100, \"%\")\n", "print(\"diff eddylosses\", eddyLosses_MS, eddyLosses_ref, eddyLosses_MS - eddyLosses_ref, (eddyLosses_MS - eddyLosses_ref)/eddyLosses_ref * 100, \"%\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 }