{"cells": [{"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import matplotlib.tri as tri\n", "import math\n", "import numpy.random as rd"]}, {"cell_type": "markdown", "metadata": {"deletable": false}, "source": ["$$\\newcommand{\\nr}[1]{\\|#1\\|}\n", "\\newcommand{\\Rsp}{\\mathbb{R}}\n", "\\newcommand{\\RR}{\\mathbb{R}}\n", "\\newcommand{\\N}{\\mathbb{N}}\n", "$$\n", "### Cours acc\u00e9l\u00e9r\u00e9 analyse num\u00e9rique - M2 AMS 2023/24\n", "\n", "# M\u00e9thode des \u00e9l\u00e9ments finis en 2D.\n", "\n", "# I - Conditions aux limites de Neumann homog\u00e8nes"]}, {"cell_type": "markdown", "metadata": {"deletable": false}, "source": ["On s'int\u00e9resse ici \u00e0 la r\u00e9solution approch\u00e9e du probl\u00e8me aux limites suivant, avec conditions aux limites de Neumann homog\u00e8ne :\n", "\n", "$$\n", "(PN)\\left\\{\\begin{aligned}\n", "&-\\Delta u+u=f,\\ \\textrm{dans }\\Omega,\\\\\n", "&\\frac{\\partial u}{\\partial n}=0, \\textrm{dans }\\partial\\Omega,\n", "\\end{aligned}\n", "\\right.\n", "$$\n", "\n", "o\u00f9 $f:\\overline{\\Omega}\\longrightarrow\\mathbb{R}$ est une fonction de classe $\\mathcal{C}^2$ donn\u00e9e et o\u00f9 $\\Omega$ est un ouvert de $\\mathbb{R}^2$ donn\u00e9, de fronti\u00e8re $\\partial\\Omega$ *r\u00e9guli\u00e8re*. \n", "\n", "**Question 1.** \u00c9crire la formulation variationnelle du probl\u00e8me $(P)$ sous la forme \n", "\\begin{equation*}\n", "(PNv)\\ \\ \\ \n", "\\begin{cases}\n", "u\\in V \\,\\textrm{tel que}\\\\\n", "a(u,v)=L(v),\\ \\,\\forall\\ v\\in V ,\n", "\\end{cases}\n", "\\end{equation*}\n", "o\u00f9 $V=H^1(\\Omega)$, $a$ est une forme bilin\u00e9aire dans $V$ et $L$ une forme lin\u00e9aire de $V,$ que l'on explicitera. \n", "\n", "On suppose d\u00e9sormais que le domaine $\\Omega$ est polygonale et on consid\u00e8re une triangulation $T_h$ de $\\Omega$, autrement dit un pavage de $\\Omega$ par des triangles tel que l'intersection de chaque deux triangles est soit vide, soit une ar\u00eate compl\u00e8te des 2 triangles, soit un sommet des deux triangles. L'indice $h$ note le diam\u00e8tre maximal des triangles de $T_h$.\n", "\n", "On consid\u00e8re l'espace $V_h$ corespondant \u00e0 l'approximation de $H^1(\\Omega)$ par des \u00e9l\u00e9ments finis $P^1$ associ\u00e9s \u00e0 la triangulation $T_h$ :\n", "\n", "\n", "$$\n", "V_h=\\{\\Phi\\in C^0(\\overline{\\Omega})\\,|\\,\\Phi_{|T}\\in\\mathbb{P}^1,\\ \\textrm{pour tout } T\\in T_h\\}.\n", "$$\n", "\n", "On note $\\{T_N\\}_{N=1,\\dots,N_{tri}}\\ \\ \\ $ les triangles de $T_h,$ $\\{S_I\\}_{I=1,\\dots,N_{Som}}\\ \\ \\ $ les sommets des triangles la triangulation et $\\{\\Phi_I\\}_{I=1,\\dots,N_{Som}}\\ \\ \\ $ les fonctions de $V_h$ d\u00e9finies par \n", "\n", "$$\n", "\\Phi_I(S_J)=\\delta_{IJ},\\ I,J=1,\\dots,N_{Som}.\n", "$$ \n", "\n", "Les fonctions $\\Phi_I$ sont l'analogue en dimension 2 des fonctions chapeaux que l'on a vu en dimension 1.\n", "\n", "On a que $\\{\\Phi_1,\\dots,\\Phi_{N_{Som}}\\ \\}$ est une base de $V_h$ et $V_h$ est donc un sous-espace de $H^1(\\Omega)$ de dimension finie $N_{Som}$. \n", "\n", "Le probl\u00e8me discret consiste alors \u00e0 chercher $u_h\\in V_h$ tel que \n", "$$\n", "(PNv_h)\\ \\ \\ \\ \\ \\ \\ \\ \\ a(u_h,\\Phi_I)=L(\\Phi_I),\\ \\,\\forall\\ I=1,\\dots,N_{Som}. \n", "$$\n", "\n", "**Question 2.** Soit $u_h=\\displaystyle{\\sum_{I=1}^{{NSom}}u_I\\Phi_I}.$ Montrer que $u_h$ est solution de $(PNv_h)$ si et seulement si le vecteur $U=(u_1,\\dots,u_{NSom})^T$ est solution d'un syst\u00e8me lin\u00e9aire \n", "\n", "$$\n", "KU+MU=F,\n", "$$\n", "\n", "o\u00f9 $K$ et $M$ sont les matrices de $\\mathcal{M}^{N_{Som}\\times N_{Som}}\\ \\ \\ (\\mathbb{R})$ d\u00e9finies par\n", "\n", "$$\n", "K_{I,J}=\\int_{\\Omega}\\nabla\\Phi_J\\cdot\\nabla\\Phi_I,\\ \\ \\ M_{I,J}=\\int_{\\Omega}\\Phi_J\\Phi_I,\n", "$$ \n", "\n", "et o\u00f9 $F$ est le vecteur de $\\mathbb{R}^{NSom}$ d\u00e9fini par \n", "\n", "$$\n", "F_I=\\int_{\\Omega}f\\Phi_I.\n", "$$ \n", "\n", "\n", "**Maillage de $\\Omega.$**\n", "\n", "Dans un premier temps on consid\u00e8re $\\Omega$ le carr\u00e9 $[0,2]\\times[0,2].$ \n", "\n", "Pour se r\u00e9p\u00e9rer dans le maillage du domaine $\\Omega$, les triangles et les sommets des triangles sont num\u00e9rot\u00e9s respectivement de $1$ \u00e0 $N_{Tri}$ et de 1 \u00e0 $N_{Som}$.\n", "\n", "La triangulation $T_h$ de $\\Omega$ est alors repr\u00e9sent\u00e9e par deux matrices. La premi\u00e8re, que l'on appellera dans le programme python $TabTri,$ contient la liste des triangles. Il s'agit d'une matrice de taille $N_{Tri}\\times3$ que, dans chaque ligne $N$, contient les 3 indices $I$ des sommets du triangle $T_N.$ La deuxi\u00e8me, que l'on appellera $TabSom,$ contient les coordonn\u00e9es de chaque sommet de la triangulation. C'est une matrice de taille $N_{Som}\\times2$ que, dans chaque ligne $I$ contient les coordonn\u00e9es du sommet $S_I.$ \n", "Le code suivant cr\u00e9e le maillage et ces matrices.\n"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"text/plain": ["Text(0.5, 1.0, 'maillage')"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["\n", "\n", "\n", "Nx=18\n", "Ny=18\n", "# On peut choisir Nx diff\u00e9rent de Ny\n", "\n", "x_m=0.\n", "x_M=2.\n", "y_m=0.\n", "y_M=2.\n", "\n", "x=np.linspace(x_m,x_M,Nx+2)\n", "y=np.linspace(y_m,y_M,Ny+2)\n", "\n", "X,Y=np.meshgrid(x,y)\n", "\n", "X=X.flatten()\n", "Y=Y.flatten()\n", "\n", "triang = tri.Triangulation(X, Y)\n", "\n", "NTri=np.shape(triang.triangles)[0]\n", "NSom=np.shape(triang.x)[0]\n", "\n", "#Tableau avec coordonnes des noeuds\n", "TabSom=np.zeros([NSom,2])\n", "TabSom[:,0]=triang.x\n", "TabSom[:,1]=triang.y\n", "\n", "#Tableau avec r\u00e9f\u00e9rence des noeuds\n", "RefSom=np.zeros([NSom,1])\n", "RefSom=np.logical_or(np.logical_or(TabSom[:,0]==x_m,TabSom[:,0]==x_M),np.logical_or(TabSom[:,1]==y_m,TabSom[:,1]==y_M))\n", "RefSom=RefSom.astype('float64')\n", "RefSom=RefSom.reshape(NSom,1)\n", "\n", "\n", "# Tableau avec noeuds des triangles\n", "TabTri=triang.triangles\n", "\n", "# Repr\u00e9sentation du maillage\n", "plt.figure(1)\n", "plt.gca().set_aspect('equal')\n", "plt.triplot(X,Y,triang.triangles, 'b-', lw=0.5)\n", "plt.title('maillage')\n", "\n", "#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", "# \n", "# Vous pouvez afficher TabTri et TabSom en r\u00e9duisant Nx et Ny pour que ce soit plus lisible\n", "#\n", "#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"]}, {"cell_type": "markdown", "metadata": {"deletable": false}, "source": ["**Les matrices \u00e9l\u00e9mentaires**\n", "\n", "Pour construire les matrices de masse M et de rigidit\u00e9 K on doit calculer des int\u00e9grales des fonctions de base sur $\\Omega$ et donc sur chaque triangle $T$ de la triangulation.\n", "\n", "On commence alors par d\u00e9finir des matrices \u00e9l\u00e9mentaires qui permettent, localement sur un triangle $T_N$ de noeuds $S_{I_1},\\ S_{I_2}$ et $S_{I_3},$ de calculer \n", "$$\n", "\\int_{T_N}\\nabla\\Phi_{I_i}\\dot\\nabla\\Phi_{I_j}\\ \\ \\ \\textrm{et}\\ \\ \\ \\ \\int_{T_N}\\Phi_{I_i}\\dot\\Phi_{I_j} \n", "$$\n", "pour $i,\\ j=1,2,3.$ Ces int\u00e9grales vont contribuer respectivement \u00e0 l'\u00e9l\u00e9ment $K_{I_j,I_i}$ et $M_{I_j,I_i}$ des matrices globales.\n", "\n", "On consid\u00e8re deux fonctions $M_{elem}(\\mathcal{S1},\\mathcal{S2},\\mathcal{S3})$ et $K_{elem}(\\mathcal{S1},\\mathcal{S2},\\mathcal{S3})$ calculant les matrices de masse et de rigidit\u00e9 \u00e9l\u00e9mentaires sur un triangle $T$ de sommets $\\mathcal{S1}=(x_1,y_1),\\ \\mathcal{S2}=(x_2,y_2)$ et $\\mathcal{S3}=(x_3,y_3).$ \n", "Pour calculer la matrice de masse \u00e9l\u00e9mentaire sur le triangle $T,$ on peut utiliser les formules ci-dessous.\n", "\n", "On peut v\u00e9rifier que les fonctions de base associ\u00e9es \u00e0 chaque sommet de $T$ sont localement sur le triangle $T$ donn\u00e9es par\n", "\n", "$$\n", "\\lambda_1(x,y)=\\frac{1}{D}(y_{23}(x-x_3)-x_{23}(y-y_3)),\\ \\ \\ \\lambda_2(x,y)=\\frac{1}{D}(y_{31}(x-x_1)-x_{31}(y-y_1)),\\ \\ \\ \\lambda_3(x,y)=\\frac{1}{D}(y_{12}(x-x_2)-x_{12}(y-y_2)),\n", "$$\n", "\n", "o\u00f9 $x_{ij}=x_i-x_j,\\ y_{ij}=y_i-y_j$ et $D=x_{23}y_{31}-x_{31}y_{23}.$ $|D|$ est \u00e9gal \u00e0 deux fois la surface du triangle.\n", "\n", "On a alors que, pour $i,\\ j=1,\\ 2,\\ 3,$ \n", "$$\n", "\\int_{T}\\lambda_i\\lambda_j=\n", "\\begin{cases}\n", "\\frac{aire(T)}{12},\\ i\\neq j,\\\\\n", "\\frac{aire(T)}{6},\\ i= j.\n", "\\end{cases}\n", "$$\n"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": ["\n", "\n", "def M_elem(S1,S2,S3):\n", " x1 = S1[0]\n", " y1 = S1[1]\n", " x2 = S2[0] \n", " y2 = S2[1]\n", " x3 = S3[0]\n", " y3 = S3[1]\n", " D = ((x2-x1)*(y3-y1) - (y2-y1)*(x3-x1))\n", " M=(1.*np.abs(D)/24)*np.ones([3,3])\n", " M[range(3),range(3)]=1.*np.abs(D)/12\n", " return M\n", "\n", "\n", "\n", "\n", "def K_elem(S1,S2,S3):\n", " x1 = S1[0]\n", " y1 = S1[1]\n", " x2 = S2[0] \n", " y2 = S2[1]\n", " x3 = S3[0]\n", " y3 = S3[1]\n", " norm = np.zeros([3, 2])\n", " norm[0, :] = np.array([y2-y3, x3-x2])\n", " norm[1, :] = np.array([y3-y1, x1-x3])\n", " norm[2, :] = np.array([y1-y2, x2-x1])\n", " D = ((x2-x1)*(y3-y1) - (y2-y1)*(x3-x1))\n", " K = np.zeros([3,3])\n", " for i in range(3):\n", " for j in range(3):\n", " K[i,j] = np.dot(norm[i,:],norm[j,:])\n", " return (1./(2*abs(D)))*K"]}, {"cell_type": "markdown", "metadata": {"deletable": false}, "source": ["**Assemblage des matrices $M$ et $K$.**\n", "\n", "Dans cette partie on met en oeuvre un algorithme pour construire les matrices $M$ et $K.$ Pour ce faire on remarque que, par exemple, \n", "$$\n", "M_{I,J}=\\sum_{N=1}^{NTri}\\int_{T_N}\\Phi_I\\Phi_J=\\sum_{N : S_I,S_J\\in T_N}\\int_{T_N}\\Phi_I\\Phi_J.\n", "$$\n", "\n", "L'algoritme de construction des matrices consiste alors \u00e0 faire la boucle suivante :\n", "\n", " Pour N= 1...NTri\n", " D\u00e9t\u00e9rmination des sommets S_I1, S_I2 et S_I3 du triangle T_N\n", " Calcul des matrices \u00e9l\u00e9mentaires associ\u00e9es au triangle T_N\n", " Pour i=1..3\n", " Pour j=1...3 \n", " Rajouter \u00e0 M(Ii,Ij) la contribution venue du triangle T_N\n", "\n", "**Question 4.** Compl\u00e9ter dans le programme l'assemblage des matrices M et K"]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": ["# \n"]}, {"cell_type": "markdown", "metadata": {"deletable": false}, "source": ["**Calcul du second membre $F.$**\n", "\n", "On peut calculer exactement les composantes $F_I$ du vecteur $F$ ou approcher ses valeurs en utilisant par exemple des formules de quadrature.\n", "\n", "Ici on fait le choix de remplacer $f$ par son interpol\u00e9 $P_1$ aux points du maillage, *i.e.* on approche $f$ par la fonction $\\displaystyle{\\sum_{I=1}^{NSom}f(S_I)\\Phi_I}.$ \n", "\n", "**Question 5.** En approchant $f$ par son interpol\u00e9, donner une approximation du second membre $F$ faisant intervenir la matrice de masse. \n", "\n", "On admet que cette approximation ne change pas l'ordre de l'approximation par \u00e9l\u00e9ments finis P1 du probl\u00e8me. \n", "\n", "**Validation : calcul d'une solution connue**\n", "\n", "On consid\u00e8re $f$ tel que la fonction\n", "$$\n", "u(x,y)=\\cos(\\pi x)\\cos(2\\pi y)\n", "$$\n", "est solution du probl\u00e8me $(PN)$ dans $\\Omega=[0,2]\\times[0,2].$ \n", "\n", "**Question 6.** Construire dans le programme une fonction $f(x,y)$ d\u00e9finissant le second membre $f$ et calculer l'approximation du vecteur $F$ obtenue comme expliqu\u00e9 dessus. Calculer le vecteur $U$ des coefficients de la solution approch\u00e9e donn\u00e9e par la m\u00e9thode des \u00e9lements finis et utiliser le code suivant pour la visualiser. Visualiser aussi la solution exacte."]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": ["# \n", "\n", "# pour r\u00e9prensenter la solution u\n", "\n", "#plt.figure(2)\n", "#plt.gca().set_aspect('equal')\n", "#plt.tripcolor(triang.x,triang.y,triang.triangles, u, shading='flat')\n", "#plt.colorbar()\n", "#plt.title('solution approchee par EF P1')"]}, {"cell_type": "markdown", "metadata": {"deletable": false}, "source": ["**Question 7.** Soit $\\Pi_h u=\\displaystyle{\\sum_{I=1}^{NSom} u(S_I)\\Phi_I}$ l'interpol\u00e9 P1 de la solution exacte $u$ de $(PN)$ aux points $S_I.$ Calculer, pour diff\u00e9rentes valeurs de $h,$ les normes $L^2$ et $H^1$ de l'erreur $u_h-\\Pi_h u.$ Remarquer que ces normes peuvent se calculer en utilisant les matrice de masse et de rigidit\u00e9 $M$ et $K$. Repr\u00e9senter, en \u00e9ch\u00e8lle logarithmique-logarithmique, ces erreurs en fonction de $h$.\n", "\n", "**D'autres structures de maillage**\n", "\n", "Vous pouvez remplacer le debut de votre programme par le code ci-dessus, pour g\u00e9n\u00e9rer un maillage non structur\u00e9 du carr\u00e9..."]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["Nx=18\n", "Ny=18\n", "\n", "x_m=0.\n", "x_M=2.\n", "y_m=0.\n", "y_M=2.\n", "\n", "Rx=rd.random([Nx+2,Nx+2])\n", "Rx[:,0]=0\n", "Rx[:,-1]=0\n", "Ry=rd.random([Ny+2,Ny+2])\n", "Ry[0,:]=0\n", "Ry[-1,:]=0\n", "x=np.linspace(x_m,x_M,Nx+2)#+4*((x_M-x_m)/(5*(Nx+2)))*Rx\n", "y=np.linspace(y_m,y_M,Ny+2)#+4*((y_M-y_m)/(5*(Ny+2)))*Ry\n", "\n", "X,Y=np.meshgrid(x,y)\n", "X=X+0.9*((x_M-x_m)/((Nx+2)))*Rx\n", "Y=Y+0.9*((y_M-y_m)/((Ny+2)))*Ry\n", "\n", "X=X.flatten()\n", "Y=Y.flatten()\n", "\n", "triang = tri.Triangulation(X, Y)\n", "\n", "NTri=np.shape(triang.triangles)[0]\n", "NSom=np.shape(triang.x)[0]\n", "\n", "#Tableau avec coordonnes des noeuds\n", "TabSom=np.zeros([NSom,2])\n", "TabSom[:,0]=triang.x\n", "TabSom[:,1]=triang.y\n", "\n", "# Tableau avec noeuds des triangles\n", "TabTri=triang.triangles\n", "\n", "plt.figure(1)\n", "plt.gca().set_aspect('equal')\n", "plt.triplot(X,Y,triang.triangles, 'b-', lw=0.5)\n", "plt.title('maillage')\n", "\n", "plt.show()\n"]}, {"cell_type": "markdown", "metadata": {"collapsed": true, "deletable": false}, "source": ["## II - Conditions aux limites de Dirichlet"]}, {"cell_type": "markdown", "metadata": {"collapsed": true, "deletable": false}, "source": ["On s'int\u00e9resse maintenant \u00e0 la r\u00e9solution approch\u00e9e du probl\u00e8me aux limites suivant pour l'\u00e9quation de Poisson, cette fois-ci avec conditions aux limites de Dirichlet homog\u00e8ne :\n", "\n", "$$\n", "(P)\\left\\{\\begin{aligned}\n", "& -\\Delta u+u=f,\\ \\textrm{dans }\\Omega,\\\\\n", "& u=0, \\textrm{dans }\\partial\\Omega\n", "\\end{aligned}\n", "\\right.\n", "$$\n", "o\u00f9 $f:\\overline{\\Omega}\\longrightarrow\\mathbb{R}$ est une fonction de classe $\\mathcal{C}^2$ donn\u00e9e et o\u00f9 $\\Omega$ est un ouvert de $\\mathbb{R}^2$ donn\u00e9. \n", "\n", "**Question 1.** \u00c9crire la formulation variationnelle du probl\u00e8me $(P)$ sous la forme \n", "\\begin{equation*}\n", "(Pv)\\ \\ \\ \n", "\\begin{cases}\n", "u\\in V \\,\\textrm{tel que}\\\\\n", "a(v,u)=L(v),\\ \\,\\forall\\ v\\in V ,\n", "\\end{cases}\n", "\\end{equation*}\n", "o\u00f9 $V=H^1_0(\\Omega)$, $a$ est une forme bilin\u00e9aire dans $V$ et $L$ une forme lin\u00e9aire de $V,$ que l'on explicitera. \n", "\n", "Comme dans le cas du probl\u00e8me de Neumann, on consid\u00e8re une triangulation $T_h$ du domaine $\\Omega$ et on consid\u00e8re $V_h$ l'approximation de $H^1(\\Omega)$ par des \u00e9l\u00e9ments finis $P^1$ associ\u00e9s \u00e0 la triangulation $T_h,$ introduite lors de l'\u00e9tude du probl\u00e8me de Neumann. \n", "\n", "On note $\\{T_N\\}_{N=1,\\dots,Ntri}\\ \\ \\ $ les triangles de $T_h,$ $\\{S_I\\}_{I=1,\\dots,NSom}\\ \\ \\ $ les sommets des triangles la triangulation et $\\{\\Phi_I\\}_{I=1,\\dots,NSom}\\ \\ \\ $ les fonctions de la base de $V_h$ d\u00e9finies par $\\Phi_I(S_J)=\\delta_{IJ},\\ I,J=1,\\dots,NSom.$ \n", "\n", "L'espace $V_h^0$ que l'on consid\u00e8rera pour approcher $H^1_0$ sera constitu\u00e9 des \u00e9l\u00e9ments $\\Phi_I$ de la base de $V_h$ tels que $I$ n'est pas un sommet appartenant au bord $\\partial \\Omega$ de $\\Omega$. Pour cela, il faut tout d'abord r\u00e9f\u00e9rencer les sommets $I$ qui sont dans le bord. \n", "\n", "**Question 2.** Compl\u00e9ter le code g\u00e9n\u00e9rateur du maillage de $\\Omega$ donn\u00e9 \u00e0 la partie I, qui g\u00e9n\u00e8re le maillage et les tableaux $TabTri,$ contenant la liste des triangles, et $TabSom,$ contenant les coordonn\u00e9es de chaque sommet de la triangulation, pour cr\u00e9er un tableau $RefSom$, de taille $NSom\\times 1$, que dans chaque ligne $I$ prendra la valeur $1$ si le sommet $I$ appartient \u00e0 $\\partial\\Omega$, et $0$ sinon."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {"deletable": false}, "source": ["On note \n", "\n", "$$\n", "S_0=\\big\\{I\\in \\{1,\\dots,NSom\\}\\ \\,:\\ \\,S_I\\notin\\partial\\Omega\\big\\}\n", "$$\n", "\n", "l'ensemble contenant les sommets de la triangulation appartenant \u00e0 l'int\u00e9rieur de $\\Omega$. \n", "\n", "On d\u00e9finit alors\n", "\n", "$$\n", "V_h^0=Vect\\big(\\{\\Phi_I\\ \\,:\\ \\,I\\in S_0\\}\\big).\n", "$$\n", "\n", "Le probl\u00e8me discret consiste alors \u00e0 chercher $u_h\\in V_h^0$ tel que \n", "$$\n", "(P_h)\\ \\ \\ \\ \\ \\ \\ \\ \\ a(\\Phi_I,u_h)=L(\\Phi_I),\\ \\,\\forall\\ I\\in S_0. \n", "$$\n", "\n", "Ces \u00e9quations originent un syst\u00e8me matricielle \u00e0 r\u00e9soudre $AU=F$, avec $A_{IJ}=a(\\Phi_J,\\Phi_I),\\ F_I=L(\\Phi_I),$ pour $I,\\ J\\in S_0$. Il s'agit d'un syst\u00e8me analogue \u00e0 celui de la discr\u00e9tisation du probl\u00e8me de Neumann, mais dont la matrice est de taille \u00e9gale au cardinal de $S_0$, au lieu de taille $NSom$. \n", "\n", "En pratique on ne construit pas cette matrice de taille plus petite. On va construire une matrice de taille $NSom$, contenant les termes $A_{IJ}=a(\\Phi_J,\\Phi_I)$ correspondant aux sommets du bord $\\partial \\Omega$, mais on va la modifier dans les lignes et colonnes correspondant \u00e0 ces noeuds. On fait le m\u00eame pour le vecteur $F$. On obtient un syst\u00e8me matricielle de taille $NSom$ et on le modifie de sort \u00e0 ce que, si $I\\notin S_0$, l'\u00e9quation $I$ de ce syst\u00e8me soit la condition de Dirichlet \n", "\n", "$$\n", "U_I=0.\n", "$$\n", "\n", "Pour ce faire, on modifie la valeur de $F_I$ en la mettant \u00e0 0, et on modifie les ligne et colonne $I$ de $A$ en mettant tous ses coefficients \u00e0 $0$, \u00e0 l'exception du coefficient $A_{II}$ que l'on met par exemple \u00e9gal \u00e0 $1$. \n", "\n", "Cette m\u00e9thode est connue sous le nom de *pseudo-\u00e9limination*.\n", "\n", "**Question 3.** Construire un programme qui donne la solution approch\u00e9e du probl\u00e8me $(P_h)$. \n", "\n", "**Question 3 bis - Calcul des matrices \u00e9l\u00e9mentaires par passage \u00e0 un triangle de r\u00e9f\u00e9rence.** Il est parfois plus simple de calculer les int\u00e9grales sur un triangle $T$ de sommets $S_{I_1},\\ S_{I_2}$ et $S_{I_3}$ en effectuant un changement de variables ramenant au calcul d'un int\u00e9grale dans un triangle dite de r\u00e9f\u00e9rence, qui est ici le triangle $T^0$ de sommets $S^0_1=(0,0),\\ S^0_2=(1,0)$ et $S^0_3=(0,1)$. Pour faire cela il faut :\n", "\n", "* D\u00e9terminer la transformation affine $\\varphi_T:\\RR^2\\longrightarrow\\RR^2$, de la forme $\\varphi_T(x,y)=B\\,\\cdot\\,(x,y)^T+b$, avec $B\\in\\mathcal{M}_2(\\RR)$ et $b=(b_1,b_2)^T\\in\\RR^2$, tel que $\\varphi_T(T^0)=T$ ;\n", "\n", "* \u00c9crire le changement de variables \n", "\n", "$$\n", "\\int_T\\lambda_i\\lambda_j=\\int_{\\varphi_T(T^0)}\\lambda_i\\lambda_j=\\int_{T^0}\\lambda_i(\\varphi_T(x,y))\\lambda_j(\\varphi_T(x,y))|\\det(B)|dx\\,dy=\\int_{T^0}\\lambda_i^0(x,y)\\lambda_j^0(x,y) |\\det(B)|dx\\,dy,\n", "$$\n", "\n", "o\u00f9 $\\lambda_1^0(x,y)=1-x-y,\\ \\lambda_2^0(x,y)=x$ et $\\lambda_3^0(x,y)=y$ sont les fonctions de base locales dans le triangle de r\u00e9f\u00e9rence $T_0$, et\n", "\n", "$$\n", "\\int_T\\nabla\\lambda_i\\cdot\\nabla\\lambda_j=\\int_{T^0}(\\nabla\\lambda_i)(\\varphi_T(x,y))\\cdot(\\nabla\\lambda_j)(\\varphi_T(x,y))|\\det(B)|dx\\,dy=\\int_{T^0}(B^T)^{-1}\\nabla\\lambda_i^0(x,y)\\cdot(B^T)^{-1}\\nabla\\lambda_j^0(x,y) |\\det(B)|dx\\,dy.\n", "$$\n", "\n", "\n", "**Question 4.** On consid\u00e8re $f$ tel que la fonction\n", "$$\n", "u(x,y)=\\sin(\\pi x)\\sin(2\\pi y)\n", "$$\n", "est solution du probl\u00e8me (P) dans $\\Omega=[0,2]\\times[0,2].$ \n", "\n", "Calculer la solution approch\u00e9e donn\u00e9e par la m\u00e9thode des \u00e9l\u00e9ments finis et repr\u00e9senter graphiquement la solution exacte et la solution approch\u00e9e. Repr\u00e9senter dans une autre figure la diff\u00e9rence, en valeur absolue, entre les deux solutions. Diminuer le pas de la discr\u00e9tisation et refaire l'exercice.\n"]}, {"cell_type": "markdown", "metadata": {"deletable": false}, "source": ["## III (pour aller plus loin) - Conditions aux limites de Robin"]}, {"cell_type": "markdown", "metadata": {"deletable": false}, "source": ["R\u00e9fl\u00e9chir dans cette partie comment adapter la m\u00e9thode \u00e0 un probl\u00e8me aux limites avec conditions aux limites de Robin :\n", "\n", "$$\n", "(\\tilde{P})\\left\\{\\begin{aligned}\n", "&-\\Delta u+u=f,\\ \\textrm{dans }\\Omega,\\\\\n", "&\\frac{\\partial u}{\\partial n}+pu=g, \\textrm{dans }\\partial\\Omega,\n", "\\end{aligned}\n", "\\right.\n", "$$\n", "\n", "o\u00f9 $p>0$ et $g$ une fonction continue sur $\\partial\\Omega$ donn\u00e9.\n", "\n", "Pour cela :\n", "\n", "- \u00c9crire la formulation variationelle de ce probl\u00e8me ;\n", "\n", "- \u00c9crire le probl\u00e8me discret associ\u00e9 \u00e0 une discr\u00e9tisation par \u00e9l\u00e9ments finis $P_1$ du domaine $\\Omega$ et le syst\u00e8me lin\u00e9aire correspondant. Celui-ci fait intervenir une nouvelle matrice de masse surfacique.\n", "\n", "- R\u00e9fl\u00e9chir \u00e0 comment faire pour assembler cette nouvelle matrice, \u00e0 l'image de ce qu'on fait pour les matrices de masse et de rigidit\u00e9.\n", "\n", "Il faut pour cela acc\u00e9der au tableau `triang.edges` qui donne dans chaque ligne les sommets des ar\u00eates des triangles composant la triangulation, et cr\u00e9er un tableau qui r\u00e9f\u00e9rence les ar\u00eates qui font partie du bord $\\partial \\Omega$.\n", "\n", "Il faut ensuite, \u00e0 l'image de ce que l'on a fait pour assembler la matrice $A$, cr\u00e9er une matrice de masse de surface \u00e9l\u00e9mentaire pour construire la matrice correspondant aux termes de bord dans la formulation variationelle. \n", "\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "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": "3.8.16"}, "celltoolbar": "None"}, "nbformat": 4, "nbformat_minor": 1}