diff --git a/U4_loesung.ipynb b/U4_loesung.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2008bfe18fbb6b6a6f7496cafde72d881bec9880 --- /dev/null +++ b/U4_loesung.ipynb @@ -0,0 +1,354 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2457c52a", + "metadata": {}, + "source": [ + "# Übung 4 - FEM Implementierung Fachwerk" + ] + }, + { + "cell_type": "markdown", + "id": "aaf32bb0", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2d991054", + "metadata": {}, + "outputs": [], + "source": [ + "# Finite Elemente Methoden - Übung 4\n", + "# Felix Steinmetz, Erik Faust, JP. Dr.-Ing. Lisa Scheunemann\n", + "# Dieses Notebook ist ausschließlich für den eigenen Gebrauch der Kursteilnehmer FEM SoSe24 bestimmt. \n", + "# Letzte Änderung: 28.05.2024\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import json\n", + "\n", + "import sys, os\n", + "\n", + "from numpy.linalg import solve" + ] + }, + { + "cell_type": "markdown", + "id": "e5fc02cc", + "metadata": {}, + "source": [ + "## Problemdefinition: Mesh, Materialien, Randbedingungen und Lasten\n", + "Hinweis: Hier muss nichts verändert werden" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "875ce8a1", + "metadata": {}, + "outputs": [], + "source": [ + "# Spezifizieren der Dateinamen -> Netz-, Material-, und RB-Daten werden in andere Dateien ausgegliedert\n", + "nodes_file = \"nodes.csv\"\n", + "elems_file = \"elems.csv\"\n", + "disp_file = \"disp.csv\"\n", + "mat_file = \"mat.csv\"\n", + "force_file = \"force.csv\"\n", + "\n", + "ndf = 2 # Festlegen der Freiheitsgrade (number degrees of freedom)\n", + "\n", + "# Laden der Materialparameter -> par ist ein Python Dictionary\n", + "# jf -> json file\n", + "with open(os.path.join(\"data\", \"par_prob.json\"), 'r') as jf:\n", + " par = json.load(jf)\n", + "\n", + "# Berechnen der Materialparameter der Stäbe -> [EA, sqrt(2)*EA]\n", + "# Notwendig, da die Stäbe unterschiedliche Materialparameter besitzen\n", + "# Beispiel mat_par[0] -> Materialparameter des Stabes mit Materialnummer 0\n", + "mat_par = []\n", + "for key in par.keys():\n", + " mat_par.append(par[key][\"E\"] * par[key][\"A\"])\n", + "\n", + "# Laden der Materialzuordnung (Index -> Materialparameter ID)\n", + "# Beispiel mat[2] -> Materialnummer des Stabs mit ID 2\n", + "# Damit können wir in mat_par indexieren um die Materialparameter zu erhalten\n", + "with open(os.path.join(\"data\", mat_file), \"r\") as mf:\n", + " mat = [int(l.strip()) for l in mf]\n", + "\n", + "# Laden der Knotenkoordinaten\n", + "# Aufbau: nodes = [[x0,y0], [x1,y1], ...]\n", + "# Zugriff: nodes[Koordinate][KnotenID]; 0-> x-Achse, 1-> y-Achse\n", + "# Beispiel: nodes[0,1] -> x1; nodes[1,3] -> y3\n", + "nodes = np.genfromtxt(os.path.join(\"data\", nodes_file), delimiter=',')\n", + "\n", + "# Laden der Elementdaten (Knotenverbindungen für die Assemblierung)\n", + "# Aufbau: elems = [[KnotenID0, KnotenID1], [KnotenID0, KnotenID1], ...]\n", + "# Zugriff: elems[ElementID] -> Knoten ID beider Knoten, die das Element verbindet\n", + "elems = []\n", + "with open(os.path.join(\"data\", elems_file), \"r\") as ef:\n", + " for line in ef:\n", + " ids = [int(i) for i in line.strip().split(\",\")]\n", + " elems.append(ids)\n", + "\n", + "# Laden der externen Kräfte\n", + "# Aufbau: ID des Knotens, Freiheitsgrad (0:x, 1:y), Betrag der Kraft\n", + "force_nodes = []\n", + "with open(os.path.join(\"data\", force_file)) as ff:\n", + " for line in ff:\n", + " sp = line.strip().split(',')\n", + " force_nodes.append([int(sp[0]), int(sp[1]), float(sp[2])])\n", + "\n", + "# Laden der Verschiebungsrandbedingungen (nur gesperrte Verschiebung)\n", + "# Aufbau: ID des Knotens, Freiheitsgrad (0:x, 1:y)\n", + "disp_nodes = []\n", + "with open(os.path.join(\"data\", disp_file), 'r') as bcf:\n", + " for line in bcf:\n", + " sp = line.strip().split(\",\")\n", + " disp_nodes.append([int(s) for s in sp])" + ] + }, + { + "cell_type": "markdown", + "id": "385bcd6b", + "metadata": {}, + "source": [ + "## Aufbau der Finite Element Rechnung" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b65cdc82", + "metadata": {}, + "outputs": [], + "source": [ + "n = len(nodes) # Anzahl der Knoten bestimmen\n", + "\n", + "#TODO: Initialisieren der globalen Steifigkeitsmatrix und des globalen Lastvektors\n", + "# in Abhängigkeit der Anzahl der (gesamt) Knoten und der (gesamt) Freiheitsgrade\n", + "K_glob = np.zeros((n*ndf, n*ndf))\n", + "F_glob = np.zeros(n*ndf)\n", + "\n", + "#Iteration über die Elemente\n", + "for elem_id, elem in enumerate(elems):\n", + " # Aufbau der Elementsteifigkeitsmatrix\n", + "\n", + " #TODO: Berechnung der Elementlänge\n", + " dx = nodes[elem[0]][0] - nodes[elem[1]][0]\n", + " dy = nodes[elem[0]][1] - nodes[elem[1]][1]\n", + " length = np.sqrt(dx**2 + dy**2)\n", + " cos = dx/length\n", + " sin = dy/length\n", + "\n", + " #TODO: Aufbau der Elementsteifigkeitsmatrix\n", + " k_elem = np.array([[cos**2, cos*sin, -cos**2, -cos*sin], \n", + " [cos*sin, sin**2, -cos*sin, -sin**2],\n", + " [-cos**2, -cos*sin, cos**2, cos*sin], \n", + " [-cos*sin, -sin**2, cos*sin, sin**2]])\n", + "\n", + " #TODO: Material berücksichtigen\n", + " k_elem = k_elem * mat_par[mat[elem_id]] / length\n", + "\n", + " #TODO: Aufbau des Elementresiduums (ohne Volumenkräfte 0)\n", + " f_elem = np.zeros(4)\n", + "\n", + " #Assemblierung (siehe Alternative unten)\n", + " \n", + " for i_node in range(2): # Index für Knoten\n", + " for i_dof in range(2): # Index für lokalen Fhg\n", + " # globale Knotennummer\n", + " I_node = elem[i_node]\n", + " # Index in Elementlastvektor \n", + " i_elem = ndf*i_node + i_dof\n", + " # globale Fhg-nummer = Index in globalem Lastvektor und globaler Steifigkeitsmatrix\n", + " I_dof = ndf*I_node + i_dof\n", + " # globaler Lastvektor\n", + " F_glob[I_dof] += f_elem[i_elem]\n", + " for j_node in range(2):\n", + " for j_dof in range(2):\n", + " # ebenso für J\n", + " J_node = elem[j_node]\n", + " j_elem = ndf*j_node + j_dof\n", + " J_dof = ndf*J_node + j_dof\n", + " # globale Steifigkeitsmatrix\n", + " K_glob[I_dof,J_dof] += k_elem[i_elem,j_elem]\n", + " \n", + " \n", + " #for I in range(4):\n", + " #for J in range(4):\n", + " #TODO: Koinzidenzschema implementieren\n", + " # Globale DOFs, die zu lokalen DOFs gehören\n", + " # Hinweis: a//b teilt a durch b und rundet das Ergebnis ab, a%b gibt den Rest der Division an\n", + " #K_glob[ndf*elem[I//ndf] + I %\n", + " # ndf, ndf*elem[J//ndf] + J % ndf] += k_elem[I, J]\n", + " #F_glob[ndf*elem[I//ndf] + I % ndf] += f_elem[I]" + ] + }, + { + "cell_type": "markdown", + "id": "6f90c5b6", + "metadata": {}, + "source": [ + "## Einarbeiten der Randbedingungen" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0c03c294", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Kraftrandbedingungen auf der rechten Seite aufbringen\n", + "for fp in force_nodes:\n", + " F_glob[ndf*fp[0] + fp[1]] = fp[2]\n", + "\n", + "# IDs der Zeilen/Spalten berechnen, bei denen die Verschiebungsrandbedingungen\n", + "# (fester Rand) aufgebracht werden\n", + "rem_rows = [ndf*bcp[0] + bcp[1] for bcp in disp_nodes]\n", + "\n", + "\n", + "# TODO: Löschen der Zeilen/Spalten im LGS - glob_red ist reduziertes LGS\n", + "K_glob_red = np.delete(K_glob, rem_rows, 0)\n", + "K_glob_red = np.delete(K_glob_red, rem_rows, 1)\n", + "F_glob_red = np.delete(F_glob, rem_rows, 0)\n", + "\n", + "#ind ist eine Liste an Indizes von 0 - Größe des LGS\n", + "ind = np.array([i for i in range(len(nodes)*ndf)])\n", + "# hier werden alle gelöschten Zeilen/Spalten des reduzierten LGS aus ind\n", + "# entfernt -> es bleiben so die ursprünglichen Indizes übrig, die noch weiter\n", + "# verwendet werden\n", + "ind_d = np.delete(ind, rem_rows, 0)\n", + "\n", + "#Initialisierung der kompletten Lösung (inkl. Randbedingungen) als 0-Vektor\n", + "result = np.zeros(len(nodes)*ndf)" + ] + }, + { + "cell_type": "markdown", + "id": "cdcf2fbf", + "metadata": {}, + "source": [ + "## Lösen des Linearen Gleichungssystems" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f4095332", + "metadata": {}, + "outputs": [], + "source": [ + "w = solve(K_glob_red, F_glob_red)\n", + "#Hier werden die Lösungen der \"freien\" Knoten in die Gesamtlösung inkl.\n", + "#Randbedingungen eingefügt\n", + "result[ind_d] = w" + ] + }, + { + "cell_type": "markdown", + "id": "22106203", + "metadata": {}, + "source": [ + "## post processing (Grafische Ausgabe)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0b368f1f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reaktion 4: -0.5714285714285713, Reaktion 5:-0.9999999999999998\n", + "Reaktion 6: -0.42857142857142844, Reaktion 7:0.9999999999999996\n" + ] + }, + { + "data": { + 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", + "text/plain": [ + "<Figure size 1000x1000 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Berechnung der Reaktionskräfte\n", + "F_full = K_glob @ result\n", + "\n", + "# Ausgabe der Reatkionskräfte, Indizes können aus Aufgabenstellung abgelesen werden\n", + "print(\"Reaktion 4: {}, Reaktion 5:{}\".format(F_full[4], F_full[5]))\n", + "print(\"Reaktion 6: {}, Reaktion 7:{}\".format(F_full[6], F_full[7]))\n", + "\n", + "# Plot des unverformten Fachwerks\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "# Plot und Beschriftung der Knoten\n", + "ax.plot(nodes[:, 0], nodes[:, 1], '.', c='black')\n", + "for i in range(nodes.shape[0]):\n", + " ax.annotate(str(i), (nodes[i, 0], nodes[i, 1]))\n", + "\n", + "# Plot der Elemente (Stäbe)\n", + "for i, e in enumerate(elems):\n", + " x = [nodes[e[pi], 0] for pi in [0, 1]]\n", + " y = [nodes[e[pi], 1] for pi in [0, 1]]\n", + " ax.plot(x, y, '-', c='black')\n", + "\n", + "# Plot des verformten Fachwerks\n", + "scale = 10\n", + "# Das Ergebnis (die Knotenverschiebungen) werden auf die ursprünglichen Koordinaten\n", + "# der Knoten addiert und zur besseren Visualisierung mit einem Faktor skaliert\n", + "nodes_dp = nodes + result.reshape(result.shape[0]//2,2) * scale\n", + "ax.plot(nodes_dp[:, 0], nodes_dp[:, 1], '.', c='red')\n", + "for i, e in enumerate(elems):\n", + " x = [nodes_dp[e[pi], 0] for pi in [0, 1]]\n", + " y = [nodes_dp[e[pi], 1] for pi in [0, 1]]\n", + " ax.plot(x, y, '-', c='red')\n", + "\n", + "plt.title(\"deformed mesh plot - scaled with factor {}\".format(scale))\n", + "plt.xlabel(\"x\")\n", + "plt.ylabel(\"y\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d485758c-2c09-4536-bb15-847cb1babb4e", + "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": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}