diff --git a/data_tower/disp.csv b/data_tower/disp.csv new file mode 100644 index 0000000000000000000000000000000000000000..063cf8b791b917bcf68dbf3686d19cfa0f45491e --- /dev/null +++ b/data_tower/disp.csv @@ -0,0 +1,8 @@ +0,0 +0,1 +1,0 +1,1 +6,0 +6,1 +7,0 +7,1 \ No newline at end of file diff --git a/data_tower/elems.csv b/data_tower/elems.csv new file mode 100644 index 0000000000000000000000000000000000000000..19970e05ae3a89a9e007f66dcda268912f6ba359 --- /dev/null +++ b/data_tower/elems.csv @@ -0,0 +1,28 @@ +0,1 +0,2 +1,2 +1,3 +2,3 +2,4 +3,4 +3,5 +4,5 +6,7 +6,9 +6,8 +7,9 +8,9 +8,5 +8,10 +9,10 +5,10 +4,11 +5,11 +5,12 +5,13 +10,13 +11,12 +12,13 +11,14 +12,14 +13,14 \ No newline at end of file diff --git a/data_tower/force.csv b/data_tower/force.csv new file mode 100644 index 0000000000000000000000000000000000000000..d1949bff1163b64bda01175e97fc657a691d4f5b --- /dev/null +++ b/data_tower/force.csv @@ -0,0 +1,3 @@ +10,0,0.1 +13,0,0.1 +14,0,0.1 diff --git a/data_tower/mat.csv b/data_tower/mat.csv new file mode 100644 index 0000000000000000000000000000000000000000..43129068a30a0a23c0b063e5727658108783b009 --- /dev/null +++ b/data_tower/mat.csv @@ -0,0 +1,28 @@ +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 \ No newline at end of file diff --git a/data_tower/nodes.csv b/data_tower/nodes.csv new file mode 100644 index 0000000000000000000000000000000000000000..327c7e1d268e6c19c99f3139e6da4e33720c1109 --- /dev/null +++ b/data_tower/nodes.csv @@ -0,0 +1,15 @@ +0,0 +1,0 +1,2 +2,2 +2,4 +3,4 +5,0 +6,0 +4,2 +5,2 +4,4 +2,6 +3,6 +4,6 +3,8 \ No newline at end of file diff --git a/data_tower/par_prob.json b/data_tower/par_prob.json new file mode 100644 index 0000000000000000000000000000000000000000..794472241fef79b33d6ed99388b7945fa1027f53 --- /dev/null +++ b/data_tower/par_prob.json @@ -0,0 +1,6 @@ +{ + "mat0":{ + "E":100, + "A":1.0 + } +} diff --git "a/\303\234bung 4 - L\303\266sung.ipynb" "b/\303\234bung 4 - L\303\266sung.ipynb" new file mode 100644 index 0000000000000000000000000000000000000000..9c5395e37c2569b8a77b14af3ea8426380aabadd --- /dev/null +++ "b/\303\234bung 4 - L\303\266sung.ipynb" @@ -0,0 +1,319 @@ +{ + "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": 1, + "id": "2d991054", + "metadata": {}, + "outputs": [], + "source": [ + "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": 2, + "id": "875ce8a1", + "metadata": {}, + "outputs": [], + "source": [ + "# Spezifizieren der Dateinamen -> Geometriedaten 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_tower\", \"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", + "with open(os.path.join(\"data_tower\", 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[Achse][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_tower\", nodes_file), delimiter=',')\n", + "\n", + "# Laden der Elementdaten (Knotenverbindungen)\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_tower\", 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_tower\", 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_tower\", 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": 3, + "id": "b65cdc82", + "metadata": {}, + "outputs": [], + "source": [ + "n = len(nodes) # Anzahl der Knoten bestimmen\n", + "\n", + "#TODO: Initialisieren der globalen Stiffness Matrix 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], [cos*sin, sin**2, -cos*sin, -sin**2],\n", + " [-cos**2, -cos*sin, cos**2, cos*sin], [-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", + " #Element Assembly\n", + "\n", + " for I in range(4):\n", + " for J in range(4):\n", + " #TODO: Koinzidenzschema implementieren\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": 4, + "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 einfach 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 gelöschenten 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": 5, + "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 eingef+gt\n", + "result[ind_d] = w" + ] + }, + { + "cell_type": "markdown", + "id": "22106203", + "metadata": {}, + "source": [ + "## post processing (Grafische Ausgabe)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0b368f1f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reaktion 4: 0.0, Reaktion 5:-4.163336342344337e-17\n", + "Reaktion 6: -5.551115123125783e-17, Reaktion 7:-2.7755575615628914e-17\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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 Ursprungskoordinaten\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 - sacled with factor {}\".format(scale))\n", + "plt.xlabel(\"x\")\n", + "plt.ylabel(\"y\")\n", + "\n", + "plt.show()" + ] + } + ], + "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", + 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