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{
"cells": [
{
"cell_type": "markdown",
"id": "0f66ea44",
"metadata": {},
"source": [
"## <font color='blue'> Anleitung zur Nutzung des Berechnungstools\n",
"\n",
"Wir nutzen die Berechnung mit der Finiten-Elemente-Methode im Stile einer Blackbox, d.h. wir werden uns nicht mit den mathematischen Hintergründen oder der Implementierung beschäftigen. Wir werden lediglich erklären, wie man die zum größten Teil auf dem öffentlichen Projekt https://github.com/SaaadRaaa/Truss-Optimization basierenden FE-Funktionen nutzt, sodass wir sie z.B. für die Optimierung mit deap Verwenden können.\n",
"\n",
"Dieses Tool erfordert numpy, matplotlib, und pandas. Sollte eines dieser Pakete noch nicht installiert sein, muss es über die Konsole (geöffnet im Jupyterlab bei Nutzung von Jupyterhub, oder der cmd bei Nutzung einer lokalen installation) mit `pip3 install numpy matplotlib pandas` installiert werden. \n",
"\n",
"Die Berechnungsmethode `FETool.Run()` erhält als Eingabewerte die Geometrie (Knoten und Stäbe), Eigenschaften der Stäbe (Querschnittsfläche und E-Modul), sowie Eigenschaften der Knoten (Rand-/Lagerbedingung und angreifende Kräfte). Sie gibt eine Liste mit Knotenverschiebungen und eine Liste mit Spannungen der Stäbe zurück. Zudem wird ein pandas dataframe zurückgegeben, in dem noch weitere Resultate, z.B. die Dehnungen enthalten sind. Diesen werden wir jedoch nicht nicht verwenden.\n",
" \n",
"Die Funktionsweise wird nun an zwei Beispielen gezeigt.\n",
" \n",
"#### <font color = 'blue'> 1. Beispiel\n",
" \n",
"Das erste Beispiel zeigt an ein einfaches Dreieck, um die Bedienung zu erklären: "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3da4d537",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Das Paket ist nicht global installiert. Die Quelldateien muessen im selben Ordner liegen, wie das Notebook, das es verwendet\n",
"import FETool\n",
"\n",
"# **** Geometrie-Definition (Knoten, Stäbe mit Flächen und E-Modul)\n",
"\n",
"Coord = [(0,0),(2,0),(1,1)] # Knotenkoordinaten (Liste mit Tupeln)\n",
"\n",
"ElmCon = [(1,2),(1,3),(2,3)] # Stäbe: Verbindungen zwischen Knoten. Knotennummern beginnen bei 1. (Liste mit Tupeln)\n",
" \n",
"A = [1,2,3] # Querschnittsflaeche der Stäbe (Gleiche Reihenfolge wie ElmCon), (Liste mit Zahlen)\n",
"\n",
"E = [100,100,100] # E-Modul der Stäbe (Gleiche Reihenfolge wie ElmCon), (Liste mit Zahlen)\n",
"\n",
"\n",
"# **** Lastfall-Definition: Lagerung und Kräfte\n",
"# Lagerung: Liste: [KnotenId, U.x: 1 oder U.y: 2, Wert = 0]\n",
"# Kraft: Liste: [KnotenId, F.x, F.y] \n",
"BC = [[1,1,0],[1,2,0],[2,2,0],[2,1,0]] # Zwei Festlager -> ueberbestimmt, das ist aber in der Elastostatik kein Problem \n",
"\n",
"F = [[3,2,1]] # An Knoten 3 eine Kraft nach rechts+oben \n",
"\n",
"# **** Berechnung\n",
"U, sigma, df = FETool.Run( Coord, ElmCon, A, E, BC, F ) # Berechnung der Verschiebungen und Spannungen\n",
"#Im zusätzlich zurückgegebenen dataframe df sind weitere Informationen enthalten, die wir hier aber nicht verwenden"
]
},
{
"cell_type": "markdown",
"id": "6bfccb50",
"metadata": {},
"source": [
"Es stehen Methoden zur Verfügung, um die Fachwerkstruktur, optional mit Querschnittsflächen, Verformungen oder Spannungen grafisch darstellen zu lassen. Diese Methoden benötigen je nach Art neben den Knoten und Verbindungen noch die Querschnittsfläche, die berechneten Verschiebungen oder die berechneten Spannungen:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1c094153",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 500x250 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"FETool.Post.ShowTruss( Coord, ElmCon ) # Stellt Fachwerk schematisch dar"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3d520aba",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 500x250 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"FETool.Post.ShowTrussCross( Coord, ElmCon, A ) # Stellt Fachwerk dar und visualisiert den Querschnitt der Stäbe an. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2724b4ca",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 500x250 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"FETool.Post.ShowDeformedTruss(Coord, ElmCon, U) # Die Verformung wird standardmaessig mit Faktor 10 dargestellt."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6ccd84b7",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 500x250 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"FETool.Post.ShowTrussStress( Coord, ElmCon, sigma ) # Rot sind Zugspannungen, Blau Druckspannungen, Weiß Nullstaebe beim aktuellen Lastfall."
]
},
{
"cell_type": "markdown",
"id": "40173a9b",
"metadata": {},
"source": [
"Zudem steht eine Methode zur Verfügung, die unter Angabe der Knoten, Stäbe, deren Materialdichten (zuvor nicht benötigt) und Querschnittsflächen das Gewicht des Fachwerks berechnet:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "709640da",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Das Gewicht des Fachwerks beträgt 45.35533905932738 kg.\n"
]
}
],
"source": [
"rho = [5 for i in range(len(ElmCon))] # Diese \"List Comprehension\" erzeugt eine Liste mit so vielen \"5\" wie Einträge in ElmCon vorhanden sind\n",
"gewicht = FETool.TotalWeight( Coord, ElmCon, rho, A )\n",
"\n",
"print(f'Das Gewicht des Fachwerks beträgt {gewicht} kg.')"
]
},
{
"cell_type": "markdown",
"id": "b17d8bce",
"metadata": {},
"source": [
"#### <font color = 'blue'> 2. Beispiel\n",
"\n",
"Das zweite Beispiel zeigt ein praxisnäheres Beispiel einer kleinen Brücke. Es hat mehr Knoten und Stäbe, sowie passendere Größen für die Parameter (im SI System). Die Bedienung funktioniert aber identisch. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b75c857d",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 500x111.111 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Ein etwas praxisnaeheres Beispiel einer kleinen Bruecke\n",
"\n",
"Coord = [(0,0),(3,0),(6,0),(9,0),(1.5,2),(4.5,2),(7.5,2)] # zuerst die unteren Knoten, dann die oberen\n",
"ElmCon = [(1,2),(2,3),(3,4),(1,5),(5,2),(2,6),(6,3),(3,7),(7,4),(5,6),(6,7)] # zuerst die untere Linie, dann die diagonalen Linien, dann die obere Linie\n",
"A = [0.0015 for x in range(len(ElmCon))] # Flaeche in m**3. Entspricht 3*5mm*100mm. Fuer alle Elemente\n",
"E = [ 210e9 for x in range(len(ElmCon))] # E-Modul von Stahl in Pa.Fuer alle Elemente\n",
"BC = [[1,1,0],[1,2,0],[4,2,0]] # Fest-Los-Lagerung\n",
"F = [[2,0,-500000],[3,0,-500000]] # ca 100t in N verteilt auf die beiden mittleren unteren Knoten\n",
"\n",
"U, sigma, df = FETool.Run(Coord, ElmCon, A, E, BC, F)\n",
"\n",
"# Eine andere Verschiebungsskalierung kann per optionalen Parameter eingestellt werden. Hier z.B. Faktor 5\n",
"FETool.Post.ShowDeformedTruss(Coord, ElmCon, U, 5)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "7a2e7134",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 500x111.111 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"FETool.Post.ShowTrussStress(Coord, ElmCon, sigma)"
]
}
],
"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": "3.8.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}