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@rnwatanabe
Created July 17, 2019 20:36
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tarefa 8\n",
"Giuliana Valéria Alves RA: 21081614\n",
"Karina Akemi Sequia RA: 21020815"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Importando os pacotes necessários\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Parâmetros do sistema\n",
"Fmax = 7400\n",
"Lceopt = 0.093\n",
"LslackSE = 0.223\n",
"LslackPE = 0.093\n",
"kpe = Fmax/(0.04*LslackPE)**2\n",
"kse = Fmax/(0.04*LslackSE)**2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"dt = 0.001 # [s]\n",
"# Vetor de tempo\n",
"t = np.arange(0, 3, dt) "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Vetor de força\n",
"F = np.zeros_like(t)\n",
"\n",
"# Vetor de comprimento\n",
"L = np.zeros_like(t)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Condição inicial\n",
"Lce0 = 0.087 # [m]\n",
"Lcenorm = Lce0/Lceopt"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Parâmetros\n",
"tdeact = 60\n",
"VMmax = 8\n",
"epM0 = 0.5\n",
"FMlen = 1.8"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# Método de Euler\n",
"a = 1.0\n",
"for i in range(0, len(t)):\n",
" \n",
" if t[i] <= 1:\n",
" Lmtnorm = 0.31/Lceopt\n",
" elif t[i] > 2:\n",
" Lmtnorm = 0.27/Lceopt\n",
" else:\n",
" Lmtnorm = (0.31 - (0.04*((t[i])-1)))/Lceopt\n",
" \n",
" L[i] = Lmtnorm * Lceopt\n",
" \n",
" Lsenorm = Lmtnorm - Lcenorm\n",
" \n",
" if Lcenorm >= LslackPE/Lceopt:\n",
" Fpenorm = kpe*(Lcenorm - LslackPE/Lceopt)**2/Fmax*Lceopt**2\n",
" else:\n",
" Fpenorm = 0 \n",
" \n",
" if Lsenorm >= LslackSE/Lceopt:\n",
" Fsenorm = kse*(Lsenorm - LslackSE/Lceopt)**2/Fmax*Lceopt**2\n",
" else:\n",
" Fsenorm = 0 \n",
" \n",
" F[i] = Fsenorm * Fmax\n",
" \n",
" Fcenorm = Fsenorm - Fpenorm\n",
" \n",
" F0 = np.exp(-(Lcenorm-1)**2/0.45)\n",
" \n",
" if Fcenorm > a*F0:\n",
" b = (2 + 2/0.25)*((a*F0*FMlen) - Fcenorm)/(FMlen - 1)\n",
" else:\n",
" b = a*F0 + (Fcenorm/0.25)\n",
" \n",
" dLcenormdt = (0.25+0.75*a)*VMmax*(Fcenorm-a*F0)/b\n",
" \n",
" Lcenorm = Lcenorm + dt*dLcenormdt\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Gráfico do Lmt\n",
"plt.figure()\n",
"plt.plot(t, L)\n",
"plt.xlabel('Tempo (s)')\n",
"plt.ylabel('Comprimento (mm)')\n",
"plt.title('Comprimento de L')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Gráfico da Força\n",
"plt.figure()\n",
"plt.plot(t, F)\n",
"plt.xlabel('Tempo (s)')\n",
"plt.ylabel('Força (N)')\n",
"plt.title('Gráfico da Força')\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",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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