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February 4, 2020 20:52
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 6.19 Tanque de agua esferico por Newton-Raphson." | |
| ] | |
| }, | |
| { | |
| "attachments": { | |
| "Tanque.PNG": { | |
| "image/png": 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" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "##### Encontrar la altura, $h$, necesaria del tanque esferico tal que $V = 30m^3$ y $R = 3m$, donde V es el volumen y R el radio del tanque. \n", | |
| "###### Formula : ######\n", | |
| "\n", | |
| "$$V = \\pi {\\color{Red} h^2} \\frac{[3R-{\\color{Red} h}]}{3} $$\n", | |
| "\n", | |
| "**Se sustityen valores y aplicamos algebra de Baldor para obtenter nuestra funcion $f(h)$ y su derivada $f´(h)$:**\n", | |
| "\n", | |
| "$$f(x) = 3\\pi h^2 -\\frac{\\pi}{3}h^3 - 30$$\n", | |
| "\n", | |
| "$$f'(x) = 6\\pi h -\\pi h^2 $$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Inserte número de iteraciones: 3\n", | |
| "Ingrese valor inicial de h: 1\n", | |
| "\n", | |
| " Iteracion Resultado (h) f(h) Ea(%)\n", | |
| "________________________________________________________________________\n", | |
| " 1 2.376525984 -21.622419590 1000.000000000\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Iteracion Resultado (h) f(h) Ea(%)\n", | |
| "________________________________________________________________________\n", | |
| " 2 2.037409988 9.174150632 14.269399877\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Iteracion Resultado (h) f(h) Ea(%)\n", | |
| "________________________________________________________________________\n", | |
| " 3 2.026918932 0.266088835 0.514921206\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "________________________________________________________________________\n", | |
| "Se encontro la altura h = 2.0269189318856173, con 3 iteraciones y Ea = 0.5149212061640462\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "import math\n", | |
| "%matplotlib inline\n", | |
| "from matplotlib import pyplot as plt\n", | |
| "from matplotlib import style\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "def grafica(x,y,hi):\n", | |
| " style.use(\"ggplot\")\n", | |
| " plt.title(\"Gafico valor de h\")\n", | |
| " plt.ylabel(\"f(h)\")\n", | |
| " plt.xlabel(\"h\")\n", | |
| " plt.scatter(hi,0,color='blue')\n", | |
| " plt.plot(x,y, linestyle='solid')\n", | |
| " plt.axhline(0, color = \"black\")\n", | |
| " plt.axvline(0, color = \"black\")\n", | |
| " \n", | |
| " plt.show()\n", | |
| "\n", | |
| "def f(h):\n", | |
| " return ((-math.pi)*(h**3)/3) + (3*(math.pi)*(h**2)) - 30\n", | |
| "\n", | |
| "def df(h):\n", | |
| " return ((-math.pi)*(h**2)) + (6*(math.pi)*h)\n", | |
| "\n", | |
| "i=0\n", | |
| "ea= 1000\n", | |
| "x = np.linspace(-2, 10, num = 100)\n", | |
| "y = f(x)\n", | |
| "\n", | |
| "# Permite operar sin mostrar errores (como divicion entre \"0\")\n", | |
| "while True:\n", | |
| " try:\n", | |
| " n = int(input(\"Inserte número de iteraciones: \"))\n", | |
| " break\n", | |
| " except ValueError:\n", | |
| " input(\"Valor ingresado invalido. Presione enter para continuar...\")\n", | |
| "while True:\n", | |
| " try:\n", | |
| " h = float(input(\"Ingrese valor inicial de h: \"))\n", | |
| " k= 1/h\n", | |
| " break\n", | |
| " except ZeroDivisionError:\n", | |
| " print(\"h debe ser diferente de 0\")\n", | |
| " input(\"Presione enter para continuar...\")\n", | |
| "#----------------------------------------------------------------------------------------------------\n", | |
| "while i < n:\n", | |
| " print(\"\")\n", | |
| " print(f\"{'Iteracion':>10s}{'Resultado (h)':>20s}{'f(h)':>20s}{'Ea(%)':>20s}\")\n", | |
| " print(\"________________________________________________________________________\")\n", | |
| " i += 1\n", | |
| " if df(h) !=0:\n", | |
| " hi = h - (f(h) / df(h))\n", | |
| " else:\n", | |
| " i=n\n", | |
| " if i > 1:\n", | |
| " ea = abs((hi-h)/h)*100\n", | |
| " print(f\"{i:10d}{hi:20.9f}{f(h):20.9f}{ea:20.9f}\")\n", | |
| " h = hi\n", | |
| " grafica(x,y,hi)\n", | |
| "print(\"________________________________________________________________________\")\n", | |
| "print(f\"Se encontro la altura h = {h}, con {n} iteraciones y Ea = {ea}\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "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": "3.7.3" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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