{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "%plot inline -w 480 -h 480\n", "format compact" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Aproksymacja - plan centralny kompozycyjny\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Belka jednostronnie utwierdzona\n", "\n", "Dana jest belka o długości $L$ z materiału o module Younga $E$ posiadająca przekrój prostokątny o momencie bezwładności $I$ (wyznaczonym na podstawie jego szerokości $w$ i wysokości $h$).\n", "Równanie różniczkowe opisujące ugięcie belki utwierdzonej na jedmym końcu (dla $x=L$) z obciążeniem ciągłym $Q$ ma postać\n", "\n", "$$\n", "\\mathrm{EI}\\,\\frac{\\partial ^4}{\\partial x^4} y\\left(x\\right)=Q\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Przemieszczenia maksymalne w belce występują na jej swobodnym końcu i wynoszą\n", "\n", "$$\n", " \\delta_{max} (w,h) =\\frac{3\\,L^4\\,Q}{2 E\\,h^3\\,w}\n", "$$\n", "\n", "Naprężenia maksymalne w belce występują na jej utwierdzonym końcu i wynoszą\n", "\n", "$$\n", " \\sigma_{max} (w,h) =\\frac{3\\,L^2\\,Q}{h^2\\,w}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Zakładając, że $L=1$ m, $E=2 \\cdot 10^{11}$ Pa, $Q= 10^{5}$ N, oraz że $w$ i $h$ zawierają się w przedziale $[0.1, 0.2]$ m " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Zapis pliku funkcyjnego opisującego model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created file 'D:\\Wprowadzenie_do_MATLAB\\model_belki.m'.\n" ] } ], "source": [ "%%file model_belki.m\n", "function [delta_max sigma_max]=model_belki(w, h)\n", "L=1;Q=1e5;E=1e11;\n", "delta_max = (3*L^4*Q)/(2*E*h^3*w);\n", "sigma_max = (3*L^2*Q)/(h^2*w);\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d =\n", " 0.0150\n", "s =\n", " 3.0000e+08\n", "\n" ] } ], "source": [ "[d s] = model_belki(0.1, 0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Eksperyment z planem centralnym kompozycyjnym pięciopoziomowym" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x_lim =\n", " 0.1000 0.2000\n", " 0.1000 0.2000\n", "\n" ] } ], "source": [ "clear all\n", "\n", "% x_lim=[min(x_1) max(x_1)\n", "% min(x_2) max(x_2)]\n", "\n", "x_lim=[.1 .2\n", " .1 .2]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x =\n", " -0.7071 -0.7071\n", " -0.7071 0.7071\n", " 0.7071 -0.7071\n", " 0.7071 0.7071\n", " -1.0000 0\n", " 1.0000 0\n", " 0 -1.0000\n", " 0 1.0000\n", " 0 0\n", "\n" ] } ], "source": [ "x=ccdesign(2,'center',1, 'type','inscribed')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Skalowanie parametrów $ t_k $ z przedziału $ [-1, 1] $ do $ [x_{k,min}, x_{k,max}] $ na podstawie zależności\n", "\n", "$$ x_{k,exp} = \\frac{x_{k,max}+x_{k,min}}{2} + x_{k} \\frac{x_{k,max}-x_{k,min}}{2} $$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x_exp =\n", " 0.1146 0.1146\n", " 0.1146 0.1854\n", " 0.1854 0.1146\n", " 0.1854 0.1854\n", " 0.1000 0.1500\n", " 0.2000 0.1500\n", " 0.1500 0.1000\n", " 0.1500 0.2000\n", " 0.1500 0.1500\n", "\n" ] } ], "source": [ "x_exp(:,1)=(x_lim(1,2)+x_lim(1,1))./2+x(:,1).*(x_lim(1,2)-x_lim(1,1))/2;\n", "x_exp(:,2)=(x_lim(2,2)+x_lim(2,1))./2+x(:,2).*(x_lim(2,2)-x_lim(2,1))/2;\n", "x_exp\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y =\n", " 1.0e+08 *\n", " 1.9909\n", " 0.7617\n", " 1.2314\n", " 0.4711\n", " 1.3333\n", " 0.6667\n", " 2.0000\n", " 0.5000\n", " 0.8889\n", "\n" ] } ], "source": [ "for k=1:length(x(:,1))\n", "[d s]=model_belki(x_exp(k,1), x_exp(k,2));\n", "y(k,1)=s;\n", "end\n", "y" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": 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U0RcuE+dnnTTnp0xxPoAzammOLl4fF5iQAMAFhnrLo7u722az0XmQ9Xr92LFjZVlbhFal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\nVpE5Ka+aF0S5g1I/BQxUGbCsIjMvdJm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CBgBgFBI0AACjkKABABiFBA0AwCgkaAAARiFB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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "subplot(2,2,1)\n", "plot3(x(:,1),x(:,2),y(:,1),'o'), xlabel('x_1'), ylabel('x_2'), zlabel('y_1'), grid on, view(3)\n", "subplot(2,2,2)\n", "plot3(x(:,1),x(:,2),y(:,1),'o'), xlabel('x_1'), ylabel('x_2'), zlabel('y_1'), grid on, view(0,90)\n", "subplot(2,2,3)\n", "plot3(x(:,1),x(:,2),y(:,1),'o'), xlabel('x_1'), ylabel('x_2'), zlabel('y_1'), grid on, view(0,0)\n", "subplot(2,2,4)\n", "plot3(x(:,1),x(:,2),y(:,1),'o'), xlabel('x_1'), ylabel('x_2'), zlabel('y_1'), grid on, view(90,0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aproksymacja punktów powierzchnią odpowiedzi " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X =\n", " 1.0000 -0.7071 -0.7071 0.5000 0.5000 0.5000\n", " 1.0000 -0.7071 0.7071 -0.5000 0.5000 0.5000\n", " 1.0000 0.7071 -0.7071 -0.5000 0.5000 0.5000\n", " 1.0000 0.7071 0.7071 0.5000 0.5000 0.5000\n", " 1.0000 -1.0000 0 0 1.0000 0\n", " 1.0000 1.0000 0 0 1.0000 0\n", " 1.0000 0 -1.0000 0 0 1.0000\n", " 1.0000 0 1.0000 0 0 1.0000\n", " 1.0000 0 0 0 0 0\n", "A =\n", " 1.0e+07 *\n", " 8.8889\n", " -3.5230\n", " -7.2672\n", " 2.3448\n", " 1.0550\n", " 3.5550\n", "f1 =\n", " function_handle with value:\n", " @(x1,x2)a00+a10*x1+a01*x2+a11*x1.*x2+a20*x1.^2+a02*x2.^2\n", "\n" ] } ], "source": [ "%Założenie postaci funkcji \n", "%f1(x1,x2) = a00+a10*x1+a01*x2+a20*x1^2+a02*x2^2\n", "%funkcje bazowe\n", "X=[ones(length(x(:,1)),1) x(:,1) x(:,2) x(:,1).*x(:,2) x(:,1).^2 x(:,2).^2]\n", "\n", "Y=y(:,1);\n", "%XA=Y\n", "A=X\\Y\n", "\n", "a00=A(1);a10=A(2);a01=A(3);a11=A(4);a20=A(5);a02=A(6);\n", "\n", "f1=@(x1,x2) a00+a10*x1+a01*x2+a11*x1.*x2+a20*x1.^2+a02*x2.^2\n", "\n", "[X1,X2]=meshgrid([-1:0.25:1]);\n", "Y1=f1(X1,X2);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": 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uIpMFfPUf0ZFrdcs3K906lixW8O3rfc59ceH83gr3ZwriuJDZOekBF07qc7dT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "surf(X1,X2,Y1)\n", "hold on\n", "plot3(x(:,1),x(:,2),y(:,1),'ok')\n", "xlabel('x_1'), ylabel('x_2'), zlabel('y_1')\n", "view(45,45)\n", "grid on" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Matlab", "language": "matlab", "name": "matlab" }, "language_info": { "codemirror_mode": "octave", "file_extension": ".m", "help_links": [ { "text": "MetaKernel Magics", "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md" } ], "mimetype": "text/x-octave", "name": "matlab", "version": "0.15.0" } }, "nbformat": 4, "nbformat_minor": 2 }