{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# OPT python exercise 1" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "In this exercise, we want to use linear and polynomial regression and compare them to the built-in curve fitting tool from scipy." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The underlying problem we are looking at is linked to mechanical testing of prismatic battery cells (see Figure below). Several experiments were carried out where an hemispherical impactor was used to deform prismatic cells. From these experiments, both the displacement of the impactor (referred to as *intrusion*) as well as the force were registered." ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "![image.png](attachment:image.png)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "In this exercise we want to use optimization to determine:\n", "\n", "1. the initial stiffness of the prismatic battery cell which can be estimated when the intrusion (i.e. displacement) is lower than 1.0 mm\n", "2. a mathematical model representing the relationship between force and intrusion for later use in finite element analysis for example.\n", "\n", "The initial stiffness can be obtained by a linear regression of the data for small intrusion levels as well as curve fitting a linear model defined as:\n", "\n", "$$ y = a x +b$$\n", "\n", "The force-intrusion response of the battery cell can be represented (as far as the data suggest) by a second order polynomial given by:\n", "\n", "$$ y = a_1 + a_2 x + a_3 x^2$$\n", "\n", "The parameters of this second order polynomial can be obtained by polynomial regression or by curve fitting as well." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "To obtain what we are looking for, you need to:\n", "\n", "1. import the data from the experiments using `numpy loadtxt`, this cell is already coded.\n", "2. extract all intrusions $x$ and forces $y$ from the data to determine the mathematical model of the battery cells, these vectors will be referred to as *large intrusion data*. This cell is already coded.\n", "3. create smaller arrays for the initial stiffness computations where $x \\le 1.0 mm$ \n", "4. visualize the data in a subplot where the small intrusion and large intrusion data are plotted independently. You should use a `scatter` plot here.\n", "5. define a function for the linear model where the independent variable $x$ is given as input as well as its parameters $a$ and $b$\n", "6. define a function for the second order polynomial model where the independent variable $x$ is given as input as well as its parameters $a_1$, $a_2$ and $a_3$\n", "7. determine the parameters of the linear model using the `curve_fit` module of `scipy` using the small data set (question 3)\n", "8. determine the parameters of the second order polynomial model using the `curve_fit` module of `scipy` using the large data set (question 2)\n", "9. plot the fitted models using a subplot (similar to question 4)\n", "10. compute and print the goodness of the fit $R^2$ for the different models\n", "11. plot the second order polynomial model up to a very large intrusion level (i.e. 26.5 mm)\n", "\n", "Bonus questions:\n", "\n", "12. Determine the parameters for the linear model using the formula presented in the lectures. This model is used for the small intrusion levels.\n", "13. Determine the parameters for the second order polynomial model using the formula presented in the lectures. This model is used for the large intrusion levels.\n", "14. print out the parameters of the different models obtained with the analytical and scipy module.\n", "15. compute and print the goodness of the fit $R^2$ for the different models" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "First we import our mandatory modules." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.linalg import solve\n", "from scipy.optimize import curve_fit" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "**Question 1**\n", "\n", "we use the numpy loadtxt function to import the text file which contains our experimental data. The columns of this text file are separated with commas meaning that we need to specify it through the keyword `delimiter`. This file contains the name of the variables as the first line. To avoid problem when reading the file, we need to specify that the first line should not be read. To this end we use the `skiprows` keyword." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data = np.loadtxt('data_OPT_1.csv',delimiter=',',skiprows=1)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "**Question 2**\n", "\n", "we extract the intrusion $x$ and force from the data array using slicing. To avoid confusion with arrays later in the notebook we will name them _large" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "x_large = data[:,0]\n", "y_large = data[:,1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. create smaller arrays for the initial stiffness computations where $x \\le 1.0 mm$ " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. visualize the data in a subplot where the small intrusion and large intrusion data are plotted independently. You should use a `scatter` plot here." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5. define a function for the linear model where the independent variable $x$ is given as input as well as its parameters $a$ and $b$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6. define a function for the second order polynomial model where the independent variable $x$ is given as input as well as its parameters $a_1$, $a_2$ and $a_3$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "7. determine the parameters of the linear model using the `curve_fit` module of `scipy` using the small data set (question 3)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8. determine the parameters of the second order polynomial model using the `curve_fit` module of `scipy` using the large data set (question 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "9. plot the fitted models using a subplot (similar to question 4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "10. compute and print the goodness of the fit $R^2$ for the different models" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "11. plot the second order polynomial model up to a very large intrusion level (i.e. 26.5 mm)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bonus questions:\n", "\n", "12. Determine the parameters for the linear model using the formula presented in the lectures. This model is used for the small intrusion levels." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "13. Determine the parameters for the second order polynomial model using the formula presented in the lectures. This model is used for the large intrusion levels." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "14. print out the parameters of the different models obtained with the analytical and scipy module." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "15. compute and print the goodness of the fit $R^2$ for the different models" ] }, { "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": "3.12.4" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }