{ "cells": [ { "cell_type": "markdown", "metadata": { "nbpages": { "level": 0, "link": "[](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html)", "section": "" } }, "source": [ "\n", "*This notebook contains material from [CBE32338](https://jckantor.github.io/CBE32338);\n", "content is available [on Github](https://github.com/jckantor/CBE32338.git).*\n" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 0, "link": "[](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html)", "section": "" } }, "source": [ "\n", "< [2.6 Four State Model](https://jckantor.github.io/CBE32338/02.06-Four-State-Model.html) | [Contents](toc.html) | [2.10 TCLab Lab 2: Model Identification](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification.html) >

\"Open

\"Download\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# IMPORT DATA FILES USED BY THIS NOTEBOOK\n", "import os, requests\n", "\n", "file_links = [(\"data/tclab-data.csv\", \"https://jckantor.github.io/CBE32338/data/tclab-data.csv\")]\n", "\n", "# This cell has been added by nbpages. Run this cell to download data files required for this notebook.\n", "\n", "for filepath, fileurl in file_links:\n", " stem, filename = os.path.split(filepath)\n", " if stem:\n", " if not os.path.exists(stem):\n", " os.mkdir(stem)\n", " if not os.path.isfile(filepath):\n", " with open(filepath, 'wb') as f:\n", " response = requests.get(fileurl)\n", " f.write(response.content)\n" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 1, "link": "[2.10 TCLab Lab 2: Model Identification](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10-TCLab-Lab-2:-Model-Identification)", "section": "2.10 TCLab Lab 2: Model Identification" } }, "source": [ "# 2.10 TCLab Lab 2: Model Identification" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 1, "link": "[2.10 TCLab Lab 2: Model Identification](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10-TCLab-Lab-2:-Model-Identification)", "section": "2.10 TCLab Lab 2: Model Identification" } }, "source": [ "For this laboratory session you will collect data from a step test experiment, then fit the data to models derived from first-principles energy balances. Fitting models to data is an engineering skill that links between the real world of engineering systems to the theory you've been learning in the classroom." ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.10.1 Procedures](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.1-Procedures)", "section": "2.10.1 Procedures" } }, "source": [ "## 2.10.1 Procedures" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.10.1 Procedures](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.1-Procedures)", "section": "2.10.1 Procedures" } }, "source": [ "1. Please work in groups of two.\n", "\n", "2. Check out a TCLab kit. The kit consists of\n", " * plastic container\n", " * Arduino device with the TCLab shield installed\n", " * 5 watt USB power supply\n", " * USB power cable\n", " * USB data cable\n", " * equipment log sheet\n", " \n", " Before going further, sign and date the equipment log sheet. \n", "\n", "3. Download a copy of this notebook to your laptop, and complete the exercises shown below. Under each exercise heading, add as many text and code cells as needed to complete the exercise. The results should be embedded in the notebook. Be sure to 'save-as-you-go' to avoid losing your work.\n", "\n", "4. Add any relevant notes to the equipment log and return the kit to equipment at the front of the lab. **Return any malfunctioning kit to the instructor for repair.**\n", "\n", "5. Submit your completed lab notebook via Sakai. The notebook should contain the name of both lab partners. Both partners should submit a copy of the notebook." ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.10.2 Exercise 1. Verify operation of the temperature control lab.](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.2-Exercise-1.-Verify-operation-of-the-temperature-control-lab.)", "section": "2.10.2 Exercise 1. Verify operation of the temperature control lab." } }, "source": [ "## 2.10.2 Exercise 1. Verify operation of the temperature control lab.\n", "\n", "Execute the following cell to verify that you have a working connection to the temperature control lab hardware. This will test for installation of TCLab.py, connection to the Arduino device, and working firmware within the Arduino." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "nbpages": { "level": 2, "link": "[2.10.2 Exercise 1. Verify operation of the temperature control lab.](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.2-Exercise-1.-Verify-operation-of-the-temperature-control-lab.)", "section": "2.10.2 Exercise 1. Verify operation of the temperature control lab." } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TCLab version 0.4.9\n", "Arduino Leonardo connected on port /dev/cu.usbmodemWUART1 at 115200 baud.\n", "TCLab Firmware 1.3.0 Arduino Leonardo/Micro.\n", "TCLab Temperatures: 23.81 23.48\n", "TCLab disconnected successfully.\n" ] } ], "source": [ "from tclab import TCLab, clock, Historian, Plotter\n", "\n", "lab = TCLab()\n", "print(\"TCLab Temperatures:\", lab.T1, lab.T2)\n", "lab.close()" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.10.3 Exercise 2. Check for steady state](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.3-Exercise-2.-Check-for-steady-state)", "section": "2.10.3 Exercise 2. Check for steady state" } }, "source": [ "## 2.10.3 Exercise 2. Check for steady state\n", "\n", "As discussed in class, for good model fitting it is essential for the TCLab hardware to be at steady state before proceeding with the step test. Run the following code to verify that the heaters are off and that the temperatures are at a steady ambient temperature." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "nbpages": { "level": 2, "link": "[2.10.3 Exercise 2. Check for steady state](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.3-Exercise-2.-Check-for-steady-state)", "section": "2.10.3 Exercise 2. Check for steady state" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "TCLab disconnected successfully.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# experimental parameters\n", "tfinal = 30\n", "\n", "# perform experiment\n", "with TCLab() as lab:\n", " h = Historian(lab.sources)\n", " p = Plotter(h, tfinal)\n", " for t in clock(tfinal):\n", " p.update(t)" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.10.4 Exercise 3. Step test.](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.4-Exercise-3.-Step-test.)", "section": "2.10.4 Exercise 3. Step test." } }, "source": [ "## 2.10.4 Exercise 3. Step test.\n", "\n", "The step test consists of turning on one heater at 50% power and recording temperature data for at least 800 seconds. Copy and paste the code from Exercise 2 into the following cell, then modify as needed to accomplish the step test. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "nbpages": { "level": 2, "link": "[2.10.4 Exercise 3. Step test.](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.4-Exercise-3.-Step-test.)", "section": "2.10.4 Exercise 3. Step test." } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "TCLab disconnected successfully.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# experimental parameters\n", "tfinal = 800\n", "Q1 = 50\n", "\n", "# perform experiment\n", "with TCLab() as lab:\n", " h = Historian(lab.sources)\n", " p = Plotter(h, tfinal)\n", " lab.Q1(Q1)\n", " for t in clock(tfinal):\n", " p.update(t)" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.10.5 Exercise 4. Verify and save data to a .csv file](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.5-Exercise-4.-Verify-and-save-data-to-a-.csv-file)", "section": "2.10.5 Exercise 4. Verify and save data to a .csv file" } }, "source": [ "## 2.10.5 Exercise 4. Verify and save data to a .csv file\n", "\n", "Run the following cell to verify and save your data to a '.csv' file. Be sure you can find and locate the data on your laptop before leaving the lab. You will need access to this data for subsequent exercises." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "nbpages": { "level": 2, "link": "[2.10.5 Exercise 4. Verify and save data to a .csv file](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.5-Exercise-4.-Verify-and-save-data-to-a-.csv-file)", "section": "2.10.5 Exercise 4. Verify and save data to a .csv file" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "t, T1, T2, Q1, Q2 = h.fields\n", "\n", "plt.plot(t, T1, t, T2, t, Q1, t, Q2)\n", "plt.legend(['T1','T2','Q1','Q2'])\n", "plt.xlabel('Time / seconds')\n", "plt.grid()\n", "\n", "h.to_csv('tclab-data.csv')" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.10.6 Exercise 5. Analysis](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.6-Exercise-5.-Analysis)", "section": "2.10.6 Exercise 5. Analysis" } }, "source": [ "## 2.10.6 Exercise 5. Analysis\n", "\n", "1.) Approximating the the step test results for T1 as a first order transfer function, estimate the time constant and gain. Write your answer in the following cell." ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.10.6 Exercise 5. Analysis](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.6-Exercise-5.-Analysis)", "section": "2.10.6 Exercise 5. Analysis" } }, "source": [ "A first order system transfer function is described by the differential equation\n", "\n", "$$ \\tau \\frac{dy}{dt} + y = K u $$\n", "\n", "where $y$ is the deviation in process output and $u$ is the deviation in process input relative to a nominal condition. In this instance, the deviation variables are $u = Q_1$ and $y = T_1 - T_{amb}$. The process gain $K$ can be estimated from the steady state condition at the end of the step test. \n", "\n", "$$K = \\frac{y_{ss}}{u_{ss}} = \\frac{54.75 - 23.81}{50} = 0.62$$\n", "\n", "The time constant $\\tau$ can be estimated as the time required to achieve 63.2% of the final response. That value of $T_1$ can be computed as \n", "\n", "$$23.81 + 0.632*(54.75-23.81) = 43.4$$\n", "\n", "This is about $\\tau = 186$ seconds from the inspection of the data. These calculations are verified in the following code cell." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "nbpages": { "level": 2, "link": "[2.10.6 Exercise 5. Analysis](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.6-Exercise-5.-Analysis)", "section": "2.10.6 Exercise 5. Analysis" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gain K = 0.6188 degrees C per percent increase in Q1\n", "63.2% of the final temperature rise corresponds to 43.36408 degrees C\n", "tau = 186.0 seconds\n" ] } ], "source": [ "K = (T1[-1] - T1[0])/Q1[0]\n", "print(\"Gain K = \", K, \"degrees C per percent increase in Q1\")\n", "\n", "T = T1[0] + 0.632*(T1[-1] - T1[0])\n", "print(\"63.2% of the final temperature rise corresponds to\", T, \"degrees C\")\n", "\n", "tau = t[min([k for k in range(0, len(T1)) if T < T1[k] ])]\n", "print(\"tau =\", tau, \"seconds\")" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.10.6 Exercise 5. Analysis](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.6-Exercise-5.-Analysis)", "section": "2.10.6 Exercise 5. Analysis" } }, "source": [ "2.) As we discussed in class, a simple energy balance model for T1 is given by\n", "\n", "$$C_p \\frac{dT_1}{dt} = U_a(T_{amb} - T_1) + P Q_1$$\n", "\n", "where the parameter $P$ has, through independent means, been determined as 0.04 watts per percent increase in $Q_1$. Use the results of this experiment to estimate values for $C_p$ and $U_a$. Write your answers in the following cell." ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.10.6 Exercise 5. Analysis](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.6-Exercise-5.-Analysis)", "section": "2.10.6 Exercise 5. Analysis" } }, "source": [ "$$K = \\frac{P}{U_a} \\implies U_a = \\frac{P}{K} = \\frac{0.04}{0.62} = 0.065 \\text{ watts/deg C}$$\n", "\n", "$$\\tau = \\frac{C_p}{U_a} \\implies C_p = \\tau U_a = \\frac{\\tau P}{K} = \\frac{186 \\times 0.04}{0.62} = 12 \\text{ J/deg C}$$" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "nbpages": { "level": 2, "link": "[2.10.6 Exercise 5. Analysis](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.6-Exercise-5.-Analysis)", "section": "2.10.6 Exercise 5. Analysis" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Heat transfer coefficient Ua = 0.06464124111182935 watts/degree C\n", "Heat capacity = 12.023270846800258 J/deg C\n" ] } ], "source": [ "P = 0.04\n", "\n", "Ua = P/K\n", "print(\"Heat transfer coefficient Ua =\", Ua, \"watts/degree C\")\n", "\n", "Cp = tau*P/K\n", "print(\"Heat capacity =\", Cp, \"J/deg C\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpages": { "level": 2, "link": "[2.10.6 Exercise 5. Analysis](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.6-Exercise-5.-Analysis)", "section": "2.10.6 Exercise 5. Analysis" } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[2.10.6 Exercise 5. Analysis](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification-Solutions.html#2.10.6-Exercise-5.-Analysis)", "section": "2.10.6 Exercise 5. Analysis" } }, "source": [ "\n", "< [2.6 Four State Model](https://jckantor.github.io/CBE32338/02.06-Four-State-Model.html) | [Contents](toc.html) | [2.10 TCLab Lab 2: Model Identification](https://jckantor.github.io/CBE32338/02.10-TCLab-Lab-2-Model-Indentification.html) >

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