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} }, "cell_type": "markdown", "id": "66b0c2d9", "metadata": {}, "source": [ "# Heat Conduction\n", "\n", "**Intputs**\n", "\n", "- `qprime`: Linear heat generation rate ($\\frac{W}{m}$)\n", "- `mdot`: Mass flow rate ($\\frac{g}{s}$)\n", "- `Tin`: Temperature of the fuel boundary ($K$)\n", "- `R`: Fuel radius ($m$)\n", "- `L`: Fuel length ($m$)\n", "- `Cp`: Heat capacity ($\\frac{J}{g \\cdot K}$)\n", "- `k`: Thermal conductivity ($\\frac{W}{m \\cdot K}$)\n", "\n", "**Output**\n", "\n", "- `T`: Fuel centerline temperature ($K$)\n", "\n", "This data set consists of 1000 points with seven inputs and one output. The data set was constructed through Latin hypercube sampling of the seven input parameters for heat conduction through a fuel rod. These samples were then used to solve for the fuel centerline temperature analytically. The geometry of the problem is illustrated in the figure below, and it is assumed volumetric heat generation is uniform radially. The problem is defined by\n", "\n", "\\begin{equation}\n", "\\frac{1}{r}\\frac{d}{dr}(kr\\frac{dT}{dr}) + q''' = 0,\n", "\\end{equation}\n", "\n", "with two boundary conditions: $\\frac{dT}{dr}|_{r = 0} = 0$ and $T(R) = T_{in}$. Therefore, the temperature profile in the fuel is\n", "\n", "\\begin{equation}\n", "T(r) = \\frac{q'}{4\\pi k}(1 - (r / R)^2) + T_{in}.\n", "\\end{equation}\n", "\n", "\n", "\n", "The following are a few standard packages and functions that will prove helpful while using pyMAISE along with pyMAISE-specific functionality. " ] }, { "cell_type": "code", "execution_count": 1, "id": "9662efe1", "metadata": { "execution": { "iopub.execute_input": "2024-08-22T19:31:58.146159Z", "iopub.status.busy": "2024-08-22T19:31:58.146047Z", "iopub.status.idle": "2024-08-22T19:32:02.288664Z", "shell.execute_reply": "2024-08-22T19:32:02.288150Z" } }, "outputs": [], "source": [ "# Importing Packages\n", "import time\n", "import cv2\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import uniform, randint\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "# pyMAISE specific imports\n", "import pyMAISE as mai\n", "from pyMAISE.datasets import load_heat\n", "from pyMAISE.preprocessing import scale_data, train_test_split, correlation_matrix\n", "\n", "# Plot settings\n", "matplotlib_settings = {\n", " \"font.size\": 12,\n", " \"legend.fontsize\": 11,\n", " \"figure.figsize\": (8, 8)\n", "}\n", "plt.rcParams.update(**matplotlib_settings)" ] }, { "cell_type": "markdown", "id": "8508a5c4", "metadata": {}, "source": [ "## pyMAISE Initialization\n", "\n", "We start by initializing pyMAISE settings and then importing the data set using `pyMAISE.datasets.load_heat()` from the pyMAISE dataset library." ] }, { "cell_type": "code", "execution_count": 2, "id": "b3928735", "metadata": { "execution": { "iopub.execute_input": "2024-08-22T19:32:02.291108Z", "iopub.status.busy": "2024-08-22T19:32:02.290621Z", "iopub.status.idle": "2024-08-22T19:32:02.303445Z", "shell.execute_reply": "2024-08-22T19:32:02.303037Z" } }, "outputs": [], "source": [ "# Initializing pyMaise settings and the problem we are solving (regression)\n", "global_settings = mai.init(\n", " problem_type=mai.ProblemType.REGRESSION, # Define a regression problem\n", " cuda_visible_devices=\"-1\" # Use CPU only\n", ")\n", "\n", "# Get data\n", "data, inputs, outputs = load_heat()" ] }, { "cell_type": "markdown", "id": "60b197eb", "metadata": {}, "source": [ "The heat conduction data set has 7 inputs" ] }, { "cell_type": "code", "execution_count": 3, "id": "c35c698c", "metadata": { "execution": { "iopub.execute_input": "2024-08-22T19:32:02.305260Z", "iopub.status.busy": "2024-08-22T19:32:02.305052Z", "iopub.status.idle": "2024-08-22T19:32:02.314859Z", "shell.execute_reply": "2024-08-22T19:32:02.314466Z" } }, "outputs": [ { "data": { "text/html": [ "
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| 17 | \n", "RF | \n", "0.998024 | \n", "4.427935 | \n", "0.342536 | \n", "6.709407 | \n", "0.533691 | \n", "0.993746 | \n", "8.205877 | \n", "0.637626 | \n", "12.314054 | \n", "0.956969 | \n", "
| 19 | \n", "RF | \n", "0.997136 | \n", "5.263513 | \n", "0.406251 | \n", "8.077905 | \n", "0.635408 | \n", "0.993728 | \n", "8.350372 | \n", "0.647859 | \n", "12.331529 | \n", "0.954462 | \n", "
| 16 | \n", "RF | \n", "0.998684 | \n", "3.552243 | \n", "0.275988 | \n", "5.476834 | \n", "0.438886 | \n", "0.993029 | \n", "8.551964 | \n", "0.666155 | \n", "13.000574 | \n", "1.011017 | \n", "
| 14 | \n", "DT | \n", "0.993846 | \n", "8.446816 | \n", "0.650752 | \n", "11.841301 | \n", "0.915109 | \n", "0.985220 | \n", "12.827502 | \n", "1.009586 | \n", "18.930021 | \n", "1.501253 | \n", "
| 13 | \n", "DT | \n", "0.993846 | \n", "8.446816 | \n", "0.650752 | \n", "11.841301 | \n", "0.915109 | \n", "0.985220 | \n", "12.827502 | \n", "1.009586 | \n", "18.930021 | \n", "1.501253 | \n", "
| 15 | \n", "DT | \n", "0.996577 | \n", "6.133811 | \n", "0.476671 | \n", "8.831676 | \n", "0.699559 | \n", "0.984935 | \n", "12.667243 | \n", "0.999214 | \n", "19.111302 | \n", "1.520477 | \n", "
| 11 | \n", "DT | \n", "0.995777 | \n", "6.739139 | \n", "0.520000 | \n", "9.808999 | \n", "0.759180 | \n", "0.984636 | \n", "12.876282 | \n", "1.016210 | \n", "19.300072 | \n", "1.535464 | \n", "
| 12 | \n", "DT | \n", "0.994587 | \n", "7.802882 | \n", "0.601207 | \n", "11.106283 | \n", "0.863034 | \n", "0.984309 | \n", "13.410189 | \n", "1.055767 | \n", "19.504322 | \n", "1.550309 | \n", "
| 28 | \n", "FNN | \n", "0.990703 | \n", "4.107766 | \n", "0.322267 | \n", "14.554742 | \n", "1.100226 | \n", "0.974040 | \n", "7.219007 | \n", "0.559021 | \n", "25.087926 | \n", "1.846443 | \n", "
| 30 | \n", "FNN | \n", "0.989318 | \n", "8.569111 | \n", "0.674231 | \n", "15.601300 | \n", "1.283347 | \n", "0.973228 | \n", "11.522918 | \n", "0.926079 | \n", "25.476912 | \n", "2.078072 | \n", "
| 26 | \n", "FNN | \n", "0.984739 | \n", "4.631119 | \n", "0.358272 | \n", "18.647505 | \n", "1.472439 | \n", "0.969190 | \n", "7.493166 | \n", "0.586952 | \n", "27.330885 | \n", "2.143828 | \n", "
| 27 | \n", "FNN | \n", "0.981239 | \n", "11.610469 | \n", "0.900106 | \n", "20.675833 | \n", "1.634222 | \n", "0.967317 | \n", "14.332504 | \n", "1.134863 | \n", "28.149507 | \n", "2.258070 | \n", "
| 29 | \n", "FNN | \n", "0.940880 | \n", "26.184468 | \n", "1.958679 | \n", "36.702944 | \n", "2.808690 | \n", "0.932436 | \n", "28.152282 | \n", "2.155911 | \n", "40.473259 | \n", "3.180488 | \n", "
| 10 | \n", "SVM | \n", "0.895306 | \n", "42.695727 | \n", "3.314075 | \n", "48.841919 | \n", "3.815481 | \n", "0.857033 | \n", "50.145252 | \n", "3.946441 | \n", "58.874553 | \n", "4.650346 | \n", "
| 8 | \n", "SVM | \n", "0.895306 | \n", "42.695727 | \n", "3.314075 | \n", "48.841919 | \n", "3.815481 | \n", "0.857033 | \n", "50.145252 | \n", "3.946441 | \n", "58.874553 | \n", "4.650346 | \n", "
| 7 | \n", "SVM | \n", "0.895306 | \n", "42.695727 | \n", "3.314075 | \n", "48.841919 | \n", "3.815481 | \n", "0.857033 | \n", "50.145252 | \n", "3.946441 | \n", "58.874553 | \n", "4.650346 | \n", "
| 6 | \n", "SVM | \n", "0.895306 | \n", "42.695727 | \n", "3.314075 | \n", "48.841919 | \n", "3.815481 | \n", "0.857033 | \n", "50.145252 | \n", "3.946441 | \n", "58.874553 | \n", "4.650346 | \n", "
| 9 | \n", "SVM | \n", "0.895306 | \n", "42.695727 | \n", "3.314075 | \n", "48.841919 | \n", "3.815481 | \n", "0.857033 | \n", "50.145252 | \n", "3.946441 | \n", "58.874553 | \n", "4.650346 | \n", "
| 5 | \n", "Lasso | \n", "0.839945 | \n", "52.227829 | \n", "4.062700 | \n", "60.390247 | \n", "4.745456 | \n", "0.850541 | \n", "52.731179 | \n", "4.168716 | \n", "60.196391 | \n", "4.797726 | \n", "
| 4 | \n", "Lasso | \n", "0.839980 | \n", "52.218883 | \n", "4.062042 | \n", "60.383614 | \n", "4.744978 | \n", "0.850534 | \n", "52.729709 | \n", "4.168467 | \n", "60.197942 | \n", "4.797652 | \n", "
| 3 | \n", "Lasso | \n", "0.840101 | \n", "52.185042 | \n", "4.059570 | \n", "60.360787 | \n", "4.743353 | \n", "0.850487 | \n", "52.726402 | \n", "4.167712 | \n", "60.207380 | \n", "4.797624 | \n", "
| 2 | \n", "Lasso | \n", "0.840165 | \n", "52.163806 | \n", "4.058018 | \n", "60.348782 | \n", "4.742518 | \n", "0.850443 | \n", "52.726202 | \n", "4.167401 | \n", "60.216178 | \n", "4.797823 | \n", "
| 1 | \n", "Lasso | \n", "0.840206 | \n", "52.148545 | \n", "4.056897 | \n", "60.340929 | \n", "4.741985 | \n", "0.850402 | \n", "52.726049 | \n", "4.167163 | \n", "60.224350 | \n", "4.798088 | \n", "
| 0 | \n", "Linear | \n", "0.840331 | \n", "52.061837 | \n", "4.050545 | \n", "60.317326 | \n", "4.740691 | \n", "0.849952 | \n", "52.749655 | \n", "4.167604 | \n", "60.315047 | \n", "4.802679 | \n", "
| 21 | \n", "KN | \n", "1.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.765921 | \n", "66.107932 | \n", "5.263937 | \n", "75.333995 | \n", "6.082310 | \n", "
| 24 | \n", "KN | \n", "1.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.762813 | \n", "65.333817 | \n", "5.200211 | \n", "75.832477 | \n", "6.125086 | \n", "
| 23 | \n", "KN | \n", "1.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.762162 | \n", "67.368345 | \n", "5.362470 | \n", "75.936563 | \n", "6.129685 | \n", "
| 22 | \n", "KN | \n", "1.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.762162 | \n", "67.368345 | \n", "5.362470 | \n", "75.936563 | \n", "6.129685 | \n", "
| 25 | \n", "KN | \n", "1.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.742511 | \n", "64.803547 | \n", "5.156468 | \n", "79.011268 | \n", "6.367217 | \n", "