210 lines
32 KiB
Text
210 lines
32 KiB
Text
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "9cf8430b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"import cantera as ct\n",
|
|
"\n",
|
|
"import ipywidgets as widgets\n",
|
|
"\n",
|
|
"from matplotlib import pyplot as plt\n",
|
|
"%matplotlib widget"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "524a6d89",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rho = 1900 # kg / m3\n",
|
|
"kCoef0 = 0.93 # W / m / K\n",
|
|
"kCoef1 = 0.698e-3 # W / m / K2\n",
|
|
"cpCoef0 = 837.2 # J / kg / K\n",
|
|
"cpCoef1 = 251.2e-3 # J / kg / K2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "a836978c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(0.0, 9.218271827661792e-07)"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "e5eb5a95c01a496fb7c866dd76d746f9",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"image/png": "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",
|
|
"text/html": [
|
|
"\n",
|
|
" <div style=\"display: inline-block;\">\n",
|
|
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
|
|
" Figure\n",
|
|
" </div>\n",
|
|
" <img src='data:image/png;base64,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' width=640.0/>\n",
|
|
" </div>\n",
|
|
" "
|
|
],
|
|
"text/plain": [
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.figure()\n",
|
|
"T = np.linspace(800, 2000, 100)\n",
|
|
"k = kCoef0 + kCoef1 * T\n",
|
|
"Cp = cpCoef0 + cpCoef1 * T\n",
|
|
"plt.plot(T, k / rho / Cp)\n",
|
|
"plt.ylim(ymin=0, ymax)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c338a370",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Mean thermal diffusivity of refractory wall (800K~1600K)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "d450d825",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"8.15323821121732e-07 m/s2\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"alpha = ( k / rho / Cp).mean()\n",
|
|
"print(f'{alpha} m/s2')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6110da16",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Grid spacing"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "5f059270",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.004375 m\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"grid_size = 0.14 / 32\n",
|
|
"print(f'{grid_size} m')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "49dc34e0",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Time step constraint"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "b17c722f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"11.73805088490246"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"0.5 *( grid_size **2) / alpha"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7a40816a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.9.12"
|
|
},
|
|
"toc": {
|
|
"base_numbering": 1,
|
|
"nav_menu": {},
|
|
"number_sections": true,
|
|
"sideBar": true,
|
|
"skip_h1_title": false,
|
|
"title_cell": "Table of Contents",
|
|
"title_sidebar": "Contents",
|
|
"toc_cell": false,
|
|
"toc_position": {},
|
|
"toc_section_display": true,
|
|
"toc_window_display": false
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|