210 lines
34 KiB
Text
210 lines
34 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a48ca14c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import cantera as ct\n",
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"\n",
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"import ipywidgets as widgets\n",
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"\n",
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"from matplotlib import pyplot as plt\n",
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"%matplotlib widget"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "3efb72e3",
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"metadata": {},
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"outputs": [],
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"source": [
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"rho = 1900 # kg / m3\n",
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"kCoef0 = 0.93 # W / m / K\n",
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"kCoef1 = 0.698e-3 # W / m / K2\n",
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"cpCoef0 = 837.2 # J / kg / K\n",
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"cpCoef1 = 251.2e-3 # J / kg / K2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "e098d30f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(0.0, 8.751177742052907e-07)"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1f391b6625084e539ed2f05764f170e8",
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"version_major": 2,
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"version_minor": 0
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},
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"image/png": 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",
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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ZXC15EBAHBWBCAuWOTjUToLvMj9rpx/5/WkKH9Av04Cr/Unh2cBAOgeBCDOyhij8poGHaloW7WrqDv1e9sh29oufLjxoIy0Dit4Qwe0rt7lD0iX15PaA7MBAAAEIDoEXvTP1t+78vl3OVlu5Q9oi7pI5A1IV6DtAgvOvwMAoHfgHdkCxhh96QRenYoras858Fyu1sALDOjnhF1+/34KDDx1sYUnlY9HAQAgERCAfYAxRqG6Jifsir9sjbridit5ZztE63JJuV6Php62gje0Lfj4/DsAAPoOAjBBVIebnLgr/rK23UpenY58WduliyyGeFtX8FoPy/Yj8AAAsBQB2Av9+dOT2nrghBN5xV/WduljUrIz3Ro6IF2Bga1RF4m8wMDWb7Mg8AAAgEQA9krvfV6u3239vMP2/v1So6IuEnmt5+H1U3oagQcAAM6OAOyFJo4cpKqrmxQY2E+BtsO0gYHpyuJjUgAAQDcgAHuhKRdna8rF2fEeBgAA6KP43iwAAADLEIAAAACWIQABAAAsQwACAABYhgAEAACwDAEIAABgGQIQAADAMgQgAACAZQhAAAAAyxCAAAAAliEAAQAALEMAAgAAWIYABAAAsAwBCAAAYBkCEAAAwDIEIAAAgGWsDsARI0bI5XJ1uM2fPz/eQwMAAIiZlHgPIJ527dql5uZm5/5HH32kG264QXfccUccRwUAABBbVgfg4MGDo+4vXbpUo0aN0rXXXhunEQEAAMSe1YeA22toaNDq1as1b948uVyueA8HAAAgZqxeAWxv/fr1qqys1D333HPGfcLhsMLhsHM/FAr1wMgAAAC6FyuAbVauXKnp06fL7/efcZ/CwkL5fD7nFggEenCEAAAA3cNljDHxHkS8ffHFF7rooou0du1azZo164z7dbYCGAgEFAwG5fV6e2KoAADgAoVCIfl8PqvfvzkELGnVqlXKycnRjBkzvnI/t9stt9vdQ6MCAACIDesPAbe0tGjVqlUqKChQSgo9DAAA+j7rA3DTpk06fPiw5s2bF++hAAAA9Ajrl7ymTZsmToMEAAA2sX4FEAAAwDYEIAAAgGUIQAAAAMsQgAAAAJYhAAEAACxDAAIAAFiGAAQAALAMAQgAAGAZAhAAAMAyBCAAAIBlCEAAAADLEIAAAACWIQABAAAsQwACAABYhgAEAACwDAEIAABgGQIQAADAMgQgAACAZQhAAAAAyxCAAAAAliEAAQAALEMAAgAAWIYABAAAsAwBCAAAYBkCEAAAwDIEIAAAgGUIQAAAAMsQgAAAAJYhAAEAACxDAAIAAFiGAAQAALCM1QF49OhR/eAHP9CgQYPUr18/ff3rX9eePXviPSwAAICYSon3AOKloqJC11xzja677jq9+eabysnJ0Weffab+/fvHe2gAAAAxZW0APvvsswoEAlq1apWzbcSIEfEbEAAAQA+x9hDw//zP/+jKK6/UHXfcoZycHF1++eV68cUX4z0sAACAmLM2AD///HO98MILuvjii/XWW2/pvvvu04MPPqiXXnrpjM8Jh8MKhUJRNwAAgETjMsaYeA8iHtLS0nTllVdq+/btzrYHH3xQu3bt0o4dOzp9zuLFi7VkyZIO24PBoLxeb8zGCgAAuk8oFJLP57P6/dvaFcC8vDxdeumlUdsuueQSHT58+IzPWbRokYLBoHMrLi6O9TABAAC6nbUXgVxzzTX65JNPorYdOHBAw4cPP+Nz3G633G53rIcGAAAQU9auAD788MN677339POf/1yffvqpXnnlFa1YsULz58+P99AAAABiytoAvOqqq7Ru3Tq9+uqrGjdunH7605/q+eef15w5c+I9NAAAgJiy9iKQ7sBJpAAAJB7evy1eAQQAALAVAQgAAGAZAhAAAMAyBCAAAIBlCEAAAADLEIAAAACWIQABAAAsQwACAABYhgAEAACwDAEIAABgGQIQAADAMgQgAACAZQhAAAAAyxCAAAAAliEAAQAALEMAAgAAWIYABAAAsAwBCAAAYBkCEAAAwDIEIAAAgGUIQAAAAMsQgAAAAJYhAAEAACxDAAIAAFiGAAQAALAMAQgAAGAZAhAAAMAyBCAAAIBlCEAAAADLEIAAAACWIQABAAAsQwACAABYxtoAXLx4sVwuV9QtNzc33sMCAACIuZR4DyCexo4dq02bNjn3k5OT4zgaAACAnmF1AKakpLDqBwAArGPtIWBJOnjwoPx+v0aOHKm77rpLn3/++VfuHw6HFQqFom4AAACJxtoAnDhxol566SW99dZbevHFF1VaWqqrr75a5eXlZ3xOYWGhfD6fcwsEAj04YgAAgO7hMsaYeA+iN6ipqdGoUaP02GOPaeHChZ3uEw6HFQ6HnfuhUEiBQEDBYFBer7enhgoAAC5AKBSSz+ez+v3b6nMA28vIyND48eN18ODBM+7jdrvldrt7cFQAAADdz9pDwKcLh8P6+OOPlZeXF++hAAAAxJS1AfhP//RP2rJliw4dOqSdO3fq7/7u7xQKhVRQUBDvoQEAAMSUtYeAjxw5otmzZ+vkyZMaPHiwJk2apPfee0/Dhw+P99AAAABiytoAXLNmTbyHAAAAEBfWHgIGAACwFQEIAABgGQIQAADAMgQgAACAZQhAAAAAyxCAAAAAliEAAQAALEMAAgAAWIYABAAAsAwBCAAAYBkCEAAAwDIEIAAAgGUIQAAAAMsQgAAAAJYhAAEAACxDAAIAAFiGAAQAALAMAQgAAGAZAhAAAMAyBCAAAIBlCEAAAADLEIAAAACWIQABAAAsQwACAABYhgAEAACwDAEIAABgGQIQAADAMgQgAACAZQhAAAAAyxCAAAAAliEAAQAALEMAtiksLJTL5dKCBQviPRQAAICYIgAl7dq1SytWrNBll10W76EAAADEnPUBWF1drTlz5ujFF1/UgAED4j0cAACAmLM+AOfPn68ZM2Zo6tSpZ903HA4rFApF3QAAABJNSrwHEE9r1qzRnj17tHv37i7tX1hYqCVLlsR4VAAAALFl7QpgcXGxHnroIb388svyeDxdes6iRYsUDAadW3FxcYxHCQAA0P1cxhgT70HEw/r163XbbbcpOTnZ2dbc3CyXy6WkpCSFw+GoxzoTCoXk8/kUDAbl9XpjPWQAANANeP+2+BDwd7/7Xe3fvz9q27333qsxY8bo8ccfP2v8AQAAJCprAzArK0vjxo2L2paRkaFBgwZ12A4AANCXWHsOIAAAgK2sXQHszObNm+M9BAAAgJhjBRAAAMAyBCAAAIBlCEAAAADLEIAAAACWIQABAAAsQwACAABYhgAEAACwDAEIAABgGQIQAADAMgQgAACAZQhAAAAAyxCAAAAAliEAAQAALEMAAgAAWIYABAAAsAwBCAAAYBkCEAAAwDIEIAAAgGUIQAAAAMsQgAAAAJYhAAEAACxDAAIAAFiGAAQAALAMAQgAAGAZAhAAAMAyBCAAAIBlCEAAAADLEIAAAACWIQABAAAsQwACAABYhgAEAACwjLUB+MILL+iyyy6T1+uV1+vV5MmT9eabb8Z7WAAAADFnbQAOHTpUS5cu1e7du7V7925df/31mjVrlv7yl7/Ee2gAAAAx5TLGmHgPorcYOHCgfvnLX+pHP/pRl/YPhULy+XwKBoPyer0xHh0AAOgOvH9LKfEeQG/Q3Nys//zP/1RNTY0mT558xv3C4bDC4bBzPxQK9cTwAAAAupW1h4Alaf/+/crMzJTb7dZ9992ndevW6dJLLz3j/oWFhfL5fM4tEAj04GgBAAC6h9WHgBsaGnT48GFVVlbqv/7rv/Rv//Zv2rJlyxkjsLMVwEAgYPUSMgAAiYZDwJYH4OmmTp2qUaNG6Xe/+12X9ucvEAAAiYf3b8sPAZ/OGBO1wgcAANAXWXsRyD//8z9r+vTpCgQCqqqq0po1a7R582Zt2LAh3kMDAACIKWsD8Pjx45o7d65KSkrk8/l02WWXacOGDbrhhhviPTQAAICYsjYAV65cGe8hAAAAxAXnAAIAAFiGAAQAALAMAQgAAGAZAhAAAMAyBCAAAIBlCEAAAADLEIAAAACWIQABAAAsQwACAABYhgAEAACwDAEIAABgGQIQAADAMgQgAACAZQhAAAAAyxCAAAAAliEAAQAALEMAAgAAWIYABAAAsAwBCAAAYBkCEAAAwDIEIAAAgGUIQAAAAMsQgAAAAJYhAAEAACxDAAIAAFiGAAQAALAMAQgAAGAZAhAAAMAyBCAAAIBlCEAAAADLEIAAAACWsTYACwsLddVVVykrK0s5OTm69dZb9cknn8R7WAAAADFnbQBu2bJF8+fP13vvvaeNGzeqqalJ06ZNU01NTbyHBgAAEFMuY4yJ9yB6gxMnTignJ0dbtmzRt7/97S49JxQKyefzKRgMyuv1xniEAACgO/D+bfEK4OmCwaAkaeDAgXEeCQAAQGylxHsAvYExRgsXLtSUKVM0bty4M+4XDocVDoed+6FQqCeGBwAA0K1YAZR0//3368MPP9Srr776lfsVFhbK5/M5t0Ag0EMjBAAA6D7WnwP4wAMPaP369dq6datGjhz5lft2tgIYCASsPocAAIBEwzmAFh8CNsbogQce0Lp167R58+azxp8kud1uud3uHhgdAABA7FgbgPPnz9crr7yi//7v/1ZWVpZKS0slST6fT+np6XEeHQAAQOxYewjY5XJ1un3VqlW65557uvQaLCEDAJB4eP+2eAXQ0u4FAADgKmAAAADbEIAAAACWIQABAAAsQwACAABYhgAEAACwDAEIAABgGQIQAADAMgQgAACAZQhAAAAAyxCAAAAAliEAAQAALEMAAgAAWIYABAAAsAwBCAAAYBkCEAAAwDIEIAAAgGUIQAAAAMsQgAAAAJYhAAEAACxDAAIAAFiGAAQAALAMAQgAAGAZAhAAAMAyBCAAAIBlCEAAAADLEIAAAACWIQABAAAsQwACAABYhgAEAACwDAEIAABgGQIQAADAMlYH4NatW3XLLbfI7/fL5XJp/fr18R4SAABAzFkdgDU1NZowYYKWLVsW76EAAAD0mJR4DyCepk+frunTp8d7GAAAAD3K6hVAAAAAG1m9AniuwuGwwuGwcz8UCsVxNAAAAOeHFcBzUFhYKJ/P59wCgUC8hwQAAHDOCMBzsGjRIgWDQedWXFwc7yEBAACcMw4BnwO32y232x3vYQAAAFwQqwOwurpan376qXP/0KFDKioq0sCBAzVs2LA4jgwAACB2rA7A3bt367rrrnPuL1y4UJJUUFCg3//+93EaFQAAQGxZHYDf+c53ZIyJ9zAAAAB6FBeBAAAAWIYABAAAsAwBCAAAYBkCEAAAwDIEIAAAgGUIQAAAAMsQgAAAAJYhAAEAACxDAAIAAFiGAAQAALAMAQgAAGAZAhAAAMAyBCAAAIBlCEAAAADLEIAAAACWIQABAAAsQwACAABYhgAEAACwDAEIAABgGQIQAADAMgQgAACAZQhAAAAAyxCAAAAAliEAAQAALEMAAgAAWIYABAAAsAwBCAAAYBkCEAAAwDIEIAAAgGUIQAAAAMsQgAAAAJYhAAEAACxjfQD+9re/1ciRI+XxeHTFFVfo3XffjfeQAAAAYsrqAHzttde0YMECPfnkk9q3b5++9a1vafr06Tp8+HC8hwYAABAzLmOMifcg4mXixIn6xje+oRdeeMHZdskll+jWW29VYWHhWZ8fCoXk8/kUDAbl9XpjOVQAANBNeP+WUuI9gHhpaGjQnj179MQTT0RtnzZtmrZv397pc8LhsMLhsHM/GAxKav2LBAAAEkPkfdviNTB7A/DkyZNqbm7WkCFDorYPGTJEpaWlnT6nsLBQS5Ys6bA9EAjEZIwAACB2qqqq5PP54j2MuLA2ACNcLlfUfWNMh20RixYt0sKFC537LS0t+vLLLzVo0KAzPud8hUIhBQIBFRcX98nlaeaX+Pr6HJlf4uvrc2R+588Yo6qqKvn9/m593URibQBmZ2crOTm5w2pfWVlZh1XBCLfbLbfbHbWtf//+sRqiJMnr9fbJf7EjmF/i6+tzZH6Jr6/PkfmdH1tX/iKsvQo4LS1NV1xxhTZu3Bi1fePGjbr66qvjNCoAAIDYs3YFUJIWLlyouXPn6sorr9TkyZO1YsUKHT58WPfdd1+8hwYAABAzVgfgnXfeqfLycv3kJz9RSUmJxo0bpz/+8Y8aPnx4vIcmt9utp59+usMh576C+SW+vj5H5pf4+vocmR8uhNWfAwgAAGAja88BBAAAsBUBCAAAYBkCEAAAwDIEIAAAgGUIwB7S1NSkH//4xxo5cqTS09N10UUX6Sc/+YlaWlqcfYwxWrx4sfx+v9LT0/Wd73xHf/nLX6JeJxwO64EHHlB2drYyMjI0c+ZMHTlypKen06mqqiotWLBAw4cPV3p6uq6++mrt2rXLeTzR5rd161bdcsst8vv9crlcWr9+fdTj3TWfiooKzZ07Vz6fTz6fT3PnzlVlZWWMZ3f2+a1du1Y33nijsrOz5XK5VFRU1OE1evP8pK+eY2Njox5//HGNHz9eGRkZ8vv9+uEPf6hjx45FvUZvnuPZ/hkuXrxYY8aMUUZGhgYMGKCpU6dq586dUfv05vlJZ59je//4j/8ol8ul559/Pmp7b57j2eZ3zz33yOVyRd0mTZoUtU8iz0+SPv74Y82cOVM+n09ZWVmaNGmSDh8+7Dzem+eXyAjAHvLss89q+fLlWrZsmT7++GP94he/0C9/+Uv95je/cfb5xS9+oeeee07Lli3Trl27lJubqxtuuEFVVVXOPgsWLNC6deu0Zs0abdu2TdXV1br55pvV3Nwcj2lF+fu//3tt3LhRf/jDH7R//35NmzZNU6dO1dGjRyUl3vxqamo0YcIELVu2rNPHu2s+d999t4qKirRhwwZt2LBBRUVFmjt3btznV1NTo2uuuUZLly4942v05vlJXz3H2tpa7d27V0899ZT27t2rtWvX6sCBA5o5c2bUfr15jmf7Zzh69GgtW7ZM+/fv17Zt2zRixAhNmzZNJ06cSIj5SWefY8T69eu1c+fOTr/aqzfPsSvzu+mmm1RSUuLc/vjHP0Y9nsjz++yzzzRlyhSNGTNGmzdv1gcffKCnnnpKHo8nIeaX0Ax6xIwZM8y8efOitt1+++3mBz/4gTHGmJaWFpObm2uWLl3qPF5fX298Pp9Zvny5McaYyspKk5qaatasWePsc/ToUZOUlGQ2bNjQA7M4s9raWpOcnGxef/31qO0TJkwwTz75ZMLPT5JZt26dc7+75vN///d/RpJ57733nH127NhhJJm//vWvMZ7VKafPr71Dhw4ZSWbfvn1R2xNpfsZ89Rwj3n//fSPJfPHFF8aYxJpjV+YXDAaNJLNp0yZjTGLNz5gzz/HIkSMmPz/ffPTRR2b48OHm17/+tfNYIs2xs/kVFBSYWbNmnfE5iT6/O++803kf7EwizS/RsALYQ6ZMmaL//d//1YEDByRJH3zwgbZt26bvfe97kqRDhw6ptLRU06ZNc57jdrt17bXXavv27ZKkPXv2qLGxMWofv9+vcePGOfvES1NTk5qbm6P+q02S0tPTtW3btoSf3+m6az47duyQz+fTxIkTnX0mTZokn8/X6+Z8ur44v2AwKJfL5XzHd1+aY0NDg1asWCGfz6cJEyZI6hvza2lp0dy5c/Xoo49q7NixHR7vC3PcvHmzcnJyNHr0aP3DP/yDysrKnMcSeX4tLS164403NHr0aN14443KycnRxIkTow4TJ/L8ejsCsIc8/vjjmj17tsaMGaPU1FRdfvnlWrBggWbPni1JKi0tlSQNGTIk6nlDhgxxHistLVVaWpoGDBhwxn3iJSsrS5MnT9ZPf/pTHTt2TM3NzVq9erV27typkpKShJ/f6bprPqWlpcrJyenw+jk5Ob1uzqfra/Orr6/XE088obvvvtv54vm+MMfXX39dmZmZ8ng8+vWvf62NGzcqOztbUt+Y37PPPquUlBQ9+OCDnT6e6HOcPn26Xn75Zf3pT3/Sr371K+3atUvXX3+9wuGwpMSeX1lZmaqrq7V06VLddNNNevvtt3Xbbbfp9ttv15YtWyQl9vx6O6u/Cq4nvfbaa1q9erVeeeUVjR07VkVFRVqwYIH8fr8KCgqc/VwuV9TzjDEdtp2uK/v0hD/84Q+aN2+e8vPzlZycrG984xu6++67tXfvXmefRJ5fZ7pjPp3t35vnfDaJOL/Gxkbdddddamlp0W9/+9uz7p9Ic7zuuutUVFSkkydP6sUXX9T3v/997dy5s9M3zIhEmd+ePXv0r//6r9q7d+85jyVR5njnnXc6v48bN05XXnmlhg8frjfeeEO33377GZ+XCPOLXAQ5a9YsPfzww5Kkr3/969q+fbuWL1+ua6+99ozPTYT59XasAPaQRx99VE888YTuuusujR8/XnPnztXDDz+swsJCSVJubq4kdfivlbKyMmeVKTc3Vw0NDaqoqDjjPvE0atQobdmyRdXV1SouLtb777+vxsZGjRw5sk/Mr73umk9ubq6OHz/e4fVPnDjR6+Z8ur4yv8bGRn3/+9/XoUOHtHHjRmf1T+obc8zIyNDXvvY1TZo0SStXrlRKSopWrlwpKfHn9+6776qsrEzDhg1TSkqKUlJS9MUXX+iRRx7RiBEjJCX+HE+Xl5en4cOH6+DBg5ISe37Z2dlKSUnRpZdeGrX9kksuca4CTuT59XYEYA+pra1VUlL0/9zJycnOfwFFImnjxo3O4w0NDdqyZYuuvvpqSdIVV1yh1NTUqH1KSkr00UcfOfv0BhkZGcrLy1NFRYXeeustzZo1q0/NT+q+f16TJ09WMBjU+++/7+yzc+dOBYPBXjfn0/WF+UXi7+DBg9q0aZMGDRoU9XhfmOPpjDHO4cNEn9/cuXP14YcfqqioyLn5/X49+uijeuuttyQl/hxPV15eruLiYuXl5UlK7PmlpaXpqquu0ieffBK1/cCBAxo+fLikxJ5fr9ez15zYq6CgwOTn55vXX3/dHDp0yKxdu9ZkZ2ebxx57zNln6dKlxufzmbVr15r9+/eb2bNnm7y8PBMKhZx97rvvPjN06FCzadMms3fvXnP99debCRMmmKampnhMK8qGDRvMm2++aT7//HPz9ttvmwkTJphvfvObpqGhwRiTePOrqqoy+/btM/v27TOSzHPPPWf27dvnXCHaXfO56aabzGWXXWZ27NhhduzYYcaPH29uvvnmuM+vvLzc7Nu3z7zxxhtGklmzZo3Zt2+fKSkpSYj5nW2OjY2NZubMmWbo0KGmqKjIlJSUOLdwOJwQc/yq+VVXV5tFixaZHTt2mL/97W9mz5495kc/+pFxu93mo48+Soj5nW2OnTn9KmBjevccv2p+VVVV5pFHHjHbt283hw4dMu+8846ZPHmyyc/P7zP/P7N27VqTmppqVqxYYQ4ePGh+85vfmOTkZPPuu+8mxPwSGQHYQ0KhkHnooYfMsGHDjMfjMRdddJF58skno95oWlpazNNPP21yc3ON2+023/72t83+/fujXqeurs7cf//9ZuDAgSY9Pd3cfPPN5vDhwz09nU699tpr5qKLLjJpaWkmNzfXzJ8/31RWVjqPJ9r83nnnHSOpw62goMAY033zKS8vN3PmzDFZWVkmKyvLzJkzx1RUVMR9fqtWrer08aeffjoh5ne2OUY+3qaz2zvvvJMQc/yq+dXV1ZnbbrvN+P1+k5aWZvLy8szMmTPN+++/H/UavXl+Z5tjZzoLwN48x6+aX21trZk2bZoZPHiwSU1NNcOGDTMFBQUdxp6o84tYuXKl+drXvmY8Ho+ZMGGCWb9+fcLML5G5jDGmO1cUAQAA0LtxDiAAAIBlCEAAAADLEIAAAACWIQABAAAsQwACAABYhgAEAACwDAEIAABgGQIQAADAMgQgAACAZQhAAAAAyxCAAAAAliEAAQAALEMAAgAAWIYABAAAsAwBCAAAYBkCEAAAwDIEIAAAgGUIQAAAAMsQgAAAAJYhAAEAACxDAAIAAFiGAAQAALAMAQgAAGAZAhAAAMAyBCAAAIBlCEAAAADLEIAAAACWIQABAAAsQwACAABYhgAEAACwDAEIAABgmf8HK0LAje65O+YAAAAASUVORK5CYII=' 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, 1600, 100)\n",
|
|
"k = kCoef0 + kCoef1 * T\n",
|
|
"Cp = cpCoef0 + cpCoef1 * T\n",
|
|
"plt.plot(T, k / rho / Cp)\n",
|
|
"plt.ylim(ymin=0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9cc2c8cf",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Mean thermal diffusivity of refractory wall (800K~1600K)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "8c99e09d",
|
|
"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": "83e89a43",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Grid spacing"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "34cd3a29",
|
|
"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": "3cb9310c",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Time step constraint"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "287b9df7",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
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