1933 lines
260 KiB
Text
1933 lines
260 KiB
Text
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "9e47daa3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"import cantera as ct\n",
|
|
"import pde\n",
|
|
"\n",
|
|
"import ipywidgets as widgets\n",
|
|
"\n",
|
|
"from matplotlib import pyplot as plt\n",
|
|
"%matplotlib widget"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "74655ac0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"grid = pde.CartesianGrid([[0, 0.14]], 32, periodic=False)\n",
|
|
"field = pde.ScalarField(grid, 0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "d447beea",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"eq = pde.PDE({\"c\": \"0.0001 * laplace(c)\"}, bc=[{\"derivative\": 1}, {\"value\": 0}])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "9d3fc1f0",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/json": {
|
|
"ascii": false,
|
|
"bar_format": null,
|
|
"colour": null,
|
|
"elapsed": 0.00999903678894043,
|
|
"initial": 0,
|
|
"n": 0,
|
|
"ncols": null,
|
|
"nrows": 29,
|
|
"postfix": null,
|
|
"prefix": "",
|
|
"rate": null,
|
|
"total": 1,
|
|
"unit": "it",
|
|
"unit_divisor": 1000,
|
|
"unit_scale": false
|
|
},
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "aad8176cab8d48de944a688aab1ccffa",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
" 0%| | 0/1.0 [00:00<?, ?it/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"solution = eq.solve(field, t_range=1, dt=0.001)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "9564e6fc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/json": {
|
|
"ascii": false,
|
|
"bar_format": null,
|
|
"colour": null,
|
|
"elapsed": 0.012967586517333984,
|
|
"initial": 0,
|
|
"n": 0,
|
|
"ncols": null,
|
|
"nrows": 29,
|
|
"postfix": null,
|
|
"prefix": "",
|
|
"rate": null,
|
|
"total": 1,
|
|
"unit": "it",
|
|
"unit_divisor": 1000,
|
|
"unit_scale": false
|
|
},
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "3fba3902e57c48f781d7b7047b32e611",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
" 0%| | 0/1.0 [00:00<?, ?it/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "29c662f4acb64118b71b289175efb823",
|
|
"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"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[-9.13274059e-01 -7.43918268e-01 -5.86270837e-01 -4.46341021e-01\n",
|
|
" -3.27877044e-01 -2.32185354e-01 -1.58399513e-01 -1.04059738e-01\n",
|
|
" -6.58146580e-02 -4.00727502e-02 -2.34909844e-02 -1.32608365e-02\n",
|
|
" -7.21098208e-03 -3.77871294e-03 -1.90906437e-03 -9.30354685e-04\n",
|
|
" -4.37592261e-04 -1.98764517e-04 -8.72410176e-05 -3.70242282e-05\n",
|
|
" -1.52022957e-05 -6.04318828e-06 -2.32719325e-06 -8.68724285e-07\n",
|
|
" -3.14546983e-07 -1.10537627e-07 -3.77240727e-08 -1.25104480e-08\n",
|
|
" -4.03442906e-09 -1.26786676e-09 -3.95715740e-10 -1.48152483e-10]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"solution = eq.solve(solution, t_range=1, dt=0.001)\n",
|
|
"solution.plot()\n",
|
|
"print(solution.gradient(bc=[{\"derivative\": 1}, {\"value\": 0}]).data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3164bd16",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 연료 조성 반영 입구 조건 계산"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "1ebaaaef",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"gas = ct.Solution('gri30.xml')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "d03cafb5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"gas.TPX = 600 + 273.15, ct.one_atm, \"H2:6.42, O2:0.39, N2:47.28, CH4:1.79, CO:24.25, CO2:19.72, C2H4:0.13, C2H6:0.04\"\n",
|
|
"fuel_comp = gas.X"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "5bddc916",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" gri30:\n",
|
|
"\n",
|
|
" temperature 873.15 K\n",
|
|
" pressure 1.0133e+05 Pa\n",
|
|
" density 0.40894 kg/m^3\n",
|
|
" mean mol. weight 29.3 kg/kmol\n",
|
|
" phase of matter gas\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -2.9427e+06 -8.6222e+07 J\n",
|
|
" internal energy -3.1905e+06 -9.3481e+07 J\n",
|
|
" entropy 8194.9 2.4011e+05 J/K\n",
|
|
" Gibbs function -1.0098e+07 -2.9587e+08 J\n",
|
|
" heat capacity c_p 1254.1 36746 J/K\n",
|
|
" heat capacity c_v 970.34 28431 J/K\n",
|
|
"\n",
|
|
" mass frac. Y mole frac. X chem. pot. / RT\n",
|
|
" --------------- --------------- ---------------\n",
|
|
" H2 0.0044164 0.064187 -19.925\n",
|
|
" O2 0.0042583 0.0038992 -31.756\n",
|
|
" CH4 0.009799 0.017896 -38.919\n",
|
|
" CO 0.23178 0.24245 -41.902\n",
|
|
" CO2 0.29614 0.19716 -83.677\n",
|
|
" C2H4 0.0012445 0.0012997 -28.665\n",
|
|
" C2H6 0.00041043 0.00039992 -50.523\n",
|
|
" N2 0.45196 0.47271 -25.262\n",
|
|
" [ +45 minor] 0 0 \n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"gas()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "40d9a0f9",
|
|
"metadata": {},
|
|
"source": [
|
|
"연료 기체 구성비와 같은 원소 1몰의 완전 연소 산소 요구량"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "5cb2f86f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2.2584483103379323\n",
|
|
"0.9072176564687062\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"consider_o_in_fuel = True\n",
|
|
"\n",
|
|
"coef_O = gas.elemental_mole_fraction('C') * 2 + gas.elemental_mole_fraction('H') / 2 - (gas.elemental_mole_fraction('O') if consider_o_in_fuel else 0)\n",
|
|
"\n",
|
|
"coef_N = coef_O * 3.762\n",
|
|
"\n",
|
|
"coef_O + coef_N\n",
|
|
"\n",
|
|
"# 공기 1몰의 원자 몰수 = 2 ; O2 + N2 만으로 구성되었으면\n",
|
|
"\n",
|
|
"# 연료 기체 1몰의 원자 몰수\n",
|
|
"\n",
|
|
"mean_natoms = sum([(gas.X[i] * sum([v for v in gas.species(i).composition.values()])) for i in range(gas.n_species)])\n",
|
|
"print(mean_natoms)\n",
|
|
"\n",
|
|
"# 연료 기체 1몰에 필요한 공기 몰수\n",
|
|
"air_fuel_ratio_st = mean_natoms * (coef_N + coef_O) / 2\n",
|
|
"print (air_fuel_ratio_st)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "bf4af7ee",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"air = ct.Solution('gri30.xml')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "a5470a8e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" gri30:\n",
|
|
"\n",
|
|
" temperature 300 K\n",
|
|
" pressure 7080.3 Pa\n",
|
|
" density 0.081894 kg/m^3\n",
|
|
" mean mol. weight 28.851 kg/kmol\n",
|
|
" phase of matter gas\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy 1907.6 55036 J\n",
|
|
" internal energy -84549 -2.4393e+06 J\n",
|
|
" entropy 7658.6 2.2095e+05 J/K\n",
|
|
" Gibbs function -2.2957e+06 -6.6231e+07 J\n",
|
|
" heat capacity c_p 1010.1 29141 J/K\n",
|
|
" heat capacity c_v 721.88 20827 J/K\n",
|
|
"\n",
|
|
" mass frac. Y mole frac. X chem. pot. / RT\n",
|
|
" --------------- --------------- ---------------\n",
|
|
" O2 0.2329 0.21 -28.895\n",
|
|
" N2 0.7671 0.79 -25.93\n",
|
|
" [ +51 minor] 0 0 \n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"air.X = \"O2:1, N2:3.762\"\n",
|
|
"air_comp = air.X\n",
|
|
"air()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "424bc158",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1240.6028257966418\n",
|
|
"\n",
|
|
" gri30:\n",
|
|
"\n",
|
|
" temperature 1513.8 K\n",
|
|
" pressure 1.0133e+05 Pa\n",
|
|
" density 0.24948 kg/m^3\n",
|
|
" mean mol. weight 30.989 kg/kmol\n",
|
|
" phase of matter gas\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -1.4987e+06 -4.6443e+07 J\n",
|
|
" internal energy -1.9048e+06 -5.9029e+07 J\n",
|
|
" entropy 8413.6 2.6073e+05 J/K\n",
|
|
" Gibbs function -1.4235e+07 -4.4112e+08 J\n",
|
|
" heat capacity c_p 1300.2 40291 J/K\n",
|
|
" heat capacity c_v 1031.9 31977 J/K\n",
|
|
"\n",
|
|
" mass frac. Y mole frac. X chem. pot. / RT\n",
|
|
" --------------- --------------- ---------------\n",
|
|
" H2 3.0791e-08 4.7331e-07 -33.192\n",
|
|
" H 4.6416e-10 1.427e-08 -16.596\n",
|
|
" O 5.5069e-07 1.0667e-06 -15.427\n",
|
|
" O2 0.049365 0.047808 -30.854\n",
|
|
" OH 2.254e-05 4.1072e-05 -32.023\n",
|
|
" H2O 0.026501 0.045587 -48.619\n",
|
|
" HO2 5.0853e-08 4.7746e-08 -47.45\n",
|
|
" H2O2 2.4769e-09 2.2566e-09 -64.046\n",
|
|
" CO 5.0256e-06 5.5601e-06 -47.675\n",
|
|
" CO2 0.28765 0.20255 -63.102\n",
|
|
" N 3.2802e-14 7.2571e-14 -13.191\n",
|
|
" NH3 9.034e-15 1.6438e-14 -62.979\n",
|
|
" NNH 1.4737e-14 1.5736e-14 -42.978\n",
|
|
" NO 0.0005737 0.0005925 -28.618\n",
|
|
" NO2 2.1637e-06 1.4575e-06 -44.045\n",
|
|
" N2O 4.8881e-08 3.4416e-08 -41.809\n",
|
|
" HNO 8.6389e-11 8.632e-11 -45.214\n",
|
|
" N2 0.63588 0.70341 -26.382\n",
|
|
" [ +35 minor] 6.3022e-15 7.2656e-15 \n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"phi = 0.70\n",
|
|
"\n",
|
|
"gas.TPX = 25+273.15, ct.one_atm, phi * fuel_comp + air_comp\n",
|
|
"\n",
|
|
"gas.equilibrate('HP')\n",
|
|
"\n",
|
|
"print(gas.T - 273.15)\n",
|
|
"gas()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "4c9a7916",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"1.4285714285714286"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"1/0.7"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "e2c9d846",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fuel = ct.Solution('gri30.xml')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "2619aaed",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fuel.TPX = 25+273.15, 1e5, fuel_comp"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "5fa14d40",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" gri30:\n",
|
|
"\n",
|
|
" temperature 298.15 K\n",
|
|
" pressure 1e+05 Pa\n",
|
|
" density 1.182 kg/m^3\n",
|
|
" mean mol. weight 29.3 kg/kmol\n",
|
|
" phase of matter gas\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -3.6069e+06 -1.0568e+08 J\n",
|
|
" internal energy -3.6915e+06 -1.0816e+08 J\n",
|
|
" entropy 6976.6 2.0442e+05 J/K\n",
|
|
" Gibbs function -5.687e+06 -1.6663e+08 J\n",
|
|
" heat capacity c_p 1051.5 30810 J/K\n",
|
|
" heat capacity c_v 767.76 22495 J/K\n",
|
|
"\n",
|
|
" mass frac. Y mole frac. X chem. pot. / RT\n",
|
|
" --------------- --------------- ---------------\n",
|
|
" H2 0.0044164 0.064187 -18.476\n",
|
|
" O2 0.0042583 0.0038992 -30.234\n",
|
|
" CH4 0.009799 0.017896 -56.545\n",
|
|
" CO 0.23178 0.24245 -69.79\n",
|
|
" CO2 0.29614 0.19716 -186.09\n",
|
|
" C2H4 0.0012445 0.0012997 -11.859\n",
|
|
" C2H6 0.00041043 0.00039992 -69.231\n",
|
|
" N2 0.45196 0.47271 -23.795\n",
|
|
" [ +45 minor] 0 0 \n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"fuel()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "e2d4686c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"h2 = fuel.species('H2')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "ce558f2c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.013281769137764726"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"h2.thermo.h(25+273.15)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "fe14a891",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4b1f191f",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 이론공연비 계산"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "60f9c29b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def stoich_AF (fuel_compostion, mole_fraction=True):\n",
|
|
" \"Stoichiometric Air/Fuel ratio (volume)\"\n",
|
|
" \n",
|
|
" gas = ct.Solution('gri30.xml')\n",
|
|
"\n",
|
|
" if mole_fraction:\n",
|
|
" gas.TPX = 600 + 273.15, ct.one_atm, fuel_compostion\n",
|
|
" else:\n",
|
|
" gas.TPY = 600 + 273.15, ct.one_atm, fuel_compostion\n",
|
|
" \n",
|
|
" fuel_comp = gas.X\n",
|
|
"\n",
|
|
" # 연료의 원자 1 몰 완전 연소에 필요한 산소 원자 몰 수\n",
|
|
" coef_O = gas.elemental_mole_fraction('C') * 2 + gas.elemental_mole_fraction('H') / 2 - (gas.elemental_mole_fraction('O'))\n",
|
|
"\n",
|
|
" # 연료의 원자 1 몰 완전 연소에 필요한 공기 원자 몰 수 = 산소 원자 몰 수 X (1 + 3.762)\n",
|
|
" coef_N = coef_O * 3.762\n",
|
|
"\n",
|
|
" # 공기 1몰의 원자 몰수 = 2 ; O2 + N2 만으로 구성되었으면\n",
|
|
" mean_natoms_air = 2\n",
|
|
"\n",
|
|
" # 연료 기체 1몰의 원자 몰수 (평균 분자당 원자 수)\n",
|
|
" mean_natoms_fuel = sum([(gas.X[i] * sum([v for v in gas.species(i).composition.values()])) for i in range(gas.n_species)])\n",
|
|
"\n",
|
|
" # 연료 기체 1몰에 필요한 공기 몰수 ()\n",
|
|
" air_fuel_ratio_st = mean_natoms_fuel * (coef_N + coef_O) / mean_natoms_air\n",
|
|
"\n",
|
|
" return (air_fuel_ratio_st)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "c633e758",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"coke_oven_fuel = \"H2:6.42, O2:0.39, N2:47.28, CH4:1.79, CO:24.25, CO2:19.72, C2H4:0.13, C2H6:0.04\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "d489bdf1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"st_AF = stoich_AF(coke_oven_fuel)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "825758e1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"air = ct.Solution('gri30.xml')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "99dbfd30",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"air.X = \"O2:1, N2:3.762\"\n",
|
|
"air_comp = air.X"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"id": "7e1a5975",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1506.856765604754\n",
|
|
"\n",
|
|
" gri30:\n",
|
|
"\n",
|
|
" temperature 1780 K\n",
|
|
" pressure 1.0133e+05 Pa\n",
|
|
" density 0.21633 kg/m^3\n",
|
|
" mean mol. weight 31.597 kg/kmol\n",
|
|
" phase of matter gas\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -1.9051e+06 -6.0194e+07 J\n",
|
|
" internal energy -2.3734e+06 -7.4994e+07 J\n",
|
|
" entropy 8525 2.6936e+05 J/K\n",
|
|
" Gibbs function -1.708e+07 -5.3966e+08 J\n",
|
|
" heat capacity c_p 1351.2 42694 J/K\n",
|
|
" heat capacity c_v 1088.1 34380 J/K\n",
|
|
"\n",
|
|
" mass frac. Y mole frac. X chem. pot. / RT\n",
|
|
" --------------- --------------- ---------------\n",
|
|
" H2 6.2424e-06 9.7839e-05 -28.347\n",
|
|
" H 9.5406e-08 2.9906e-06 -14.173\n",
|
|
" O 1.3675e-06 2.7007e-06 -17.795\n",
|
|
" O2 0.00073214 0.00072297 -35.589\n",
|
|
" OH 6.1066e-05 0.00011345 -31.968\n",
|
|
" H2O 0.033613 0.058955 -46.141\n",
|
|
" HO2 1.1974e-08 1.1463e-08 -49.763\n",
|
|
" H2O2 1.4101e-09 1.3099e-09 -63.936\n",
|
|
" CO 0.0014344 0.0016181 -41.208\n",
|
|
" CO2 0.3634 0.26091 -59.003\n",
|
|
" HCO 2.8143e-12 3.0644e-12 -55.381\n",
|
|
" CH2O 6.3467e-14 6.6788e-14 -69.555\n",
|
|
" N 9.3579e-12 2.111e-11 -13.467\n",
|
|
" NH 1.4448e-12 3.0404e-12 -27.64\n",
|
|
" NH2 1.006e-12 1.9838e-12 -41.814\n",
|
|
" NH3 1.3308e-11 2.469e-11 -55.987\n",
|
|
" NNH 3.8031e-12 4.1406e-12 -41.107\n",
|
|
" NO 0.00020133 0.000212 -31.262\n",
|
|
" NO2 4.7618e-08 3.2705e-08 -49.056\n",
|
|
" N2O 1.5885e-08 1.1404e-08 -44.729\n",
|
|
" HNO 5.0425e-10 5.1373e-10 -45.435\n",
|
|
" HCN 4.3708e-14 5.1101e-14 -51.054\n",
|
|
" HNCO 1.5701e-11 1.153e-11 -68.848\n",
|
|
" NCO 1.2119e-13 9.1136e-14 -54.675\n",
|
|
" N2 0.60055 0.67737 -26.934\n",
|
|
" [ +28 minor] 1.1611e-14 8.5342e-15 \n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"phi = 1\n",
|
|
"\n",
|
|
"mix = ct.Solution('gri30.xml')\n",
|
|
"\n",
|
|
"mix.TPX = 25+273.15, ct.one_atm, phi * fuel_comp + st_AF * air_comp\n",
|
|
"\n",
|
|
"mix.equilibrate('HP')\n",
|
|
"\n",
|
|
"print(mix.T - 273.15)\n",
|
|
"mix()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "a20ce147",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def exhaust_stoichiometry (phi = 1, return_unburned=False):\n",
|
|
"\n",
|
|
" mix = ct.Solution('gri30.xml')\n",
|
|
"\n",
|
|
" mix.TPX = 25+273.15, ct.one_atm, phi * fuel_comp + st_AF * air_comp\n",
|
|
" \n",
|
|
" element_X = {ename: mix.elemental_mole_fraction(ename) for ename in mix.element_names}\n",
|
|
" \n",
|
|
" exhaust = ct.Solution('gri30.xml')\n",
|
|
" exhaust.X = {\n",
|
|
" \"CO2\" : element_X['C'],\n",
|
|
" \"H2O\" : element_X['H']/2,\n",
|
|
" \"O2\" : (element_X['O'] - 2*element_X['C'] - element_X['H']/2)/2,\n",
|
|
" \"N2\" : element_X['N']/2,\n",
|
|
" }\n",
|
|
" \n",
|
|
" if return_unburned:\n",
|
|
" return mix.mole_fraction_dict(threshold=-1), exhaust.mole_fraction_dict(threshold=-1)\n",
|
|
" else:\n",
|
|
" return exhaust.mole_fraction_dict(threshold=-1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"id": "c49236bf",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from scipy import optimize as opt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "4e5bd0e1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" converged: True\n",
|
|
" flag: 'converged'\n",
|
|
" function_calls: 8\n",
|
|
" iterations: 7\n",
|
|
" root: 0.6547375739201831\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"f_found = opt.root_scalar(lambda x: exhaust_stoichiometry(x)[\"O2\"] - 0.045, \n",
|
|
" bracket=[1e-300, 1])\n",
|
|
"\n",
|
|
"print(f_found)\n",
|
|
"\n",
|
|
"phi_O2_045 = f_found.root"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "c3d58dd5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def exhaust_equilibrium (phi = 1):\n",
|
|
"\n",
|
|
" mix = ct.Solution('gri30.xml')\n",
|
|
"\n",
|
|
" mix.TPX = 25+273.15, ct.one_atm, phi * fuel_comp + st_AF * air_comp\n",
|
|
" \n",
|
|
" mix.equilibrate(\"HP\")\n",
|
|
" \n",
|
|
" return mix.mole_fraction_dict(threshold=-1)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "dc4ca92f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
" converged: True\n",
|
|
" flag: 'converged'\n",
|
|
" function_calls: 8\n",
|
|
" iterations: 7\n",
|
|
" root: 0.6526852820518209"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"f_found = opt.root_scalar(lambda x: exhaust_equilibrium(x)[\"O2\"] - 0.045, \n",
|
|
" bracket=[0.5, 1])\n",
|
|
"\n",
|
|
"f_found"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"id": "f9c07f28",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"1.3856202738392207"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"1 / 0.7216984471721393"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"id": "447502a7",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"1.4285714285714286"
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"1/0.7"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"id": "87a2118d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fuel = ct.Solution('gri30.xml')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"id": "9a5b2b68",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fuel.TPX = 25+273.15, 1e5, fuel_comp"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"id": "14224e71",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" gri30:\n",
|
|
"\n",
|
|
" temperature 298.15 K\n",
|
|
" pressure 1e+05 Pa\n",
|
|
" density 1.182 kg/m^3\n",
|
|
" mean mol. weight 29.3 kg/kmol\n",
|
|
" phase of matter gas\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -3.6069e+06 -1.0568e+08 J\n",
|
|
" internal energy -3.6915e+06 -1.0816e+08 J\n",
|
|
" entropy 6976.6 2.0442e+05 J/K\n",
|
|
" Gibbs function -5.687e+06 -1.6663e+08 J\n",
|
|
" heat capacity c_p 1051.5 30810 J/K\n",
|
|
" heat capacity c_v 767.76 22495 J/K\n",
|
|
"\n",
|
|
" mass frac. Y mole frac. X chem. pot. / RT\n",
|
|
" --------------- --------------- ---------------\n",
|
|
" H2 0.0044164 0.064187 -18.476\n",
|
|
" O2 0.0042583 0.0038992 -30.234\n",
|
|
" CH4 0.009799 0.017896 -56.545\n",
|
|
" CO 0.23178 0.24245 -69.79\n",
|
|
" CO2 0.29614 0.19716 -186.09\n",
|
|
" C2H4 0.0012445 0.0012997 -11.859\n",
|
|
" C2H6 0.00041043 0.00039992 -69.231\n",
|
|
" N2 0.45196 0.47271 -23.795\n",
|
|
" [ +45 minor] 0 0 \n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"fuel()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"id": "3e637e4c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"h2 = fuel.species('H2')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"id": "49a5dca8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.013281769137764726"
|
|
]
|
|
},
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"h2.thermo.h(25+273.15)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0b31ec1b",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 배가스 산소 농도 4.5% 조건"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e22e5512",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 미연혼합가스 / 기연가스 조성"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"id": "2f732468",
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"Xu, Xb = exhaust_stoichiometry(phi=phi_O2_045, return_unburned=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f7c5063d",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 미연혼합가스(연료+공기)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"id": "53b2c1b9",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" gri30:\n",
|
|
"\n",
|
|
" temperature 298.15 K\n",
|
|
" pressure 1.0133e+05 Pa\n",
|
|
" density 1.1869 kg/m^3\n",
|
|
" mean mol. weight 29.039 kg/kmol\n",
|
|
" phase of matter gas\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -1.5255e+06 -4.4299e+07 J\n",
|
|
" internal energy -1.6109e+06 -4.6778e+07 J\n",
|
|
" entropy 6999.5 2.0326e+05 J/K\n",
|
|
" Gibbs function -3.6124e+06 -1.049e+08 J\n",
|
|
" heat capacity c_p 1027.5 29837 J/K\n",
|
|
" heat capacity c_v 741.17 21523 J/K\n",
|
|
"\n",
|
|
" mass frac. Y mole frac. X chem. pot. / RT\n",
|
|
" --------------- --------------- ---------------\n",
|
|
" H2 0.0018679 0.026906 -19.333\n",
|
|
" O2 0.1362 0.1236 -26.764\n",
|
|
" CH4 0.0041445 0.0075018 -57.401\n",
|
|
" CO 0.098029 0.10163 -70.646\n",
|
|
" CO2 0.12525 0.082645 -186.95\n",
|
|
" C2H4 0.00052634 0.00054482 -12.715\n",
|
|
" C2H6 0.00017359 0.00016764 -70.088\n",
|
|
" N2 0.63381 0.657 -23.453\n",
|
|
" [ +45 minor] 0 0 \n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"gas_u = ct.Solution('gri30.xml')\n",
|
|
"gas_u.TPX = 25 + 273.15, ct.one_atm, Xu\n",
|
|
"gas_u()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1d54eebc",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 완전 연소 기연가스"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"id": "a1bc3d15",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" gri30:\n",
|
|
"\n",
|
|
" temperature 298.15 K\n",
|
|
" pressure 1.0133e+05 Pa\n",
|
|
" density 1.2684 kg/m^3\n",
|
|
" mean mol. weight 31.031 kg/kmol\n",
|
|
" phase of matter gas\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -2.9803e+06 -9.2481e+07 J\n",
|
|
" internal energy -3.0602e+06 -9.496e+07 J\n",
|
|
" entropy 6565.1 2.0372e+05 J/K\n",
|
|
" Gibbs function -4.9377e+06 -1.5322e+08 J\n",
|
|
" heat capacity c_p 997.71 30960 J/K\n",
|
|
" heat capacity c_v 729.77 22645 J/K\n",
|
|
"\n",
|
|
" mass frac. Y mole frac. X chem. pot. / RT\n",
|
|
" --------------- --------------- ---------------\n",
|
|
" O2 0.046403 0.045 -27.775\n",
|
|
" H2O 0.026987 0.046486 -123.33\n",
|
|
" CO2 0.2928 0.20645 -186.03\n",
|
|
" N2 0.63381 0.70206 -23.387\n",
|
|
" [ +49 minor] 0 0 \n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"gas_b = ct.Solution('gri30.xml')\n",
|
|
"gas_b.TPX = 25 + 273.15, ct.one_atm, Xb\n",
|
|
"gas_b()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3e537f99",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 미연혼합가스의 저위발열량 (J/kg)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"id": "067ae187",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'1.454825772118082 MJ/kg'"
|
|
]
|
|
},
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"heating_value_J_kg = gas_u.enthalpy_mass - gas_b.enthalpy_mass\n",
|
|
"f'{heating_value_J_kg*1e-6} MJ/kg'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"id": "f8713062",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'80 GJ / rev to a battery'"
|
|
]
|
|
},
|
|
"execution_count": 41,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"heat_to_battery = 80 # GJ / Rev\n",
|
|
"f'{heat_to_battery} GJ / rev to a battery'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"id": "42f68b81",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'3636.3636363636365 MJ / hr to a combustion chamber'"
|
|
]
|
|
},
|
|
"execution_count": 42,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"heat_to_chamber = heat_to_battery * 1000 * 3 / 66 # MJ / hr\n",
|
|
"f'{heat_to_chamber} MJ / hr to a combustion chamber'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "30be4bfc",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 연소실당 질량 유량 kg/hr"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"id": "fd85f6dc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'2499.5182970050437 (kg/hr) = 3636.3636363636365 (MJ/hr) / 1.454825772118082 (MJ/kg)'"
|
|
]
|
|
},
|
|
"execution_count": 43,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"mfr_to_chamber = heat_to_chamber * 1e6 / heating_value_J_kg # kg / hr\n",
|
|
"f'{mfr_to_chamber} (kg/hr) = {heat_to_chamber} (MJ/hr) / {heating_value_J_kg * 1e-6} (MJ/kg)'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a52e7ccf",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 연소실 평형 온도"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "363f68e8",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 단열 화염 온도 (역반응 무시)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"id": "debc8fe3",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" gri30:\n",
|
|
"\n",
|
|
" temperature 2010 K\n",
|
|
" pressure 1.0133e+05 Pa\n",
|
|
" density 0.18814 kg/m^3\n",
|
|
" mean mol. weight 31.031 kg/kmol\n",
|
|
" phase of matter gas\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -8.9328e+05 -2.7719e+07 J\n",
|
|
" internal energy -1.4318e+06 -4.4431e+07 J\n",
|
|
" entropy 8781.6 2.725e+05 J/K\n",
|
|
" Gibbs function -1.8544e+07 -5.7543e+08 J\n",
|
|
" heat capacity c_p 1348.9 41856 J/K\n",
|
|
" heat capacity c_v 1080.9 33542 J/K\n",
|
|
"\n",
|
|
" mass frac. Y mole frac. X chem. pot. / RT\n",
|
|
" --------------- --------------- ---------------\n",
|
|
" O2 0.046403 0.045 -31.884\n",
|
|
" H2O 0.026987 0.046486 -45.031\n",
|
|
" CO2 0.2928 0.20645 -56.851\n",
|
|
" N2 0.63381 0.70206 -27.302\n",
|
|
" [ +49 minor] 0 0 \n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"testgas = ct.Solution('gri30.xml')\n",
|
|
"testgas.TPX = 600+273.15, ct.one_atm, Xu\n",
|
|
"testgas.HPX = None, None, Xb\n",
|
|
"testgas()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ed0ec84b",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 단열 화염 온도 (평형 계산)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"id": "fa7ef7a2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" gri30:\n",
|
|
"\n",
|
|
" temperature 1990.3 K\n",
|
|
" pressure 1.0133e+05 Pa\n",
|
|
" density 0.18983 kg/m^3\n",
|
|
" mean mol. weight 31.002 kg/kmol\n",
|
|
" phase of matter gas\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -8.9328e+05 -2.7694e+07 J\n",
|
|
" internal energy -1.427e+06 -4.4241e+07 J\n",
|
|
" entropy 8783.1 2.7229e+05 J/K\n",
|
|
" Gibbs function -1.8374e+07 -5.6963e+08 J\n",
|
|
" heat capacity c_p 1347.2 41767 J/K\n",
|
|
" heat capacity c_v 1079 33452 J/K\n",
|
|
"\n",
|
|
" mass frac. Y mole frac. X chem. pot. / RT\n",
|
|
" --------------- --------------- ---------------\n",
|
|
" O 6.69e-05 0.00012964 -15.939\n",
|
|
" O2 0.045092 0.043688 -31.879\n",
|
|
" OH 0.00048954 0.00089239 -30.541\n",
|
|
" H2O 0.02669 0.045932 -45.143\n",
|
|
" CO 0.001079 0.0011942 -41.097\n",
|
|
" CO2 0.29111 0.20507 -57.036\n",
|
|
" NO 0.003114 0.0032174 -29.575\n",
|
|
" N2 0.63235 0.6998 -27.272\n",
|
|
" [ +45 minor] 8.9232e-06 7.3917e-05 \n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"gas_chamber = ct.Solution('gri30.xml')\n",
|
|
"gas_chamber.TPX = 600 + 273.15, ct.one_atm, Xu\n",
|
|
"gas_chamber.equilibrate(\"HP\")\n",
|
|
"gas_chamber(threshold=1e-4)\n",
|
|
"\n",
|
|
"eq_state = gas_chamber.TPX\n",
|
|
"\n",
|
|
"Tad, P0, X0 = eq_state"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"id": "1d6c95a7",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Tad = 1717.1055126606984 `C\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(f'Tad = {eq_state[0] - 273.15} `C')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"id": "e437f3b4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"NO: 3217.3809731942233 ppm\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(f'NO: {gas_chamber.mole_fraction_dict()[\"NO\"] * 1e6} ppm')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"id": "683e1479",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"564968.6851296029 1057170.3471485006\n",
|
|
"258568.46996560355\n",
|
|
"239.0022179980857 1363443.6972422504\n",
|
|
"-612434.5630397515\n",
|
|
"400326.765370211 1148091.5363109885\n",
|
|
"3005.361043723591\n",
|
|
"398382.53608438675 1149155.5265008416\n",
|
|
"-2.858431953645777\n",
|
|
"398384.3836843984 1149154.51548965\n",
|
|
"0.000179249735083431\n",
|
|
"398384.3835685452 1149154.5155530453\n",
|
|
"8.731149137020111e-10\n",
|
|
"398384.38356854365 1149154.515553046\n",
|
|
"-1.6298145055770874e-09\n",
|
|
"496.511875 converged: True\n",
|
|
" flag: 'converged'\n",
|
|
" function_calls: 7\n",
|
|
" iterations: 6\n",
|
|
" root: 1558.4107622013973\n",
|
|
"Outlet Gas Temperature: 1285.2607622013975 `C\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"htc = 568 # W / m2 / K\n",
|
|
"\n",
|
|
"Twall0 = 1100 + 273.15\n",
|
|
"Twall1 = 1100 + 273.15\n",
|
|
"\n",
|
|
"area_chamber_wall = 6.7 * 16.7\n",
|
|
"\n",
|
|
"htcA = htc * area_chamber_wall / 2 / 2 / 2 / 2 / 2 / 2 / 2# W / K\n",
|
|
"\n",
|
|
"def chamber_energy_balance (Tout, hA, Twall0, Twall1):\n",
|
|
" \n",
|
|
" gas_chamber.TPX = eq_state\n",
|
|
" h0 = gas_chamber.enthalpy_mass\n",
|
|
" \n",
|
|
" gas_chamber.TP = Tout, gas_chamber.P\n",
|
|
" h1 = gas_chamber.enthalpy_mass\n",
|
|
" \n",
|
|
" '''print(h0 - h1, \n",
|
|
" mfr_to_chamber/3600 * (h0 - h1) - htc * area_chamber_wall * (((eq_state[0] - Tout)/2 - Twall0) + ((eq_state[0] - Tout)/2 - Twall1))\n",
|
|
")'''\n",
|
|
" \n",
|
|
" print (mfr_to_chamber/3600 * (h0 - h1), \n",
|
|
" - hA * (((eq_state[0] - Tout)/2 - Twall0) + ((eq_state[0] - Tout)/2 - Twall1)))\n",
|
|
" \n",
|
|
" print (mfr_to_chamber/3600 * (h0 - h1) \n",
|
|
" - hA * (((eq_state[0] + Tout)/2 - Twall0) + ((eq_state[0] + Tout)/2 - Twall1)))\n",
|
|
"\n",
|
|
" return (mfr_to_chamber/3600 * (h0 - h1) \n",
|
|
" - hA * (((eq_state[0] + Tout)/2 - Twall0) + ((eq_state[0] + Tout)/2 - Twall1)))\n",
|
|
"\n",
|
|
"f_found = opt.root_scalar(lambda x: chamber_energy_balance(x, htcA, Twall0, Twall1),\n",
|
|
" bracket=[min(Twall0, Twall1), 1990])\n",
|
|
"\n",
|
|
"print(htcA, f_found)\n",
|
|
"\n",
|
|
"\n",
|
|
"print(f'Outlet Gas Temperature: {f_found.root - 273.15} `C')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"id": "badfe727",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class CombustionChamber:\n",
|
|
" def __init__ (self, mdot, unburned_state, hA=600):\n",
|
|
" T0, P0, X0 = unburned_state\n",
|
|
" self.mdot = mdot # kg/s\n",
|
|
" self.T_preheat = T0 # K, preheat temperature\n",
|
|
" self.P0 = P0 # Pa, pressure\n",
|
|
" self.X0 = X0 # Composition in mole fractions, Fuel + Air\n",
|
|
" self.gas = ct.Solution(\"gri30.xml\") # gas object\n",
|
|
" self.gas.TPX = T0, P0, X0\n",
|
|
" self.gas.equilibrate('HP')\n",
|
|
" self.eq_state = self.gas.TPX\n",
|
|
" self.h0 = self.gas.enthalpy_mass # inlet enthalpy\n",
|
|
" self.T0 = self.gas.T # K, adiabatic flame temperature\n",
|
|
" self.hA = hA # HTC x Area\n",
|
|
" self.Twall0 = 1100 + 273.15\n",
|
|
" self.Twall1 = 1100 + 273.15\n",
|
|
" self.Area = 6.7 * 16.7\n",
|
|
"\n",
|
|
" def update_mdot (self, mdot_new):\n",
|
|
" if mdot_new : self.mdot = mdot_new\n",
|
|
"\n",
|
|
" def update_Twall (self, Twall0=None, Twall1=None):\n",
|
|
" if Twall0: self.Twall0 = Twall0\n",
|
|
" if Twall1: self.Twall1 = Twall1\n",
|
|
"\n",
|
|
" def energy_balance_equation (self, Tout):\n",
|
|
" \n",
|
|
" self.gas.TP = Tout, None\n",
|
|
" h1 = self.gas.enthalpy_mass\n",
|
|
" q1, q2 = self.heat(Tout)\n",
|
|
"\n",
|
|
" return (self.mdot * (self.h0 - h1) - q1 - q2)\n",
|
|
"\n",
|
|
" def solve (self, ):\n",
|
|
" \"\"\" Iteratively solve for outlet temperature that balance with heat loss to walls \"\"\"\n",
|
|
" f_found = opt.root_scalar(self.energy_balance_equation, \n",
|
|
" bracket=[max(self.Twall0, self.Twall1), self.T0])\n",
|
|
" self.T1 = f_found.root\n",
|
|
"\n",
|
|
" return f_found.root\n",
|
|
"\n",
|
|
" def heat (self, Tout=None):\n",
|
|
" if Tout is None: Tout = self.T1\n",
|
|
" \n",
|
|
" Tgas = (self.T0 + Tout) / 2\n",
|
|
" \n",
|
|
" return self.hA * (Tgas - self.Twall0), self.hA * (Tgas - self.Twall1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 50,
|
|
"id": "8ce5b3bc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.6943106380569566 1273.15 1273.15\n",
|
|
"Outlet Gas Temperature 1095.5947728980304 `C\n",
|
|
"Heat to Combustion Chamber Walls (284445.0999455549, 284445.0999455549) J/s\n",
|
|
"45.0561038313759 GJ/rev from chambers to walls\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"chmbr = CombustionChamber(mfr_to_chamber/3600, (600+273.15, ct.one_atm, Xu), hA=700)\n",
|
|
"chmbr.update_mdot(None)\n",
|
|
"chmbr.update_Twall(1273.15, 1273.15)\n",
|
|
"print (chmbr.mdot, chmbr.Twall0, chmbr.Twall1)\n",
|
|
"chmbr.solve()\n",
|
|
"print(f'Outlet Gas Temperature {chmbr.T1 - 273.15} `C')\n",
|
|
"print(f'Heat to Combustion Chamber Walls {chmbr.heat()} J/s')\n",
|
|
"print(60 * 20 * 66 * 2 * chmbr.heat()[0] / 1e9, \"GJ/rev from chambers to walls\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9a42fe1c",
|
|
"metadata": {},
|
|
"source": [
|
|
"chamber = CombustionChamber()\n",
|
|
"wall0 = Brick()\n",
|
|
"wall1 = Brick()\n",
|
|
"\"Iterate\"\n",
|
|
"\"Integrate for 1 timestep\"\n",
|
|
"chamber.solve()\n",
|
|
"\n",
|
|
"wall0.heat_to_coke()\n",
|
|
"wall1.heat_to_coke()\n",
|
|
"\n",
|
|
"wall0.update_coke_side() coke_model.brick_temperature(cum_heat)\n",
|
|
"wall1.update_coke_side() coke_model.brick_temperature(cum_heat)\n",
|
|
" \n",
|
|
"wall0.solve() # advance 1 timestep\n",
|
|
"wall1.solve() # advance 1 timestep\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c20ac3d8",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Heat diffusion equation with Coke Oven Brick Properties"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "fcd17e59",
|
|
"metadata": {},
|
|
"source": [
|
|
"## For constant $k$, $C_p$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e87a0bbf",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"\\partial_t T = \\alpha \\Delta T\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "116d5038",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"\\partial_t T = \\frac{k}{\\rho C_p} \\Delta T\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dc87d038",
|
|
"metadata": {},
|
|
"source": [
|
|
"## For varying $k$, $C_p$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d8d70c8f",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"\\partial_t T = \\frac{1}{\\rho C_p} \\nabla \\cdot \\left( k \\nabla T \\right)\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f9ed2160",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"\\partial_t T = \\frac{1}{\\rho C_p} \\left(\\nabla k \\cdot \\nabla T + k \\nabla^2 T \\right)\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1e703f35",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"\\partial_t T = \\frac{1}{\\rho C_p} \\left( c_1 \\nabla T \\cdot \\nabla T + k \\nabla^2 T \\right)\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "511804d4",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"k = c_1 T + c_0\n",
|
|
"$$\n",
|
|
"\n",
|
|
"$$\n",
|
|
"\\nabla k = c_1 \\nabla T\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ab5e4123",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"C_p = d_1 T + d_0\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 58,
|
|
"id": "5a43dfeb",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class CokeOvenBrickHeatEqn(pde.PDEBase):\n",
|
|
" \"\"\"Implementation of the Heat equation\"\"\"\n",
|
|
"\n",
|
|
" def __init__ (self, bc=\"auto_periodic_neumann\"):\n",
|
|
" self.bc = bc\n",
|
|
" self.rho = 1900 # kg / m3\n",
|
|
" self.kCoef0 = 0.93 # W / m / K\n",
|
|
" self.kCoef1 = 0.698e-3 # W / m / K2\n",
|
|
" self.cpCoef0 = 837.2 # J / kg / K\n",
|
|
" self.cpCoef1 = 251.2e-3 # J / kg / K2\n",
|
|
"\n",
|
|
" def k (self, T):\n",
|
|
" return T * self.kCoef1 + self.kCoef0\n",
|
|
" \n",
|
|
" def cp (self, T):\n",
|
|
" return T * self.cpCoef1 + self.cpCoef0\n",
|
|
" \n",
|
|
" def update_bc (self, gradT_chamber=None, T_oven=None):\n",
|
|
" bc0, bc1 = self.bc\n",
|
|
" if gradT_chamber:\n",
|
|
" self.bc[0] = {\"derivative\": gradT_chamber} \n",
|
|
" if T_oven:\n",
|
|
" self.bc[1] = {\"value\": T_oven}\n",
|
|
"\n",
|
|
" def evolution_rate(self, state, t=0):\n",
|
|
" \"\"\"implement the python version of the evolution equation\"\"\"\n",
|
|
" state_lap = state.laplace(bc=self.bc)\n",
|
|
" state_grad = state.gradient(bc=self.bc)\n",
|
|
" \n",
|
|
" k = self.kCoef1 * state + self.kCoef0\n",
|
|
" cp = self.cpCoef1 * state + self.cpCoef0\n",
|
|
" \n",
|
|
" k_grad = self.kCoef1 * state_grad\n",
|
|
" \n",
|
|
" return (state_grad.dot(k_grad) + k * state_lap) / self.rho / cp\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cd2e51d5",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Test run parameters"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 59,
|
|
"id": "8f2a53b4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"T0 = 800 + 273.15 # Initial Brick Temperature\n",
|
|
"T_preheat = 600 + 273.15 # Unburned Gas Preheat Temperature\n",
|
|
"simulation_time = 72 * 60 * 60. # 24 hours in seconds\n",
|
|
"dt = 1. # timestep 1 second\n",
|
|
"bc_update_period = 60. # update bc every 60 seconds\n",
|
|
"bc_update_stride = int(bc_update_period / dt) # For convenience\n",
|
|
"brick_thickness = 0.14 # m, \n",
|
|
"n_grid_brick = 32 # Number of Grid points inside\n",
|
|
"\n",
|
|
"grid = pde.CartesianGrid([[0, brick_thickness]], n_grid_brick, periodic=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 60,
|
|
"id": "f2d5e137",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Twall_table = np.loadtxt('./CokeOvenWallTemperature.csv', delimiter=',').T"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 61,
|
|
"id": "b2f3006a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Twall_model = lambda x: np.interp(x, Twall_table[0] * 60 * 60 * (66/80) / (100/80), Twall_table[1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "70971eb5",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"q = -k\\nabla T\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 62,
|
|
"id": "a6240b56",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.6943106380569566 1073.15 1073.15\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Initial Condition\n",
|
|
"# Brick Temperature\n",
|
|
"field = pde.ScalarField(grid, T0)\n",
|
|
"\n",
|
|
"# BC-0: Heat Flux from chamber to wall\n",
|
|
"chmbr = CombustionChamber(mfr_to_chamber/3600, (T_preheat, ct.one_atm, Xu), hA=700)\n",
|
|
"chmbr.update_mdot(None)\n",
|
|
"chmbr.update_Twall(T0, T0)\n",
|
|
"print (chmbr.mdot, chmbr.Twall0, chmbr.Twall1)\n",
|
|
"chmbr.solve()\n",
|
|
"q1, q2 = chmbr.heat()\n",
|
|
"\n",
|
|
"\n",
|
|
"# BC-1: Oven side brick temperature\n",
|
|
"Toven0 = Twall_model(0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 63,
|
|
"id": "454af53c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"eq = CokeOvenBrickHeatEqn(bc=[{\"derivative\": 100}, {\"value\": T0}])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 64,
|
|
"id": "d28a0363",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"eq.update_bc(gradT_chamber=q1/eq.k(T0), T_oven=Toven0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 65,
|
|
"id": "00b419af",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"times = np.arange(0, simulation_time, dt)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 66,
|
|
"id": "7ad5a41a",
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"solutions = [field]\n",
|
|
"for i, t in enumerate(times[::bc_update_stride]):\n",
|
|
" Twall0 = solutions[-1].get_boundary_values(axis=0, upper=False, bc=eq.bc)\n",
|
|
" # chabmer.solve(sol) \n",
|
|
" eq.update_bc(T_oven=T0 + t/2000.)\n",
|
|
" solution = eq.solve(solutions[-1], t_range=bc_update_stride*dt, dt=dt, tracker='consistency')\n",
|
|
" solutions.append(solution)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 67,
|
|
"id": "ecd3d85f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "cf1e92571ba5457ab22cb045cf72bb08",
|
|
"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",
|
|
"for sln in solutions[::100]:\n",
|
|
" plt.plot(grid.cell_coords, sln.data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b68e032a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sln.get_boundary_values(axis=0, upper=False, bc=eq.bc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "cee34fb8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sln.data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "868e60e7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sln.get_boundary_values(axis=0, upper=True, bc=eq.bc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "aa06579f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sln.data.size"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "36ddf849",
|
|
"metadata": {},
|
|
"source": [
|
|
"solution = eq.solve(solution, t_range=10000, dt=0.01)\n",
|
|
"solution.plot()\n",
|
|
"plt.ylim(1000, 1100)\n",
|
|
"print(solution.gradient(bc=[{\"derivative\": 1}, {\"value\": 0}]).data)"
|
|
]
|
|
}
|
|
],
|
|
"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": {
|
|
"height": "calc(100% - 180px)",
|
|
"left": "10px",
|
|
"top": "150px",
|
|
"width": "384px"
|
|
},
|
|
"toc_section_display": true,
|
|
"toc_window_display": true
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|