2341 lines
255 KiB
Text
2341 lines
255 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": "9e47daa3",
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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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"import pde\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\n",
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"\n",
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"# 사용자 운영체제 확인\n",
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"import os\n",
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"os.name\n",
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"# 운영체제별 한글 폰트 설정\n",
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"if os.name == 'posix': # Mac 환경 폰트 설정\n",
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" plt.rc('font', family='AppleGothic')\n",
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"elif os.name == 'nt': # Windows 환경 폰트 설정\n",
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" plt.rc('font', family='Malgun Gothic')\n",
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"\n",
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"plt.rc('axes', unicode_minus=False) # 마이너스 폰트 설정\n",
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"\n",
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"# 글씨 선명하게 출력하는 설정\n",
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"%config InlineBackend.figure_format = 'retina'"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3164bd16",
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"metadata": {},
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"source": [
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"# 연료 조성 반영 입구 조건 계산"
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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": "1ebaaaef",
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"metadata": {},
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"outputs": [],
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"source": [
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"gas = ct.Solution('gri30.xml')"
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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": 3,
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"id": "d03cafb5",
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"metadata": {},
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"outputs": [],
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"source": [
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"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",
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"fuel_comp = gas.X"
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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": 4,
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"id": "5bddc916",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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" gri30:\n",
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"\n",
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" temperature 873.15 K\n",
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" pressure 1.0133e+05 Pa\n",
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" density 0.40894 kg/m^3\n",
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" mean mol. weight 29.3 kg/kmol\n",
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" phase of matter gas\n",
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"\n",
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" 1 kg 1 kmol \n",
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" --------------- ---------------\n",
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" enthalpy -2.9427e+06 -8.6222e+07 J\n",
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" internal energy -3.1905e+06 -9.3481e+07 J\n",
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" entropy 8194.9 2.4011e+05 J/K\n",
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" Gibbs function -1.0098e+07 -2.9587e+08 J\n",
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" heat capacity c_p 1254.1 36746 J/K\n",
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" heat capacity c_v 970.34 28431 J/K\n",
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"\n",
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" mass frac. Y mole frac. X chem. pot. / RT\n",
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" --------------- --------------- ---------------\n",
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" H2 0.0044164 0.064187 -19.925\n",
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" O2 0.0042583 0.0038992 -31.756\n",
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" CH4 0.009799 0.017896 -38.919\n",
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" CO 0.23178 0.24245 -41.902\n",
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" CO2 0.29614 0.19716 -83.677\n",
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" C2H4 0.0012445 0.0012997 -28.665\n",
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" C2H6 0.00041043 0.00039992 -50.523\n",
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" N2 0.45196 0.47271 -25.262\n",
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" [ +45 minor] 0 0 \n",
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"\n"
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]
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}
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],
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"source": [
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"gas()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "40d9a0f9",
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"metadata": {},
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"source": [
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"연료 기체 구성비와 같은 원소 1몰의 완전 연소 산소 요구량"
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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": "5cb2f86f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2.2584483103379323\n",
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"0.9072176564687062\n"
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]
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}
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],
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"source": [
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"consider_o_in_fuel = True\n",
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"\n",
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"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",
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"\n",
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"coef_N = coef_O * 3.762\n",
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"\n",
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"coef_O + coef_N\n",
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"\n",
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"# 공기 1몰의 원자 몰수 = 2 ; O2 + N2 만으로 구성되었으면\n",
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"\n",
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"# 연료 기체 1몰의 원자 몰수\n",
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"\n",
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"mean_natoms = sum([(gas.X[i] * sum([v for v in gas.species(i).composition.values()])) for i in range(gas.n_species)])\n",
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"print(mean_natoms)\n",
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"\n",
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"# 연료 기체 1몰에 필요한 공기 몰수\n",
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"air_fuel_ratio_st = mean_natoms * (coef_N + coef_O) / 2\n",
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"print (air_fuel_ratio_st)\n"
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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": 6,
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"id": "bf4af7ee",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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" gri30:\n",
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"\n",
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" temperature 300 K\n",
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" pressure 7080.3 Pa\n",
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" density 0.081894 kg/m^3\n",
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" mean mol. weight 28.851 kg/kmol\n",
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" phase of matter gas\n",
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"\n",
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" 1 kg 1 kmol \n",
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" --------------- ---------------\n",
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" enthalpy 1907.6 55036 J\n",
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" internal energy -84549 -2.4393e+06 J\n",
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" entropy 7658.6 2.2095e+05 J/K\n",
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" Gibbs function -2.2957e+06 -6.6231e+07 J\n",
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" heat capacity c_p 1010.1 29141 J/K\n",
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" heat capacity c_v 721.88 20827 J/K\n",
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"\n",
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" mass frac. Y mole frac. X chem. pot. / RT\n",
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" --------------- --------------- ---------------\n",
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" O2 0.2329 0.21 -28.895\n",
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" N2 0.7671 0.79 -25.93\n",
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" [ +51 minor] 0 0 \n",
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"\n",
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"\n",
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" gri30:\n",
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"\n",
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" temperature 300 K\n",
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" pressure 6971.7 Pa\n",
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" density 0.081894 kg/m^3\n",
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" mean mol. weight 29.3 kg/kmol\n",
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" phase of matter gas\n",
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"\n",
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" 1 kg 1 kmol \n",
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" --------------- ---------------\n",
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" enthalpy -3.6049e+06 -1.0562e+08 J\n",
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" internal energy -3.6901e+06 -1.0812e+08 J\n",
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" entropy 7738.9 2.2675e+05 J/K\n",
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" Gibbs function -5.9266e+06 -1.7365e+08 J\n",
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" heat capacity c_p 1052.3 30831 J/K\n",
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" heat capacity c_v 768.49 22517 J/K\n",
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"\n",
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" mass frac. Y mole frac. X chem. pot. / RT\n",
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" --------------- --------------- ---------------\n",
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" H2 0.0044164 0.064187 -21.14\n",
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" O2 0.0042583 0.0038992 -32.897\n",
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" CH4 0.009799 0.017896 -59.022\n",
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" CO 0.23178 0.24245 -72.178\n",
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" CO2 0.29614 0.19716 -187.77\n",
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" C2H4 0.0012445 0.0012997 -14.653\n",
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" C2H6 0.00041043 0.00039992 -71.686\n",
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" N2 0.45196 0.47271 -26.459\n",
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" [ +45 minor] 0 0 \n",
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"\n"
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]
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}
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],
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"source": [
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"air = ct.Solution('gri30.xml')\n",
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"air.X = \"O2:1, N2:3.762\"\n",
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"air_comp = air.X\n",
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"air()\n",
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"\n",
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"fuel = ct.Solution('gri30.xml')\n",
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"fuel.X = \"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",
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"fuel_comp = fuel.X\n",
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"fuel()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4b1f191f",
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"metadata": {},
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"source": [
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"# 이론공연비 계산"
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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": 7,
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"id": "60f9c29b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stoich_AF (fuel_compostion, mole_fraction=True):\n",
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" \"Stoichiometric Air/Fuel ratio (volume)\"\n",
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" \n",
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" gas = ct.Solution('gri30.xml')\n",
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"\n",
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" if mole_fraction:\n",
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" gas.TPX = 600 + 273.15, ct.one_atm, fuel_compostion\n",
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" else:\n",
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" gas.TPY = 600 + 273.15, ct.one_atm, fuel_compostion\n",
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" \n",
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" fuel_comp = gas.X\n",
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"\n",
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" # 연료의 원자 1 몰 완전 연소에 필요한 산소 원자 몰 수\n",
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" coef_O = gas.elemental_mole_fraction('C') * 2 + gas.elemental_mole_fraction('H') / 2 - (gas.elemental_mole_fraction('O'))\n",
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"\n",
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" # 연료의 원자 1 몰 완전 연소에 필요한 공기 원자 몰 수 = 산소 원자 몰 수 X (1 + 3.762)\n",
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" coef_N = coef_O * 3.762\n",
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"\n",
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" # 공기 1몰의 원자 몰수 = 2 ; O2 + N2 만으로 구성되었으면\n",
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" mean_natoms_air = 2\n",
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"\n",
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" # 연료 기체 1몰의 원자 몰수 (평균 분자당 원자 수)\n",
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" 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",
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"\n",
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" # 연료 기체 1몰에 필요한 공기 몰수 ()\n",
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" air_fuel_ratio_st = mean_natoms_fuel * (coef_N + coef_O) / mean_natoms_air\n",
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"\n",
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" return (air_fuel_ratio_st)\n"
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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": 8,
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"id": "c633e758",
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"metadata": {},
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"outputs": [],
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"source": [
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"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\""
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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": 9,
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"id": "d489bdf1",
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"metadata": {},
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"outputs": [],
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"source": [
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"st_AF = stoich_AF(coke_oven_fuel)"
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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": 10,
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"id": "825758e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"air = ct.Solution('gri30.xml')"
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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": 11,
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"id": "99dbfd30",
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"metadata": {},
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"outputs": [],
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"source": [
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"air.X = \"O2:1, N2:3.762\"\n",
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"air_comp = air.X"
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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": 12,
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"id": "7e1a5975",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1506.856765604754\n",
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"\n",
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" gri30:\n",
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"\n",
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" temperature 1780 K\n",
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" pressure 1.0133e+05 Pa\n",
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" density 0.21633 kg/m^3\n",
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" mean mol. weight 31.597 kg/kmol\n",
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" phase of matter gas\n",
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"\n",
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" 1 kg 1 kmol \n",
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" --------------- ---------------\n",
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" enthalpy -1.9051e+06 -6.0194e+07 J\n",
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" internal energy -2.3734e+06 -7.4994e+07 J\n",
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" entropy 8525 2.6936e+05 J/K\n",
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" Gibbs function -1.708e+07 -5.3966e+08 J\n",
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" heat capacity c_p 1351.2 42694 J/K\n",
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" heat capacity c_v 1088.1 34380 J/K\n",
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"\n",
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" mass frac. Y mole frac. X chem. pot. / RT\n",
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" --------------- --------------- ---------------\n",
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" H2 6.2424e-06 9.7839e-05 -28.347\n",
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" H 9.5406e-08 2.9906e-06 -14.173\n",
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" O 1.3675e-06 2.7007e-06 -17.795\n",
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" O2 0.00073214 0.00072297 -35.589\n",
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" OH 6.1066e-05 0.00011345 -31.968\n",
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" H2O 0.033613 0.058955 -46.141\n",
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" HO2 1.1974e-08 1.1463e-08 -49.763\n",
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" H2O2 1.4101e-09 1.3099e-09 -63.936\n",
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" CO 0.0014344 0.0016181 -41.208\n",
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" CO2 0.3634 0.26091 -59.003\n",
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" HCO 2.8143e-12 3.0644e-12 -55.381\n",
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" CH2O 6.3467e-14 6.6788e-14 -69.555\n",
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" N 9.3579e-12 2.111e-11 -13.467\n",
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" NH 1.4448e-12 3.0404e-12 -27.64\n",
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" NH2 1.006e-12 1.9838e-12 -41.814\n",
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" NH3 1.3308e-11 2.469e-11 -55.987\n",
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" NNH 3.8031e-12 4.1406e-12 -41.107\n",
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" NO 0.00020133 0.000212 -31.262\n",
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" NO2 4.7618e-08 3.2705e-08 -49.056\n",
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" N2O 1.5885e-08 1.1404e-08 -44.729\n",
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" HNO 5.0425e-10 5.1373e-10 -45.435\n",
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" HCN 4.3708e-14 5.1101e-14 -51.054\n",
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" HNCO 1.5701e-11 1.153e-11 -68.848\n",
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" NCO 1.2119e-13 9.1136e-14 -54.675\n",
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" N2 0.60055 0.67737 -26.934\n",
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" [ +28 minor] 1.1611e-14 8.5342e-15 \n",
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"\n"
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]
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}
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],
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"source": [
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"phi = 1\n",
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"\n",
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"mix = ct.Solution('gri30.xml')\n",
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"\n",
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"mix.TPX = 25+273.15, ct.one_atm, phi * fuel_comp + st_AF * air_comp\n",
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"\n",
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"mix.equilibrate('HP')\n",
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"\n",
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"print(mix.T - 273.15)\n",
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"mix()"
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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": 13,
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"id": "a20ce147",
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"metadata": {},
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"outputs": [],
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"source": [
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"def exhaust_stoichiometry (phi = 1, return_unburned=False):\n",
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"\n",
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" mix = ct.Solution('gri30.xml')\n",
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"\n",
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" mix.TPX = 25+273.15, ct.one_atm, phi * fuel_comp + st_AF * air_comp\n",
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" \n",
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" element_X = {ename: mix.elemental_mole_fraction(ename) for ename in mix.element_names}\n",
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" \n",
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" exhaust = ct.Solution('gri30.xml')\n",
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" exhaust.X = {\n",
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" \"CO2\" : element_X['C'],\n",
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" \"H2O\" : element_X['H']/2,\n",
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" \"O2\" : (element_X['O'] - 2*element_X['C'] - element_X['H']/2)/2,\n",
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" \"N2\" : element_X['N']/2,\n",
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" }\n",
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" \n",
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" if return_unburned:\n",
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" return mix.mole_fraction_dict(threshold=-1), exhaust.mole_fraction_dict(threshold=-1)\n",
|
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" else:\n",
|
|
" return exhaust.mole_fraction_dict(threshold=-1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "c49236bf",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from scipy import optimize as opt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"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": 16,
|
|
"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": 17,
|
|
"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": 17,
|
|
"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": "markdown",
|
|
"id": "4c6c962f",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 발열량 계산"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"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": [
|
|
"## 배가스 산소 농도 4.5% 연료+공기 혼합 기체 "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"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": [
|
|
"## 배가스 산소 농도 4.5% 완전 연소 배가스"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"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": [
|
|
"## 배가스 산소 농도 4.5% 혼합 기체 kg 당 저위발열량 (J/kg)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "c4cb7bc6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"-2980321.8153982754"
|
|
]
|
|
},
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"gas_b.enthalpy_mass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "e6e9641d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'C2H4': 0.0005448226887749458,\n",
|
|
" 'C2H6': 0.00016763775039229108,\n",
|
|
" 'CH4': 0.007501789330055024,\n",
|
|
" 'CO': 0.10163038617532642,\n",
|
|
" 'CO2': 0.08264541094339947,\n",
|
|
" 'H2': 0.02690585893796271,\n",
|
|
" 'N2': 0.6569994864303017,\n",
|
|
" 'O2': 0.1236046077437873}"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"gas_u.mole_fraction_dict()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "e52dd204",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"-1525496.0432801933"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"gas_u.enthalpy_mass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"id": "067ae187",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'1.454825772118082 MJ/kg'"
|
|
]
|
|
},
|
|
"execution_count": 24,
|
|
"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": 25,
|
|
"id": "eb4e75d7",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"1.4548257721180817"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"1454825.7721180818e-6"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "b925399a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"3681.8181818181815"
|
|
]
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"67500000.0 / 66 / 1e6 * 3600"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "f8713062",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"heat_to_battery = 80 # GJ / Rev"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "42f68b81",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'3636.3636363636365 MJ / hr to a combustion chamber'"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"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": 30,
|
|
"id": "fd85f6dc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'2499.5182970050437 (kg/hr) = 3636.3636363636365 (MJ/hr) / 1.454825772118082 (MJ/kg)'"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"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": "ed0ec84b",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 단열 화염 온도 (평형 계산)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"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": 32,
|
|
"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": 33,
|
|
"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": "markdown",
|
|
"id": "a52e7ccf",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 연소실 평형 온도"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"id": "683e1479",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"567407.1211925298 1520734.4676495316\n",
|
|
"169566.12238822447\n",
|
|
"239.0022179980857 1954446.7344226132\n",
|
|
"-831314.263359389\n",
|
|
"473431.18022539583 1594212.6857824458\n",
|
|
"2111.9632881762227\n",
|
|
"472242.3473923314 1595137.058483179\n",
|
|
"-1.2422456212807447\n",
|
|
"472243.0462926644 1595136.5150916716\n",
|
|
"4.621868720278144e-05\n",
|
|
"472243.0462666627 1595136.515111888\n",
|
|
"5.820766091346741e-10\n",
|
|
"472243.04626666114 1595136.5151118895\n",
|
|
"-1.979060471057892e-09\n",
|
|
"700 converged: True\n",
|
|
" flag: 'converged'\n",
|
|
" function_calls: 7\n",
|
|
" iterations: 6\n",
|
|
" root: 1476.6996866989646\n",
|
|
"Outlet Gas Temperature: 1203.5496866989647 `C\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"htc = 568 # W / m2 / K\n",
|
|
"\n",
|
|
"Twall0 = 1370.4110474670265\n",
|
|
"Twall1 = 1421.9112286545476\n",
|
|
"\n",
|
|
"area_chamber_wall = 6.7 * 16.7\n",
|
|
"\n",
|
|
"htcA = 700 # 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": 35,
|
|
"id": "badfe727",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class CombustionChamber:\n",
|
|
" def __init__ (self, mdot, T0, P0, X0, hA=600):\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",
|
|
" \n",
|
|
" def update_mdot (self, mdot_new):\n",
|
|
" 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 (Tout, Twall0, Twall1):\n",
|
|
" # mdot * (dh) - self.heat()\n",
|
|
"\n",
|
|
" self.gas.TP = Tout, gas_chamber.P\n",
|
|
" h1 = self.gas.enthalpy_mass\n",
|
|
" Tgas = (self.T0 + Tout)/2\n",
|
|
"\n",
|
|
" print (self.mdot * (h0 - h1) - self.hA * ((Tgas - Twall0) + (Tgas - Twall1)))\n",
|
|
"\n",
|
|
" return (self.mdot * (h0 - h1) - self.hA * ((Tgas - Twall0) + (Tgas - Twall1)))\n",
|
|
"\n",
|
|
" def solve (self, ):\n",
|
|
" f_found = opt.root_scalar(lambda x: chamber_energy_balance(x, htcA, Twall0, Twall1),\n",
|
|
" bracket=[max(Twall0, Twall1), 1990])\n",
|
|
"\n",
|
|
" def heat (self, Tgas, Twall0, Twall1):\n",
|
|
" # Tgas = (T0 + T1) / 2\n",
|
|
" # heat_to_Twall0 + heat_to_Twall1\n",
|
|
" pass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "60c37bac",
|
|
"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": "1470c1e2",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 공급 열량 오더 추정"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"id": "858a862e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"346.5 Mcal / ton\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# 50'C 장입탄 1 톤을 1100'C 코크스로 건류시키기 위해 필요한 열량\n",
|
|
"heat_coking_mcal_per_ton = (1100 - 50) * 0.33 # Mcal / t-c\n",
|
|
"print(f'{heat_coking_mcal_per_ton} Mcal / ton')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"id": "d9a3443d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"925.2 Mcal / ton\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# 50`C 물 1톤을 800`C 수증기로 증발하는데 필요한 열량\n",
|
|
"# 액체 가열 + 증발 + 증기 가열\n",
|
|
"heat_evap_mcal_per_ton = 1 * (100 - 50) + 539.2 + 0.48*(800-100)\n",
|
|
"heat_evap_mcal_per_ton\n",
|
|
"print(f'{heat_evap_mcal_per_ton} Mcal / ton')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"id": "bc35e30a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"90.223776 GJ / charge\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# 1~4 기 장입 습탄 34 톤 중 수분 5.9% (2 톤) 제외 건탄 32 톤\n",
|
|
"# 1 문당 장입에서 추출까지 필요한 열량\n",
|
|
"heat_total_mcal = 32 * heat_coking_mcal_per_ton + 2 * heat_evap_mcal_per_ton # Mcal\n",
|
|
"heat_total_gj = heat_total_mcal / 1000 * 4.184 / 0.6 # GJ\n",
|
|
"print(f'{heat_total_gj} GJ / charge')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"id": "c5a556cd",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# 1 문에 시간당 공급되는 "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"id": "2b8e5d35",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"59.4 rev / charge\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# 건류 시간 동안 rev 횟수\n",
|
|
"n_rev_total = 3 * 24 * 66 / 80\n",
|
|
"\n",
|
|
"n_rev_total\n",
|
|
"print(f'{n_rev_total} rev / charge')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"id": "9e3eee55",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'100.24864 GJ / rev'"
|
|
]
|
|
},
|
|
"execution_count": 41,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 66 문에 rev 당 공급되는 열량\n",
|
|
"f'{66 * heat_total_gj / n_rev_total} GJ / rev'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"id": "4ae27703",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# 80 GJ/rev 는 모든 문에 공급되는 열량이 맞다"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"id": "d552c14d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"water = ct.Water()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"id": "c4d73576",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<cantera.liquidvapor.Water.<locals>.WaterWithTransport at 0x133b86e9300>"
|
|
]
|
|
},
|
|
"execution_count": 44,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"water"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"id": "4c82456a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" water:\n",
|
|
"\n",
|
|
" temperature 313.15 K\n",
|
|
" pressure 1.0132e+05 Pa\n",
|
|
" density 992.27 kg/m^3\n",
|
|
" mean mol. weight 18.016 kg/kmol\n",
|
|
" vapor fraction 0\n",
|
|
" phase of matter liquid\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -1.5803e+07 -2.8471e+08 J\n",
|
|
" internal energy -1.5803e+07 -2.8471e+08 J\n",
|
|
" entropy 4092.4 73728 J/K\n",
|
|
" Gibbs function -1.7085e+07 -3.078e+08 J\n",
|
|
" heat capacity c_p 4175.6 75228 J/K\n",
|
|
" heat capacity c_v 4067.9 73288 J/K\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"water.TP = 40 + 273.15, ct.one_atm\n",
|
|
"\n",
|
|
"water()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"id": "9dd982a4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" water:\n",
|
|
"\n",
|
|
" temperature 313.15 K\n",
|
|
" pressure 7367.2 Pa\n",
|
|
" density 0.051107 kg/m^3\n",
|
|
" mean mol. weight 18.016 kg/kmol\n",
|
|
" vapor fraction 1\n",
|
|
" phase of matter liquid-gas-mix\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -1.3397e+07 -2.4135e+08 J\n",
|
|
" internal energy -1.3541e+07 -2.4395e+08 J\n",
|
|
" entropy 11778 2.1219e+05 J/K\n",
|
|
" Gibbs function -1.7085e+07 -3.078e+08 J\n",
|
|
" heat capacity c_p 1884.7 33955 J/K\n",
|
|
" heat capacity c_v 1414.4 25481 J/K\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"water.TQ = 40 + 273.15, 1\n",
|
|
"h_vapor = water.enthalpy_mass\n",
|
|
"water()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"id": "70b5436f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" water:\n",
|
|
"\n",
|
|
" temperature 313.15 K\n",
|
|
" pressure 7367.2 Pa\n",
|
|
" density 992.23 kg/m^3\n",
|
|
" mean mol. weight 18.016 kg/kmol\n",
|
|
" vapor fraction 0\n",
|
|
" phase of matter liquid-gas-mix\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -1.5803e+07 -2.8471e+08 J\n",
|
|
" internal energy -1.5803e+07 -2.8471e+08 J\n",
|
|
" entropy 4092.4 73729 J/K\n",
|
|
" Gibbs function -1.7085e+07 -3.078e+08 J\n",
|
|
" heat capacity c_p 4176.1 75236 J/K\n",
|
|
" heat capacity c_v 4067.9 73287 J/K\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"water.TQ = 40 + 273.15, 0\n",
|
|
"h_water = water.enthalpy_mass\n",
|
|
"water()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"id": "1c18625b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"2406727.679574404"
|
|
]
|
|
},
|
|
"execution_count": 48,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"h_vapor - h_water"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"id": "17f8781a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" water:\n",
|
|
"\n",
|
|
" temperature 282.88 K\n",
|
|
" pressure 1.0133e+05 Pa\n",
|
|
" density 999.71 kg/m^3\n",
|
|
" mean mol. weight 18.016 kg/kmol\n",
|
|
" vapor fraction 0\n",
|
|
" phase of matter liquid\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -1.593e+07 -2.8699e+08 J\n",
|
|
" internal energy -1.593e+07 -2.8699e+08 J\n",
|
|
" entropy 3667 66064 J/K\n",
|
|
" Gibbs function -1.6967e+07 -3.0568e+08 J\n",
|
|
" heat capacity c_p 4201.1 75688 J/K\n",
|
|
" heat capacity c_v 4197.7 75626 J/K\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"water.TP = 40+273.15, ct.one_atm\n",
|
|
"h0 = water.enthalpy_mass\n",
|
|
"water.HP = h0 - (h_vapor - h_water) * 5 / 95, ct.one_atm\n",
|
|
"water()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 50,
|
|
"id": "f4e62efb",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" water:\n",
|
|
"\n",
|
|
" temperature 338.15 K\n",
|
|
" pressure 24986 Pa\n",
|
|
" density 0.16107 kg/m^3\n",
|
|
" mean mol. weight 18.016 kg/kmol\n",
|
|
" vapor fraction 1\n",
|
|
" phase of matter liquid-gas-mix\n",
|
|
"\n",
|
|
" 1 kg 1 kmol \n",
|
|
" --------------- ---------------\n",
|
|
" enthalpy -1.3353e+07 -2.4056e+08 J\n",
|
|
" internal energy -1.3508e+07 -2.4335e+08 J\n",
|
|
" entropy 11352 2.0451e+05 J/K\n",
|
|
" Gibbs function -1.7191e+07 -3.0972e+08 J\n",
|
|
" heat capacity c_p 1926.9 34715 J/K\n",
|
|
" heat capacity c_v 1443.6 26008 J/K\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"water.TQ = 65 + 273.15, 1\n",
|
|
"water()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"id": "54881653",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.8110541903465941"
|
|
]
|
|
},
|
|
"execution_count": 51,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"20 / (24986 / ct.one_atm * 100)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "20389a41",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Oven Dimension"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "31ce464b",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 1~4 Coke Ovens"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 52,
|
|
"id": "94eac653",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"oven_height = 6.7 # m\n",
|
|
"oven_width_mid = 0.450 # m\n",
|
|
"oven_width_pusher = 0.412 # m\n",
|
|
"oven_width_coke = 0.488 # m"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b072182b",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Oven Wall Temperature"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 53,
|
|
"id": "87510579",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Twall_model = np.loadtxt(\"./CokeOvenWallTemperature.csv\", delimiter=',')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 71,
|
|
"id": "b0f627f1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0, 0.5, '탄화실 벽면 온도 $ ^\\\\circ C$')"
|
|
]
|
|
},
|
|
"execution_count": 71,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "a46db8f3014d4b83b74cfa56780a8e77",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"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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" <img 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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",
|
|
"elapsed_time = np.linspace(0, 19.8+9, 1000)\n",
|
|
"plt.plot(elapsed_time, np.interp(elapsed_time, Twall_model.T[0] * 19.8 / 30 , Twall_model.T[-1]), '-')\n",
|
|
"plt.xlim(0, 19.8 + 9)\n",
|
|
"plt.xlabel(r\"장입 후 경과 시간 (hours)\")\n",
|
|
"plt.ylabel(r\"탄화실 벽면 온도 $ ^\\circ C$\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "87511f84",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 실제 정수 기록"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 56,
|
|
"id": "34154c31",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import pandas as pd"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 57,
|
|
"id": "dd45d825",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"heat_140205_4A = pd.read_csv('./Heat_History_140205_4A.csv', delimiter='\\t', parse_dates=[['Date', 'Time']])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 58,
|
|
"id": "a999b3a6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"heat_program = np.zeros((2*heat_140205_4A.Q.size-1, 2))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 59,
|
|
"id": "7f312ec7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"heat_program[::2,0] = heat_140205_4A.Date_Time\n",
|
|
"heat_program[1::2,0] = heat_140205_4A.Date_Time[1:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 60,
|
|
"id": "f917ae2c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"heat_program[::2,1] = heat_140205_4A.Q\n",
|
|
"heat_program[1::2,1] = heat_140205_4A.Q[:-1]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 61,
|
|
"id": "45351178",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def data_to_program (filename):\n",
|
|
" heat_140205_4A = pd.read_csv(filename, delimiter='\\t', parse_dates=[['Date', 'Time']])\n",
|
|
"\n",
|
|
" heat_program = np.zeros((2*heat_140205_4A.Q.size-1, 2))\n",
|
|
"\n",
|
|
" heat_program[::2,0] = heat_140205_4A.Date_Time\n",
|
|
" heat_program[1::2,0] = heat_140205_4A.Date_Time[1:]\n",
|
|
"\n",
|
|
" heat_program[::2,1] = heat_140205_4A.Q\n",
|
|
" heat_program[1::2,1] = heat_140205_4A.Q[:-1]\n",
|
|
" \n",
|
|
" return heat_program"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 62,
|
|
"id": "46d37540",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"hp2 = data_to_program('./Heat_Mod_140205_4A.csv')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 73,
|
|
"id": "bae02afd",
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "21aa4bc690934cd0a67e5aef541e899b",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"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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' 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",
|
|
"plt.plot(heat_program.T[0].astype('datetime64[ns]'), heat_program.T[1])\n",
|
|
"plt.plot(hp2.T[0].astype('datetime64[ns]'), hp2.T[1], \"--\")\n",
|
|
"# plt.plot(heat_140205_4A.Date_Time, heat_140205_4A.Q, 'o')\n",
|
|
"plt.xticks(rotation=45)\n",
|
|
"plt.ylabel(r'열량, $GJ/rev$')\n",
|
|
"plt.xlabel(r'14년 2월 5일~6일 시각')\n",
|
|
"plt.ylim(40, 80)\n",
|
|
"plt.xlim(np.datetime64(\"2014-02-05 00:00:00\"), np.datetime64(\"2014-02-07 00:00:00\"), )\n",
|
|
"plt.tight_layout()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 64,
|
|
"id": "5cdfeb1f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.0 75.0\n",
|
|
"3.0 75.0\n",
|
|
"3.0 68.21\n",
|
|
"4.333333333333333 68.21\n",
|
|
"4.333333333333333 61.58\n",
|
|
"6.0 61.58\n",
|
|
"6.0 54.78\n",
|
|
"7.333333333333333 54.78\n",
|
|
"7.333333333333333 48.43\n",
|
|
"8.666666666666666 48.43\n",
|
|
"8.666666666666666 42.25\n",
|
|
"20.0 42.25\n",
|
|
"20.0 48.43\n",
|
|
"22.0 48.43\n",
|
|
"22.0 54.78\n",
|
|
"26.0 54.78\n",
|
|
"26.0 61.58\n",
|
|
"30.0 61.58\n",
|
|
"30.0 68.21\n",
|
|
"33.333333333333336 68.21\n",
|
|
"33.333333333333336 62.75\n",
|
|
"34.333333333333336 62.75\n",
|
|
"34.333333333333336 64.3\n",
|
|
"35.333333333333336 64.3\n",
|
|
"35.333333333333336 59.16\n",
|
|
"36.666666666666664 59.16\n",
|
|
"36.666666666666664 54.85\n",
|
|
"39.666666666666664 54.85\n",
|
|
"39.666666666666664 56.82\n",
|
|
"40.666666666666664 56.82\n",
|
|
"40.666666666666664 61.13\n",
|
|
"43.333333333333336 61.13\n",
|
|
"43.333333333333336 65.28\n",
|
|
"44.666666666666664 65.28\n",
|
|
"44.666666666666664 70.53\n",
|
|
"46.0 70.53\n",
|
|
"46.0 75.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for t, q in heat_program:\n",
|
|
" print(f'{(t - heat_program[0,0]).astype(\"timedelta64[ns]\")/np.timedelta64(1, \"h\")} {q}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 65,
|
|
"id": "ea0b3748",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.0 75.0\n",
|
|
"3.0 75.0\n",
|
|
"3.0 68.21\n",
|
|
"4.333333333333333 68.21\n",
|
|
"4.333333333333333 61.58\n",
|
|
"6.0 61.58\n",
|
|
"6.0 54.78\n",
|
|
"7.333333333333333 54.78\n",
|
|
"7.333333333333333 48.43\n",
|
|
"8.666666666666666 48.43\n",
|
|
"8.666666666666666 42.25\n",
|
|
"20.0 42.25\n",
|
|
"20.0 45.4\n",
|
|
"25.0 45.4\n",
|
|
"25.0 48.55\n",
|
|
"30.0 48.55\n",
|
|
"30.0 51.7\n",
|
|
"36.666666666666664 51.7\n",
|
|
"36.666666666666664 54.85\n",
|
|
"42.666666666666664 54.85\n",
|
|
"42.666666666666664 56.82\n",
|
|
"43.666666666666664 56.82\n",
|
|
"43.666666666666664 61.13\n",
|
|
"46.333333333333336 61.13\n",
|
|
"46.333333333333336 65.28\n",
|
|
"47.666666666666664 65.28\n",
|
|
"47.666666666666664 70.53\n",
|
|
"49.0 70.53\n",
|
|
"49.0 75.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for t, q in hp2:\n",
|
|
" print(f'{(t - heat_program[0,0]).astype(\"timedelta64[ns]\")/np.timedelta64(1, \"h\")} {q}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 66,
|
|
"id": "1f01c5cb",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x133c5c6cca0>]"
|
|
]
|
|
},
|
|
"execution_count": 66,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "399ab23628fb41cf84fc7aa5eede058f",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"image/png": 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",
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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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" <img src='data:image/png;base64,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' 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" </div>\n",
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" "
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],
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"text/plain": [
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"hours, qs = zip(*[((t - heat_program[0,0]).astype(\"timedelta64[ns]\")/np.timedelta64(1, \"h\"), q) for t, q in heat_program])\n",
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"plt.figure()\n",
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"plt.plot(hours, qs)"
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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": 83,
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"id": "fe4ac3fd",
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"metadata": {},
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"outputs": [],
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"source": [
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"temperature_140205_4A = pd.read_csv('./Temperature_History_140205_4A.csv', delimiter='\\t', parse_dates=[['Date', 'Time']])"
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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": 84,
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"id": "2484d2d7",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
|
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|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
|
" <th>Date_Time</th>\n",
|
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" <th>T</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
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" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>2014-02-05 00:00:00</td>\n",
|
|
" <td>1256</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>2014-02-05 09:00:00</td>\n",
|
|
" <td>1211</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>2014-02-05 13:20:00</td>\n",
|
|
" <td>1204</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>2014-02-05 16:20:00</td>\n",
|
|
" <td>1197</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>2014-02-05 20:20:00</td>\n",
|
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" <td>1215</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>2014-02-06 09:20:00</td>\n",
|
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" <td>1263</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>2014-02-06 15:20:00</td>\n",
|
|
" <td>1230</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>2014-02-07 00:20:00</td>\n",
|
|
" <td>1267</td>\n",
|
|
" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
|
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"</div>"
|
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],
|
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"text/plain": [
|
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" Date_Time T\n",
|
|
"0 2014-02-05 00:00:00 1256\n",
|
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"1 2014-02-05 09:00:00 1211\n",
|
|
"2 2014-02-05 13:20:00 1204\n",
|
|
"3 2014-02-05 16:20:00 1197\n",
|
|
"4 2014-02-05 20:20:00 1215\n",
|
|
"5 2014-02-06 09:20:00 1263\n",
|
|
"6 2014-02-06 15:20:00 1230\n",
|
|
"7 2014-02-07 00:20:00 1267"
|
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]
|
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},
|
|
"execution_count": 84,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
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}
|
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],
|
|
"source": [
|
|
"temperature_140205_4A"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 87,
|
|
"id": "3231a843",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0 0 days 00:00:00\n",
|
|
"1 0 days 09:00:00\n",
|
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"2 0 days 13:20:00\n",
|
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"3 0 days 16:20:00\n",
|
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"4 0 days 20:20:00\n",
|
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"5 1 days 09:20:00\n",
|
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"6 1 days 15:20:00\n",
|
|
"7 2 days 00:20:00\n",
|
|
"Name: Date_Time, dtype: timedelta64[ns]"
|
|
]
|
|
},
|
|
"execution_count": 87,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"temperature_140205_4A.Date_Time - temperature_140205_4A.Date_Time[0]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 85,
|
|
"id": "73587ebf",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0 2014-02-05 00:00:00\n",
|
|
"1 2014-02-05 09:00:00\n",
|
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"2 2014-02-05 13:20:00\n",
|
|
"3 2014-02-05 16:20:00\n",
|
|
"4 2014-02-05 20:20:00\n",
|
|
"5 2014-02-06 09:20:00\n",
|
|
"6 2014-02-06 15:20:00\n",
|
|
"7 2014-02-07 00:20:00\n",
|
|
"Name: Date_Time, dtype: datetime64[ns]"
|
|
]
|
|
},
|
|
"execution_count": 85,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"temperature_140205_4A.Date_Time.apply(pd.Timestamp)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 96,
|
|
"id": "532eeb34",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"hp2 = data_to_program('./Heat_History_221128_3A.csv')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 98,
|
|
"id": "93d14543",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
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"text/plain": [
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]
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},
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"execution_count": 98,
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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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"\n",
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" <div style=\"display: inline-block;\">\n",
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" Figure\n",
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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": [
|
|
"hours, qs = zip(*[((t - hp2[0,0]).astype(\"timedelta64[ns]\")/np.timedelta64(1, \"h\"), q) for t, q in hp2])\n",
|
|
"plt.figure()\n",
|
|
"plt.plot(hours, qs)\n",
|
|
"plt.axvline(9)\n",
|
|
"plt.axvline(9+13)\n",
|
|
"plt.axvline(9+13+18)\n",
|
|
"plt.axvline(9+13+18+4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 101,
|
|
"id": "518202df",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def print_program(hp2):\n",
|
|
" for t, q in hp2:\n",
|
|
" print(f'{(t - hp2[0,0]).astype(\"timedelta64[ns]\")/np.timedelta64(1, \"h\")} {q}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 102,
|
|
"id": "9720059c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.0 85.0\n",
|
|
"4.0 85.0\n",
|
|
"4.0 75.0\n",
|
|
"6.0 75.0\n",
|
|
"6.0 65.0\n",
|
|
"8.0 65.0\n",
|
|
"8.0 55.0\n",
|
|
"23.0 55.0\n",
|
|
"23.0 50.0\n",
|
|
"32.0 50.0\n",
|
|
"32.0 60.0\n",
|
|
"36.0 60.0\n",
|
|
"36.0 55.0\n",
|
|
"43.0 55.0\n",
|
|
"43.0 65.0\n",
|
|
"47.666666666666664 65.0\n",
|
|
"47.666666666666664 75.0\n",
|
|
"49.666666666666664 75.0\n",
|
|
"49.666666666666664 85.0\n",
|
|
"52.666666666666664 85.0\n",
|
|
"52.666666666666664 65.0\n",
|
|
"56.666666666666664 65.0\n",
|
|
"56.666666666666664 62.5\n",
|
|
"57.666666666666664 62.5\n",
|
|
"57.666666666666664 85.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print_program(hp2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 112,
|
|
"id": "70043c15",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.0 85.0\n",
|
|
"4.0 85.0\n",
|
|
"4.0 75.0\n",
|
|
"6.0 75.0\n",
|
|
"6.0 65.0\n",
|
|
"8.0 65.0\n",
|
|
"8.0 55.0\n",
|
|
"41.0 55.0\n",
|
|
"41.0 65.0\n",
|
|
"46.0 65.0\n",
|
|
"46.0 75.0\n",
|
|
"52.0 75.0\n",
|
|
"52.0 85.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print_program(data_to_program('./Heat_Plan_221128_3A-1.csv'))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 113,
|
|
"id": "0477e4f9",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.0 85.0\n",
|
|
"4.0 85.0\n",
|
|
"4.0 75.0\n",
|
|
"6.0 75.0\n",
|
|
"6.0 65.0\n",
|
|
"8.0 65.0\n",
|
|
"8.0 55.0\n",
|
|
"10.0 55.0\n",
|
|
"10.0 50.0\n",
|
|
"23.0 50.0\n",
|
|
"23.0 60.0\n",
|
|
"36.0 60.0\n",
|
|
"36.0 55.0\n",
|
|
"41.0 55.0\n",
|
|
"41.0 65.0\n",
|
|
"46.0 65.0\n",
|
|
"46.0 75.0\n",
|
|
"52.0 75.0\n",
|
|
"52.0 85.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print_program(data_to_program('./Heat_Plan_221128_3A-2.csv'))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 114,
|
|
"id": "eb8c780d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x133cf569850>]"
|
|
]
|
|
},
|
|
"execution_count": 114,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "789d202efb88473295d4e3dd7122a380",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"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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' 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": [
|
|
"plan1 = data_to_program('./Heat_Plan_221128_3A-1.csv')\n",
|
|
"plan2 = data_to_program('./Heat_Plan_221128_3A-2.csv')\n",
|
|
"\n",
|
|
"plt.figure()\n",
|
|
"\n",
|
|
"hours, qs = zip(*[((t - plan1[0,0]).astype(\"timedelta64[ns]\")/np.timedelta64(1, \"h\"), q) for t, q in plan1])\n",
|
|
"plt.plot(hours, qs)\n",
|
|
"\n",
|
|
"\n",
|
|
"hours, qs = zip(*[((t - plan2[0,0]).astype(\"timedelta64[ns]\")/np.timedelta64(1, \"h\"), q) for t, q in plan2])\n",
|
|
"plt.plot(hours, qs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 115,
|
|
"id": "45499b94",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.lines.Line2D at 0x133cf57b640>"
|
|
]
|
|
},
|
|
"execution_count": 115,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.axvline(9)"
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|