2022 lines
188 KiB
Text
2022 lines
188 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": 54,
|
|
"id": "b0f627f1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0, 0.5, '탄화실 벽면 온도 $ ^\\\\circ C$')"
|
|
]
|
|
},
|
|
"execution_count": 54,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "4599e46e119f431ebbd16b5665df4526",
|
|
"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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yo/P1/jxo3Txo0b25x7//33NWnSJMXGxmrkyJFtzldUVOjYsWPKzc3t8HUjIyMVHx/f5tHq7JZEAPiysnJ7u5a/s1XUNaqsvPuT0ACguwIiAO7a1bJYqtPp1IIFC+R0OnXTTTfplltu0bp167R+/XpJ0ltvvaXdu3frhhtukCQVFBSouLhYJ06cUFNTk+bNm6c5c+YoJiam2zW4yX8AOmAYhj47flJ/23q4S9cfbzh3SAQAM/j9JBBJ+uUvf6kNGzYoPDxcV1xxhVatWqXQ0FANGzZMr7zyiu655x7Z7XZlZ2erpKREsbGxkqTCwkIdOXJEI0aMUFhYmL773e9q4cKFPaqBBkAArSrrGvX+Z9V6f2+1Nn5Wo8r6roe6pLioPqwMAFpYDPoue6y+vr5lMsj9y7XmP76lUbb48z8JQL/X0TItoeeYmFF3ulml+6r1/mc1en9vtfZVnWpzPiIsROMuGKgdR+p10uHs8DUsklKsUdrwkyvPeS8Avdf693ddXV2b4VzBJCBaAPsDZgEDgaGjZVps1igV5edo+mibJOlMk0tbDtj1/mc12ri3WjuO1LXpBQixSGNSrfpadqImZyXq0oxBigoP9cwCltpOHGuNe0X5OYQ/AF5BADQJ+Q/wf60B7ez/nCvrGnX30q36zsU2HT/p0NYDJ9Tkcre5JjtpgCZnDdbXshM1cfhgWaPD273+9NE2LZ49tl3ATDkrYAJAXyMAmoQACPi3cy3T0nrs9X9WeI7ZrFH6WlaiJmcP1teyEpVi7drYvemjbZqWk9KtLmYAMBsB0CR0AQP+rXRv9XmXaZGk2ydnaPbEdGUmxvZ4t47QEIsmZQ3u0XMBwAwEQJMQAAH/c9Lh1LufVOntXce0emdll56TmzZQw4cM6OPKAKBvEQBNQvwD/MOx+ka9veuY3t51TKV7a9qN5TsflmkBEAgIgCZhNR3Au7q6VIthGNpzrEF//zz0fXS4rs35jMExmpaTrKsuStb9y7brWH1jh/9D17pMy/jMhL55QwDgRQRAk7ATCOA951uqxely64P9tS0tfR9X6pD9TJvnX3LBQE3LSda0UcnKThrgGcv3y+/kaO7SrbKIZVoABDYCoEncJEDAK863VMuEzATtOdagE6ebPeciwkI0JTuxpaVvVFKn3bgs0wIgWBAATUL8A/peV5Zq2VxulyQNignXlRcla1pOkr5+4RDFRnbt1x3LtAAIBgRAkzALGOh7ZeX2Li3V8otrR+nWSRkKCw3p0X1YpgVAoCMAmoT8B/SdmpMOrf5XpV54f3+Xrh88ILLH4Q8AggEB0CQEQMBcJ043ac2/KvXGPyu0cW+NXN0YZ8tSLQBwbgRAk9AFDHSuq0u21J1p1tp/VerNHRXa8Gm1nF8KfaNT43X1GJv+8v5+VTc4WKoFAHqBAGgSAiDQsfMt2dLQ2Ky/f3xMb3xUoXc/rVKz64v/li5KiVN+7lBdPcamzMRYSdLwxFiWagGAXiIAmoT8B7R3viVbcodZ9XFlg5qcX+zGMSJ5gK4ZM1TXXGxTdlL7LddYqgUAeo8AaBKDhWCANrqyZEvrrhzDh8Tq2ouH6tqLbRqRHHfe12apFgDoHQKgSdzd204UCHhdXbLlN98boxsvTfPsxtFVLNUCAD3HOgkmYQwg8AWny62/7zrWpWujwkO7Hf4AAL1DC6BJiH+AdLyhUcvKDumlsoNdav2TWLIFAHyBAGgSgxZABKjzLeFiGC3nX9x0QKt3VnqWbhkUE64ml1unHK4OX5clWwDAdwiAJunGGrWA3zjXEi6TsxP1v9uO6MVNB/TJsZOe8+PSB+n7E9P17TEpemf3cc1dulUSS7YAQH9CADQJYwARaDpbwqXi8yVcIsNC5Ph8+Zbo8FDNuGSoZk9M11eGWj3XsmQLAPRPBECTkP8QSM61hEsrh9OtzMQY3TopQ9eNHSZrdHiH17FkCwD0PwRAk9ACiEDS1SVcfj1zjCZlJZ73OpZsAYD+hWVgTEL+QyDZeaSuS9cdb3D0cSUAgL5AC6BJ2AkE/s4wDJXurdFzG8q1fvfxLj2HJVwAwD8RAE3CTiDor863jIvD6VLJRxV6fkO5Pq6o9xz/8iSPs7GECwD4NwKgSRgDiP7oXMu4jM8crP/ZdEBLNh1Q1eddudHhobrh0mH64eRM7amsZwkXAAhQBECTkP/Q35xvGZewEItn0eaU+Cj94GsZmjU+TQNjIiRJmYmxLOECAAGKAGgSxgCiP+nKMi5Ot6HRQ+M15/LhunqMTeGh7eeEsYQLAAQmAqBJ2AkE/UlXl3F55JpR513GhSVcACDwsAyMSRgDiP7kWP35w5/EMi4AEKxoATQJ+Q/9gdttaNXOSv1+7Z4uXc8yLgAQnAiAJjFIgOhj51rOxeU29OaOCv1h3af69PhJSS2zdTv7VrKMCwAENwKgSRgDiL7U2XIuP79mlJpdhv6w/lPtrTolSYqPCtPtUzKVNihGD/31I0ks4wIAaCsgAuCRI0d09913a+vWrYqMjNQPf/hD/ed//qckKTc3V9XV1QoPb9mo/pJLLtHKlSs9z120aJGeeuopnTlzRpdddpmee+45JSaef2/TszEGEH3lXMu53PvSNs/P1uhw3TklUz+YnKH4qJbve2xkKMu4AADaCYgAeOutt2rChAl6/fXXVVtbqyuvvFJpaWm67bbbVFtbqw0bNigzM7Pd85YvX64lS5aorKxMVqtV9913nwoKCvTqq692uwbyH/pCV5ZzsVikB6aN0G1fy1Dc58GvFcu4AAA6EhABcNu2bXryySdlsViUkJCga6+9Vlu2bNFtt90mu92ugQMHdvi8RYsWqaioSAkJLeOg5s+fL5vNJrvd7jnWVbQAoi90ZTkXw5AuTU9oF/5asYwLAOBsAbEMzM0336ynnnpKTU1NOnDggF577TVdf/31am5u1unTp2W1Wts9x+l0asuWLZo8ebLnWGJiojIyMrRjx45u10D+Q1843tDV5Vy6dh0AAFKABMD58+dr3bp1GjhwoDIzM3XFFVfoG9/4hux2uywWi7KysjRixAjdeeedqqyslCRVVVXJ5XK1G++XlJSkmpqaDu/jcDhUX1/f5tGKnUBgtjNNLv1jz/EuXctyLgCA7vD7AOhyufTNb35Tc+fOVV1dnY4cOaKPPvpITz75pJKTk+V0OlVeXq7S0lKFhoYqPz9fhmHI5XJJar98i8vlksXS8fioBQsWyGq1eh5paWmec8wCRne53IZK99bote1HVLq3Rq7Pv0SGYeitHRWa+vg/9Oq2o+d8DYtaZgOznAsAoDv8fgzg+vXr1dTUpAceeECSZLPZ9MQTTyg/P1+FhYWeMDd48GA9/fTTslqtKi8vV3JysgzDUG1tbZvxflVVVUpJSenwXvPmzfPcR5Lq6+s9IZAxgOiOzpZ1mfP14fr7x8e0cW9LK3TqwGhdMyZFf3qvXBLLuQAAzOH3AbCpqUlhYW3fRkhIiJqamtpdaxiG3G63IiIiFBsbq5EjR2rjxo269tprJUkVFRU6duyYcnNzO7xXZGSkIiMjOzxH/kNXnWtZl1+9sUuSFBEWorvzsjQ3L0vREaEamz6I5VwAAKbx+wA4ZcoUVVZW6qWXXtLNN9+shoYGPfLII7rxxhu1d+9euVwujRgxQg6HQw888IAmTJigYcOGSZIKCgpUXFysKVOmKCYmRvPmzdOcOXMUExPT7TrYCQRd0ZVlXSLDQrTm/suVkRjrOcZyLgAAM/n9GECr1ao1a9bohRdeUEZGhnJzczVq1Cg99thjstvtuvrqq5WamqqcnBw5nU6tWLHC89zCwkLl5eVpxIgRysjIUHR0tBYuXNijOhgDiK7oyrIuDqe7w2tal3P57ldTNSlrMOEPANBjft8CKEmjR4/W2rVr2x2/7LLL9Nlnn3X6vJCQED322GN67LHHel0DYwDRFSzrAgDoD/y+BbC/IP+hK045nF26jmVdAAB9KSBaAPsDxgCilctttBur1+xy6w/rP9Xi/9t7zuda1DK5g2VdAAB9iQBoEsYAQup4eZfBsREKC7XoWL1DkjT2goHaevCELGJZFwCAbxAATcJOIOhseZeaUy1LEsVFhel311+s6aNtHQZFlnUBAHgLAdAktAAGt64s7xITEappOS2LjLOsCwDAl7w+CeTTTz/VvHnzdOLECW/fuk8xCzi4dWV5l2P1DpWV2z0/s6wLAMBXvB4AFy5cqD179mjgwIHtzjU2NmrXrl3eLskU5L/gxvIuAAB/4vUA+I9//EP//u//3uG5qKgo3X333fr1r3/t5ap6j1nAwe1w7ekuXcfyLgCA/sDrAfDIkSPKysrq9Pxdd92l119/3YsVmYMxgMHJ6XJr4ard+t2aT855nUWSjeVdAAD9hNcDYEJCgioqKjo9P378+HPu3tFfMQYw+Byrb9TNf9qsZ/7RsrbfFSOHyKIvlnNpxfIuAID+xusB8PLLL9cLL7zQ6fmQkBA5HA7vFWQS8l9w2fBpta5+8j2V7bdrQGSYnrr5Ev3lh+O1ePZYpVjbdvOmWKO0ePZYlncBAPQbXl8G5qGHHtLEiRP11a9+VQUFBe3Ol5aWavjw4d4uq9cYAxh4OtrRQ5KeWv+ZFq37RIYhXZQSp6dvGavhQwZIYnkXAIB/8HoAHDdunBYvXqy7775bf/3rX3Xvvfdq7NixGjBggN577z395Cc/UWFhobfL6jXGAAaWjhZqToqL1OABEfq4okGS9G+XpemX3/mKosJD2zy3dXkXAAD6K58sBH3nnXfqoosu0gMPPKDrrrtOFktL64hhGPrmN7+pH//4x74oq1cYAxg4OtvR43iDQ8cbHAoPtWjBdRfr+nHDfFIfAAC95bOdQKZMmaKysjLt3r1bW7du1enTpzV69GhNnDjRVyX1CvEvMHRlR4+B0RGaeUmq12oCAMBsPt8K7qKLLtJFF13k6zJ6jTGAgaErO3pUnWzZ0YNuXgCAv/L6LOBA5Xb7ugKYgR09AADBgABoEoNO4IDQ1Z062NEDAODPCIAmYRZwYGhyutot5Pxl7OgBAAgEXh0D6HQ6tWTJEhmGoVtvvVXh4eHevH2fYhaw/1v2wUE9snKnpy3XoraTe9jRAwAQKLzaAvizn/1MgwcP1sCBA/XTn/7Um7fuc+Q//+V2G/rdmt36yd92yOk29N2vDtUfZn2VHT0AAAHLqy2Abrdbp0+fVkhIiNwBNmuCWcD+qbHZpYdX/FMlHx2VJP37ldn68bQRslgsunrMUHb0AAAEJK8GwAULFmjZsmVyOp36zW9+481b9znGAPqf2lNNKnhxiz7YX6uwEIt+fd0Y3Xhpmuc8O3oAAAKVVwNgeHi4Zs+e7c1beg1jAP3L/upT+uELH6i8+pTiIsP0zPfHaXJ2oq/LAgDAK3y+EHSgIP75jw8P2DVnyYeyn2pS6sBo/eWHl2lEcpyvywIAwGsIgCZhDGD/43Ib7cbwrd5ZqR8v364mp1sXD7PquR9cypp+AICgQwA0SYDNafF7q3dWqLhkV5tt3eKiwtTQ6JQkTR2VrP+e9VXFRPCfAAAg+PC3n0kYA9h/rN5ZoblLt7brlm8Nf1dcNER//P44ZvQCAIIWO4GYhPjXP7jchopLdp3z38fuigav1QMAQH9EADQJYwD7h7Jye5tu345U1DWqrNzupYoAAOh/+l0A9NcgxTqA/cPxhnOHv+5eBwBAIOp3ATA9Pd3XJfSIvwbXQNPVGb3M/AUABDOfB8BPP/20zc/+GqRoAewfLrLFKTy088kdFkk2a8uSMAAABCuvBsALL7yw3bG8vLw2P1ss/jkzk1nAvnfS4dTtL3ygZlfH/y5av1lF+TnMAAYABDWvBsCTJ0+2O+avLX5nC5C34bdONzn1w7+UadvBE7JGh+uRq0fJZm3bzZtijdLi2WM1fbTNR1UCANA/eHUdwI5a9/y1xe9sBgvB+MyZJpfueGGLPthfq7ioMC29Y4LGDLPq9imZ7XYCoeUPAIB+MAbQMAw9/vjjevzxx/X73/++w1bC8zly5Ijy8/OVmpqq4cOHa/78+Z5z27Zt08SJE5Wenq6cnBytXbu2zXMXLVqk7OxspaamasaMGaquru7R+2AnEN9obHap4MUtKt1XowGRYVpy+3iNGWaVJIWGWDQpa7C++9VUTcoaTPgDAOBzPg+AknTo0CEdOnRIhw8flsvl6vbzb731Vo0ZM0aHDx/Wli1b9Le//U0vvPCCGhoalJ+fr0cffVQHDhzQ4sWLdeONN6qyslKStHz5ci1ZskRlZWU6ePCgbDabCgoKevQeGAPofQ6nS3OXfqj3Pq1WTESoXvjhZbrkgkG+LgsAgH7P51vBWSwWPfHEE56fV6xY0e3X2LZtm5588klZLBYlJCTo2muv1ZYtW9TU1KTLLrtMU6dOldQy4eTyyy/XsmXLVFhYqEWLFqmoqEgJCS0zQufPny+bzSa73e451lXEP+9qcrp17/9s0zt7qhQVHqI/33aZLs1gZi8AAF3RL1oAv6wnYwJvvvlmPfXUU2pqatKBAwf02muv6frrr1dpaakmT57c5toJEyZo+/btcjqd2rJlS5vziYmJysjI0I4dO7pdQ6BMZumPXG5DpXtr9Nr2IyrdWyNHs0uFr2zT3z8+psiwED3/g8s0cfhgX5cJAIDf8GoLYGVlpeLj4z0/G4ahM2fO9Pp158+fr/Hjx2vgwIFqbGzUfffdp2984xtasGCBrrzyyjbXJiUlafPmzaqqqpLL5VJiYmK78zU1NR3ex+FwyOFweH6ur6/3/Jl1APvG6p0VKi7Z1WZ7t6jwEDU2uxURGqI/fn+cJmcnnuMVAADA2bzaAlhbW+sZ79c65q+zsNVVLpdL3/zmNzV37lzV1dXpyJEj+uijj/Tkk0/K5XK1a5lzuVyyWCyesYadne/IggULZLVaPY+0tDTPOcYAmm/1zgrNXbq13d6+jc0tM27u/HqmvjEyyRelAQDg17waAL8cnr78+LLudqWuX79eTU1NeuCBBxQeHi6bzaYnnnhCv/3tb5WQkNBuVm9VVZVSUlI0aNAgGYah2traDs93ZN68eaqrq/M8Dh069KW6u1U2zsPlNlRcsuucYytXbjsiF02vAAB0W78bA7hp06ZuXd/U1KSwsLY92SEhIWpqatK4ceO0cePGNufef/99TZo0SbGxsRo5cmSb8xUVFTp27Jhyc3M7vFdkZKTi4+PbPFoxBtBcZeX2di1/Z6uoa1RZud1LFQEAEDj6RQD8cnhKTU3t1nOnTJmiyspKvfTSS5KkhoYGPfLII7rxxht1yy23aN26dVq/fr0k6a233tLu3bt1ww03SJIKCgpUXFysEydOqKmpSfPmzdOcOXMUExPT7fdAQ5S5jjecO/x19zoAAPCFfhEA09PTe/xcq9WqNWvW6IUXXlBGRoZyc3M1atQoPfbYYxo2bJheeeUV3XPPPUpKStKjjz6qkpISxcbGSpIKCwuVl5enESNGKCMjQ9HR0Vq4cGGP6mAnEHMlxUWd/6JuXAcAAL5gMXzQd3nmzBlFR0d7fk5LS2sznk5qGdt39gze/qa+vr5lMsj9yzU6I0VvFX7d1yUFDJfb0IRf/13VJ5s6PG9Ry96+G35yJTt8AAC6pfXv77q6ujbDuYKJV1sA7Xa7xo0bp/j4eF133XVqamr5y72jWbezZ8/2Zmm9xixgc9WfaVZnsa71eFF+DuEPAIAe8GoA/P3vf69vfetbqq+vV2xsrJ5//nlJHU+gYFJF8GpyujX3fz5U1ckmDY6NUFJcZJvzKdYoLZ49VtNH23xUIQAA/s2rC0G/8cYbeu+99xQdHa2HH35YP/3pTzV37twOr+3JjiC+RAugOQzDUNHr/9KmfXbFRoTqpTkTlZ00QGXldh1vaFRSXJTGZybQ8gcAQC94NQA2NTV5+tovvPBCbdu2TY8//rinK9ifMQvYHC9s3K+Xyw7KYpH+e9YlGpkSJ0malMVWbwAAmMWrXcBf7tZtXavv0KFDcrvd3iyjT9AC2Hv/+KRK89/YJUn62bdH6apRyT6uCACAwOTVABgaGurZ+3f//v0aN26cnnjiCUVFBcBSHuS/XvnseIPue2mr3IZ0w7hhuvPrmb4uCQCAgOXVADh16lT96U9/kiT98Y9/1LRp07x5+z5FC2DXudyGSvfW6LXtR1S6t0bVDQ7d8f9tUUOjU5dlDNKjM0f73RhQAAD8iVfHAD700EOaMmWK/uu//kvp6emeHTpa/7K/9957tWrVKhmGoaqqKm+W1muMAeya1TsrVFyyq802bxGhFjW5DA0bFK1nZo9TZFioDysEACDweTUApqWl6ZNPPtGBAwd04YUXtmvl+cUvfqEHHnjAmyWZhp1Azm/1zgrNXbq13SfV5Go58sPJmRo8ILL9EwEAgKm8GgAlKTIyUiNGjGhzrHVySHJyspKT/XPgfwDMY+lTLreh4pJd54zJz723T7d9LYMlXgAA6GP9Yi/gTZs2+bqEXmPh6nMrK7e36fbtSEVdo8rK7V6qCACA4NUvAmBqaqqvS+g1xgCe2/GGc4e/7l4HAAB6rl8EwEDAGMBzS4rr2lI/Xb0OAAD0HAHQJLQAntv4zATZrJ2HO4skm7VlmzcAANC3CIAmYQzguYWGWHTHlI4Xd26d8lGUn8MEEAAAvIAAaBLy37k1Nru07INDkqTIsLZfuxRrlBbPHqvpo22+KA0AgKDj9WVgAhU7gZzbf735sT49flJD4iL15o+maG/VKR1vaFRSXEu3Ly1/AAB4T78NgOXl5crM9J/9YBkD2Lm3dx3Ti5sOSJIevzFXSfFRSopnsgcAAL7itQB4xRVXnHd/15tuukl33XWXJCkvL08HDx70RmmmoAWwY8fqG/UfKz6SJM35eqa+fuEQH1cEAAC8FgB//vOfe/555513KiMjQ/PmzdNdd92ljIwMSVJ6errner+bVOFn5XqD223ogeXbVXu6WV8ZGq+HvjXS1yUBAAB5MQBeddVVkqRFixZp0qRJysnJ0eDBgz1/Ptv5Wgv7G1oAW7Z7Kyu3e8b2bT9Uq/c/q1F0eKj+e9YligwL9XWJAABAXh4DePDgQTU2NqqiokIDBgzw/PmCCy7QgAEDvFmK6YJ9DODqnRUqLtnV4XZvRfk5yhri3/9+AQAIJF5dBmb48OHavXu3brvtNk2ePFmfffaZbrjhBj377LPeLKNPBPNOIKt3Vmju0q2d7vVrjQ73ckUAAOBcvNoCOGTIEB06dKjNsd///vfeLKHPBGsLoMttqLhkV6fx1yLpV2/s0je/ksJSLwAA9BNebQH0t3F93eF3k1ZMUlZu77TlT2qZG1NR16iycrv3igIAAOfUb9YBPHbsmK6//npZLBYZhqGqqipfl9QtwdoCeLyh8/DXk+sAAEDf82oAdLlcKi8vb9NaZrfbNXjwYA0aNEiPPvqoN8sxVbC2ACbFdW1B565eBwAA+p5XA2B0dLSuuuqqdmHp5z//uSIiIpSXl+fNckwVrC2A4zMTZLNGddoNbFHLXr/jMxO8WxgAAOiUVwPg/v37vXk7rzMMI6DHOXYkNMSiovwc3b10a7tzrZ9EUX4OE0AAAOhHvDoJJNAFaS+wBkR2vMxLijVKi2eP1fTRNi9XBAAAzqXfTAIJBG7DUIiCq6XL6XLrV2/8S5J066R0fXu0zbMTyPjMBFr+AADohwiAJirdW6OvZScGVehZuumAPjl2UoNiwvXgtJGyxrDoMwAA/Z3XAuDy5cv1zDPPdHjupptu0ttvvy27/Yu14lJTU/Xiiy96qzxTfP/PZbJZo1SUnxMU3Z72U016/O1PJEkPfpPwBwCAv/BaAJw0aZISEhI0f/583XPPPTpz5oyee+45FRcXKz09Xb/85S+1dOlSz/UxMTHeKs1UlXWNmrt0a1CMfXv87T2qb3TqopQ4zRp/ga/LAQAAXeS1AJiWlqa0tDT98Y9/1Ne//nU1NDTo9ddf11VXXSWpZZeQ1j/7M0Mts1+LS3ZpWk7gbn/2cUW9Xtp8UJL0y+98JWDfJwAAgcirs4DfffddVVdXq7S0VB988IGqq6u1cePGXr1mSUmJMjIy2jySk5M1YMAASVJubq5SU1M952bOnNnm+YsWLVJ2drZSU1M1Y8YMVVdX96oeKfC3PzMMQ8Ul/5LbkK4ZY9PE4YN9XRIAAOgGr04CKSoqkiTdcsstmjRpkkJDQ3XNNdeotra2xztp5OfnKz8/v82xu+++W4mJiZKk2tpabdiwQZmZme2eu3z5ci1ZskRlZWWyWq267777VFBQoFdffbVHtZwtULc/W7WzUpv22RUZFqJ5V1/k63IAAEA3eTUAvvPOO5Ikm83m+XNSUpKp99i3b59WrlypTz5pmZxgt9s1cODADq9dtGiRioqKlJDQskvF/PnzZbPZZLfbPcd6IxC3P2tsdum/3vxYknRXXpaGDfLPsZoAAAQznywE3Ze7ZSxcuFD33nuvrFarmpubdfr0aVmt1nbXOZ1ObdmyRZMnT/YcS0xMVEZGhnbs2NGrGiySbAG6/dmz7+7TkRNnZLNGaW5elq/LAQAAPRBQ6wBWVVVp2bJl+vTTTyW1tP5ZLBZlZWUpPDxcl19+uR599FGlpKSoqqpKLpfL01XcKikpSTU1NR2+vsPhkMPh8PxcX1/f7ppA3v7s6Ikzevr/PpMkzbt6lKIjQn1cEQAA6AmvTwJ599135XA49N577+kf//iH3G63JGnatGm9fv0XX3xRM2fO9HQrJycny+l0qry8XKWlpQoNDVV+fr4Mw5DL5ZKkdmMPXS5Xpy2UCxYskNVq9TzS0tLaXRPI258tXLVbjc1ujc9IUP7Fgff+AAAIFhajp7MveuCKK65odyw6OlpvvfWWKa8/ZswYPf74452GSZfLJavVqn/+859KTk5WXFycqqur24z3y8rK0tKlSzVp0qR2z++oBTAtLU2jfvo3nTYi9bvrL9Z1Y4cFXMufJH2w364bnimVxSKV3DdFo1Pbd6sDAOAP6uvrZbVaVVdXp/j4eF+X4xM+mQTSF7Zv364jR450GDJbGYYht9utiIgIxcbGauTIkdq4caOuvfZaSVJFRYWOHTum3NzcDp8fGRmpyMjIdsejw0J1ulnKGRofkOHP5Tb0y9db9vv9t8vSCH8AAPg5n0wC6QurV69WXl6ewsK+yLR79+71zAZ2OBwqLCzUhAkTNGzYMElSQUGBiouLdeLECTU1NWnevHmaM2dOt3chCQtt+RidLq81pnrVX7cc0r+O1isuKkwPfXOkr8sBAAC9FDABcPPmzRo7dmybY3a7XVdffbVSU1OVk5Mjp9OpFStWeM4XFhYqLy9PI0aMUEZGhqKjo7Vw4cJu3zs8tKXVr9nl7t2b6IfqzjTrd2v2SJLunzpCgwe0bwEFAAD+xatjAANN6xiCvP96U/vrDb08Z6ImZfn/rhgut6GycruONzRqzc5KvbWzUllDYrX6/ssVHhow/88AAAhSjAEMsGVgfCUsxCLJkNPt/y2Aq3dWqLhklyrq2u5icvUYG+EPAIAAwd/oJmgNRv7eBbx6Z4XmLt3aLvxJ0lPrP9PqnRU+qAoAAJiNAGiCsJDWMYD+25vuchsqLtmlc72D4pJdcrn99z0CAIAWBEATBMIs4LJye4ctf60MSRV1jSort3uvKAAA0CcIgCaICIAu4OMNnYe/nlwHAAD6L69NAvnss8+0ZMmSdsenTZumTZs2qaGhQZKUkJCg+++/XxMmTNDmzZu9VV6vhH6+Ja4/B8CkuChTrwMAAP2X11oALRaLQkND2z0k6YknnlBUVJQiIyP13//935KkgwcPequ0XgsL+bwL2I/Hx43PTJDN2nm4s0iyWaM0PjOh02sAAIB/8FoLYFZWloqKijo8ZxiGfvazn0mSnn/+eW+VZJqIAFgIOjTEol9cm6O5/7O13bnWze2K8nMCcqs7AACCjU/HAPpLF+/5tLYA+vMsYEmKCm9pkT074qVYo7R49lhNH23zflEAAMB0PlsIurq6WrNmzdK//vUvX5VgmtDPWwCdftwCaBiGnnrnM0nSHV/P1FUXJet4Q6OS4lq6fWn5AwAgcPgkADY3N+v73/++FixYoOjoaF+UYKpAmAW8aZ9dHx6oVURYiAq+PlxJ8Uz2AAAgUHk1AK5du1aHDx/WM888o+uuu0433XSTpJYJImfr6Fh/FRbq/wtB/7/PW/9uujSN8AcAQIDzagBcvHixjh49qsOHD+trX/ua57hhfBGcamtr9atf/UonT570Zmm98sUsYP9sAdx2sFYbPqtWWIhFd+UN93U5AACgj3l1EsjKlSu1efNmrVq1Svfee682btwoSXrooYc819x///0yDEMPPvigN0vrlfAw/94JpLX1b8YlqRo2KMbH1QAAgL7mkzGAubm5Wrlypb73ve+prKysTdjrbKmY/izs8+7qJj8cA/hxRb3+/vFxWSzS3G9k+bocAADgBT5bBiY7O1u33HKL9u7d66sSTOPPewG3tv5dM8amrCEDfFwNAADwBp8tAyNJ//Ef/+HL25smvHUZGD8bA7iv6qTe3FEhSbr3imwfVwMAALzFpwtBu1wu/e1vf/NlCaZonQXc5Oz/LYAut6HSvTV6bfsRFZf8S4YhTR2VpFG2eF+XBgAAvMSnLYCNjY2aN2+evve97/myjF4LC/GPFsDVOytUXLJLFXWNbY6PvWCQjyoCAAC+4LUAuGTJknbHHA6H6uvrOzwnSbNmzVJ4eHhfl9Zr4X4wBnD1zgrNXbpVHVX4uzV7NHxILFu9AQAQJLwWAN9+++0Oj0+bNq3DcxaLRddff71fBMDWSSD9dRawy22ouGRXh+GvVXHJLk3LSWHLNwAAgoDXAuCLL7543mvq6+v13HPP6YEHHvBCRebxdAH30wBYVm5v1+37ZYakirpGlZXbNSlrsPcKAwAAPuHVSSAffvihrr76aknSmTNn9Oabb2rbtm2e8263W8uWLfNmSab4YhZw/+wCPt7QefjryXUAAMC/+WQSiMPh0MSJE5WYmKgjR46ooKBADzzwgGJiYnTq1ClflNQrrWMAm5z9swUwKa5re/t29ToAAODfvL4MjGEYWrp0qSZMmKB169bpww8/1DPPPCOn06mIiAg1NvpfK1SoZy/g/tkCOD4zQTZrlDob3WeRZLNGaXxmgjfLAgAAPuK1FsAf//jHOnz4sCwWi3bs2KFrr71WkhQbG6tLL71Ut956qwYOHCiHw+Gtkkzj6QLup2MAQ0MsKsrP0d1Lt7Y71xoKi/JzmAACAECQ8FoL4JgxYzRixAgZhqGIiIg2Qa+pqUkXXXSRcnNzvVWOqTxdwP14GZjpo20aZYtrdzzFGqXFs8eyBAwAAEHEay2At99+uz788ENt27ZNEydO1HPPPafrr79eR44c0datW7V8+XKFhITo17/+tbdKMk1/nwUsSZ8ca9DHFQ2ySHry374qQy1j/sZnJtDyBwBAkPHJJJCZM2fqpZdeUkpKilwul55++mmFfD6OrvWf/iSsn88ClqS/vF8uSZo+OkXf+Wqqj6sBAAC+5NUAGBMTo+HDh8tisWjFihU6cuSI4uPjFRfXvmvSn/T3WcD2U016desRSdLtUzJ9XA0AAPA1rwbAUaNG6amnnpIk1dbWKjk5WWFhbUv485//7M2STNHf9wJ+ueygHE63xqRadWk6+/4CABDsfNbf+oMf/EBbt7aflXrFFVf4oJre6c97ATc53VpSul+SdPuUDFksjPcDACDYea0FcN++fW1+PnXqlA4fPqzExETPsSFDhni6gx9//HG/2RKudQxgcz+cBLJqZ4WO1TuUFBepa8YM9XU5AACgH/BaALzqqqtksVhkGF+0kj344IOeP1ssFv3qV7/S7NmzJUmPPfaY3wTA8M8nrjT3sxZAwzD0/IaWyR+3TkpXRJj/TbABAADm81oALC8v79b1Xw6K/V1rr2qT06XSvTU+X1rF5TZUVm7X5n01+ufhOkWEWjRr/AU+qwcAAPQvPlkG5svKyso0YMAA5eTktDnuT2PVZj+3WVKYXIY060+bZLNGqSg/xyeLK6/eWaHikl2qqPtiS73QkBB9sN/OYs8AAECSlyeBDB3aMgatpqZG27ZtkyStW7dO77zzTo9fs6SkRBkZGW0eycnJGjBggCR5Fp5OT09XTk6O1q5d2+b5ixYtUnZ2tlJTUzVjxgxVV1d3u4aqk01tfq6sa9TcpVu1emdFj99XT6zeWaG5S7e2CX+SdKbZ5ZN6AABA/+TVANjarbtz50498cQTkqTo6GidPn26x6+Zn5+v/fv3t3nMnDlT999/vxoaGpSfn69HH31UBw4c0OLFi3XjjTeqsrJSkrR8+XItWbJEZWVlOnjwoGw2mwoKCnr/Pj//Z3HJLrm8tDi0y22ouGSXznU3b9YDAAD6L68GwI66dcPDw9XU1NTB1T2zb98+rVy5Ug8//LBefvllXXbZZZo6daokKS8vT5dffrmWLVsmqaX1r6ioSAkJCQoNDdX8+fNVUlIiu93e6zoMSRV1jSor7/1rdUVZub1dy58v6wEAAP2Xz8cAhoaGyul06syZM6qoaOmiNAxDLperR6+3cOFC3XvvvbJarSotLdXkyZPbnJ8wYYK2b98up9OpLVu2tDmfmJiojIwM7dixQ3l5eT1/U19yvKHzUGamrt7HW/UAAID+y+cB0GKxyO12a8OGDSooKPAsFRMTE9Pt16qqqtKyZcv06aefSpKOHj2qK6+8ss01SUlJ2rx5s6qqquRyudqsQ9h6vqampsPXdzgccjgcnp/r6+vPW1NSXFR330aPdPU+3qoHAAD0X14NgMePH9fw4cN15swZnTx5UsOHD1dDQ4Pmzp2radOmdXupmLO9+OKLmjlzppKSkiRJLper3XIyLpdLFovF08JoGEabrunW8x1ZsGCBiouLu1SLRVKKNUrjMxN68E66b3xmgmzWqE67gb1dDwAA6L+8GgD37NnT4fGEBHNCyV/+8hc9/vjjbV737Fm9VVVVSklJ0aBBg2QYhmpra9vcv/V8R+bNm9dmcer6+nqlpaXp7LjY+nNRfo7X1gMMDbGoKD9Hdy9tv72eL+oBAAD9l1cngWRlZXX4GDRoUK9fe/v27Tpy5EibvYTHjRunjRs3trnu/fff16RJkxQbG6uRI0e2OV9RUaFjx44pNze3w3tERkYqPj6+zUOSkuIj21yXYo3S4tljvb7u3vTRNqUOjG533Ff1AACA/snnYwDNsnr1auXl5Sks7Iu3dMstt2jhwoVav369rrzySr311lvavXu3brjhBklSQUGBiouLNWXKFMXExGjevHmaM2dOt8cfrv1xnqY9Vabqk02aP2O0bh5/gU9a2nZX1uvIiTMKD7Ho6dljdbrJpaS4KJ/vTAIAAPqXgAmAmzdv1tixY9scGzZsmF555RXdc889stvtys7OVklJiWJjYyVJhYWFOnLkiEaMGKGwsDB997vf1cKFC7t979AQi+KjwlV9skkjk+N8FrZWbjsiSbpyVJKm5XTcjQ0AABAwAXDlypUdHv/Wt76l3bt3d3guJCREjz32mB577LFe3z8irKU3vcnp7vVr9YTLbei1bUclSTMvGeaTGgAAgH/w6hjAQBYe2vJRNrt8EwA37atRZX2jrNHhuuKiIT6pAQAA+AcCoElaWwAdPmoBfHVrS/fvNRfbFBkW6pMaAACAfyAAmiQ8tGXcny9aAM80ubR6Z8suKtddkur1+wMAAP9CADRJxOetbr4YA7h2V6VONbmUlhCtcem9X1IHAAAENgKgSSJ82ALYOvt35ldTO93FBAAAoBUB0CSeWcBeDoBVDQ6992nLbicz6P4FAABdQAA0SessYG93AZd8dFQut6HctIEaPmSAV+8NAAD8EwHQJBGhvmkBbO3+ZfIHAADoKgKgSVq7gJudhtfu+dnxBu04UqewEIvyc4d67b4AAMC/EQBN4ukCdrm8ds/Wtf++MXKIEmIjvHZfAADg3wiAJon08lZwbreh17a3bP3G5A8AANAdBECTfLEVnHe6gMv223XkxBnFRYZp6qhkr9wTAAAEBgKgSby9FdzKz7t/rx5jU1Q4W78BAICuIwCa5IsWwL4PgI3NLr21o2XrN7p/AQBAdxEATRL2+U4g+6pOqnRvjVxu87uCXW5DpXtr9JtVu9XgcGqoNUoTMhNMvw8AAAhsYb4uIBC8vatSf1hXLknaevCEZv1pk2zWKBXl52j6aJsp91i9s0LFJbtUUdfoOVbX2Ky1uypNuwcAAAgOtACa4IFlH6m+0dnmWGVdo+Yu3arVOyt6/fqrd1Zo7tKtbcKfJJ1yuEy7BwAACB4EQBN01Nnbeqy4ZFevuoNdbkPFJbs6vEer3t4DAAAEFwJgHzIkVdQ1qqzc3uPXKCu3t2v5M/seAAAguBAAveB4Q+cBzqzn9uYeAAAguBAAvSApLqrPn9ubewAAgOBCADSB5RzHbdYoje/FUi3jMxNks0b16T0AAEBwIQD2kdbAVpSfo9CQzuLb+YWGWFSUn9PhJBCz7gEAAIILAdAEj9+Uq8QBEW2OpVijtHj2WFPW6Js+2qYbxg1rd9zMewAAgODBQtAmmJaTostGDNPlv31HYSEWvXjHBI3PTDC1VW5/zSlJ0g8mpWts+iAlxUWZfg8AABAcCIAmiY0IlSQ53YYmZCYoxMRgdqy+UVsO1EqS7v5GlmzWaNNeGwAABB+6gE0S/XkAlKRGp8vU117zr0oZhjT2goGEPwAA0GsEQJNEhX0pADa7TX3tt3a0bPV29RjG+gEAgN4jAJokJMSiiLCWj/NMszktgC63odU7K7R5X8suH9Nykk15XQAAENwIgCaKDm9pBWw0IQCu3lmhKb9Zr7uXbvUsAfNvz27S6p0VvX5tAAAQ3AiAJooK/7wFsKl3AXD1zgrNXbq13R7AlXWNmrt0KyEQAAD0CgHQRK0tgI5eTAJxuQ0Vl+zqcOHn1mPFJbvkcnd0BQAAwPkRAE0U9XkAPNPU80kgZeX2di1/X2ZIqqhrVFm5vcf3AAAAwY0AaCJPAOzFGMDjDZ2Hv55cBwAAcDYCoInMmASSFBdl6nUAAABnIwCayDMJpBcBcHxmgmzWzsOdRZLN2rINHAAAQE8ETAAsKyvT5ZdfrvT0dA0dOlSvvvqqJCk3N1epqanKyMhQRkaGZs6c2eZ5ixYtUnZ2tlJTUzVjxgxVV1f3uIbW3UAcPQyALrehsnK7po9O6fB86+ZyRfk57AEMAAB6LCD2At69e7dmzJihJUuWaOrUqWpqatKJEyckSbW1tdqwYYMyMzPbPW/58uVasmSJysrKZLVadd9996mgoMATHrurdTeQnrQArt5ZoeKSXeecAJJijVJRfo6mj2ZHEAAA0HMBEQAfeeQR/ehHP9LUqVMlSREREUpKSpIk2e12DRw4sMPnLVq0SEVFRUpIaOlOnT9/vmw2m+x2u+dYd0R83gW8/eAJle6t0fjMhC611LWu+9fZwi53TM7Q1JyULr8eAADAuVgMw/DrBeUcDofi4+N18OBBJSe33SqtublZkZGRcjqdCglp29vtdDoVExOjo0ePKjEx0XP8wgsv1HPPPae8vLzz3ru+vl5Wq1V1dXXaePCUHlz+kU59aRFoWxda7FxuQ1N+s77Tlj+LWlr+NvzkSsIfAAAm+PLf3/Hx8b4uxyf8fgzgnj17FB0drfXr1+viiy/W8OHDddddd6m+vl52u10Wi0VZWVkaMWKE7rzzTlVWVkqSqqqq5HK52oQ/SUpKSlJNTU2H93I4HKqvr2/zkKS3d1Vq7tKtbcKf1LWdO1j3DwAAeJvfB8CGhgY5nU5t3rxZmzdv1kcffaSqqioVFhYqOTlZTqdT5eXlKi0tVWhoqPLz82UYhlyulrB2dgOoy+WSxdJxS9uCBQtktVo9j7S0NEnSwlW7e7xzB+v+AQAAb/P7AJiYmCiHw6Hf/va3io6OVlxcnIqLi/X6669LkifMDR48WE8//bQ+/vhjlZeXa9CgQTIMQ7W1tW1er6qqSikpHc/CnTdvnurq6jyPQ4cOSZKO1Ts6re98LXis+wcAALzN7wNgenq6oqKidPr0ac8xi8WiqKj2gckwDLndbkVERCg2NlYjR47Uxo0bPecrKip07Ngx5ebmdnivyMhIxcfHt3l0VWcteK3r/nU2uo91/wAAgNn8PgBGRUXptttu08MPPyyn0ymHw6GioiLNnj1be/fu1SeffCKpZfxeYWGhJkyYoGHDhkmSCgoKVFxcrBMnTqipqUnz5s3TnDlzFBMTY3qdnbXghYZYVJSf0+E51v0DAAB9we8DoCQtXLhQDodDqamp+spXvqLs7GzNnz9fdrtdV199tVJTU5WTkyOn06kVK1Z4nldYWKi8vDyNGDFCGRkZio6O1sKFC7t9/+T4yF614E0fbdPi2WMV8/lC0q1SrFFaPHss6/4BAABT+f0yML7UOo18RekePfS/n7abCNIaCs8X4lp3APnxK9tU2eDQD7+WoW9+hXX/AADoCywDEyALQfvatJwULR4Qp0dW7lTNqSbP8a7s3NHRDiBv7qjQhOGEPwAA0DdoAeyFs/8PYsfhOuU/tUHxUWH64/cvPW8LXmc7gHS15RAAAHQfLYABMgawv4iLamlQdRvSpKzB5wx/Lreh4pJdPV4/EAAAoKcIgCZqncRxusnZboHps7EDCAAA8BUCoImiPw+AbkNyON3nvJYdQAAAgK8QAE0UE/HFnJrTZ+0LfDZ2AAEAAL5CADRRaIhFkWEtH+npJuc5r2UHEAAA4CsEQJO1jgM8c54WQHYAAQAAvkIANFlrN/CpDgKgy22odG+NXtt+RKV7a1rWD5w91jN2sBU7gAAAgL7EQtAm+/JM4C/raMHngdHh+uHkDF2UPEDbDtXp1knp+vZoGzuAAACAPkUANFlHXcCdLfh84kyznvj7p56fZ42/QKNswbkgJQAA8B66gE12dhfwuRZ8PtsnlfV9WBkAAEALWgBNFh3ekqlL91ZryIBIuQ3jnAs+f9mPl3+k8NBQXX0xY/8AAEDfIQCaaPXOCm3cVyNJernskF4uO6SB0eFdfr7bkO55aaueCWECCAAA6Dt0AZukdZxfY3PbHUBOnGnu9muxBzAAAOhLBEATdGecX1ewBzAAAOhLBEATfLi/tsvj/LqKPYABAEBfIQCaoOqk+WGNPYABAEBfYRKICYYMMC+sWdSyEwh7AAMAgL5CC6AJxmUMks0apd7u3cEewAAAwBsIgCYIDbGoKD+n16/DHsAAAMAb6AI2yfTRNi2ePVaP/O9O1Zxs6vLz7rsiSxcmxykpLoo9gAEAgFcQAE00fbRNI5LjdOXv/6GwEMnpPv9zJmcP0aSswX1fHAAAwOfoAjZZQmyEpJbwlxLf+bhAiyQbkz0AAIAPEABNFhf1xdZvD35zRIfXMNkDAAD4EgHQZKEhFsVFtvSsj0sfpMWzx8p61n7ATPYAAAC+xBjAPhAfHa4Gh1N1Z5o1fbRNeyob9MTfP9XEzAQVTh3BZA8AAOBTtAD2gdYWv7ozzZKkmlMts4IvzUjQpKzBhD8AAOBTBMA+cHYAPF7vkCQlxUf6rCYAAIBWBMA+0BoA61sDYEPLXsFJcQRAAADge4wB7AMDokIlSZv21Sg7KU7H6lsC4JA48/YMBgAA6CkCoMlW76zQqh2VkqQ3d1Tqzc//LNECCAAA+ge6gE20emeF5i7dqlNNrg7PL//goFxuw8tVAQAAtEUANInLbai4ZJfOFe/+8M5eTV64Xqt3VnitLgAAgLMRAE1SVm5XRV3jea+rrG/U3KVbCYEAAMBnAiYAlpWV6fLLL1d6erqGDh2qV199VZK0bds2TZw4Uenp6crJydHatWvbPG/RokXKzs5WamqqZsyYoerq6h7dv3Wmb1cVl+yiOxgAAPhEQATA3bt3a8aMGfrFL36hAwcOaP/+/ZoyZYoaGhqUn5+vRx99VAcOHNDixYt14403qrKyZWLG8uXLtWTJEpWVlengwYOy2WwqKCjoUQ1J3Zjha0iqqGtUWbm9R/cCAADojYAIgI888oh+9KMfaerUqZKkiIgIJSUl6eWXX9Zll13mOZ6Xl6fLL79cy5Ytk9TS+ldUVKSEhASFhoZq/vz5Kikpkd3e/WA2PjNBNmuUurPHR3dbDQEAAMzg9wHQ4XDojTfe0O23397uXGlpqSZPntzm2IQJE7R9+3Y5nU5t2bKlzfnExERlZGRox44d3a4jNMSiovycbj2nO62GAAAAZvH7ALhnzx5FR0dr/fr1uvjiizV8+HDdddddqq+v19GjR5WcnNzm+qSkJNXU1Kiqqkoul0uJiYkdnu+Iw+FQfX19m8eXTR9t0+LZY5V8ni3fLJJs1iiNz0zo/hsGAADoJb8PgA0NDXI6ndq8ebM2b96sjz76SFVVVSosLJTL5ZJhtJ1o4XK5ZLFY5HK1rNXX2fmOLFiwQFar1fNIS0trd8300TZt/OlVig4P7fA1Wl+5KD9HoSHd6TAGAAAwh98HwMTERDkcDv32t79VdHS04uLiVFxcrNdff10JCQntZvVWVVUpJSVFgwYNkmEYqq2t7fB8R+bNm6e6ujrP49ChQx1eFxpi0bBB0R2eS7FGafHssZo+2taDdwsAANB7fh8A09PTFRUVpdOnT3uOWSwWRUVFady4cdq4cWOb699//31NmjRJsbGxGjlyZJvzFRUVOnbsmHJzczu8V2RkpOLj49s8OjMoJsLz5xCL9MRNX9XLcyZqw0+uJPwBAACf8vsAGBUVpdtuu00PP/ywnE6nHA6HioqKNHv2bN1yyy1at26d1q9fL0l66623tHv3bt1www2SpIKCAhUXF+vEiRNqamrSvHnzNGfOHMXExPS6roEx4Z4/26zRmnlJqiZlDabbFwAA+JzfB0BJWrhwoRwOh1JTU/WVr3xF2dnZmj9/voYNG6ZXXnlF99xzj5KSkvToo4+qpKREsbGxkqTCwkLl5eVpxIgRysjIUHR0tBYuXGhKTV9uAbRZme0LAAD6D4tx9iwIdFl9fb2sVqvq6uradQcvXLVbz/xjryTp2otteurmsb4oEQAAnOVcf38Hi4BoAeyPrNFhbX5m2zcAANBfEAD7wOqdFXrmH/s8P7/xzwpN+c16rd5Z4cOqAAAAWhAATbZ6Z4XmLt2qujPNbY5X1jVq7tKthEAAAOBzBEATudyGikt2qaPO3tZjxSW76A4GAAA+RQA0UVm5XRV1jZ2eNyRV1DWqrNzuvaIAAADOQgA00fGGzsNfT64DAADoCwRAEyXFdW29v65eBwAA0BcIgCYan5kgmzVKne31YVHLotDjMxO8WRYAAEAbBEAThYZYVJSfI0ntQmDrz0X5OWwHBwAAfIoAaLLpo21aPHusUs7a/i3FGqXFs8dq+mibjyoDAABoEXb+S9Bd00fbNC0nRWXldh1vaFRSXEu3Ly1/AACgPyAA9pHQEIsmZQ32dRkAAADt0AUMAAAQZAiAAAAAQYYACAAAEGQIgAAAAEGGAAgAABBkCIAAAABBhgAIAAAQZAiAAAAAQYYACAAAEGTYCaQXDMOQJNXX1/u4EgAA0FWtf2+3/j0ejAiAvVBTUyNJSktL83ElAACguxoaGmS1Wn1dhk8QAHshISFBknTw4MGg/QL1hfr6eqWlpenQoUOKj4/3dTkBg8+1b/C59g0+177DZ9vS8tfQ0KChQ4f6uhSfIQD2QkhIyxBKq9UatP8R9aX4+Hg+1z7A59o3+Fz7Bp9r3wn2zzbYG26YBAIAABBkCIAAAABBhgDYC5GRkSoqKlJkZKSvSwkofK59g8+1b/C59g0+177DZwtJshjBPAcaAAAgCNECCAAAEGQIgAAAAEGGAAgAABBkCIA9dObMGRUUFCg9PV3Dhg3Tww8/LLfb7euy/NoTTzwhq9WqjIwMz2Pv3r2+LstvGYahJUuWaOLEiW2Ob9u2TRMnTlR6erpycnK0du1aH1Xonzr7XHNzc5Wamur57s6cOdNHFfqn9evXa/LkycrOzlZWVpb+8Ic/eM7xne25c32ufGeDnIEemTt3rnHHHXcYzc3NxokTJ4xLL73UePLJJ31dll/7z//8T+MXv/iFr8sICKtWrTJGjx5tDB8+3Bg5cqTneH19vZGammq8/fbbhmEYxv/93/8ZVqvVqKio8FWpfqWzz9UwDCMtLc3Yt2+fjyrzf3feeaexa9cuwzAMY+/evcbQoUONVatW8Z3tpc4+V8PgOxvsmAXcAydPnlRycrIOHjyowYMHS5JeffVVzZ8/X9u2bfNxdf7rvvvuU1ZWln784x/7uhS/t2LFCkVGRiouLk533323du/eLUl69tlntWrVKq1cudJz7Xe+8x1dddVVKiws9FW5fqOzz1WSBgwYoEOHDmnQoEE+rDBwPPDAAwoLC1N2djbfWRO1fq6//e1v+c4GObqAe+DDDz9UZmamJ/xJ0oQJE7Rz5045nU4fVubfamtrNXDgQF+XERCuv/565efntzteWlqqyZMntzk2YcIEbd++3UuV+bfOPtfm5madPn066LeWMlNVVZWsVivfWZO1fq58Z0EA7IGjR48qOTm5zbGkpCQ5nU7V19f7qCr/V1tbq0ceeURpaWm68sortW7dOl+XFHA6++7W1NT4qKLAYLfbZbFYlJWVpREjRujOO+9UZWWlr8vyW2VlZXrjjTd0880385010Zc/V76zIAD2gMvl0tk95y6XS5JksVh8UVJAeOONN3T06FGVl5frRz/6ka677jpt3brV12UFlM6+u3xveyc5OVlOp1Pl5eUqLS1VaGio8vPz233WOL+//vWv+u53v6slS5YoMzOT76xJzv5c+c4izNcF+KOEhARVV1e3OVZVVaXo6Gia03shJKTl/0fCwsI0c+ZMrVmzRv/7v/+rsWPH+riywNHZdzclJcVHFQWO1kAyePBgPf3007JarSovL9fw4cN9XJl/cLlc+vd//3e98847Wrt2rcaMGSOJ72xvdfa5Snxngx0tgD0wduxY7dmzR7W1tZ5j77//vsaPH+8JMeg9l8uliIgIX5cRUMaNG6eNGze2Ofb+++9r0qRJPqooMBmGIbfbzfe3GwoLC7V3716VlZW1CSl8Z3uns8/1bHxng5DP5h/7ue985zvG3XffbTQ3NxtVVVXGmDFjjJUrV/q6LL+2evVqw+VyGYZhGGvWrDESEhI8yxegZ9555502y5UcOnTIGDhwoLFu3TrDMAzjzTffNNLT042TJ0/6qkS/dPbn+tlnnxl79uwxDMMwGhsbjXvuucf4xje+4avy/M7p06eN0NBQo7Kyst05vrM9d67Ple8s6ALuoeeff1533HGHbDabYmNj9dBDD2nGjBm+LsuvPfHEE/r+97+vmJgYpaen67XXXtOoUaN8XVZAGTZsmF555RXdc889stvtys7OVklJiWJjY31dml+z2+2aNWuWzpw5o6ioKE2dOlUrVqzwdVl+o7y8XG63WxMmTGhzPCsrS+vWreM720Pn+lwXLlzIdzbIsQ4gAABAkGHAGgAAQJAhAAIAAAQZAiAAAECQIQACAAAEGQIgAABAkCEAAgAABBkCIAAAQJAhAAIAAAQZAiAAAECQIQACAAAEGQIgAABAkCEAAgAABBkCIAAAQJAhAAIAAAQZAiAAAECQIQACAAAEGQIgAABAkCEAAgAABBkCIAAAQJAhAAIAAAQZAiAAAECQIQACAAAEGQIgAABAkCEAAgAABBkCIAAAQJAhAAIAAAQZAiAAAECQIQACAAAEGQIgAABAkCEAAgAABBkCIAAAQJAhAAIAAASZ/x9+lzcPNIegyQAAAABJRU5ErkJggg==' 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(Twall_model.T[0] * 19.8 / 30 , Twall_model.T[-1], '-o')\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": "e1d9254d",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 실제 정수 기록"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 56,
|
|
"id": "0412e4da",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import pandas as pd"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 57,
|
|
"id": "841a4910",
|
|
"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": "45311f29",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"heat_program = np.zeros((2*heat_140205_4A.Q.size-1, 2))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 59,
|
|
"id": "bfe3bc04",
|
|
"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": "24cc8840",
|
|
"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": "b129f59a",
|
|
"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": "8356bc94",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"hp2 = data_to_program('./Heat_Mod_140205_4A.csv')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 63,
|
|
"id": "31afc8da",
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "f653e716651445abb4eb37f891324af4",
|
|
"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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" Figure\n",
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f1se2OpIgpAAACAmjBRafMSq50COAcQAADAYSgAAQAAHIYCEAAAwGEoAAEAAByGAhAAAMBhUuYq4E8//VT33nuvNm/erFAopGeffVZXXXWVzjrrLO3atUter1eS1KVLF82ZM8fmaOtIWoYuDz4qSZqZlmFzMAAAHBtjjEpDkWq/LlBW/df8SCBSKCC5PJL3sO/Vsv2Vv8blif3r9Sc2llqWEgXg+vXrdcUVV+jVV19V//79VVZWpr1790qS9uzZoyVLluikk06yN0g7uD1abU6JtwEASDXGGA1+fpk+37zH7kCk6QOlgnyp0yDpmlet537XsvLXtR8gDZslPfidtY6p4BLjwQcf1KhRo9S/f39Jks/nU4sWLSRJhYWFaty4sY3RAQCAY1UaitS4+Oue20SZ3hoOhIQCseLPIZJ+BDAYDGr+/PmaOnXqEc+FQiEFAgFlZ2fbEFkSiJTpNs+8g22mggMApLblD/WX31f9Qi7T65HL5UpcIJdNKb/8wPbK+7pS8whc0lcMX331lTIzM7V48WJNnDhR+/bt00UXXaQnnnhCpaWlcrlcOuWUU+T1etWnTx9NmDBBJ5xwQqXbCwaDCgaD8eXi4uQfpq1UJKQHvK9LkgKRx20OBgCAmvH7PPL7kqA0SUsvv+xrYE8ctSjpDwGXlJQoHA4rPz9f+fn5WrVqlXbu3KnRo0fr+OOPVzgc1qZNm7Rs2TJ5PB7l5eXJGFPp9iZOnKjs7Oz4Iycnpw7/NwAAAPZL+gKwefPmCgaDmjRpkjIzM9WwYUONGzdO77zzjiTFh3ybNWumqVOnat26ddq0aVOl2xs7dqyKiorij4KCgjr5fwAAACSLJBhnPbrc3FxlZGQoEAjI5/NJihV9GRlH3vbEGKNoNBrvV5H09HSlp6dX+jwAAEB9l/QjgBkZGRo+fLjuu+8+hcNhBYNBPfLII7r++uu1ceNGbdiwQVLs3L7Ro0erZ8+eat26tc1RAwCAlOL1xy72eGB7yt3T71gkfQEoSY899piCwaBatWql008/Xe3atdOjjz6qwsJCXXLJJWrVqpU6deqkcDis2bNn2x0uAABINS5X7GIPX4NYu55L+kPAktSgQQO9+uqrR6zv0aOHvv76axsiAgCksurOPJHw24wANkuJAhCVSMvQ0LKHJEnTmQoOAKrkWGae6J7bRLNG9KYIrM/CQWnemFg7b8qRt4KpZygAU5nbo0+ineJtAMCPO5aZJ5Zv3qPSUCQ57lGH2hENS6tei7UvfVISBSAAAPXSj808ESiLqPuED+swIqBuUACmskhIN3gWHmz3Ez9OAKiepJl5AqhjCb8K+Gg3YUaCRcr0qPcVPep9RYqU2R0NAABIEQkvADt27KgxY8Zo165did40AAAAEiDhBeC///1vrVmzRqeccop++9vfKhAIJPotAAAAUAMJLwB79OihDz74QLNmzdLbb7+tdu3a6YUXXlA0Gk30WwEAAOAY1NpMIAMGDNBnn32myZMn66mnnlKnTp301ltv1dbbAQAAHDuvX7pvY+zBVHA1d+mll+qll15S06ZNNWTIkNp+OwAAgOpzuaQGzWMPB9zwO+HXvk+fPl1r1qzR2rVrtWbNGm3btk0ul0tt2rTRZZddlui3AwAAQDUlvAAcO3aszjjjDJ155pm6+uqrdeaZZ+qMM85QgwYNEv1WSEvXTWX3SZL+WM+nrAEAoFaFg9L7D8TaA3/HVHDV9f333yd6k6iMO03/iHaJtwEAwDGKhqXP/hRrXzRe9X0quFo/BxAAAADJJeEF4N69e3XbbbepXbt26tixo7777rtEvwUOiYQ02PORBns+kiIhu6MBgKQQKIsoUBY+yiNid4iA7RJ+3HDkyJH65ptvNGnSJF1//fUqLS2VJI0ZM0YnnXSSRo8enei3dK5ImZ70TpMkBSIPSsq0Nx4ASALdJ3xodwhA0kv4CODf//53TZ06VVdddZU8Hk98/c9+9jPNmDEj0W8HAIAyvR51z21Srdd0z22iTK/nxzsC9VCtXDmQlZV1xLr27dvr66+/ro23AwA4nMvl0qwRvVUaqvrh3UyvRy4H3O8NqEjCRwAvueQSvfbaa0es37dvHx80AECtcblc8vvSqvzgOwlOlvARwMcee0zdunWTJBlj5HK5VFpaqvHjx6tr166JfjsAAICaS8uURq+22vVcQgvASCSi/Px8ffDBB7rnnnsUCAR0zjnnqKSkRI0aNdKCBQsS+XYAAACJ4XZLTXLtjqLOJLQA9Hg8uv7667VmzRp98MEH2rJli1atWiWv16uePXuqSZPqnaALAACAxEv4IeBzzjlHmzZt0sknn6w2bdqoTZs2iX4LHJKWrjvLfilJerKeT1kDAECtCpdJi8fH2v0eltJ89sZTyxJ+Ecgvf/lLPfDAAyooKEj0pvFD7jQtiPbSgmgvpoIDAKAmoiHp4z/EHtH6P7lCwquGIUOGSJJOP/10XX755brgggvUpUsXnXnmmfL56nc1DQAAkAoSXgBu2rRJK1eu1KpVq7Ry5UpNnDhR3377rTwejzp06KDVq1cn+i2dKxrWJe5P5HcFVVpyjpT+gwLb45M83nhfhYOVb+tgX+6LBdQ9Y0y17l9nJ6ZRA+qHhBeAubm5ys3N1aBBg+LrSkpKtHLlSoq/RAsHNdX3TKz9h2lHPH1v6HbNjvSVJF3oXqGXfU9UuqnfhIZrRmSAuuc20awRvSkCgTpijNHg55fp88177A4FgIMk7BzABx54QJ9++mmFzzVs2FDnn3++Ro4cmai3g6RMf0Ot83ZK6DaXb96TMiMRQH1QGoqkZPHHNGpAakvYCOB3332nyy67TB6PR3l5eRo0aJD69++v9HSuTq0tLrdbHcYuVSBQUuHz4z0+jY8fAv6pAuFRlW5rdFj676QXDvbtr1qaJRDAUSx/qL/8vtQoqjhdBEhtCfuWf/nll2WM0ZIlSzRv3jzdc8892rZtmy666CJdfvnluuyyy9S8efNEvR0Ocrnd8mdlV6FnmqSMyp/eV6SZvgmSpED4NkkU7kBd8/s88vv44wv2qutzUis8r9QYKRQ4cn25c9sjUvhA5Rt2e61buVSlr8MkdE/jcrl0/vnn6/zzz9ekSZO0bt06zZs3Ty+++KJuv/129ezZU5dffrmuvfZatWrVKpFvDQAAaigpzkk1Rpo+UCrIP/K5S56UzvlFrL35Y+nPl1W+nYvGS+eOjrW/Wym92K/yvn1/LfX9lXTnJ7FlpoKrmY4dO6pjx466//77tXPnTr3zzjt65513JEn33ntvbb41AACoJjvPSY2fVxoKVFz81Ta3W2rRse7f1yYJKwBHjBihrl27qkuXLurcufMR5/4dd9xxuuWWW3TLLbck6i0BAEAtqetzUis8r/TeryWf31r2HHa7s9yfSA9sr3yDhx/WPfHsqvd1iIQVgCtWrNCMGTNUWlqqtLQ0dejQQV27do0XhV26dFFWVlai3g4AANQi285J9aRL170Za2c2kTyVxOD2SL4GVdtmdfo6RMJ+svn5+YpGo1q/fr1WrFgRf8ybN0979uyR2+1Wu3bt1L9/f40aNUqnnXZaot4aAADUF5406dSBdkdR7yW0tHe73erUqZM6deqkYcOGxddv3rxZK1as0Oeff6733ntP06dP18KFC3Xeeecl8u0BAABQBQm7EfTR5Obm6oorrtCjjz6qzz77TGPHjtWvfvWrunhrVJXHq9+FrtXvQtdal9gDAFDXIiFpxV9jj0jI7mjqrYQVgAsWLNBZZ50VX7733nv1wgsvKD8/X4FA+Xv53HjjjVq1alW1tv/pp5+qT58+ys3NVcuWLfXWW29Jip172KtXL+Xm5qpTp05auHBhzf8zTuTx6YVInl6I5JU/yRYAgLoUKZPm3hl7RMrsjqbeStgh4Oeff14333xzfHnatGmKRCI6cOCA3G63TjvtNOXn5ysrK0u5ublatmxZlbe9fv16XXHFFXr11VfVv39/lZWVae/evSopKVFeXp5eeeUV9e/fXx999JEGDRqk9evX64QTTkjUfw0AAKBeSdgI4OrVq9WrV69y67744gt98803mjNnjjIyMvTyyy/HnzvzzDOrvO0HH3xQo0aNUv/+/SVJPp9PLVq00Ouvv64ePXrE1/ft21d9+vTRG2+8kYD/kcNEI+rs2qjOro2xO6YDAIB6K2EjgDt27FDLli2tDaelyeVyqW3btmrbtq3279+vP/zhDxo1qvL5aCsSDAY1f/58TZ069Yjnli1bpnPPPbfcup49e2rlypXH9H9wtPABvZP+G0lSIPxzMRUcAAD1V8JGAJs3b67NmzfHl3fs2KHc3Nz48tlnn621a9dWe7tfffWVMjMztXjxYnXu3Fknn3yybr/9dhUXF2v79u06/vjjy/Vv0aKFdu/eXen2gsGgiouLyz0AAACcJGEFYL9+/TR9+vT4cnp6ujwe6w7ibrdboVD1r+YpKSlROBxWfn6+8vPztWrVKu3cuVOjR49WJBKRMaZc/0gkcuSdxA8zceJEZWdnxx85OTnVjgkAACCVJawAvO+++/TXv/5VU6ZMqfD5pUuX6uSTT672dps3b65gMKhJkyYpMzNTDRs21Lhx4/TOO++oadOm2rVrV7n+O3fuPOoFIGPHjlVRUVH8UVBQUO2YAAAAUlnCCsAzzzxTf/nLX3T//ferR48emj17tgoKCrR9+3a9+eabGjt2rG644YZqbzc3N1cZGRnlbiXjcrmUkZGhbt266eOPPy7Xf+nSperdu3el20tPT1ejRo3KPQAAQJLwpEtDXok9PJyPXlsSeiPoIUOGKD8/Xw0aNNA111yjtm3bKicnR0OHDlXv3r111113VXubGRkZGj58uO677z6Fw2EFg0E98sgjuv766zVs2DAtWrRIixcvlhS7F+H69es1ZMiQRP63AABAXfGkSadfGXtUNg8waizhme3SpYv++c9/asuWLfriiy9UUlKiM844Q2ecccYxb/Oxxx7THXfcoVatWqlhw4a6+uqr9eijj8rn82nmzJm68847VVhYqHbt2mnevHlq0IAJnwEAACpTa6V1mzZt1KZNm4Rsq0GDBnr11VcrfG7gwIFav359Qt7H0TxeTQlfJUm6jangAAB2iYSl9fNi7Q55jALWErKKGI9PU8KDJUm3MRUcABwhUHZsN8nP9HqOencK/EAkKM0aHms/sJ0CsJaQVQAAqqD7hA+P7XW5TTRrRG+KQCSVhF4EghRmomrv2qr2rq2SidodDQAkhUyvR91zm9RoG8s371FpiCk2kVwYAURMqFQfpN8vSQqEhkrpHAYGAJfLpVkjeh9TARcoixzzqCFQ2+qsACwrK5PPR1EBAEgtLpdLfh/jJahf6uwQ8JNPPqmzzz5b48aN0+rVq+vqbQEAAPADdVYAPvDAA5o9e7Zyc3P1zDPP1NXbAgAA4AcSNqZdUlKihg0bSpIKCwvVtGnTI/q0a9dO7dq10/DhwxP1tgAAoD7x+KRBU602akXCRgBHjx4tSdq9e7d69eqVqM0CAAAn8XilLsNiDyYmqDU1LgD37t2rLVu2aN++fSooKFBBQYHC4bAKCgq0e/fueJ958+Zp+/btNQ4YAAAANVPjQ8BPP/20/vSnP0mSfvKTn8TX/+QnP9Fll12mCRMmqGvXrmrRooU2b96sf/zjHzr99NNr+rZINI9X08KXSpJu4C8uAIBdImFp46JY+5SfMhNILalxVh955BE98sgjlT7/+OOPa/jw4Ro3bpxee+01/f73v9dLL71U07dFonl8mhgeJkm6gXMuAAB2iQSl166JtZkKrtbUylXAoVBIN9xwgyRp8eLFuvHGGyVJQ4YM0bJly2rjLQEAAFBFCSmrb7755nh7+vTp8nq9Wrp0qSTp+++/V+vWrSVJXq9X0SjTjCUlE1Vr1854GwAA1F8JGQGcO3euzj33XM2fPz++zuPxKBqNxv9FkguVakn6aC1JHy2FSu2OBgAA1KKEFIANGjTQLbfcogYNGsTXeTwehcNhnXjiidq0aZMkaf/+/fJ4PIl4SwAAAByjWpsJxO12KxqN6uKLL9bUqbEbOr744ov66U9/WltvCQAAgCqo1UtrotGofv7zn+vcc89Vs2bN5Pf79fHHH9fmWwIAAOBHJKQANMbE23369JExRlu2bJEkZWVl6fPPP9fatWvVvn17ZWZmJuItAQBAfeTxSZc8abVRKxJSAM6aNUuSNHPmTB04cCC+/lCxl5aWps6dOyfirQAAQH3m8Urn/MLuKOq9hBSAh+b+7dmzZyI2BwAAgFpU57fX/v7773X88cfX9dvix7jT9Gr4IknSYDd3XQcA2CQakTYfvF4g9yeSm7uH1IYaf9O/+eabev755yt8btKkSbr//vslSS1atNDMmTPVpUsXbd++vaZvi0RLS9fD4ZskSYPT0m0OBgDgWOED0p8vi7Uf2C75Ghy9P45JjQvA3r17q2nTprrxxhs1Y8YMbdiwQXPnztX999+vk08+WatXr9bf/va3+NRwh18wAgAAapcxRqWhSJX6Bsqq1u8YA5FCAWs5LcMa3QuXSdFQrF0WOPK1SLgaF4A5OTnKyclRRkaGfvrTn6px48b67LPP1K9fP0mSz+dT3759axwoapkxaqrieBsAkPqMMRr8/DJ9vnmP3YFI0wdKBfnWul8sllp1i7Xzn5M+eNie2BwqYSd7lZSU6PHHH9fevXsTtUnUpVBA/8kYIUkKhC6R0rNtDggAUFOlocgxFX/dc5so05vAc+9CgfLFX1Xk9JK8/sTFgHISUgD+4Q9/UFZWlr7//ntt3bpVy5cv52IPAACSyPKH+svvq1pRl+n1yOVy1U4g934t+fyxQ8CH9LxD6nFr+X5ev1RbMaDmBeD//d//afLkyVq5cqWys2OjRn/4wx/08MMPa9q0aTUOEAAA1Jzf55HflwR3efD5j7ywI80niZs+16UazwX8+eef66KLLooXf5L085//XJ988kmF/WvtLwoAAABUSY3/FDjuuOO0e/fucut2796t4447TpJ11e///d//qV+/fiosLKzpWwIAgFTi9kp9f221YbsaF4Bnn322vvrqK82dO1eDBg1SSUmJRo8eraFDh0qS3nrrLUnS+++/X9O3AgAAqSjNJ1041u4ocJgaF4BpaWl68803deONN+qWW25ROBzWL3/5S916a+xkzt69e0sSt4IBAABIEgk5G7Rjx4767LPPVFRUpKysLHk8TNuSctxpmh3pI0m6hKngAACJFI1Ku76KtZufJrlrfAkCaiih3/SHXwiCFJOWrntDsfsAXsJUcACARAqXSlN7xdpM75YUKMEBAAAchmN9iDFGmToQbwMAgPqLAhAxoYDWZdwsSQqEtjAVHAAA9VhKHAKePHmysrOz1bZt2/hj48aNkqSzzjpLrVq1iq+/8sorbY4WAAAguaXECOCePXs0ZswYjRs3rsLnlixZopNOOsmGyAAAAFJPSowAFhYWqnHjxtV+DgAAAEdKmRHAioq8UCikQCDA7WcAAEhmbq/0k1FWG7ZLiRHAPXv26MEHH1ROTo769eunRYsWSYqN/rlcLp1yyik69dRTdeutt2rHjh1H3VYwGFRxcXG5BwAAqEVpPmnAhNgjzWd3NFCKjADOnz9fbrdb4XBY8+bN01VXXaV//OMf6tq1q8LhsFwul3bv3q0HHnhAeXl5+vTTT+VyuSrc1sSJEys8lxAAgMQzylCZAmURa1WoVDLRyl9y+E2Sq9M3fECKRso9HSiLWLf4OlzogGQiR64/xOuXDn2PhoNSNJyYvtwAOmmkRAHoPjhlTFpamq688kq9//77evvtt9W1a9d4odesWTNNnTpV2dnZ2rRpk04++eQKtzV27Fjdfffd8eXi4mLl5OTU/n8i2bk9ejdyjiSpZ1jSvqKj9lVahrVctl+Z/oZyMbUPkoAxRqWho3yxJZlyhQHqF2M02zdOJ7gK1X2CNcPSXN9DOsv9TYUv2W0aqltwWnx5pu9R9XKvq7BvwKSrU/Dl+PJ07yT186ws18cvad3B3XVAu60n5twmrZ1beeyHz9Yxb4y06rXK+963UWrQPNZ+/wHpsz9V3K/7LdK5o6XsHKaCSwIpUQD+UCQSkc935BCyMUbRaLTC5w5JT09XejpTnR0hLUMjQ2Ni7ceW6NuM6yrtujhytm4O3R9fXpt+k9b7TlGHsUspAmErY4wGP79Mn2/eY3cogDIVVHf3hoPtAypVxo+8opbj8XpsfX8tf0n6fo1083v2xgFJKVIAvv/++7rooovkdru1cOFCvfXWW1qyZIk2btyoSCSiU089VcFgUHfffbd69uyp1q1b2x1yysn0etQ9t4mWV/OLM1MH5HcF1TG0VoFAifxZXJAD+5SGIilb/HXPbWL/FzQS6vBTkT5/qL81ohbqo0Alh3UzJa0tdwi48r76Yd9wXwWiFY8oZ3o95U+NuvIF6YrnKg/e67faeVOkS5+sWt+Bv5MuGn/0vpWcooW6lRIF4OTJk3XDDTfI7/crNzdXc+fOVceOHfXZZ5/p2muvVWlpqTIyMtS/f3/Nnj3b7nBTksvl0qwRveOHzgJlWyrt28vt0dqDh4AD+4qlZ+okRKBalj/UX35f6hRUR3xBo17x+9Ik38GvXF/Dqr+wWn2zqt7XW43RyLR0SVU8cladvrBVShSA771X8XBxjx499PXXX9dxNPWXy+WK7aQkyVfFkbwU+oKFs/h9Huv3GQBQDidsAQAAOAwFIAAAgMNwfAQAgFrhko7rYLWBJEIBiJpxufVJtKMkqbOLAWUAiPP5pZH5dkcBVIgCEDXjzdTQst9IktZ6M20OBgAAVAVDNgAAAA5DAQgAQG0oC0h/7Bl7lAXsjgYoh0PAqJmy/fo8/faD7TVVv38gANR7Rtq53moDSYQCEDXWzFUiSeLvWwAAUgOHgAEAAByGAhAAAMBhKAABAAAchgIQAADAYbgIBACAWuGSsttYbSCJUACiZlxurYqeLElqz1RwAGDx+aW7vrA7CqBCFICoGW+mBpVNkMRUcAAApAqGbAAAAByGAhAAgNoQKpVeuCD2CJXaHQ1QDoeAUTOhgJak//Jge4Xka2RvPACQLExU2r7CagNJhAIQNWOMWrt2SZIChrkuAQBIBRwCBgAAcBgKQAAAAIehAAQAAHAYCkAAAACH4SIQAED1GCOFAhU/5/ZKab5YOxqVwke5/Um1+qZJaek//v7V7evySN4Ma7lsf4L6Hhxf8TervA9gIwpA1IzLpQ3RVpKk1i7mugTqPWOk6QOlgvyKn+/7a+nCsbH2rq+kqb0q39ZPRkkDYjMJqahAerpz5X173Cpd+lSsHdgtPXFK5X3Puk668rlYOxSQftey8r6dBknXvGotH61v+wHSsFnW8hPtKi8uc8+TbnpXuv+byrcH2IgCEDXj9WtA2ROSpLVev83BAKh1oUDlxR+AlEEBCACourQM6ReLpbKAdOJZkttT/nm312o3P016YHvl2zq8b3bOj/Q97OvK36zqfb3+o/d1/SD+6vS97+uj9OUUeyQ3CkAAQNW5PVKrblXs65Z8DRLf1+Wqnb5S7fUFkgwFIGomFNBC330H2+czFRwAACmAAhA1Y4xOdW+TxFRwgCOEy6T8gxdY9LzDuooXQEqhAAQAVF00JH3wcKzd41ZJFIBAKuIsVQAAAIehAAQAAHAYCkAAAACHoQAEAABwGC4CQc24XNpqmkuSmjIVHAAAKYECEDXj9eu84DOSmAoOAIBUkRKHgCdPnqzs7Gy1bds2/ti4caMkacWKFerVq5dyc3PVqVMnLVy40OZoAaAeS8uQfj4/9kjLsDsaAMcoJUYA9+zZozFjxmjcuHHl1peUlCgvL0+vvPKK+vfvr48++kiDBg3S+vXrdcIJJ9gULQDUY26PdNL5dkcBoIZSYgSwsLBQjRs3PmL966+/rh49eqh///6SpL59+6pPnz5644036jhCBwuVaq7vIc31PSSFSu2OBgAAVEHKjABWVAAuW7ZM5557brl1PXv21MqVK+smMEgmqrPc30iSAiZqczAAal0kJH3+Sqzdbbjk8doZDYBjlBIjgHv27NGDDz6onJwc9evXT4sWLZIkbd++Xccff3y5vi1atNDu3bsr3VYwGFRxcXG5BwCgiiJl0oJ7Y49Imd3RADhGKTECOH/+fLndboXDYc2bN09XXXWV/vGPfygSicgYU65vJBKR6yi3I5k4ceIR5xICAAA4SUqMALrdsTDT0tJ05ZVX6tprr9Xbb7+tpk2bateuXeX67ty586gXgIwdO1ZFRUXxR0FBQa3GDgAAkGxSogD8oUgkIp/Pp27duunjjz8u99zSpUvVu3fvSl+bnp6uRo0alXsAAAA4SUoUgO+//76i0dgFBgsXLtRbb72lq6++WsOGDdOiRYu0ePFiSdKCBQu0fv16DRkyxM5wAQAAklpKnAM4efJk3XDDDfL7/crNzdXcuXPVsWNHSdLMmTN15513qrCwUO3atdO8efPUoEEDmyN2lt2moSQp0+Y4AABA1aREAfjee+9V+tzAgQO1fv36OowG5fgaqFtwmiRprY/CGwCAVJASBSAAIEl40qXr3rTaAFISBSAAoOo8adKpA+2OAkANUQCiZkKlmul79GC7j+RraG88AADgR1EAomZMVL3c6yQxFRzgCJGQtPrgIeDO1zAVHJCiKAABAFUXKZPm3hlrn34FBSCQolLiPoAAAABIHEYAAaA2GCOFAuXXpWVKB6e2VLhMioYqf31ahuT2VL9vJBQbpauMJz12IUe1+4alSFAqC1TeH0DKoABEwgTKIlJZOLYQPiBFI5V3PuyegZlej1wuVy1Hh2RjjFFp6Ci/I8cgUJbY7R0zY6TpA6WC/PLr7/xEahG7ib3+/ZT00WOVb+MXi6VW3WLt/OekDx6uvO/P50snnR9rf/6KtODeyvte96Z1Fe/qN63DuRUZ8op0+pWx9vp50qzhlfcFkFIoAJEw50/6h0qVIUma7p2kfp6VlfZte+C1eLt7bhPNGtGbItBBjDEa/Pwyfb55j92h1I5Q4Mjir77J6SV5/XZHAeAYUQCiRjL9DbXO20m5ZRvjxV9VpatMv/dOlbZLpaVz5Pdn1VKUSDaloUitFn/dc5so0+upte1Xy71fS76DhVLaYRMmnn+PdO4vK39d2mGfp553SD1urVrfbsOls6+rvO/hN2/ufE3sQo6q9O2QJz2w3Vr2+iX+aANSFgUgasTldqvD2KUqDZSUnwou3FeBoxwCXutroMC+YjV/ZrgkHbUv6rflD/WX35fYYi2pTivw+cud8hCX5pPkq9o2qtPX4636lbnV6ptmnQ8IIOXxaUaNudxu+bOyy6/0VWE0L8Ff+khNfp9Hfl892xW5vdJPRlltAEgy9WyvCwBJIM0nDZhgdxQAUCnuAwgAAOAwjAACQKJFo1JRQaydnWPd+w8AkgQFIAAkWrhUerpzrP3A9oovAgEAG/FnKQAAgMMwAgj7eP3qeGC6JOlzbigLAECdoQCEfVwu6+bRyXLPNgAAHIBDwAAAAA7DCCDsEw7qSe/zB9sXSPXtZsAAACQpvnFhn2hYgz3/kiQFomGbgwEAwDkoAAEg0dxpUo9brTYAJBn2TACQaGnp0qVP2R0FAFSKi0AAAAAchhFAAEg0Y6TA7ljb34zbHAFIOhSAAJBooYD0xCmxNlPBAUhCHAIGAABwGEYAYR+vX10PxO4DuISp4AAAqDMUgLCPy6VCNYq3AQBA3eAQMAAAgMMwAgj7hIMan/bywfYFTAUHAEAd4RsX9omGdWPaB5KYCg4AgLpEAQgAieZOk866zmoDQJJhzwQAiZaWLl35nN1RAECluAgEAADAYRgBBGATo0wFY7Nm+BpZq8sCkkwlr3FJvsPuGRkqlUy08rc4fAaOavU9IJnIsfc1JvZ+Pr/k9XObIwBJJ+UKwBEjRuif//yn1q9fL0k666yztGvXLnm9XklSly5dNGfOHDtDBPBjjNFs3zh1d29Q9OXTpP/91HruxQulnesrfl12G+muL6zlly+Wtq+ouK+/mXT/N9byXwZLm5dU3Nfrlx78zlp+8wbpvwsrj///FVntObdJa+dW3pep4AAkoZQqALds2aIZM2YoJycnvm7Pnj1asmSJTjrpJBsjA1AtoYC6uzfYHUXty+kVKy4BIMm4jDGVHWtJOldffbVOPPFEffjhh/ERwKysLBUUFKhJkybHtM3i4mJlZ2erqKhIjRo1+vEXIGECwTIN+H+vSZIW/r/r5E/32RwR6kpgX5H8T7aJtcdskL/x8daT9eEQ8CEc/gUcKRVqi5QZAZw/f74KCws1atQoffjhh5KkUCikQCCg7Oxsm6PDMXG5tdUcF2/DodIyyi/7qjFi5s2spb4ZP97nWPoCQJJIiW/d7du3a+TIkXr++efLrS8sLJTL5dIpp5yiU089Vbfeeqt27Nhx1G0Fg0EVFxeXewAAADhJ0heA0WhU1113ne677z6ddtpp5Z47/vjjFQ6HtWnTJi1btkwej0d5eXk62lHtiRMnKjs7O/44/HxC1LFImcam/VVj0/4qRcrsjgYAAMdI+gJw/PjxatiwoUaOHFnh866D59c0a9ZMU6dO1bp167Rp06ZKtzd27FgVFRXFHwUFBbUSN6ogEtLtae/q9rR3pUjI7mhQl1wubTXNtdU05xw5ALBB0p8DOG3aNO3fvz9+kUc4HFZpaakaN26szz77TO3bt4/3NcYoGo3K56v8YoL09HSlp6fXetwAjsLr13nBZyRJa7lKFgDqXNKPAH733XcqLi7W3r17tXfvXs2fP1/t27fX3r175Xa7tWFD7FYSwWBQo0ePVs+ePdW6dWubowYAAEheSV8AHk1hYaEuueQStWrVSp06dVI4HNbs2bPtDgsAACCpJf0h4B+64IIL4vcA7NGjh77++mubIwJQbaFSzfU9dLDdR/I1tDceAHCYlCsAAdQDJqqz3LFp2gJHuzkzAKBWpPQhYAAAAFQfI4CwjzdTFwUnSZLmVmeWBgAAUCMUgLCPy63/mtbxNgAAqBt86wIAADgMI4CwT6RMY9IO3rYncqH4dQQAoG7wjQv7REIak/aWJCkQmWJvLKhzu03s1i+c/QkAdY8CEEDd8zVQt+A0SdJaXwObgwEA5+EcQAAAAIehAAQAAHAYDgEDqHuhUs30PXqwzVRwAFDXKAAB1D0TVS/3OklMBQcAduAQMAAAgMMwAgj7pGXo8mDsMODMtAybgwEAwDkoAGEft0erzSnxNgAAqBscAgYAAHAYRgBhn0iZbvPMO9hmKjgAAOoK37iwTySkB7yvS5ICkcdtDgZ1LWDS7Q4BAByLAhBJoXR/ibWQlmGdExgJSZGyyl+Yli6506rfNxqWwsHK+3p8ksd7DH0jUvjAUfp6Y/2r29dEpVBpYvq602K5kCRjpFAgQX09sZ/dIWX7K+0aCEvdgy9LYio4ALADBSCSQrPnTo+3h5Y9pE+inSRJN3gW6lHvK5W+7qay+/SPaBdJ0mDPR3rSO63SvneW/VILor0kSZe4P9FU3zOV9r03dLtmR/pKki50r9DLvicq7fub0HDNiAyQJPVyr9VM34RK+/4udK1eiORJkjq7Nuqd9N9U2ndK+CpNCQ+WJLV3bdUH6fdX2nda+FJNDA+TJLV27dSS9NGV9n01fJEeDt8kSWqqYv0nY0SlfWdH+ujeUOz5TB3QuoybK+37buQcjQyNiS9/m3FdpX0/iZwtqfL/DwCgdnERCGyT6W+odd5OdocBG3XPbaJML1eAA0BdcxljjN1B2Km4uFjZ2dkqKipSo0aN7A7HcUw0qtJASfmVHAI+sm89OwR8qG+m1yOXy1V5PwBIQalQW3AIGLZyud3yZ2UfpUeapMwqbq26fat68+nq9q3qxQ3V6Ssp3VdLfY+W/xr09VWjLwCgTnEIGAAAwGEoAAEAAByGAhAAAMBhKAABAAAchgIQAADAYSgAAQAAHIYCEAAAwGEoAAEAAByGAhAAAMBhKAABAAAchgIQAADAYSgAAQAAHIYCEAAAwGEoAAEAABwm5QrAESNGqEOHDvHlFStWqFevXsrNzVWnTp20cOFCG6MDAABIfilVAG7ZskUzZsyIL5eUlCgvL08TJkzQ5s2b9dxzz+maa67Rjh07bIwSAAAguaVUAXjXXXfppptuii+//vrr6tGjh/r37y9J6tu3r/r06aM33njDrhABAACSXsoUgPPnz1dhYaEGDx4cX7ds2TKde+655fr17NlTK1eurOPoAAAAUkea3QFUxfbt2zVy5EgtXLhQ3333Xbn1/fr1K9e3RYsWys/Pr3RbwWBQwWAwvlxUVCRJKi4uTnDUAADAiQ7VFMYYmyOpXNIXgNFoVNddd53uu+8+nXbaaeUKwEgkckRyI5GIXC5XpdubOHGixo0bd8T6nJycxAUNAAAcb/fu3crOzrY7jAolfQE4fvx4NWzYUCNHjjziuaZNm2rXrl3l1u3cuVMnnHBCpdsbO3as7r777vjy3r17lZubqy1btiTtD6muFBcXKycnRwUFBWrUqJHd4diKXFjIhYVcxJAHC7mwkAtLUVGR2rRpo6ZNm9odSqWSvgCcNm2a9u/fryZNmkiSwuGwSktL1bhxY40dO1Yff/xxuYJu6dKlGjp0aKXbS09PV3p6+hHrs7OzHf8Le0ijRo3IxUHkwkIuLOQihjxYyIWFXFjc7uS91CJ5Izvou+++U3Fxsfbu3au9e/dq/vz5at++vfbu3athw4Zp0aJFWrx4sSRpwYIFWr9+vYYMGWJz1AAAAMkr6UcAj6Z169aaOXOm7rzzThUWFqpdu3aaN2+eGjRoYHdoAAAASSvlCsALLrhA69evjy8PHDiw3HJ1paen65FHHqnwsLDTkAsLubCQCwu5iCEPFnJhIReWVMiFyyTzNcoAAABIuKQ/BxAAAACJRQEIAADgMBSAAAAADkMBCAAA4DAUgMfgwIED2rdvn91hJIU1a9bo9ddfVzQatTsU20UiEYXDYbvDSAplZWU6cOCA3WEkBfYXFvYXFvYXFvYXlrrcX1AAVtMXX3yhQYMG6ac//alGjRqlv/71r3aHZJs1a9Zo4MCB2rp1a1Lf7bwurF27VjfeeKMuueQSTZw4UQUFBXaHZJs1a9Zo6NCh+tnPfqZf//rX2rBhg90h2Yb9hYX9hYX9hYX9haWu9xfO/hRW0zfffKMhQ4Zo8ODBmjRpkvx+v1577TXddddddodW5/bv368xY8Zo/Pjxuu++++wOx1br1q3ToEGDdOaZZ2rYsGGaO3eunnnmGbvDssWGDRt0+eWXa8CAAXrooYf06aefau7cuXaHZQv2Fxb2Fxb2Fxb2FxZb9hcGVfb222+bkSNHxpdLSkrMwoULzQUXXGBGjBhhY2R1r7Cw0FxwwQXm22+/ja9bu3atWb16tdm0aZN9gdWxsrIyM2bMGPPiiy/G161bt840a9bM/Otf/7IxMns8++yz5p577okvv/XWW+bWW281u3fvNqFQyMbI6h77Cwv7ixj2F+Wxv7DYsb9gBLAaduzYoXXr1sWXs7Ky1K9fP40fP14bN27U448/bmN0dcvtdistLU2RSESSNHHiRN1yyy0aOnSoRo0apfHjx9scYd3wer3y+/1q2rSppNj5Gx06dNBFF12kUChkc3R1b8eOHVq+fHl8eenSpfroo4904YUX6uabb9aECRNsjK5usb+wsL+IYX9RHvsLix37CwrAarjmmmt04MABvfzyy/F1Ho9HPXr00PXXX69169apuLjYxgjrTnZ2tsLhsB5//HH95z//0fz58zV79mwtWLBAN998s7744gvl5+fbHWatOnQC929+8xtdddVVkqSMjAxJ0rfffqu9e/faFVqdO/TFfuedd2r37t0aMGCA8vLy9Oc//1lvv/22Xn/9dV1++eX67LPP9NFHH9kcbd1gf2Fhf8H+4nDsL45kx/6CAvAoNm7cqCeeeEIvvfSS/v73v6tJkyYaPHiwPv74Y3344YfxfhkZGbrwwgv10Ucflftrpj45PBcLFiyQJD3zzDPaunWrnnrqKd1xxx1q2bKlcnNz1a9fP+3atatGczQns927d0uS0tLSFA6H4ztxc9isio0aNVLbtm3j6+bNm6d333237oOtZYdy4fF4FAqFdOKJJ2rJkiUaOXKkOnTooBdeeEGdOnVSp06d1L9/f+3evVtfffWVzVHXDvYXFvYXFvYXFvYXlmTYX1AAVmLNmjXq06eP1qxZow8//FA33nijHn30UV188cVyu92aNWtWuQ9oTk6OBgwYIL/fb2PUteOHuRg+fLjGjh2rE088URdddJE++eQTLVq0KN4/Oztb3bt3V2Zmpo1RJ54xRqFQSFdddZXuvvtuSSp3WMvlcikSiejbb7/Vtm3b1LZtW7lcLr300ku666671KFDBzvDT6iKcuH1ehUKhZSdna1BgwZp69at+uSTT+Kvady4sbp27Vrvfi8k9heHY38Rw/7Cwv6ivKTZX9TKmYUpLhgMmhtuuME899xz8XUrV640LVu2NI888oj55JNPzMiRI80VV1xhHn74YbNt2zbz7LPPmpYtW5Y7ybk+OFouxo4dazZs2GCeeuop07dvX/PrX//abNq0yTz77LOmdevW5uuvv7Yx8tpz3nnnGbfbbW655Zb4unA4HG9v3rzZnH322cYYY1599VVz6qmnmlWrVtV5nHWholwcOnn79ddfN7fddpuZO3euMcaYl156ybRt27be/V6wv7CwvzgS+wsL+4vk2l+kJbacrB98Pp/S0tKUlZUlSQoGgzrrrLP0z3/+U5dccokk6bHHHtP8+fP12GOPaenSpdq1a5feffdd5ebm2hl6wh0tF5deeqkk6Xe/+53OOeccPfLII9qwYYO+//57vfvuuzrllFPsDL1WvPrqq/J4PFqzZo0uvvhi3XbbbXrhhRfk8XgUiUTk8XiUk5OjRo0aqV+/ftq2bZtmzZqlzp072x16wlWWi7S02G6lW7du+vzzz/XQQw/pj3/8o77//nvNnTu33v1esL+wsL8oj/2Fhf1FTFLtLxJaTtYD0WjUhEIhc8cdd5iHH344vq6srMwYY8xXX31lsrKyzMsvvxx/TUlJiSkuLrYj3FpV1VwcfkuDUChk9u3bZ0u8deHjjz82f/7zn40xxqxevdq0bt3a/OIXv4g/f+DAAWOMMdOnTzfdu3evt3/JG/PjuTDGmP3795vt27ebjRs3ml27dtkRZq1if2Fhf3Ek9hcW9hfJt7+gAKzE559/bho0aGD+8pe/xNcFg0FjjDEzZ8401157rSkqKjLRaNSuEOtMVXMRiUTsCrHOBAIBU1RUFF9eu3ZthTuyPXv2mMLCwroOr05VJRdOuZcX+wsL+wsL+wsL+wtLsuwvKAArcGjH9Oqrr5p27dqZN998s9z6f//73+ayyy5zxC8rufhxh3ZkTru5b0WcmAs+IxZy8eOc+BmpjBNzkUyfEc4BlBSNRsvNTXmoPXjwYO3bt0+//OUvVVRUpFtuuUVS7J5N4XBYBw4ciB/Hry/IheWHuahMx44d9cEHH6hbt25KT0/XlClTaj+4OkYuLHxGLOTCwmfEQi5iSktLj7iKOZk+Iy5jDrsZkcNs3bpVWVlZaty4caV9wuGw5syZo9tvvz3+S7p8+XItWLBAXbt2rbtgaxm5sFQlFxXZsGGDXC6X2rdvXzuB2YBcWDZu3KjMzEy1bNmy0j5O+YyQC0tVclERp35GKlIfc/H111/rt7/9rSZNmqTmzZvL5XId0cf2z0itjzEmqS+//NKcfvrp5oMPPqhS/40bN5r58+ebmTNnmo0bN9ZydHWLXFiqm4v6jFxYvvzyS3PccceZP/7xj1XqX98/I+Qiprq5qM/IheXLL780OTk5xuVymXXr1v1of7s+I44sANesWWNOPfVUM23atCr1r88nK5MLS3VzUZ+RC8v3339vzjjjjHJX5h1Nff6MkAtLdXNRn5ELy6F951tvvWUef/xx86tf/coYU/lnwc7PiONmAtm6dat+9rOf6Z577tFtt91WYR/zg6PiVTmXIRWRC8ux5KK+Ihflbdu2TSeddJKGDx8eX7d3714VFxdXmIf6+hmRyMXhqpuL+oxcxKxdu1aXX3657r77bl155ZVq2bKl1q5dK6nyz4KdnxHHXQQSjUbl8XhUVlYWXzdnzhxt2rRJ69at08iRI3X22WfbF2AdIhcWcmEhF+VFIhGVlJTElydPnqx///vfWrt2ra6++moNHDhQffr0sTHCukMuLOTCQi6k4uJi3X///frVr36lX/ziF5KkIUOG6KmnntLMmTM1dOhQmyM8kuMKwDZt2mjOnDnq16+fsrKy5PV69fjjj+viiy9WUVGRLrjgAs2fP1/nnXeejDEVnrhZX5ALC7mwkIvyjjvuOK1YsULvv/++otGoXnjhBb3xxhtauXKlvvzyS82aNUtdunRRw4YN7Q611pELC7mwkAupUaNGeu6555STkyNJKisrU3p6ugYNGqSNGzdKqvrV0XXGniPPdWvr1q3mww8/NP/5z3/M9u3bjTGxu5K7XC5z/PHHmy1btsT7jh8/3nTr1i1+h/b6hlxYyIWFXFgO5WLFihXmu+++M8YYM2nSJHPzzTebO+64w3zyySfxvsuWLTOnnnpqlU70TkXkwkIuLOTCsnXrVrNw4UKzcuVKs2PHDmOMic/sYYwxH330kTn++OPN8uXL7QqxUvV+BPCLL77QoEGD1LZtW4VCIYVCIU2ZMkW9e/fWv//9b+Xm5qp169YKBALy+/3q16+fVq5cqfT0dLtDTzhyYSEXFnJhqSgXzz//vP7nf/5HDz74oN577z395Cc/Uc+ePSVJvXr1UpcuXerleU7kwkIuLOTCUlEunn32WXXv3l3RaFQul0t9+vTR8OHDNWPGDLVv316NGjWyO2yLvfVn7dqzZ485//zzzYwZM4wxxvz3v/81v/nNb4zX6zULFiyI9wuHw/H2c889Z6655hpTWlpar6ZtIhcWcmEhF5aKcvHQQw+Z9PR0s2TJErNy5Upz+eWXm+HDh5vFixcbY4z505/+ZE499dT4KEh9QS4s5MJCLixH23f+/e9/N8ZY+80333zT9OvXL+lGAet1AVhcXGwuv/zy+OGrQ19WU6ZMMV6v1/zrX/+Kry8rKzNPPPGEOemkk8wXX3xhW8y1hVxYyIWFXFgqy8XkyZNNenq6+c9//mO2bNlixo8fb1q3bm2uvvpq065dO7Nq1So7w64V5MJCLizkwlJZLp5++uly+85DBgwYYHr16pVUfzTX2wIwGo2a7du3m549e5pPP/3UGFN+oumJEyeaE044wWzYsMEYY8yHH35oevToYVasWGFHuLWKXFjIhYVcWH4sF7/97W9Ny5YtzebNm40xxhQUFJhvv/223o1qGEMuDkcuLOTCUt1956HXbNq0qa5DPap6WwAe8sgjj5jTTz/dFBQUGGPK/5BGjRplnnvuOWOMMfv37zdFRUW2xFhXyIWFXFjIheVouRg5cqSZOnVqvb658eHIhYVcWMiF5cf2nVOnTj1ifTJJouuRE8scPOH0nnvu0XnnnacxY8bou+++U1paWvz+Zo0aNdLq1aslSX6/P7lOzkwgcmEhFxZyYalKLho3bqwvv/wyuW7jUAvIhYVcWMiFpar7zi+//FKSlJaWnNfb1tuf0qF7kzVs2FC33HKLGjdurFtvvVUFBQXy+XySpOOPP15+v1+RSMTOUGsdubCQCwu5sFQ1F5mZmYpGo3aGWuvIhYVcWMiFpb7kIjnL0gQJhULyer3q0aOHsrKy9NRTT6lz5866/fbbtW/fPr3xxhtatGiRPB6P3aHWOnJhIRcWcmGpai7q++iGRC4ORy4s5MJSH3KRvJHVUCQSkdfrlSRNnTpVp5xyiv70pz/p97//vdLT05WZmamPPvpInTt3tjnS2kcuLOTCQi4s5MJCLizkwkIuLPUlFy5jUvfujOvXr9fTTz+tPXv2qG/fvhoxYoRcLle56VZGjRqlxYsXa9WqVUl7HD4RyIWFXFjIhYVcWMiFhVxYyIXFCblI2RHAdevW6dJLL1XTpk3Vo0cPjRs3TuPHj5ek+A/n3nvv1erVq+M/nHA4bGfItYZcWMiFhVxYyIWFXFjIhYVcWByTC5uuPq6RQCBgbrrpJjN9+vT4uhUrVpjmzZub//znP8aY2B24p0yZEr/8+vCZDOoTcmEhFxZyYSEXFnJhIRcWcmFxUi5Sb8xSUmZmpk444QSdfPLJkqTS0lKdffbZ6tGjR/zqHI/Ho9GjR0uSwuFwSg7PVgW5sJALC7mwkAsLubCQCwu5sDgpF6kZtaTbbrtNbdu2lRT7gUnS9u3btXv37iP6puoPp6rIhYVcWMiFhVxYyIWFXFjIhcUpuUiZyDdt2qTFixerWbNmatOmjbp27SqpfPXdvHnz+A9Nkj788ENlZWWpV69edoRca8iFhVxYyIWFXFjIhYVcWMiFxbG5sPsYdFV8+eWX5sQTTzRDhw41/fr1M507dzaPPfZY/PlQKGS++OIL06FDB1NSUmKMMebFF180J510kvn222/tCrtWkAsLubCQCwu5sJALC7mwkAuLk3OR9AXg/v37zaBBg8y0adOMMcbs2rXLvP/++8bv95t777033m/FihXm7LPPNsYY8+c//9mcdtppZuXKlbbEXFvIhYVcWMiFhVxYyIWFXFjIhcXpuUj6AtAYY2644Qbzr3/9yxhjTaq8Zs0ak5WVZR5++GFjjDElJSWmf//+ZvDgwaZ9+/Zm1apVtsVbm8iFhVxYyIWFXFjIhYVcWMiFxcm5SOr7ABpjVFpaql27dik/P19S7ITLUCikTp06adGiRZo2bZpee+01ZWVlqWfPnvrqq6/0t7/9LenvwF1d5MJCLizkwkIuLOTCQi4s5MJCLpQa5wD+85//NG3atDHvvPNOfN2hSv2ZZ54x999/vzHGmK1bt5rvv//elhjrCrmwkAsLubCQCwu5sJALC7mwODkXST0CeEifPn00ZswYTZ48We+9954k627cLVu21Jo1axQOh9WqVSu1aNHCzlBrHbmwkAsLubCQCwu5sJALC7mwODkXKXEbGJfLpZtuukn79+/X+PHjVVxcrGuuuUaStG/fPjVo0ECRSCSl78dTVeTCQi4s5MJCLizkwkIuLOTC4uhc2D0EWR1FRUXmueeeM36/31xxxRXmf/7nf8yJJ54Yn57FSciFhVxYyIWFXFjIhYVcWMiFxYm5cBljjN1FaHV9+eWXWrZsmaLRqPr166f27dvbHZJtyIWFXFjIhYVcWMiFhVxYyIXFSblIyQIQAAAAxy4lLgIBAABA4lAAAgAAOAwFIAAAgMNQAAIAADgMBSAAAIDDUAACAAA4DAUgAACAw1AAAgAAOAwFIAAAgMNQAAIAADgMBSAAAIDDUAACAAA4DAUgAACAw1AAAgAAOAwFIAAAgMNQAAIAADgMBSAAAIDD/H+lsB/jKXEeHwAAAABJRU5ErkJggg==' 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.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": "563d7b16",
|
|
"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": "7ef46c1b",
|
|
"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": "93afa7a3",
|
|
"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
|
|
},
|
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"image/png": 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",
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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 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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"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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