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- import numpy as np
- try:
- import pymc as pm
- except:
- pass
- from ._base_components import BaseComponents
- from ..tools.enthalpy import get_RH_from_Tdb_and_Hr
- from ..tools.enthalpy import get_Dew_from_HumRatio
- class WheelS2(BaseComponents):
-
- def __init__(self, name):
- super().__init__(name)
-
- @classmethod
- def model(
- cls,
- TinP,HinP,FP,
- TinR,HinR,FR,
- engine : str,
- param : dict,
- ):
- FUNC = cls.get_func_by_engine(engine)
- EXP = FUNC['EXP']
- RinP = get_RH_from_Tdb_and_Hr(TinP,HinP,engine)
-
- beta_Q1 = param['beta_Q1']
- beta_H1 = param['beta_H1']
- beta_H2 = param['beta_H2']
- beta_H3 = param['beta_H3']
- beta_H4 = param['beta_H4']
-
- # 转轮的温度
- T_avg = TinR * (1 - beta_H4) + TinP * beta_H4
-
- # 出风露点(除湿量)
- ## 处理侧
- Hdiff_mu = beta_H1 * RinP ** beta_H2 * EXP(-beta_H3 / T_avg) / 1000 # kg水蒸气/kg干空气
- HoutP_mu = HinP - Hdiff_mu
- DoutP_mu = get_Dew_from_HumRatio(HoutP_mu,engine)
- ## 再生侧
- HoutR_mu = HinR + Hdiff_mu * (FP / FR)
- DoutR_mu = get_Dew_from_HumRatio(HoutR_mu,engine)
-
- # 出风温度
- # 处理侧
- Q_latent = Hdiff_mu * cls.CONSTANT['h_ads'] # Kj/kg 潜热
- Q_sensible = beta_Q1 * (TinR - TinP) # Kj/kg 显热(影响升焓的部分)
- Q = (Q_latent + Q_sensible) * FP # Kj
- ToutP_mu = TinP + Q / (FP * cls.CONSTANT['c_p_air'])
- # 再生侧
- ToutR_mu = TinR - Q / (FR * cls.CONSTANT['c_p_air'])
-
- return {
- 'ToutP':ToutP_mu,'HoutP':HoutP_mu,'DoutP':DoutP_mu,
- 'ToutR':ToutR_mu,'HoutR':HoutR_mu,'DoutR':DoutR_mu,
- }
-
- def prior(self):
- param = {
- 'beta_Q1': pm.HalfNormal(f'{self.name}_beta_Q1',sigma=10),
- 'beta_H1': pm.HalfNormal(f'{self.name}_beta_H1',sigma=10),
- 'beta_H2': pm.HalfNormal(f'{self.name}_beta_H2',sigma=10),
- 'beta_H3': pm.HalfNormal(f'{self.name}_beta_H3',sigma=10),
- 'beta_H4': pm.Uniform(f'{self.name}_beta_H4',lower=0,upper=1),
- }
- return param
- class WheelS3(BaseComponents):
- def __init__(self, name):
- super().__init__(name)
-
- @classmethod
- def model(
- cls,
- TinP,HinP,FP,
- TinR,HinR,FR,
- TinC,HinC,FC,
- engine : str,
- param : dict,
- ):
- FUNC = cls.get_func_by_engine(engine)
- EXP = FUNC['EXP']
-
- beta_P1 = param['beta_P1']
- beta_P2 = param['beta_P2']
- beta_P3 = param['beta_P3']
- beta_P4 = param['beta_P4']
- beta_P5 = param['beta_P5']
- beta_C1 = param['beta_C1']
- beta_C2 = param['beta_C2']
- beta_C3 = param['beta_C3']
- beta_C4 = param['beta_C4']
-
- RinP = get_RH_from_Tdb_and_Hr(TinP,HinP,engine)
- RinC = get_RH_from_Tdb_and_Hr(TinC,HinC,engine)
-
- # 处理侧
- HdiffP = (beta_P1 * RinP**beta_P4 * HinP * TinR * EXP(-beta_P5 * FP) + beta_P2)/1000
- WdiffP = HdiffP * FP
- HoutP = HinP - HdiffP
- DoutP = get_Dew_from_HumRatio(HoutP,engine)
- Q_lat_P = WdiffP * cls.CONSTANT['h_ads']
- Q_sen_P = beta_P3 * (TinR - TinP) * FP #TODO
- TdiffP = (Q_lat_P + Q_sen_P) / (FP * cls.CONSTANT['c_p_air'])
- ToutP = TinP + TdiffP
-
- # 冷却侧
- TdiffC = beta_C1 * EXP(-beta_C2 * EXP(-beta_C3 * (TinR - TinC))) * EXP(-beta_C4 * FC)
- ToutC = TinC + TdiffC
- HdiffC = (beta_P1 * RinC**beta_P4 * HinC * TinR * EXP(-beta_P5 * FC) + beta_P2)/1000
- WdiffC = HdiffC * FC
- HoutC = HinC - HdiffC
- DoutC = get_Dew_from_HumRatio(HoutC,engine)
- Q_total_C = TdiffC * FC * cls.CONSTANT['c_p_air']
-
- # 再生侧
- WdiffR = WdiffP + WdiffC
- HoutR = (HinR * FR + WdiffR) / FR
- DoutR = get_Dew_from_HumRatio(HoutR,engine)
- Q_total_R = Q_lat_P + Q_sen_P + Q_total_C
- TdiffR = Q_total_R / (FR * cls.CONSTANT['c_p_air'])
- ToutR = TinR - TdiffR
-
- return {
- 'ToutP':ToutP,'HoutP':HoutP,'DoutP':DoutP,'FP':FP,
- 'ToutR':ToutR,'HoutR':HoutR,'DoutR':DoutR,'FR':FR,
- 'ToutC':ToutC,'HoutC':HoutC,'DoutC':DoutC,'FC':FC,
- }
-
- def prior(self):
- param = {
- 'beta_P1': pm.TruncatedNormal(f'{self.name}_beta_P1',mu=5,sigma=10,initval=5,lower=0),
- 'beta_P2': pm.TruncatedNormal(f'{self.name}_beta_P2',mu=0.5,sigma=1,initval=0.02,lower=0),
- 'beta_P3': pm.TruncatedNormal(f'{self.name}_beta_P3',mu=1,sigma=2,initval=1.5,lower=0),
- 'beta_P4': pm.TruncatedNormal(f'{self.name}_beta_P4',mu=1,sigma=0.3,initval=1,lower=0),
- 'beta_P5': pm.TruncatedNormal(f'{self.name}_beta_P5',mu=5,sigma=2,initval=5,lower=0),
- 'beta_C1': pm.TruncatedNormal(f'{self.name}_beta_C1',mu=60,sigma=10,initval=60,lower=10),
- 'beta_C2': pm.TruncatedNormal(f'{self.name}_beta_C2',mu=30,sigma=10,initval=30,lower=1),
- 'beta_C3': pm.TruncatedNormal(f'{self.name}_beta_C3',mu=0.05,sigma=0.1,initval=0.05,lower=0),
- 'beta_C4': pm.TruncatedNormal(f'{self.name}_beta_C4',mu=1,sigma=1,initval=1,lower=0),
- }
- return param
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