def adiabatic_cooling(ti, rhi, to, rho):

    V = duct_area * air_velocity * 60 * 60  #Volumetric Air Flow [m3/h]
    pv = HAPropsSI('P_w', 'T', to, 'P', 101325, 'R',
                   rho) / 100000  # Partial pressure of water vapor
    va = (287 * ti) / ((1.0135 - pv) * 100000)  #[m3/kg of dry air]

    hi = HAPropsSI('H', 'T', ti, 'P', 101325, 'R',
                   rhi) / 1000  # Inlet Air Enthalpy[kJ/kg of dry air]
    ho = HAPropsSI('H', 'T', to, 'P', 101325, 'R',
                   rho) / 1000  # Outlet Air Enthalpy[kJ/kg of dry air]

    W1 = HAPropsSI('W', 'T', ti, 'P', 101325, 'R', rhi)  #[kg/kg of dry air]
    W2 = HAPropsSI('W', 'T', to, 'P', 101325, 'R', rho)  #[kg/kg of dry air]

    spray_water = (W2 - W1) / va  #
    amount_of_water_required_per_hour = spray_water * V  #[kg/h]
    Qadiabatic = abs(p_air * (ho - hi) * V / 3600)

    print('\n')
    print('Air Volumetric Rate = {:.2f} m3/h'.format(V))
    print('Inlet Air Enthalpy = {:.3f} kJ/kg of dry air'.format(hi))
    print('Outlet Air Enthalpy = {:.3f} kJ/kg of dry air'.format(ho))
    print('Partial Pressure of Water Vapour = {:.3f} bar'.format(pv))
    print('Specific Humidity Inlet Air = {:.5f} kg/kg of dry air'.format(W1))
    print('Specific Humidity Outlet Air = {:.5f} kg/kg of dry air'.format(W2))
    print('Spray Water = {:.5f} kg of moisture/m3 of air'.format(spray_water))
    print('Amount of Water Required per hour = {:.3f} kg/h'.format(
        amount_of_water_required_per_hour))
    print('Adiabatic Cooler Power Output = {:.2f} kW'.format(Qadiabatic))
    print('\n')
示例#2
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 def plot(self,ax):
     for H in self.H_values:
         #Line goes from saturation to zero humidity ratio for this enthalpy
         T1 = HAPropsSI('T','H',H,'P',p,'R',1.0)-273.15
         T0 = HAPropsSI('T','H',H,'P',p,'R',0.0)-273.15
         w1 = HAPropsSI('W','H',H,'P',p,'R',1.0)
         w0 = HAPropsSI('W','H',H,'P',p,'R',0.0)
         ax.plot(numpy.r_[T1,T0],numpy.r_[w1,w0],'r',lw=1)
示例#3
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 def plot(self,ax):
     xv = Tdb #[K]
     for RH in self.RH_values:
         yv = [HAPropsSI('W','T',T,'P',p,'R',RH) for T in Tdb]
         y = HAPropsSI('W','P',p,'H',self.h,'R',RH)
         T_K,w,rot = InlineLabel(xv, yv, y=y, axis = ax)
         string = r'$\phi$='+'{s:0.0f}'.format(s=RH*100)+'%'
         #Make a temporary label to get its bounding box
         bbox_opts = dict(boxstyle='square,pad=0.0',fc='white',ec='None',alpha = 0.5)
         ax.text(T_K-273.15,w,string,rotation = rot,ha ='center',va='center',bbox=bbox_opts)
示例#4
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def gibbs_HA(T,P,W, T_0):
    # This function calculates the flow exergy of a humid air mixture, on a basis of humid air
    # When T_0 = T, the specific gibbs function is equivalent to the flow exergy.
    # T: Temperature in K
    # P: Pressure in Pa
    # rh: Relative humidity [0,1]
    # T_0: Dead state temperature
    h = HAPropsSI('Hha','T', T, 'P', P, 'W',W)
    s = HAPropsSI('Sha','T', T, 'P', P, 'W',W)
    g = h-T_0*s
    return g
def calc_prop_of(counter, xdata, ydata):
    a = 'Point: ' + str(counter + 1)
    b = "-- R = " + str(
        round(100 * HAPropsSI('R', 'T', xdata + 273, 'P', 101325, 'W', ydata),
              2)) + ' %'
    c = '-- T = ' + str(round(xdata, 2)) + ' [C]'
    d = '-- W = ' + str(round(ydata, 4))
    e = '-- H = ' + str(
        round(
            (HAPropsSI('H', 'T', xdata + 273, 'P', 101325, 'W', ydata) / 1000),
            3)) + ' kJ/kg'
    #       f =' W = '+ str(100*HAPropsSI('Twb','T',xdata+273,'P',101325,'W',ydata)) +' [C]'
    return (str(a + b + c + d + e))
 def calc_prop(self, xdata, ydata):
     #        from CoolProp.HumidAirProp import HAPropsSI
     a = 'Point: ' + str(self.counter + 1)
     b = "'-- R = " + str(
         round(
             100 * HAPropsSI('R', 'T', xdata + 273, 'P', 101325, 'W',
                             ydata), 2)) + ' %'
     c = '-- T = ' + str(round(xdata, 2)) + ' [C]'
     d = '-- W = ' + str(round(ydata, 4))
     e = '-- H = ' + str(
         round((HAPropsSI('H', 'T', xdata + 273, 'P', 101325, 'W', ydata) /
                1000), 3)) + ' kJ/kg'
     #       f =' W = '+ str(100*HAPropsSI('Twb','T',xdata+273,'P',101325,'W',ydata)) +' [C]'
     return (str(a + b + c + d + e))
示例#7
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def min_recovery(T,rh):


    recovery = np.linspace(0.01,0.9,20)
    DEW = []
    rec = []
    for r in recovery:
          W = HAPropsSI('W', 'T', T, 'P', P, 'R', rh)
          dw = r*W
          W_reject = W * (1 - r)
          T_dp = HAPropsSI('D', 'T', T, 'P', P, 'W', W_reject)
          if T_dp > 273.1:
            rec.append(r)
            DEW.append(calcDcooling(T,P,W,r))
    loc = np.argmin(DEW)
    min_r = recovery[loc]
    return (min_r)
示例#8
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def calcDcooling(T,P,W,r):
    # This function calculates the minimum energy requirement for direct cooling without icing, and the optimal recovery ratio
    # The algorithm finds the maximum recovery to avoid icing. Then it slowly minimizes r until energy is sufficiently minimized
    # T: Ambient temperature in K
    # P: Ambient pressure in Pa
    # rh: Ambient relative humidity [0,1]
    # r: recovery ratio

    alpha = 0.005 #Predefined gradient descent coefficient
    dw = r*W
    W_reject = W * (1 - r)
    T_dp = HAPropsSI('D', 'T', T, 'P', P, 'W', W_reject)
    while T_dp - 273.15 < 5e-4 :
      r = r*(1-alpha)
      dw = r*W
      W_reject = W * (1 - r)
      T_dp = HAPropsSI('D', 'T', T, 'P', P, 'W', W_reject)
      if (r <=0):
        return('end')
    
    G_p = gibbs_satv(T_dp, T)
    G_f = gibbs_HA(T, P, W, T)
    G_b = gibbs_HA(T_dp, P, W_reject, T)
    sep = (G_p + G_b * (1 - dw) / dw - G_f / dw) / 1000

    sep_new = sep - 1.1e-2  #Small numerical addition to enter the while loop condition
    r_new = r

    while sep - sep_new > 1e-2:
      sep  = sep_new
      r = r_new
      r_new = r*(1-alpha)

      dw = r_new*W
      W_reject = W * (1 - r_new)

      T_dp = HAPropsSI('D', 'T', T, 'P', P, 'W', W_reject)

      G_p = gibbs_satv(T_dp, T)
      G_b = gibbs_HA(T_dp, P, W_reject, T)
      sep_new = (G_p + G_b * (1 - dw) / dw - G_f / dw) / 1000
      if r<= 1e-2:
        return r,sep
    return r, sep
def RelativeHumidty(
    W,
    Tdb,
    P=101325
):  # returns relative humidity of air for W:kg/kg humidity ratio and T in Kelvin
    try:
        result = HAPropsSI('R', 'W', W, 'T', Tdb, 'P', P)  # unitless
    except ValueError:
        result = 1.0
    return result
def heating_and_cooling(ti, rhi, to, rho, air_velocity, duct_area):

    V = duct_area * air_velocity  # Volumetric Flow Rate [m3/s]
    pv = HAPropsSI('P_w', 'T', ti, 'P', 101325, 'R',
                   rhi) / 100000  # Partial pressure of water vapor
    ma = ((1.0135 - pv) * V) * 100000 / (287 * ti)  # Air Mass Flow [kg/s]

    global Wi
    global Wo

    Wi = HAPropsSI('W', 'T', ti, 'P', 101325, 'R',
                   rhi)  # Inlet Humidity Ratio[kg/kg of dry air]
    Wo = HAPropsSI('W', 'T', to, 'P', 101325, 'R',
                   rho)  # Outlet Humidity Ratio[kg/kg of dry air]

    hi = HAPropsSI('H', 'T', ti, 'P', 101325, 'R',
                   rhi) / 1000  # Inlet Air Enthalpy [kJ/kg]
    ho = HAPropsSI('H', 'T', to, 'P', 101325, 'R',
                   rho) / 1000  # Outlet Air Enthalpy [kJ/kg]

    Qs = Cp * ma * (to - ti)  #Sensible Heat Added/Removed from the Air [kW]
    Ql = hwe * ma * (Wo - Wi)  # Latent Heat Added/Removed from the Air [kW]
    Qt = ma * (ho - hi)  # Total Heat Added/Removed from the Air [kW]

    print('Partial Pressure of Water Vapour = {:.3f} bar'.format(pv))
    print('Mass of dry air = {:.2f} kg/s'.format(ma))

    print('Inlet Air Humidity Ratio = {:.5f} kg/kg of dry air'.format(Wi))
    print('Outlet Air Humidity Ratio = {:.5f} kg/kg of dry air'.format(Wo))

    print('Inlet Air Enthalpy = {:.3f} kJ/kg of dry air'.format(hi))
    print('Outlet Air Enthalpy = {:.3f} kJ/kg of dry air'.format(ho))

    print('Sensible Heat added/removed = {:.3f} kJ/s or kW'.format(Qs))
    print('Latent Heat added/removed = {:.3f} kJ/s or kW'.format(Ql))
    print('Amount of Heat added/removed = {:.2f} kJ/s or kW'.format(Qt))
    print('\n')
示例#11
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def calc_enth(df: pd.DataFrame) -> pd.Series:
    """Calculate enthalpy.
    """
    Tn = 273.15  # absolute temp.
    df_drop = df[["air_temperature", "dew_temperature",
                  "sea_level_pressure"]].dropna()
    # NOTE: unit of temperature and pressure must be [K] and kPa respectively.
    enth = HAPropsSI("H",\
              "T", df_drop["air_temperature"].values + Tn,\
              "D", df_drop["dew_temperature"].values + Tn,\
              "P", df_drop["sea_level_pressure"].values * 10.0
             )
    enth = pd.Series(enth, index=df_drop.index)
    df = df.assign(enth=enth)
    return df["enth"]
示例#12
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length = 150                          # Specifies  the resolution of the contour plot
T = np.linspace(280,325,length)       # Ambient temperature in K
W = np.linspace(0.002,0.04,length)    # Ambient Relative Humidity [0,1]

# Initialize arrays
DC = [] 
recovery = []
T_df = []
relhum = []
abshum = []
r_ref = []
LW = []

# Find the saturation line
W_sat = HAPropsSI('W','T',T,'P',P, 'R',1)

# Loop through all conditions
counter = 0
for hum in W:
    idx = 0
    for temp in T:
        # Check if the dewpoint is above freezing and the mixture is not super saturated
        if (HAPropsSI('D', 'T', temp, 'P', P, 'W', hum)>273.2) & (hum<=W_sat[idx]):
            RH = HAPropsSI('R', 'T', temp, 'P', P, 'W', hum)
            T_dp = HAPropsSI('D', 'T', temp, 'P', P, 'W', hum)
            LW.append(calcLW(temp, P, hum, 0.2))
            r_temp, dc_temp = calcDcooling(temp,P,hum,0.9)
            DC.append(dc_temp)
            recovery.append(r_temp)
            T_df.append(temp)
示例#13
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def plot_results(my_dataframe):
    import matplotlib.pyplot
    import CoolProp.Plots
    import CoolProp.Plots.PsychChart
    import CoolProp.Plots.PsychScript
    #CoolProp.Plots.TwoStage('water',1,Te=280,Tc=300,DTsh=2,DTsc=2,eta_oi=0.9,f_p=0.1,Tsat_ic=290,DTsh_ic=2)
    matplotlib.pyplot.close('all')
    if True:
        from CoolProp.HumidAirProp import HAPropsSI
        from CoolProp.Plots.Plots import InlineLabel
        #from CoolProp.Plots.Common import BasePlot
        #bp = BasePlot()
        #InlineLabel = bp.inline_label

        p = 101325
        Tdb = numpy.linspace(-10,40,100)+273.15

        # Make the figure and the axes
        fig=matplotlib.pyplot.figure(figsize=(10,8))
        ax=fig.add_axes((0.1,0.1,0.85,0.85))

        # Saturation line
        w = [HAPropsSI('W','T',T,'P',p,'R',1.0) for T in Tdb]
        ax.plot(Tdb-273.15,w,lw=2)

        # Humidity lines
        RHValues = [0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
        for RH in RHValues:
            w = [HAPropsSI('W','T',T,'P',p,'R',RH) for T in Tdb]
            ax.plot(Tdb-273.15,w,'r',lw=1)

        # Humidity lines
        for H in [-20000, -10000, 0, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000]:
            #Line goes from saturation to zero humidity ratio for this enthalpy
            T1 = HAPropsSI('T','H',H,'P',p,'R',1.0)-273.15
            T0 = HAPropsSI('T','H',H,'P',p,'R',0.0)-273.15
            w1 = HAPropsSI('W','H',H,'P',p,'R',1.0)
            w0 = HAPropsSI('W','H',H,'P',p,'R',0.0)
            ax.plot(numpy.r_[T1,T0],numpy.r_[w1,w0],'r',lw=1)

        ax.set_xlim(Tdb[0]-273.15,Tdb[-1]-273.15)
        ax.set_ylim(0,0.03)
        ax.set_xlabel(r"$T_{db}$ [$^{\circ}$C]")
        ax.set_ylabel(r"$W$ ($m_{w}/m_{da}$) [-]")

        xv = Tdb #[K]
        for RH in [0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
            yv = numpy.array([HAPropsSI('W','T',T,'P',p,'R',RH) for T in Tdb])
            #y = HAPropsSI('W','P',p,'H',65000.000000,'R',RH)
            y = HAPropsSI('W','P',p,'H',45000.000000,'R',RH)
            T_K,w,rot = InlineLabel(xv, yv, y=y, fig=fig, axis = ax)
            string = r'$\phi$='+'{s:0.0f}'.format(s=RH*100)+'%'
            bbox_opts = dict(boxstyle='square,pad=0.0',fc='white',ec='None',alpha = 0.9)
            ax.text(T_K-273.15,w,string,rotation = rot,ha ='center',va='center',bbox=bbox_opts)

    ax.plot(my_dataframe.T_air-273.15,
            my_dataframe.HumRat_air,
            'k.-')
    ax.plot(my_dataframe.T_water-273.15,
            my_dataframe.HumRat_interface,
           'm.-')
    matplotlib.pyplot.show()
    
def get_real_obs(api_args: dict, meta_data_: dict, obs_space_vars : list, scaler, period, log):

	try:
		# arguements for the api query
		time_args = {'trend_id' : '2681', 'save_path' : 'data/trend_data/alumni_data_deployment.csv'}
		start_fields = ['start_'+i for i in ['year','month','day', 'hour', 'minute', 'second']]
		end_fields = ['end_'+i for i in ['year','month','day', 'hour', 'minute', 'second']]
		end_time = datetime.now(tz=pytz.utc)
		time_gap_minutes = int((period+3)*5)
		start_time = end_time - timedelta(minutes=time_gap_minutes)
		for idx, i in enumerate(start_fields):
			time_args[i] = start_time.timetuple()[idx]
		for idx, i in enumerate(end_fields):
			time_args[i] = end_time.timetuple()[idx]
		api_args.update(time_args)

		# pull the data into csv file
		try:
			dp.pull_online_data(**api_args)
			log.info('Deploy Control Module: Deployment Data obtained from API')
		except Exception:
			log.info('Deploy Control Module: BdX API could not get data: will resuse old data')

		# get the dataframe from a csv
		df_ = read_csv('data/trend_data/alumni_data_deployment.csv', )
		df_['time'] = to_datetime(df_['time'])
		to_zone = tz.tzlocal()
		df_['time'] = df_['time'].apply(lambda x: x.astimezone(to_zone)) # convert time to loca timezones
		df_.set_index(keys='time',inplace=True, drop = True)
		df_ = a_utils.dropNaNrows(df_)

		# add wet bulb temperature to the data
		log.info('Deploy Control Module: Start of Wet Bulb Data Calculation')
		rh = df_['WeatherDataProfile humidity']/100
		rh = rh.to_numpy()
		t_db = 5*(df_['AHU_1 outdoorAirTemp']-32)/9 + 273.15
		t_db = t_db.to_numpy()
		T = HAPropsSI('T_wb','R',rh,'T',t_db,'P',101325)
		t_f = 9*(T-273.15)/5 + 32
		df_['wbt'] = t_f
		log.info('Deploy Control Module: Wet Bulb Data Calculated')

		# rename the columns
		new_names = []
		for i in df_.columns:
			new_names.append(meta_data_["reverse_col_alias"][i])
		df_.columns = new_names

		# collect current set point
		hist_stpt = df_.loc[df_.index[-1],['sat_stpt']].to_numpy().copy().flatten()

		# clean the data
		df_cleaned = dp.online_data_clean(
			meta_data_ = meta_data_, df = df_
		)

		# clip less than 0 values
		df_cleaned.clip(lower=0, inplace=True)

		# aggregate data
		rolling_sum_target, rolling_mean_target = [], []
		for col_name in df_cleaned.columns:
			if meta_data_['column_agg_type'][col_name] == 'sum' : rolling_sum_target.append(col_name)
			else: rolling_mean_target.append(col_name)
		df_cleaned[rolling_sum_target] =  a_utils.window_sum(df_cleaned, window_size=6, column_names=rolling_sum_target)
		df_cleaned[rolling_mean_target] =  a_utils.window_mean(df_cleaned, window_size=6, column_names=rolling_mean_target)
		df_cleaned = a_utils.dropNaNrows(df_cleaned)

		# Sample the last half hour data
		df_cleaned = df_cleaned.iloc[[-1],:]

		# also need an unscaled version of the observation for logging
		df_unscaled = df_cleaned.copy()

		# scale the columns: here we will use min-max
		df_cleaned[df_cleaned.columns] = scaler.minmax_scale(df_cleaned, df_cleaned.columns, df_cleaned.columns)

		# create avg_stpt column
		stpt_cols = [ele for ele in df_cleaned.columns if 'vrf' in ele]
		df_cleaned['avg_stpt'] = df_cleaned[stpt_cols].mean(axis=1)
		# drop individual set point cols
		df_cleaned.drop( columns = stpt_cols, inplace = True)
		# rearrange observation cols
		df_cleaned = df_cleaned[obs_space_vars]

		# create avg_stpt column
		stpt_cols = [ele for ele in df_unscaled.columns if 'vrf' in ele]
		df_unscaled['avg_stpt'] = df_unscaled[stpt_cols].mean(axis=1)
		# drop individual set point cols
		df_unscaled.drop( columns = stpt_cols, inplace = True)
		# rearrange observation cols
		df_unscaled = df_unscaled[obs_space_vars] 

		return df_cleaned, df_unscaled, hist_stpt

	except Exception as e:
		log.error('Deploy Control Module: %s', str(e))
		log.debug(e, exc_info=True)
示例#15
0
# Density of carbon dioxide at 100 bar and 25C
D = PropsSI('D', 'T', 298.15, 'P', 100e5, 'CO2')
print(D)

# Saturated vapor enthalpy [J/kg] of R134a at 25C
H = PropsSI('H', 'T', 298.15, 'Q', 1, 'R134a')
print(H)

#%%

# import the things you need
from CoolProp.HumidAirProp import HAPropsSI

# Enthalpy (J per kg dry air) as a function of temperature, pressure,
#    and relative humidity at STP
h = HAPropsSI('H', 'T', 298.15, 'P', 101325, 'R', 0.5)
print(h)

# Temperature of saturated air at the previous enthalpy
T = HAPropsSI('T', 'P', 101325, 'H', h, 'R', 1.0)
print(T)

# Temperature of saturated air - order of inputs doesn't matter
T = HAPropsSI('T', 'H', h, 'R', 1.0, 'P', 101325)
print(T)

#%%

import CoolProp
from CoolProp.Plots import PropertyPlot
plot = PropertyPlot('R290', 'ph')
示例#16
0
def HotAir(num):
    from CoolProp.HumidAirProp import HAPropsSI
    if num=='8':
        Temp=str(200)
        T=200+273.15
    elif num=='9':
        Temp=str(320)
        T=320+273.15
    print 'A.'+num+'.1 Psychrometric Properties of Moist Air at 101.325 kPa '
    print 'Dry Bulb temperature of '+Temp+'C'
    s5=' '*5
    print '================================================================'
    print "{W:10s}{Twb:10s}{v:10s}{h:10s}{s:10s}{R:10s}".format(W=s5+' W',Twb=s5+'Twb',v=s5+'  v',h=s5+'h',s=s5+' s',R=s5+'RH')
    print "{W:10s}{Twb:10s}{v:10s}{h:10s}{s:10s}{R:10s}".format(W='  kgw/kg_da',Twb='      C',v='   m3/kgda',h='  kJ/kgda',s=' kJ/kgda/K',R='    %')
    print '----------------------------------------------------------------'
    for W in [0.0,0.05,0.1,0.20,0.30,0.40,0.50,0.60,0.70,0.80,0.90,1.0]:
        h=HAPropsSI('H','T',T,'W',W,'P',101325)/1000
        Twb=HAPropsSI('Twb','T',T,'W',W,'P',101325)-273.15
        R=HAPropsSI('R','T',T,'W',W,'P',101325)*100
        v=HAPropsSI('V','T',T,'W',W,'P',101325)
        s=HAPropsSI('S','T',T,'W',W,'P',101325)/1000
        print "{W:10.2f}{Twb:10.2f}{v:10.3f}{h:10.2f}{s:10.4f}{R:10.4f}".format(W=W,Twb=Twb,v=v,h=h,s=s,R=R)
    print '================================================================'
    print ' '
    print 'A.'+num+'.2 Psychrometric Properties of Moist Air at 1000 kPa '
    print 'Dry Bulb temperature of '+Temp+'C'
    print '================================================================'
    print "{W:10s}{Twb:10s}{v:10s}{h:10s}{s:10s}{R:10s}".format(W=s5+' W',Twb=s5+'Twb',v=s5+'  v',h=s5+'h',s=s5+' s',R=s5+'RH')
    print "{W:10s}{Twb:10s}{v:10s}{h:10s}{s:10s}{R:10s}".format(W='  kgw/kg_da',Twb='      C',v='   m3/kgda',h='  kJ/kgda',s=' kJ/kgda/K',R='    %')
    print '----------------------------------------------------------------'
    for W in [0.0,0.05,0.1,0.20,0.30,0.40,0.50,0.60,0.70,0.80,0.90,1.0]:
        h=HAPropsSI('H','T',T,'W',W,'P',1000e3)/1000
        Twb=HAPropsSI('Twb','T',T,'W',W,'P',1000e3)-273.15
        R=HAPropsSI('R','T',T,'W',W,'P',1000e3)*100
        v=HAPropsSI('V','T',T,'W',W,'P',1000e3)
        s=HAPropsSI('S','T',T,'W',W,'P',1000e3)/1000
        print "{W:10.2f}{Twb:10.2f}{v:10.3f}{h:10.2f}{s:10.4f}{R:10.4f}".format(W=W,Twb=Twb,v=v,h=h,s=s,R=R)
    print '================================================================'
    print ' '
    s5=' '*5
    print 'A.'+num+'.3 Psychrometric Properties of Moist Air at 2000 kPa '
    print 'Dry Bulb temperature of '+Temp+'C'
    print '================================================================'
    print "{W:10s}{Twb:10s}{v:10s}{h:10s}{s:10s}{R:10s}".format(W=s5+' W',Twb=s5+'Twb',v=s5+'  v',h=s5+'h',s=s5+' s',R=s5+'RH')
    print "{W:10s}{Twb:10s}{v:10s}{h:10s}{s:10s}{R:10s}".format(W='  kgw/kg_da',Twb='      C',v='   m3/kgda',h='  kJ/kgda',s=' kJ/kgda/K',R='    %')
    print '----------------------------------------------------------------'
    for W in [0.0,0.05,0.1,0.20,0.30,0.40,0.50,0.60,0.70,0.80,0.90,1.0]:
        h=HAPropsSI('H','T',T,'W',W,'P',2000e3)/1000
        Twb=HAPropsSI('Twb','T',T,'W',W,'P',2000e3)-273.15
        R=HAPropsSI('R','T',T,'W',W,'P',2000e3)*100
        v=HAPropsSI('V','T',T,'W',W,'P',2000e3)
        s=HAPropsSI('S','T',T,'W',W,'P',2000e3)/1000
        print "{W:10.2f}{Twb:10.2f}{v:10.3f}{h:10.2f}{s:10.4f}{R:10.4f}".format(W=W,Twb=Twb,v=v,h=h,s=s,R=R)
    print '================================================================'
    print ' '
    s5=' '*5
    print 'A.'+num+'.4 Psychrometric Properties of Moist Air at 5000 kPa '
    print 'Dry Bulb temperature of '+Temp+'C'
    print '================================================================'
    print "{W:10s}{Twb:10s}{v:10s}{h:10s}{s:10s}{R:10s}".format(W=s5+' W',Twb=s5+'Twb',v=s5+'  v',h=s5+'h',s=s5+' s',R=s5+'RH')
    print "{W:10s}{Twb:10s}{v:10s}{h:10s}{s:10s}{R:10s}".format(W='  kgw/kg_da',Twb='      C',v='   m3/kgda',h='  kJ/kgda',s=' kJ/kgda/K',R='    %')
    print '----------------------------------------------------------------'
    if Temp=='200':
        Wrange = [0.0,0.05,0.1,0.15,0.20,0.25,0.30]
    else:
        Wrange = [0.0,0.05,0.1,0.15,0.20,0.25,0.30,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
    for W in Wrange:
        h=HAPropsSI('H','T',T,'W',W,'P',5000e3)/1000
        Twb=HAPropsSI('Twb','T',T,'W',W,'P',5000e3)-273.15
        R=HAPropsSI('R','T',T,'W',W,'P',5000e3)*100
        v=HAPropsSI('V','T',T,'W',W,'P',5000e3)
        s=HAPropsSI('S','T',T,'W',W,'P',5000e3)/1000
        print "{W:10.2f}{Twb:10.2f}{v:10.3f}{h:10.2f}{s:10.4f}{R:10.4f}".format(W=W,Twb=Twb,v=v,h=h,s=s,R=R)
    print '================================================================'    
    print ' '
    s5=' '*5
    print 'A.'+num+'.5 Psychrometric Properties of Moist Air at 10,000 kPa '
    print 'Dry Bulb temperature of '+Temp+'C'
    print '================================================================'
    print "{W:10s}{Twb:10s}{v:10s}{h:10s}{s:10s}{R:10s}".format(W=s5+' W',Twb=s5+'Twb',v=s5+'  v',h=s5+'h',s=s5+' s',R=s5+'RH')
    print "{W:10s}{Twb:10s}{v:10s}{h:10s}{s:10s}{R:10s}".format(W='  kgw/kg_da',Twb='      C',v='   m3/kgda',h='  kJ/kgda',s=' kJ/kgda/K',R='    %')
    print '----------------------------------------------------------------'
    
    if Temp=='200':
        Wrange = [0.0,0.05,0.1]
    else:
        Wrange = [0.0,0.05,0.1,0.15,0.20,0.25,0.30,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
    for W in Wrange:
        h=HAPropsSI('H','T',T,'W',W,'P',10000e3)/1000
        Twb=HAPropsSI('Twb','T',T,'W',W,'P',10000e3)-273.15
        R=HAPropsSI('R','T',T,'W',W,'P',10000e3)*100
        v=HAPropsSI('V','T',T,'W',W,'P',10000e3)
        s=HAPropsSI('S','T',T,'W',W,'P',10000e3)/1000
        print "{W:10.2f}{Twb:10.2f}{v:10.3f}{h:10.2f}{s:10.4f}{R:10.4f}".format(W=W,Twb=Twb,v=v,h=h,s=s,R=R)
    print '================================================================'
示例#17
0
from CoolProp.HumidAirProp import HAPropsSI
import numpy as np

print ' Replicating the tables from ASHRAE RP-1485'
print '  '
print 'A.6.1 Psychrometric Properties of Moist Air at 0C and Below'
print 'Saturated air at 101.325 kPa'
s5=' '*5
print '===================================================='
print "{T:8s}{W:10s}{v:10s}{h:10s}{s:10s}".format(W=s5+' Ws',v=s5+'  v',h=s5+'h',s=s5+' s',T='   T')
print "{T:8s}{W:10s}{v:10s}{h:10s}{s:10s}".format(W='  kgw/kg_da',v='   m3/kgda',h='  kJ/kgda',s=' kJ/kgda/K',T='   C')
print '----------------------------------------------------'
for T in np.linspace(-60,0,13)+273.15:
    h = HAPropsSI('H','T',T,'R',1.0,'P',101325)/1000
    Twb = HAPropsSI('Twb','T',T,'R',1.0,'P',101325)-273.15
    W = HAPropsSI('W','T',T,'R',1.0,'P',101325)
    v = HAPropsSI('V','T',T,'R',1.0,'P',101325)
    s = HAPropsSI('S','T',T,'R',1.0,'P',101325)/1000
    print "{T:8.0f}{W:10.7f}{v:10.4f}{h:10.3f}{s:10.4f}".format(W=W,T=T-273.15,v=v,h=h,s=s)
print '===================================================='
print ' '
print 'A.6.2 Psychrometric Properties of Moist Air at 0C and Above'
print 'Saturated air at 101.325 kPa'
s5=' '*5
print '===================================================='
print "{T:8s}{W:10s}{v:10s}{h:10s}{s:10s}".format(W=s5+' Ws',v=s5+'  v',h=s5+'h',s=s5+' s',T='   T')
print "{T:8s}{W:10s}{v:10s}{h:10s}{s:10s}".format(W='  kgw/kg_da',v='   m3/kgda',h='  kJ/kgda',s=' kJ/kgda/K',T='   C')
print '----------------------------------------------------'
for T in np.linspace(0,90,19)+273.15:
    h=HAPropsSI('H','T',T,'R',1.0,'P',101325)/1000
    Twb=HAPropsSI('Twb','T',T,'R',1.0,'P',101325)-273.15
#%% m cooling tower
mCTtot = mCW.sum(axis=1)
figure()
mCTtot.plot()
title('mCT total [gpm?]')
savefig('mCT_tot_gpm.png')

#%% weather data
Toa = data['Toa'][startdate:enddate].resample('15T').mean()
RH = data['RH'][startdate:enddate].resample('15T').mean()
Weather = Toa.merge(RH, on='Date', how='inner')
Weather.columns = ['Toa', 'RH']
Weather = Weather.dropna()
from CoolProp.HumidAirProp import HAPropsSI
Twb = HAPropsSI(
    'B', 'T', Weather.iloc[:, 0].to_numpy() + 273.15, 'R',
    Weather.iloc[:, 1].to_numpy() * 1. / 100, 'P', 101325
)  #HumRat(AirH2O,T=T_a_in , P=p_atm,B=T_wb_in ) "lbm/lbm"                "Humidity ratio"
Weather['Twb'] = k2c(Twb)

#%% Cooling Tower Power << sequencing.
PCT = data['PCT'][startdate:enddate].resample('15T').mean()
PCT[[k for k in PCT.columns.to_list() if 'Power' in k]].plot()
PCTtot = PCT[[k for k in PCT.columns.to_list() if 'Power' in k]].sum(axis=1)

PCT.plot()
title('PCT')

figure()
PCTtot.plot()
title('PCT')
savefig('PCT_total_kW.png')
示例#19
0
# water and air density
rho_c = fpw.density(t_w) * 1000
rho_b = fpa.moist_air_density(
    p_standard, rH * fpa.temperature2saturation_vapour_pressure(t_in), t_mean)

# initial value for critical radius
r_max = np.full(re.shape, 0.0016)

# initial values for Bond number, droplet aspect ratio, minimum contact angle and drag coefficient
bo = Bo(rho_c, r_max, surf_tens)
beta = aspect_ratio(bo)
theta_m_0 = minimum_contact_angle(bo, theta_a)
c_d = np.vectorize(c_drag)(r_max, theta_a, theta_m_0, re, d_h)

# bulk velocity based on reynolds number
u = re * HAPropsSI('mu', 'T', t_mean + 273.15, 'P', p_standard, 'R', rH) / \
    (d_h * fpa.moist_air_density(p_standard, rH * fpa.temperature2saturation_vapour_pressure(t_in), t_mean))

# variable initialisation for forces and iteration
f_g, f_s, f_d = np.zeros(re.shape), np.zeros(re.shape), np.zeros(re.shape)
epsilon_1 = np.ones(re.shape)

# a lot of debugging info
logger.debug('flow direction: {a}'.format(a=flow_direction))
logger.debug('hydraulic diameter: {a}'.format(a=d_h))
logger.debug('duct height: {a}'.format(a=h))
logger.debug('diameter ratio: {a}'.format(a=d_h / h))
logger.debug('rho_water: {a}'.format(a=rho_c))
logger.debug('rho_air: {a}'.format(a=rho_b))
logger.debug('surface tension: {a}'.format(a=surf_tens))
logger.debug('Bo_0: {a}'.format(a=bo))
示例#20
0
def HumidityRatio(
    Tdb,
    R,
    P=101325
):  # returns Kg-of-moisture/Kg-DryAir at sea level eg HumidityRatio(293.15,0.6)
    return HAPropsSI('W', 'T', Tdb, 'R', R, 'P', P)  # Kg moisture/Kg Dry Air
示例#21
0
def check_type(Tvals, wvals):
    HAPropsSI('H', 'T', Tvals, 'P', 101325, 'W', wvals)
示例#22
0
def check_HAProps(*args):
    val = HAPropsSI(*args)
示例#23
0
# Humid air example from Sphinx
from CoolProp.HumidAirProp import HAPropsSI
h = HAPropsSI('H','T',298.15,'P',101325,'R',0.5); print(h)
T = HAPropsSI('T','P',101325,'H',h,'R',1.0); print(T)
T = HAPropsSI('T','H',h,'R',1.0,'P',101325); print(T)

# Verification script 
import CoolProp.CoolProp as CP
import numpy as np
import itertools
from multiprocessing import Pool

def generate_values(TR,P=101325):
    """ Starting with T,R as inputs, generate all other values """
    T,R = TR
    psi_w = CP.HAPropsSI('psi_w','T',T,'R',R,'P',P)
    other_output_keys = ['T_wb','T_dp','Hda','Sda','Vda','Omega']
    outputs = {'psi_w':psi_w,'T':T,'P':P,'R':R}
    for k in other_output_keys:
        outputs[k] = CP.HAPropsSI(k,'T',T,'R',R,'P',P)
    return outputs

def get_supported_input_pairs():
    """ Determine which input pairs are supported """
    good_ones = []
    inputs = generate_values((300, 0.5))
    for k1, k2 in itertools.product(inputs.keys(), inputs.keys()):
        if 'P' in [k1,k2] or k1==k2:
            continue
        args = ('psi_w', k1, inputs[k1], k2, inputs[k2], 'P', inputs['P'])
示例#24
0
def calculate_wbt(all_args):
    t_db, rh, cpu_id = all_args
    proc = psutil.Process()
    proc.cpu_affinity([cpu_id])
    T = HAPropsSI('T_wb', 'R', rh, 'T', t_db, 'P', 101325)
    return T
示例#25
0
# This file was auto-generated by the PsychChart.py script in wrappers/Python/CoolProp/Plots

if __name__ == '__main__':
    import numpy, matplotlib
    from CoolProp.HumidAirProp import HAPropsSI
    from CoolProp.Plots.Plots import InlineLabel

    p = 101325
    Tdb = numpy.linspace(-10, 60, 100) + 273.15

    # Make the figure and the axes
    fig = matplotlib.pyplot.figure(figsize=(10, 8))
    ax = fig.add_axes((0.1, 0.1, 0.85, 0.85))

    # Saturation line
    w = [HAPropsSI('W', 'T', T, 'P', p, 'R', 1.0) for T in Tdb]
    ax.plot(Tdb - 273.15, w, lw=2)

    # Humidity lines
    RHValues = [0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
    for RH in RHValues:
        w = [HAPropsSI('W', 'T', T, 'P', p, 'R', RH) for T in Tdb]
        ax.plot(Tdb - 273.15, w, 'r', lw=1)

    # Humidity lines
    for H in [
            -20000, -10000, 0, 10000, 20000, 30000, 40000, 50000, 60000, 70000,
            80000, 90000
    ]:
        # Line goes from saturation to zero humidity ratio for this enthalpy
        T1 = HAPropsSI('T', 'H', H, 'P', p, 'R', 1.0) - 273.15
def plot_psy_chart(x_low_limit=-10,
                   x_upp_limit=60,
                   y_low_limit=0,
                   y_upp_limit=0.03,
                   p=101325,
                   RH_lines='y',
                   H_lines='y',
                   WB_lines='y'):
    Tdb = np.linspace(x_low_limit, x_upp_limit, 100) + 273.15

    # Make the figure and the axes
    fig, ax = plt.subplots(figsize=(10, 8))
    fig.patch.set_alpha(0.9)
    ax.plot()
    ax.set_xlim(Tdb[0] - 273.15, Tdb[-1] - 273.15)
    ax.set_ylim(y_low_limit, y_upp_limit)
    ax.set_xlabel(r"$T_{db}$ [$^{\circ}$C]")
    ax.set_ylabel(r"$W$ ($m_{w}/m_{da}$) [-]")

    # Saturation line
    w = [HAPropsSI('W', 'T', T, 'P', p, 'R', 1.0) for T in Tdb]
    ax.plot(Tdb - 273.15, w, lw=2)

    # Enthalpy lines
    if H_lines == 'y':
        H_lines = [
            -20000, -10000, 0, 10000, 20000, 30000, 40000, 50000, 60000, 70000,
            80000, 90000
        ]
        for H in H_lines:
            #Line goes from saturation to zero humidity ratio for this enthalpy
            T1 = HAPropsSI('T', 'H', H, 'P', p, 'R', 1.0) - 273.15
            T0 = HAPropsSI('T', 'H', H, 'P', p, 'R', 0.0) - 273.15
            w1 = HAPropsSI('W', 'H', H, 'P', p, 'R', 1.0)
            w0 = HAPropsSI('W', 'H', H, 'P', p, 'R', 0.0)
            ax.plot(np.r_[T1, T0], np.r_[w1, w0], 'go--', lw=1, alpha=0.5)
            string = r'$H$=' + '{s:0.0f}'.format(s=H / 1000) + ' kJ/kg'
            bbox_opts = dict(boxstyle='square,pad=0.0',
                             fc='white',
                             ec='None',
                             alpha=0)
            ax.text(T1 - 2,
                    w1 + 0.0005,
                    string,
                    size='x-small',
                    ha='center',
                    va='center',
                    bbox=bbox_opts)

    # Wet-blub temperature lines
    if WB_lines == 'y':
        WB_lines = np.linspace(0, 55, 12) + 273.15
        for WB in WB_lines:
            #Line goes from saturation to zero humidity ratio for this enthalpy
            T1 = HAPropsSI('T', 'Twb', WB, 'P', p, 'R', 1.0) - 273.15 - 2
            T0 = HAPropsSI('T', 'Twb', int(WB), 'P', p, 'R', 0.0) - 273.15
            wb1 = HAPropsSI('W', 'Twb', WB, 'P', p, 'R', 1.0) + 0.002
            wb0 = HAPropsSI('W', 'Twb', int(WB), 'P', p, 'R', 0.0)
            ax.plot(np.r_[T1, T0], np.r_[wb1, wb0], 'm--', lw=1, alpha=0.5)
            string = r'$WB$=' + '{s:0.0f}'.format(s=(WB - 273)) + ' [C]'
            bbox_opts = dict(boxstyle='square,pad=0.0',
                             fc='white',
                             ec='None',
                             alpha=0)
            ax.text(T1 - 2,
                    wb1 + 0.0005,
                    string,
                    size='x-small',
                    ha='center',
                    va='center',
                    bbox=bbox_opts)

    # Humidity lines
    if RH_lines == 'y':
        RH_lines = [0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
        for RH in RH_lines:
            w = [HAPropsSI('W', 'T', T, 'P', p, 'R', RH) for T in Tdb]
            ax.plot(Tdb - 273.15, w, 'r--', lw=1, alpha=0.5)
            yv = [HAPropsSI('W', 'T', T, 'P', p, 'R', RH) for T in Tdb]
            T_K = Tdb[round(95.4082 - 40.8163 * RH)]
            w = yv[round(95.4082 - 40.8163 * RH)]
            string = r'$\phi$=' + '{s:0.0f}'.format(s=RH * 100) + '%'
            bbox_opts = dict(boxstyle='square,pad=0.0',
                             fc='white',
                             ec='None',
                             alpha=0)
            ax.text(T_K - 273.15,
                    w,
                    string,
                    size='x-small',
                    ha='center',
                    va='center',
                    bbox=bbox_opts)
#    plt.close('all')
    return (fig, ax)
示例#27
0
 def plot(self,ax):
     w = [HAPropsSI('W','T',T,'P',p,'R',1.0) for T in Tdb]
     ax.plot(Tdb-273.15,w,lw=2)
示例#28
0
 def plot(self,ax):
     for RH in self.RH_values:
         w = [HAPropsSI('W','T',T,'P',p,'R',RH) for T in Tdb]
         ax.plot(Tdb-273.15,w,'r',lw=1)
示例#29
0
def get_ws(Ts, RHs):
    #    assert(len(Ts) == len(RHs))
    ws = HAPropsSI('W', 'P', p_ref, 'T', Ts + C2K, 'R', RHs / 100) * PropsSI(
        'D', 'P', p_ref, 'T', Ts + C2K, "air")
    return ws