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')
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)
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)
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))
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)
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')
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"]
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)
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)
# 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')
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 '================================================================'
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')
# 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))
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
def check_type(Tvals, wvals):
HAPropsSI('H', 'T', Tvals, 'P', 101325, 'W', wvals)
def check_HAProps(*args):
val = HAPropsSI(*args)
# 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'])
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
# 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)
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)
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)
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