Heat Pump Example
How to calculate a heat pump with carbatpy. There are two storages (cold and hot) running between prescribed temperatures with defined fluids.
[1]:
import carbatpy as cb
You are using this custom set ('CARBATPY_BASE_DIR') base directory for carbatpy: C:\Users\atakan\sciebo\Python\carbatpy\src\carbatpy
Select the input file (yaml or json works):
[2]:
DEFAULT_DIR = cb.CB_DEFAULTS["General"]["CB_DIR"]+"\\data\\"
DEFAULT_FILE = DEFAULT_DIR+"hp-input-dictvariables"
The content of the (here: json) file is shown below. It includes the wanted minimum separation between fluid temperatures D_T_MIN, superheating before compressor, isentropic efficiency (=const. at the moment), the pressures, fluids and their composition in both storages and the working fluid. If P_Working[“setting”] ==”auto” , the pressures will be determined acording to the storage temperatures, but this can fail. Alternatively “setting” can be anything else, then the two pressures (p_low for the evaporator, p_high for the condenser, all in Pa) will be used together with the temperatures (and T-differences) listed. Again, if the values are not chosen properly, this will fail, therefore check the warning (see below).
{
"D_T_MIN": 4.0,
"D_T_SUPER": 5,
"ETA_S_C": 0.7,
"P_IN_STORAGE_COLD": 500000.0,
"P_IN_STORAGE_HOT": 500000.0,
"P_WORKING": {
"optimize": "None",
"setting": "auto",
"p_low": 0.0,
"p_high": 0.0
},
"Q_DOT_MIN": 1000.0,
"T_IN_STORAGE_COLD": 288.15,
"T_IN_STORAGE_HOT": 288.15,
"T_OUT_STORAGE_COLD": 250.15,
"T_OUT_STORAGE_HOT": 363.0,
"fluids_all": {
"WORKING": [
"Propane * Butane * Pentane * Hexane",
[
0.75,
0.05,
0.15,
0.05
]
],
"STORAGE_HOT": [
"Water",
[
1.0
]
],
"STORAGE_COLD": [
"Methanol",
[
1.0
]
]
}
}
Read the inputs from the file and create an input dictionary:
[3]:
inputs = cb.hp_simple.HpVal.load_from_file(DEFAULT_FILE+".json")
# print(DEFAULT_FILE)
INPUTS = inputs.to_dict()
Create a new instance of HeatPump with the wanted data:
[4]:
hpn = cb.hp_simple.HeatPump( INPUTS)
print(hpn.evaluation)
{'Q_DOT_MIN': 1000.0, 'Power': 0.0, 'T_OUT_STORAGE_HOT': 363.0, 'T_OUT_STORAGE_COLD': 250.15, 'exergy_loss_rate': 0}
Calculate the cop and further values of the heat pump and plot it:
[5]:
cop_n = hpn.calc_heat_pump(verbose=True)
hpn.hp_plot()
print(f"COP: {cop_n:.3f}")
Start:
Propane * Butane * Pentane * Hexane, composition: [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997]
Temperature : 284.15
Pressure : 103162.80738655275
spec_Enthalpy : 515638.559622378
spec_Volume : 0.4413119378111834
spec_Entropy : 2324.4430369865
quality_mass : -9999990.0
spec_internal_Energy : 470111.5811845765
x : None
y : None
z : array('d', [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997])
After compressor
Propane * Butane * Pentane * Hexane, composition: [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997]
Temperature : 402.2488899394503
Pressure : 1587011.8598801694
spec_Enthalpy : 709434.0144803056
spec_Volume : 0.03591669528516554
spec_Entropy : 2473.6676586767635
quality_mass : -9999990.0
spec_internal_Energy : 652433.7930950456
x : None
y : None
z : array('d', [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997])
after heat exchanger: condenser
Propane * Butane * Pentane * Hexane, composition: [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997]
Temperature : 292.14999998080066
Pressure : 1587011.9475793857
spec_Enthalpy : 161113.98428590625
spec_Volume : 0.0018393440674969955
spec_Entropy : 875.8441286552104
quality_mass : -9999990.0
spec_internal_Energy : 158194.92327507926
x : None
y : None
z : array('d', [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997])
power:353.43493617583744 W, heat-Evap. 646.5650638241625 W
after throttle:
Propane * Butane * Pentane * Hexane, composition: [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997]
Quality: 0.295
Temperature : 239.98538804712933
Pressure : 103162.80738655283
spec_Enthalpy : 161113.97387927683
spec_Volume : 0.1248742636863252
spec_Entropy : 938.871830958696
quality_mass : 0.295401228556919
spec_internal_Energy : 148231.5766939051
x : None
y : None
z : array('d', [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997])
None
after heat exchanger: evaporator
Propane * Butane * Pentane * Hexane, composition: [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997]
Temperature : 284.1499999999687
Pressure : 103162.80738521798
spec_Enthalpy : 515638.55962237855
spec_Volume : 0.44131193781699385
spec_Entropy : 2324.443036988574
quality_mass : -9999990.0
spec_internal_Energy : 470111.5811845667
x : None
y : None
z : array('d', [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997])
heat flow rate evaporator:646.5650828356768,power:353.43493617583744
Propane * Butane * Pentane * Hexane, composition: [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997]
Temperature : 284.1499999999687
Pressure : 103162.80738521798
spec_Enthalpy : 515638.55962237855
spec_Volume : 0.44131193781699385
spec_Entropy : 2324.443036988574
quality_mass : -9999990.0
spec_internal_Energy : 470111.5811845667
x : None
y : None
z : array('d', [0.771733787, 0.0222759488, 0.178685867, 0.027304397199999997])
COP: 2.829
Get the results
Further Evaluation results are found here:
[6]:
hpn.evaluation
[6]:
{'Q_DOT_MIN': 1000.0,
'Power': np.float64(353.43493617583744),
'T_OUT_STORAGE_HOT': 363.0,
'T_OUT_STORAGE_COLD': 250.15,
'exergy_loss_rate': np.float64(196.09456356383058),
'condenser': {'states-in-out': array([[[ 2.92150000e+02, 1.58701190e+06, 1.61113984e+05,
1.83934407e-03, 8.75844129e+02, -9.99999000e+06,
1.58194923e+05],
[ 4.02248890e+02, 1.58701186e+06, 7.09434014e+05,
3.59166953e-02, 2.47366766e+03, -9.99999000e+06,
6.52433793e+05]],
[[ 2.88150000e+02, 4.99999995e+05, 6.34584406e+04,
1.00071182e-03, 2.24387988e+02, -9.99999000e+06,
6.29580847e+04],
[ 3.63000000e+02, 4.99999996e+05, 3.76741387e+05,
1.03563325e-03, 1.19081770e+03, -9.99999000e+06,
3.76223571e+05]]]),
'mass-flow-rates': array([0.00182375, 0.003192 ]),
'exergy-loss-rate': np.float64(1728.5774985398261),
'dT-min': np.float64(3.9999999892039),
'dT-mean': np.float64(19.15954540872633)},
'evaporator': {'states-in-out': array([[[ 2.84150000e+02, 1.03162807e+05, 5.15638560e+05,
4.41311938e-01, 2.32444304e+03, -9.99999000e+06,
4.70111581e+05],
[ 2.39985388e+02, 1.03162807e+05, 1.61113964e+05,
1.24874264e-01, 9.38871861e+02, 2.95401228e-01,
1.48231584e+05]],
[[ 2.88150000e+02, 4.99999994e+05, -1.30221024e+05,
1.25617917e-03, -4.17976648e+02, -9.99999000e+06,
-1.30849114e+05],
[ 2.50150000e+02, 4.99999993e+05, -2.20917466e+05,
1.20227386e-03, -7.55240424e+02, -9.99999000e+06,
-2.21518603e+05]]]),
'mass-flow-rates': array([0.00182375, 0.00712889]),
'exergy-loss-rate': np.float64(1420.9412672182882),
'dT-min': np.float64(4.000000001905164),
'dT-mean': np.float64(-12.761387621687966)},
'p_high': np.float64(1587011.859880169),
'p_low': np.float64(103162.80738655283),
'sec_fluid_states_in_out': array([[[ 2.88150000e+02, 4.99999995e+05, 6.34584406e+04,
1.00071182e-03, 2.24387988e+02, -9.99999000e+06,
6.29580847e+04],
[ 3.63000000e+02, 4.99999996e+05, 3.76741387e+05,
1.03563325e-03, 1.19081770e+03, -9.99999000e+06,
3.76223571e+05]],
[[ 2.88150000e+02, 4.99999994e+05, -1.30221024e+05,
1.25617917e-03, -4.17976648e+02, -9.99999000e+06,
-1.30849114e+05],
[ 2.50150000e+02, 4.99999993e+05, -2.20917466e+05,
1.20227386e-03, -7.55240424e+02, -9.99999000e+06,
-2.21518603e+05]]]),
'sec_fluid_m_dots': array([0.003192 , 0.00712889]),
'COP': np.float64(2.829375077687538)}
Mass flow rates are calculated for a fixed value of the heat flow rate at high temperature \(\dot Q_h\). The mean temperature differences are calculted for each heat exchanger using the (typically) 50 points along the heat exchanger. It can be used to estimate heat exchanger sizes: \(\dot Q = UA \Delta T_{mean}\)
Check for warnings
If the temperature curves cross with the given T-p-values, you will get warnings and you should not use the results. warning is a list with the problem of each device listed, if there are some:
[7]:
if hpn.warning:
print(f"Problems: {hpn.warning}")
Information about all the calculated states (typically 50 points per heat exchanger) and the modelled components/devices can also be obtained: (They are not printed here, because the output is quite long.)
[8]:
all_plotted_states = hpn.all_states
component_info = hpn.components
Storing
Finally you can store all these things (in part this is done automatically, check your Results folder (if it is not set as environment variable, they are in your TEMP folder):
[9]:
hpn.save_to_file(cb.CB_DEFAULTS["General"]["RES_DIR"]+"\\my_results.json")
RESULTS_DIR = cb.CB_DEFAULTS["General"]["RES_DIR"]
check_res_folder = False # set this to True, if you want check yours
if check_res_folder:
import os
try: # results directory set, Environment variable?
print (os.environ['CARBATPY_RES_DIR'])
except:
pass
print("RESULTS folder :", cb.CB_DEFAULTS["General"]["RES_DIR"])
The automatically stored results can be read to a dictionary:
[10]:
my_dict = cb.hp_simple.read_hp_results(RESULTS_DIR +
"\\last_T_H_dot_plot_hp_evaluation_dict.npy")
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