Compressor - Constant Isentropic Efficiency Model¶
Model description¶
The constant isentropic efficiency model is a simple model based on the assumption that the isentropic efficiency stays constant.
where \(\varepsilon_{is}\) is the isentropic efficiency, \(h_{su}\) is the supply specific enthalpy, \(h_{ex, is}\) is the isentropic exhaust specific enthalpy and \(h_{ex}\) is the exhaust specific enthalpy.
Based on the isentropic efficiency definition, the exhaust specific enthalpy can be calculated and thus also the exhaust temperature.
Class description¶
- class component.compressor.compressor_csteff.CompressorCstEff[source]¶
Component: Compressor
Model: Constant isentropic efficiency
Descritpion:
This model determines the exhaust specific enthalpy and the exhaust temperature of a compressor. This model can be used for on-design models of systems.
Assumptions:
Steady-state operation.
Isentropic efficiency stays constant for all the conditions.
Connectors:
su (MassConnector): Mass connector for the suction side.
ex (MassConnector): Mass connector for the exhaust side.
W (WorkConnector): Work connector for the mechanical work.
Parameters:
eta_is: Isentropic efficiency. [-]
Inputs:
P_su: Suction side pressure. [Pa]
T_su: Suction side temperature. [K]
P_ex: Exhaust side pressure. [Pa]
fluid: Working fluid. [-]
m_dot: Mass flow rate of working fluid. [kg/s]
Ouputs:
h_ex: Exhaust side specific enthalpy. [J/kg]
T_ex: Exhaust side temperature. [K]
Example of use¶
from labothappy.component.compressor.compressor_csteff import CompressorCstEff
# Example usage
CP = CompressorCstEff()
# CP.print_setup()
"If the inputs are not set directly BUT through the connectors"
# CP.su.set_properties(P=319296.5575177148, T=331.033964665788, fluid='R1233ZDE', m_dot = 0.1)
# CP.ex.set_properties(P=606240.1433176235)
"If the inputs are set directly"
CP.set_inputs(
P_su=319296.5575177148,
T_su=331.033964665788,
P_ex=606240.1433176235,
fluid='R1233ZDE', # Make sure to include fluid information
m_dot=0.1 # Mass flow rate
)
CP.set_parameters(eta_is=0.8)
# CP.print_setup()
CP.solve()
CP.print_results()
fig = CP.plot_Ts()
fig.show()
References¶
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