Data-driven regression of thermodynamic models in entropic form using physics-informed machine learning
E.C. Bunschoten (TU Delft - Flight Performance and Propulsion)
A. Cappiello (TU Delft - Flight Performance and Propulsion)
M. Pini (TU Delft - Flight Performance and Propulsion)
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Abstract
This article presents a data-driven method to evaluate thermodynamic properties of pure fluids and mixtures of fixed composition in the ideal- and nonideal thermodynamic states. Thermodynamic consistency is ensured by computing the fluid properties on the basis of the entropy potential and its first- and second- order derivatives, calculated with a physics-informed neural network. The computational performance of the method was investigated by implementing the resulting data-driven model in the open-source SU2 CFD software and by performing RANS simulations of the nonideal compressible flows through an organic Rankine cycle turbine cascade. Compared to using a multiparameter equation of state through a thermodynamic library coupled with SU2, the method was found to be 60 % more computationally efficient while maintaining high accuracy.
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