Work

Data-driven regression of thermodynamic models in entropic form using physics-informed machine learning

Year: 2025

Type: article

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.

Source: Computers & Fluids

DOI: 10.1016/j.compfluid.2025.106932

Authors: Evert Bunschoten, Alessandro Cappiello, Matteo Pini

Institution: Delft University of Technology

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,700

Funder: Robert Bosch