Predictive fluid–property modeling of supercritical power cycle performance using the Extended Corresponding States framework

This paper presents a predictive framework for evaluating how thermophysical fluid properties influence the performance of supercritical Brayton and recuperated power cycles. Understanding these relationships is essential for developing high-efficiency energy systems and extending cycle design beyond well-characterized CO 2 and CO 2 -based mixtures. Leveraging Extended Corresponding States (ECS) theory, the study establishes a physically interpretable, data-driven model that links fluid properties to thermodynamic cycle efficiency across a broad range of synthetic and real working fluids. A large dataset of ECS-generated fluids is created by varying nine independent thermophysical parameters within physically meaningful bounds and coupling them with a systematic sweep of operating conditions, including pressure ratio, turbine inlet temperature, and component isentropic efficiencies. Cycle simulations are performed for both Brayton and recuperated configurations, and the resulting data are analyzed using statistically validated regression models to quantify the influence of fluid and operating parameters on thermal efficiency. The results show that operating parameters dominate overall performance, with pressure ratio and isentropic efficiencies being most influential in Brayton cycles, while pressure ratio, isentropic efficiency, and turbine inlet temperature dominate in recuperated cycles. Fluid–property effects govern efficiency trends primarily through the ideal-gas specific heat capacity, its temperature dependence, and the critical temperature (for recuperated cycles). The model accurately predicts performance trends across real fluids within the validated ECS evaluation domain and captures the key thermophysical dependencies governing both cycle configurations. The resulting generalized fluid–property–performance relationships provide a physically interpretable framework for working-fluid pre-selection and cycle optimization of supercritical power systems.