Development of an improved turbulent heat flux model for the heat transfer of supercritical fluids

For decades, enhancing coolants or working fluid properties has been a main research interest. Operating fluids above their critical point enhance their thermophysical properties, making them suitable for advanced thermal systems. These fluids are referred to as supercritical fluids (SCF). SCF can be utilized in many industrial applications, such as jet propulsion systems, conventional power cycles, and nuclear reactors. However, the inclusion of SCF in such applications is restricted by the challenging heat transfer behavior driven by the drastic change in thermophysical properties. Predicting the heat transfer in such fluids is essential for the safe operation of thermo-sensitive systems like nuclear reactors. Many attempts have been made in open literature to generate numerical methods for predicting the heat transfer of SCF; however, the proposed methods lacked generality and reasonable accuracy. The current work presents an improved advanced turbulent heat flux model in the framework of the Reynolds-Averaged Navier-Stokes (RANS) approach for predicting the heat transfer of SCF. In this regard, an Algebraic Heat Flux Model (AHFM) has been considered as a base for the model. Although the AHFM modeling approach is advanced, it has never reached the level of being used in the generalized form to cater to a wide range of industrial applications. Nevertheless, in this research work, a wide range of numerical studies have been performed to study the complex heat transfer phenomena in SCF. In total, 45 different upward flow cases were considered under various conditions. Existing standard and advanced turbulent heat flux models were found to inadequately predict heat transfer. Therefore, a new formulation for AHFM has been proposed, hereafter referred to as AHFM-APS, to accurately predict the heat transfer in SCF at different conditions. This new model has shown promising results and has significantly improved the accuracy of heat transfer prediction to a wide range of flow conditions.