An overview of the prediction methods for the heat transfer of supercritical fluids

Abstract

Enhancing coolants or working fluid properties has been of interest for decades. One of the most convenient ways is to use fluids operating at conditions higher than the thermodynamically defined critical point. Such fluids are called supercritical fluids. Supercritical fluids can be used in multiple engineering applications, such as jet propulsion systems, power plants, and nuclear reactors. One of the Generation IV reactors is the Supercritical Water Reactor, which utilizes supercritical water as a coolant. However, the inclusion of supercritical fluids in such industries is constrained by the vague nature of heat transfer when used in thermal systems. Predicting heat transfer is vital, especially in thermally sensitive systems like nuclear reactors. Until now, the scientific community has lacked a generalized and accurate method to predict the heat transfer of these fluids. This paper aims to provide an overview of the recent attempts to understand and predict the heat transfer of supercritical fluids by different methods. These methods include experiments, Computational Fluid Dynamics, and machine learning, which are used to generate models for predicting heat transfer.