An intelligent strategy for phase change heat and mass transfer: Application of machine learning

Reliable and cost-effective measurement and characterization of phase change processes have always been challenging and expensive. Likewise, due to the complex nature of these processes, the fundamental understanding of processes such as boiling and condensation remains limited. Therefore, a need exists in the phase change heat and mass transfer research community to develop new techniques which can achieve both more accurate and simpler heat transfer measurements. Furthermore, a need exists to develop a better understanding of the relevant physical mechanisms governing these processes. Conventional methods for measuring and characterizing phase change heat transfer are often complex and lead to high measurement uncertainty, and their use is limited to narrow conditions. However, in the past decade, the field of engineering has seen a surge in the application of machine learning and computer vision techniques in various areas such as material science, biomedical, manufacturing, and autonomous driving. Recently, these techniques have shown promising results in the field of thermofluidic sciences. This chapter aims to review traditional phase change heat transfer measurement and characterization methods, highlighting their challenges and limitations. Furthermore, we discuss the potential of machine learning and computer vision models in phase change processes including their generalizability, and cost of the machine learning models compared to conventional methods. This chapter is intended to provide a strong argument for the need for new characterization techniques in phase change processes and why machine learning has the potential to augment or replace other methods. We also hope that this chapter is informative for those seeking to apply machine learning in the domain of phase change heat and mass transfer.