Machine learning predictions of critical heat fluxes for pillar-modified surfaces

For convection research, one important topic is the maximum heat flux achieved on a boiling surface, known as the critical heat flux (CHF). This phenomenon is characterized by the formation of a blanket of heat-blocking vapor on the surface. Over several decades, numerous surface structures have been fabricated to enhance the CHF for various high-power cooling applications. However, the complexity of the surface structures and many other factors (e.g., capillary wicking flux) restrict the prediction of the CHF using theoretical models. In this work, three popular machine learning (ML) methods are employed to analyze and further predict the CHFs for a given surface modified with micro-structures. Among these, the random forest regression method consistently produced the best fitting models of previously published data. The importance analysis algorithm developed for random forest models facilitated efficient discovery of the most important descriptors predicting the CHF. One key descriptor used in these models was the mean beam length (MBL), a terminology borrowed from radiative heat transfer, which effectively described the characteristic spacing between adjacent surface features. The models showed greatest sensitivity to the MBL and the height of the features, compared to the other surface descriptors.