A Machine Learning Approach to the Prediction of Flow Boiling Heat Transfer Coefficients in Small to Micro-Tubes

Abstract

Flow boiling in microchannels plays an important role in the future of cooling systems. However, to design efficient devices that exploit the benefits of latent heat cooling, it is necessary to develop a detailed understanding of the several complex phenomena that interact with each other and are extremely challenging to capture mathematically with physics-based models. This study explores the application of machine learning (ML) algorithms to demonstrate their predictive abilities in the absence of detailed deterministic knowledge. The work leverages the extensive Brunel Two-phase Flow Database to extract the explanatory variables needed for predictions and uses various regression models to predict the heat transfer coefficient in single small to micro tubes with diameters ranging from 0.52 to 4.26 mm. The preliminary results demonstrate that the ML algorithm can predict accurately, albeit caution is needed when extrapolating beyond the ranges of the data used for training.