Reliable predictions of bubble departure frequency in subcooled flow boiling: A machine learning-based approach

Abstract

• A large database for bubble departure frequency is amassed. • Nine machine learning algorithms are developed. • Optimal models performed better than prior universal correlations. • The effect of parameters on departure frequency was studied. Boiling is a very effective heat transfer mechanism, which has been widespread applicable to many engineering and industrial fields. Moreover, a critical parameter in nucleate boiling is the bubble departure frequency, which arrives at accumulations of heat carried out by vapor bubbles from the heating surface. However, because of the complex behaviors in the phase change process, there are few reliable approaches to predict bubble departure frequency. In this study, a consolidated dataset is utilized to predict bubble departure frequency in subcooling flow boiling using machine learning-based approaches. A consolidated dataset of bubble departure frequency, including four working fluids in subcooling flow boiling, is formed. Nine machine learning-based regression models are well discussed. As well as the input parameters, including geometric and dimensionless parameters, are also contrasted for a suitable approach. Generally speaking, the XGBoost model shows the most significant performance at predicting bubble departure frequency, better than highly reliable generalized prediction correlations. The machine learning-based approach unlocks a reliable tool for bubble departure frequency prediction.