Machine learning based prediction of subcooled bubble condensation behavior, validation with experimental and numerical results

Abstract

Measuring a full life cycle of condensing subcooled bubbles using either the experimental and/or numerical approaches is a very challenging problem. In present study this problem is solved through Machine Learning techniques using existing data sets from both experiment and numerical results. Two different machine leaning methods, Linear Regression (LR) and Gaussian Process Regression (GPR) are trained to predict the bubble condensing life-history. The models are trained with 70% of data and validated using 30 % data from the collected datasets. The predicted results are compared with both numerical and experimental results and model prediction obtained good agreement. Additionally, the validated machine learning models are used to predict various bubble diameters ranging between 1 and 6 mm. These predicted results give a much better understanding of subcooled bubble condensation behavior without the need for extensive experiments and numerical studies.