Prediction of the Minimum Film Boiling Temperature of Quenching Vertical Rods in Water Using Random Forest Machine Learning Algorithm

A great amount of research is focused, nowadays, on experimental, theoretical, and numerical analysis of transient pool boiling. Knowing the minimum film boiling temperature ( T min ) for rods with different substrate materials that are quenched in distilled water pools at various system pressures is known to be a complex and highly non-linear process. This work aims to develop a new correlation to predict the T min in the above process: Random forest machine learning technique is applied to predict the T min . The approach trains a machine learning algorithm using a set of experimental data collected from the literature. Several parameters such as liquid subcooling temperature ( T sub ), fluid to the substrate material thermophysical properties (β f / β w ), and system saturated pressure ( P sat ) are collected and used as inputs, whereas T min is measured and used as the output. Computational results show that the algorithm achieves superior results compared to other correlations reported in the literature.