AUTHOR=Alotaibi Sorour , Ebrahim Shikha , Salman Ayed 

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

JOURNAL=Frontiers in Energy Research

VOLUME=9

YEAR=2021

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2021.668227

DOI=10.3389/fenrg.2021.668227

ISSN=2296-598X

ABSTRACT=<p>A great amount of research is focused, nowadays, on experimental, theoretical, and numerical analysis of transient pool boiling. Knowing the minimum film boiling temperature (<italic>T</italic><sub><italic>min</italic></sub>) 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 <italic>T</italic><sub><italic>min</italic></sub> in the above process: Random forest machine learning technique is applied to predict the <italic>T</italic><sub><italic>min</italic></sub>. The approach trains a machine learning algorithm using a set of experimental data collected from the literature. Several parameters such as liquid subcooling temperature (<italic>T</italic><sub><italic>sub</italic></sub>), fluid to the substrate material thermophysical properties (β<italic><sub><italic>f</italic></sub>/</italic>β<italic><sub><italic>w</italic></sub></italic>), and system saturated pressure (<italic>P</italic><sub><italic>sat</italic></sub>) are collected and used as inputs, whereas <italic>T</italic><sub><italic>min</italic></sub> is measured and used as the output. Computational results show that the algorithm achieves superior results compared to other correlations reported in the literature.</p>