Accurate and robust predictions of pool boiling heat transfer with micro-structured surfaces using probabilistic machine learning models

Abstract

The accurate and reliable prediction of enhanced heat transfer performance of micro-structured surfaces is crucial to optimally design and operate pool boiling systems. However, the existing empirical models predict the enhanced pool boiling heat transfer with very large errors up to ±81 % even using the experimental data from the same study, mainly due to the complex nature of the pool boiling processes. More importantly, the existing models predict only limited coolant types, surface geometries, and operating conditions. To overcome these challenges, this study examines three deterministic and two probabilistic machine learning (ML) models for accurate and reliable enhanced pool boiling prediction, while using carefully selected key six dimensionless numbers. The models were trained and tested using 1,241 data from 20 experimental studies with 80/20 % of train/test ratio, and the pre-trained models were tested for additional 519 data from 6 studies to evaluate the models reliability. The predicted mean absolute percentage error (MAPE) shows that Bayesian, deep, and 1-D convolutional neural network (1D-CNN) models outperform the random forest and natural gradient (NG) boost models due to their extended hidden layers. The machine learning models improve the MAPE by up to 30 % compared to the existing correlations. Furthermore, a parameter sensitivity analysis is performed using explainable artificial intelligence showing that the boiling Reynolds number is the most critical parameter followed by the kinetic Reynolds and Bond numbers. The probabilistic ML models can also provide the uncertainties to improve prediction reliability compared to the deterministic ones.