Decrypting the boiling crisis through data-driven exploration of high-resolution infrared thermometry measurements

Abstract

We develop a neural network model capable of predicting the margin to the boiling crisis (i.e., the departure from nucleate boiling ratio, DNBR) from high-resolution infrared measurements of the bubble dynamics on surfaces with different morphologies and wettability (or wickability). We use a feature ranking algorithm, i.e., minimum redundancy maximum relevance, to elucidate the importance of fundamental boiling parameters, i.e., nucleation site density, bubble departure frequency, growth time, and footprint radius, in predicting the boiling crisis. We conclude that these parameters are all necessary and equally important. This result has profound implications, as it undermines the general validity of many observations and mechanistic models that attempt to predict the critical heat flux (CHF) by describing how a single boiling parameter changes with the heat flux or from one surface to another. Notably, the neural network model can predict the DNBR on CHF-enhancing surfaces of different wickability without using any input information related to the surface properties. This result suggests that, at least on the considered surfaces, surface wickability enhances the CHF by modifying the bubble dynamics, i.e., the aforesaid boiling parameters, rather than acting as an additional heat removal mechanism.