A Novel Vision-Based Framework for Detecting Changes in Heat Flux in Pool Boiling Using Morphological Similarity

This work presents a robust visual diagnostic framework for analyzing pool boiling phenomena, aimed at quantifying relative changes in heat flux and providing a foundation for future predictive models with limited temperature data. Instead of directly estimating heat flux from visual features, the proposed approach focuses on identifying relative changes in boiling visual patterns to detect a shift in heat flux and infer proximity to the critical heat flux (CHF), which is a key threshold beyond which thermal performance rapidly deteriorates. At the core of this framework is a novel metric, morphological similarity, which captures variations in bubble shape and spatial organization using Scale Invariant Feature Transform (SIFT)-based feature matching between image pairs. Experimental validation on two different heated surfaces demonstrates that morphological similarity reliably tracks changes in heat flux and strongly correlates with surface superheat, a key thermal parameter. Requiring only minimal data, this method generalizes across two surfaces and offers a scalable, real-time alternative for thermal diagnostics in electronics cooling systems.