A novel visual data-based diagnostic approach for estimation of regime transition in pool boiling

TL;DR

This paper presents the Index of Visual Similarity (IVS), a new visual data-based metric that accurately detects boiling regime transitions in pool boiling using only high-speed images, offering a rapid and non-intrusive diagnostic tool.

Contribution

The study introduces the IVS metric combining morphological and physical visual features, validated against heat transfer data, for real-time boiling regime detection.

Findings

IVS correlates strongly with heat transfer coefficient-based metrics.

IVS effectively detects boiling regime transitions, including nucleate boiling onset.

The approach is validated on different surface types, demonstrating generalizability.

Abstract

This study introduces a novel metric, the Index of Visual Similarity (IVS), to qualitatively characterize boiling heat transfer regimes using only visual data. The IVS is constructed by combining morphological similarity, through SIFT-based feature matching, with physical similarity, via vapor area estimation using Mask R-CNN. High-speed images of pool boiling on two distinct surfaces, polished copper and porous copper foam, are employed to demonstrate the generalizability of the approach. IVS captures critical changes in bubble shape, size, and distribution that correspond to transitions in heat transfer mechanisms. The metric is validated against an equivalent metric, $\Phi$, derived from measured heat transfer coefficients (HTC), showing strong correlation and reliability in detecting boiling regime transitions, including the onset of nucleate boiling and proximity to critical heat flux (CHF). Given experimental limitations in precisely measuring changes in HTC, the sensitivity of IVS to surface superheat is also examined to reinforce the credibility of IVS. IVS thus emerges as a powerful, rapid, and non-intrusive tool for real-time, image-based boiling diagnostics, with promising applications in phase change heat transfer.