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Data for: Nonintrusive heat flux quantification using acoustic emissions during pool boiling

Dunlap, Christy1; Pandey, Hari1; Weems, Ethan1; Hu, Han1

Published Jun 13, 2025 on Dryad. https://doi.org/10.5061/dryad.q573n5tvq

Data files

Jun 13, 2025 version files 9.60 GB

Abstract

Monitoring two-phase cooling systems is crucial to avoid thermal runaways and device failures. Nonintrusive monitoring methods using remote sensing, e.g., optical and acoustic sensors, are desired to avoid interfering with bubble dynamics and ease replacement. Compared to image-based technologies, sound-based sensors are cheaper and do not require the same environment as cameras. Acoustic signals during pool boiling have been used to identify boiling states, but acoustic-based quantitative predictions have been challenging. The present work presents a machine learning framework to determine the heat flux during pool boiling using acoustic signals captured through a hydrophone. This framework investigates and compares the performance and computational cost of six machine learning models by coupling two feature extraction algorithms (fast Fourier transform and convolution) and three different regressors (multilayer perceptron, random forest, and Gaussian process regression). The fast Fourier transform-Gaussian process regression model is found to be the most promising, with high accuracy and the lowest computational cost. A parametric study is performed to investigate the effect of the temporal length and sampling rates on the model predictions. It is found that the model’s performance is improved with increasing temporal lengths of the acoustic sequences for all sampling rates. Acoustic features below 512 Hz are found to be most significant for heat flux predictions. For sampling rates beyond 512 Hz, the model performance is dictated by the temporal length of the acoustic sequences.