Data for: A temporal–spatial framework for efficient heat flux monitoring of transient boiling

Two-phase cooling offers superior heat dissipation compared to
conventional single-phase cooling methods. Nevertheless, the occurrence of
critical heat flux (CHF) during boiling introduces reliability concerns,
potentially leading to system failure. To improve system reliability,
optical imaging is employed to analyze and monitor cooling systems without
disrupting the boiling dynamics. These methods involve analyzing images of
the boiling process to identify boiling regimes and evaluate heat flux.
However, current optical-based methods are limited to static images,
thereby missing out on the valuable temporal information captured by
high-speed imaging. Inspired by the successful integration of temporal
information in other fields, this work aims to exploit the temporal
information from transient pool boiling captured via high-speed imaging
for enhanced heat flux monitoring. For this purpose, two frameworks,
comprising six different machine-learning models, have been developed for
a comparative analysis. Specifically, the first framework includes two
models that use static images for monitoring, serving as a representation
of existing methodologies and a benchmark against which the second
framework is measured. The remaining four models within the dynamic
image-based framework (the 2nd framework) leverage sequences of images to
capture temporal information. To evaluate the advantage of incorporating
temporal information, transient boiling experiments were conducted to
construct the dataset. A comparative analysis confirmed that temporal
information significantly enhances the accuracy of the developed heat flux
monitoring models. Among these models, the developed principal components
(PCs)-convolutional neural network (CNN) stands out with a superior
determination coefficient of 97.4% and a mean absolute percentage error of
7.0%, achieving an excellent balance between monitoring accuracy and
computational efficiency.