Data from: Generalizable physical descriptors of pool boiling heat transfer from unsupervised learning of images

Boiling processes are notoriously difficult to analyze visually due to the
complex interactions between vapor bubbles and the surface. To aid in the
quantitative analysis of these phenomena, this repository provides the
high-speed videos, manually annotated pool boiling images, and MATLAB
analysis toolkit associated with the study "Generalizable physical
descriptors of pool boiling heat transfer from unsupervised learning of
images" (International Journal of Heat and Mass Transfer, 255 (2026)
127894). The dataset comprises experiments conducted with different
working fluids (water and HFE-7100) and heater surfaces (plain and
microstructured copper and silicon) to investigate the effect on bubble
morphology. Conventional physical descriptors, such as bubble size, bubble
count, and vapor area fraction, as well as the descriptors derived from
Principal Component Analysis (PCA), were extracted from the abovementioned
dataset. The results demonstrate strong positive correlations between the
PCA-derived descriptors and the conventional parameters, confirming that
dominant amplitude correlates with bubble size and vapor area fraction,
while dominant frequency correlates with bubble count. The dataset and
accompanying tools therefore provide a basis for applying and validating
an unsupervised learning approach that can act as a robust surrogate for
traditional, time-consuming manual labeling techniques.