UARK-NED3/PoolBoiling-HighSpeedVideo

Pool Boiling High-Speed Video Datasets and Analysis Toolkit

Four high-speed video datasets of pool boiling across two working fluids and four heater surfaces, the manually annotated bubble-contour subsets derived from them, and the MATLAB toolkit that extracts both conventional and PCA-derived physical descriptors.

Dataset DOI: 10.5061/dryad.kh18932mw · Primary article: International Journal of Heat and Mass Transfer 255, 127894, 10.1016/j.ijheatmasstransfer.2025.127894 · License: CC0 1.0 (public domain dedication)

Mirror of the Dryad deposit published 30 October 2025.

The measurement problem

Boiling is hard to quantify from images. Bubbles overlap, merge, and leave the field of view, so the conventional descriptors — bubble count, mean bubble area and radius, vapor area fraction — are recovered by hand-annotating contours frame by frame, which does not scale to the thousands of frames a high-speed camera produces in a second.

The associated study asks whether an unsupervised decomposition can stand in for that labor. Principal component analysis over image sequences yields two descriptors — Dominant Amplitude (Dₐ) and Dominant Frequency (D𝑓) — taken from the FFT of the leading PC's time series. The paper reports strong positive correlations between these and the manual measurements: Dₐ with bubble size and vapor area fraction, D𝑓 with bubble count.

There is no pretrained model in this deposit, and none is published alongside it. The PCA is re-fit at run time, per dataset and per heat load, and the scripts write PC scores to CSV rather than persisting a basis. The method lives in the toolkit below, not in a set of weights.

Datasets

Two boiling regimes appear across the videos: steady-state nucleate boiling at lower heat loads, and the transient excursion to film boiling at the highest. Videos were captured with Phantom high-speed cameras and are distributed as downsized .mp4 converted from the camera's native format with Phantom Camera Control (PCC).

Dataset	Surface	Fluid	Videos	Heat loads	Annotated images	Size
FCu-H2O	Copper foam	Deionized water	7	15, 30, 60, 100, 150, 200, 220 W	91	11 GB
PCu-H2O	Plain copper	Deionized water	7	10, 40, 60, 90, 110, 120, 130 W	91	2.3 GB
PSi-HFE	Plain silicon	HFE-7100	9	6, 8, 10, 12, 14, 16, 17.6, 20, 22 W	119	3.5 GB
SSi-HFE	Structured silicon	HFE-7100	8	6, 12, 18, 24, 30, 36, 42, 44 W	56	1.5 GB

Provenance differs by fluid, and the difference matters when interpreting cross-dataset comparisons:

• Cu-H₂O (PCu-H2O, FCu-H2O) — Nano Energy and Data-Driven Discovery Laboratory, University of Arkansas (Prof. Han Hu).
• Si-HFE (PSi-HFE, SSi-HFE) — Cooling Technologies Research Center, Purdue University (Prof. Justin Weibel).

Fluid, surface, facility, and optical arrangement all vary together between the water and HFE-7100 groups. Treat a water-versus-HFE difference as a compound effect, not a fluid-only one.

Layout

DataProcessTools/
  Conventional_descriptors/   bubbles_annotator_v6.m, bubbleStat_calculator_v4.m
  Generalized_descriptors/    PC_calculator_v4.m, dominantDescriptor_calculator_v4.m
<Dataset>/                    downsized .mp4 videos, one per heat load
<Dataset>/annotatedBubbles/   megapixel .jpg frames + <Dataset>-BubbleContours.json

Images are named <Dataset>_heatLoad_imageSequenceNum.jpg. Each regime's BubbleContours.json holds one object per image, carrying a FileName and a Bubbles object with the coordinates of each labeled bubble (b0001, b0002, …).

Path note. The deposit README documents the PCA scripts under ./Generalizable_descriptors/. The directory is actually named Generalized_descriptors. Use the latter.

Toolkit

MATLAB R2021a or newer, with the Image Processing Toolbox and the Statistics and Machine Learning Toolbox.

Conventional descriptors

bubbles_annotator_v6.m — a GUI for drawing and labeling bubble contours. Prompts for a source image folder and a destination, then opens each image: left-click places polygon vertices, Backspace undoes the last point, double-click finalizes a bubble. Writes annotated images and a single bubble_labeled_data.json.

bubbleStat_calculator_v4.m — reads that JSON and computes bubble count (N_b), mean bubble area (A_b), mean bubble radius (R_b), and vapor area fraction (VAF), grouped by heat load. Set the pixel resolution l_px for your dataset before running — the physical statistics scale directly with it. Optionally writes contour overlays to a BubbleContours subfolder.

Generalizable descriptors

PC_calculator_v4.m — runs PCA over image frames in subsets. Configure MainData_DIR, the heatLoads array, totalImages, numSubsets, and numPCs; frames must be named sequentially (00000.jpg). Writes a ./PC_Results directory of CSV PC scores.

dominantDescriptor_calculator_v4.m — FFTs the time series of one principal component (typically PC1) to extract Dₐ and D𝑓. Requires PC_calculator_v4.m to have run first. Set MainData_DIR, heatLoads, frameRate, and pcToAnalyze. Writes ./Dominant_Descriptors, one CSV per heat load.

Workflow

1. Extract frames from the videos into per-heat-load subfolders (e.g. ./FCu-H2O/15W/).
2. Annotate a representative subset with bubbles_annotator_v6.m.
3. Run bubbleStat_calculator_v4.m for the conventional statistics.
4. Run PC_calculator_v4.m, then dominantDescriptor_calculator_v4.m.

Note that step 1 is a prerequisite the deposit does not ship: the videos are distributed as .mp4, and the scripts consume extracted frame sequences.

Relationship to BoilingBench-CV

The annotated subsets here also appear in UARK-NED3/BoilingBench-CV, which lists this deposit as one of its upstream sources. The overlap is substantial and deliberate: all 357 annotated images, all 4 contour JSON files, and 8 of the 9 PSi-HFE videos are common to both.

Which to use:

• This repository for the high-speed videos, the original contour JSON format, and the MATLAB descriptor toolkit.
• BoilingBench-CV for canonical COCO-style annotations, grouped benchmark splits with a documented leakage rule, and segmentation evaluation records.

The two carry different licenses — this deposit is CC0 1.0, BoilingBench-CV is CC BY 4.0 — so check the terms for the copy you actually use.

Loading

The Dataset Viewer is disabled: the payload is .mp4 video and megapixel .jpg frames with contours in a bespoke JSON schema, none of which the viewer renders.

from huggingface_hub import snapshot_download

# annotations and toolkit only, skipping ~18 GB of video
path = snapshot_download(
    "UARK-NED3/PoolBoiling-HighSpeedVideo", repo_type="dataset",
    allow_patterns=["*/annotatedBubbles/*", "DataProcessTools/*"],
)

# one regime's videos
path = snapshot_download(
    "UARK-NED3/PoolBoiling-HighSpeedVideo", repo_type="dataset",
    allow_patterns=["SSi-HFE/*.mp4"],
)

Limitations

• Videos are downsized conversions of the Phantom native recordings, not the original acquisitions. Frame rate and scale are not recorded in the file metadata; recover them from the paper or the acquisition records.
• bubbleStat_calculator_v4.m needs a per-dataset pixel resolution that the deposit does not tabulate. Conventional descriptors are not reproducible without it.
• Folder labels are acquisition heat loads, not validated onset or critical-heat-flux markers.
• The annotations are 2-D projected contours in image-pixel coordinates, not three-dimensional vapor measurements.

Citation

Zhang, L., Soori, T., Bongarala, M., Li, C., Hu, H., Weibel, J. A., and Sun, Y. 2026. Generalizable physical descriptors of pool boiling heat transfer from unsupervised learning of images. International Journal of Heat and Mass Transfer 255, 127894. https://doi.org/10.1016/j.ijheatmasstransfer.2025.127894

Data: Zhang, Lige; Soori, Tejaswi; Bongarala, Manohar; Li, Changgen; Hu, Han; Weibel, Justin; Sun, Ying. Data from: Generalizable physical descriptors of pool boiling heat transfer from unsupervised learning of images. Dryad. https://doi.org/10.5061/dryad.kh18932mw

Affiliations: Drexel University (Zhang, Soori, Sun); Purdue University West Lafayette (Bongarala, Weibel); University of Arkansas (Li, Hu); University of Cincinnati (Sun).

Funding

• U.S. National Science Foundation, CBET-2300317 and CBET-2323023 (Division of Chemical, Bioengineering, Environmental, and Transport Systems)
• Office of Naval Research, N000141613109
• U.S. National Science Foundation, 22-EPS4-0028, Arkansas EPSCoR DART
• U.S. National Science Foundation, OIA-1946391 (Office of Integrative Activities)

Contact

Han Hu, Associate Professor of Mechanical Engineering, University of Arkansas — hanhu@uark.edu