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Abstract: This dataset contains 30 transient saturated pool-boiling experiments with deionized water on flat copper, straight-microchannel copper, and square-micropillar copper surfaces at nominal vapor-space pressures of 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 kPa. One transient is provided for each surface-pressure combination. In each standard test, a single electrical-heating step was applied until a CHF-related thermal excursion was identified, after which the heater was turned off and the cooling/recovery response was recorded. The archive includes native thermocouple and pressure records, processed thermal histories, reconstructed surface temperature, heat flux, wall superheat, heat-transfer coefficient, selected boiling-event fields, side-view high-speed videos, NumPy data archives, and selected image-derived BubbleID quantities. The data support analysis of CHF, post-CHF recovery, boiling hysteresis, thermal history, pressure effects, and surface-structure effects. The boiling-hysteresis ratio used in the companion manuscript is $$H=q''_{NBR}/q''_{CHF}$$, where NBR denotes the sustained return to the nucleate-boiling branch. Reconstructed thermal quantities and event labels are derived outputs, while projected vapor fraction and bubble counts are side-view image metrics rather than volumetric void fraction or wall dry-area measurements. The dataset is not a replicated performance database and should be analyzed with condition-aware methods because pressure sequence, surface history, and run order were not fully randomized. The companion analysis and figure-generation workflow is available in BoilingLab (https://github.com/UARK-NED3/BoilingLab/tree/main/manuscripts/boiling_hysteresis_subatmospheric). 

Abstract: The ned3-019-BoilingHysteresis10kPa dataset contains five extended-heating saturated pool-boiling experiments on a flat copper surface at approximately 10 kPa and 60 W. Unlike the 30 standard tests, in which the heater was turned off shortly after CHF detection, these experiments intentionally prolonged heating after CHF to produce progressively higher post-CHF maximum surface temperatures, from approximately 75.6 to 250.1 °C. Each case includes native and processed thermal and pressure measurements, reconstructed heat-flux and surface-temperature histories, high-speed side-view video, hydrophone signals, acoustic-emission data, synchronization metadata, and derived plots. The dataset was developed to examine how post-CHF thermal history affects the return to nucleate boiling and boiling hysteresis, and serves as an external protocol-validation dataset rather than an independent set of fitting cases. This dataset is the five-case protocol-perturbation dataset used to test the boiling-hysteresis correlation in: *Pressure-Adjusted Post-CHF Apparent Base-Wall Superheat Correlates with Boiling Hysteresis in Subatmospheric Pool Boiling.* The dataset has DOI [10.5281/zenodo.22235808](https://doi.org/10.5281/zenodo.22235808) and is released under the [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/). The companion 30-case standard dataset is stored separately in `ned3-013_SubatmosphericBoiling`. The standard cases use one transient for each of three copper surfaces and ten nominal pressures. The present five cases use flat copper at approximately 10 kPa and intentionally extend the heating period after the CHF indication to produce different post-CHF maximum wall temperatures. They were excluded from fitting and used only as withheld protocol-validation cases. ## Cases and experimental design | Directory | Case identifier in raw/plot filenames | Nominal condition | Measured maximum surface temperature | |---|---|---|---:| | `1-Tmax_at_CHF` | `Boiling-420` | baseline; approximately 10 kPa; 60 W | 75.6 degC | | `2_Tmax_100C` | `Boiling-421` | approximately 100 degC target; approximately 10 kPa; 60 W | 101.7 degC | | `3_Tmax_150C` | `Boiling-422` | approximately 150 degC target; approximately 10 kPa; 60 W | 150.5 degC | | `4_Tmax_200C` | `Boiling-423` | approximately 200 degC target; approximately 10 kPa; 60 W | 198.7 degC | | `5_Tmax_250C` | `Boiling-424` | approximately 250 degC target; approximately 10 kPa; 60 W | 250.1 degC | The temperatures in the last column are derived values read from the retained processed summaries. The target temperature and the measured maximum temperature are not identical for every case. Use the measured value for quantitative analysis. 

Abstract: Overview    BoilingBench-CV is a benchmark dataset package for evaluating computer-vision methods for two-dimensional bubble instance segmentation and per-frame bubble morphometry. It is designed to measure within-domain performance and generalization across water and HFE-7100 imaging domains. This package does not yet establish a tracking or bubble-dynamics benchmark. Per-image contours do not encode reviewed temporal bubble identities, coalescence/breakup events, or departure times. Contents - `raw/`: immutable copies of source annotated-image subsets and their original annotation formats. - `derived/`: canonical COCO-style annotations and manifests generated from the raw data. - `splits/`: immutable grouped benchmark partitions. - `metadata/`: source, rights, annotation, and quality-assurance records. - `docs/`: data-card and release notes. The source video collections are deliberately not duplicated here. The benchmark uses annotated still images; original videos remain in their source archives. Data domains The initial pool-boiling component includes FCu-H2O, PCu-H2O, PSi-HFE, and SSi-HFE annotated subsets. They form a valuable but compound domain shift: fluid, surface, image geometry, optical arrangement, and facility may differ. Water-versus-HFE results must not be interpreted as a fluid-only causal effect. Use rules 1. Preserve `raw/` unchanged. 2. Use only the supplied grouped split files for reported benchmark results. 3. Do not split frames from the same `(regime, source video/power condition)` across partitions. 4. Report performance by regime, not only a pooled score. 5. Treat annotations flagged in `metadata/annotation_qa.md` according to the    benchmark evaluator's documented ignore policy. Attribution When using the package, cite the BoilingBench-CV dataset version and the source dataset records listed in `metadata/data_sources.csv`, together with the assigned Zenodo DOI: [10.5281/zenodo.22264378](https://doi.org/10.5281/zenodo.22264378). 

Abstract: BoilingBench-Multimodal Lite is a versioned, machine-learning-oriented release from the NED³ Laboratory at the University of Arkansas. It contains seven related benchmark tracks covering pool boiling, bubble-image annotation, acoustic sensing, and infrared immersion cooling. BoilingBench-1 through BoilingBench-4 contain multimodal boiling experiments; BoilingBench-5 contains human-annotated boiling images; BoilingBench-6 is a hydrophone-focused pool-boiling dataset; and BoilingBench-7 is an infrared immersion-cooling dataset. This Lite profile omits original Phantom `.cine` recordings to reduce distribution size. It retains the released raw files, processed analysis products, annotations, manifests, plots, and machine-readable exports included in the directory. The complete archive and any omission details are documented in the accompanying README and release manifest. Raw acquisition files are measurements or source observations. Heat flux, reconstructed surface temperature, synchronization records, spectra, spectrograms, event times, hysteresis metrics, and other analysis products are derived data unless explicitly identified otherwise. BoilingLab-generated heat-flux products require users to inspect thermocouple locations, geometry, calibration, material assumptions, time alignment, sign convention, and quality fields before treating them as labels. BoilingBench-3 and BoilingBench-4 use legacy MISTRAS USB AE acquisition and do not contain continuous AE waveform exports. BoilingBench-3 contains microphone data. BoilingBench-1 and BoilingBench-2 contain continuous AE waveform products where documented. Users must split examples by independent experiment/run or physical condition rather than randomly splitting adjacent temporal windows. The dataset is intended for reproducible research in heat-transfer prediction, multimodal sensing, boiling-regime and transition analysis, bubble-image analysis, acoustic heat-flux estimation, sequence regression, and cross-domain generalization. It is not a clinical, personal, or safety-certified dataset. 

The growing power density of microelectronics has intensified the demand for high-performance cooling solutions. Subatmospheric pool boiling of water offers a promising pathway to extend the use of water-based cooling by reducing saturation temperature. However, reduced critical heat flux (CHF) and heat transfer coefficient (HTC) at low pressures remain key challenges. In this work, we experimentally investigate the coupled effects of pressure and surface structures on pool boiling performance and boiling hysteresis. Transient pool boiling experiments were conducted on flat copper, copper microchannel, and copper micro-pin-fin surfaces over a pressure range of 10 – 100 kPa. The results show that both CHF and HTC increase monotonically with pressure for all surfaces, while surface structures consistently enhance boiling performance across the entire pressure range. Distinct roles of pressure-structure coupling are identified in CHF and HTC. CHF is predominantly governed by surface structures, with structured surfaces at 10 kPa exhibiting higher CHF than a flat surface at atmospheric pressure. In contrast, HTC is influenced by both pressure and surface structure, with notable overlap between their effects. In addition, CHF-induced boiling hysteresis is substantially mitigated at low pressures (10 – 30 kPa), which leads to rapid recovery to nucleate boiling with minimal overheating. These findings demonstrate that the degradation of boiling performance at subatmospheric pressures can be effectively mitigated through surface engineering and that the complementary roles of surface structures (passive control) and pressure (active control) promise decoupled and targeted control of CHF and HTC. The results provide new insight into pressure-structure coupling in pool boiling and support subatmospheric operation combined with surface microstructuring as a viable strategy for reliable, high-performance water-based cooling of high-power-density electronic systems. 

Abstract: This paper presents Hit2Flux, a machine learning framework for boiling heat flux prediction using acoustic emission (AE) hits generated through threshold-based transient sampling. Unlike continuously sampled data, AE hits are recorded when the signal exceeds a predefined threshold and are thus discontinuous in nature. Meanwhile, each hit represents a waveform at a high sampling frequency (∼1 MHz). In order to capture the features of both the high-frequency waveforms and the temporal distribution of hits, Hit2Flux involves i) feature extraction by transforming AE hits into the frequency domain and organizing these spectra into sequences using a rolling window to form “sequences-of-sequences,” and ii) heat flux prediction using a long short-term memory (LSTM) network with sequences of sequences. The model is trained on AE hits recorded during pool boiling experiments using an AE sensor attached to the boiling chamber. Continuously sampled acoustic data using a hydrophone were also collected as a reference data set for this study. Results demonstrate that the proposed AE-based method achieves performance comparable to hydrophones, validating its potential for heat flux monitoring. Additionally, it is shown that the inclusion of multiple acoustic emission hits as model inputs leads to higher performance. The Hit2Flux model is also compared to methods pairing various signal preparation techniques with state-of-the-art models. This comparison further highlighted the superior accuracy of the proposed approach. The developed Hi2Flux algorithm can be applied to other transient sampling events, such as structural health monitoring, detection of electromagnetic pulses, among others. TechnicalInfo: # Data for: Hit2flux: A machine learning framework for boiling heat flux prediction using hit-based acoustic emission sensing Dataset DOI: [10.5061/dryad.g79cnp628](10.5061/dryad.g79cnp628) ## Description of the data and file structure This dataset includes acoustic emission hit data and waveforms, hydrophone data, pressure, and temperature data from transient pool boiling tests on copper microchannels. The pool boiling test facility includes (a) a heating element that consists of a copper block and cartridge heaters; (b) a closed chamber with flow loops for a chiller (Thermo Scientific Polar ACCEL 500 Low/EA) connecting Graham condenser (Ace glass 5953-106) with an adapter (Ace glass 5838-76) and an in-house built coiled copper condenser; and (c) a synchronized multimodal sensing system. The copper block is submerged in deionized water and heated by nine cartridge heaters (Omega Engineering HDC19102), each with a power rating of 50 W, inserted from the bottom. The cartridge heaters are connected to the DC power supply (MagnaPower SL200-7.5) for controlling the heating power. The copper tube coiled condenser is used to keep the vapor pressure in check during the boiling test. This is done by controlling the flow rate passing through it manually using the flow metering valve with a Vernier handle (Swagelok SS-SS4-VH). Two screw-plug immersion heaters (McMaster-Carr 4668T54) are immersed in the liquid pool for degassing and auxiliary heating. A variable-voltage transformer (McMaster-Carr 6994K17) is used for adjusting the voltage required for the immersion heaters within the given range. The test facility is installed on a Thorlab Nexus optical table (T46V) with passive legs (PTP703) for vibration isolation. The following sensors are installed in the pool boiling facility.  * A high-accuracy pressure transducer (Omega Engineering PX409030A5V) with an NI-9239 module is used to measure the vapor pressure in the boiling chamber. * Four T-type thermocouples (Omega Engineering Tj36-CPSS-032U-6) are inserted near the boiling surface of the copper block to measure the temperature gradient and calculate the heat flux and boiling surface temperature. Two T-type threaded thermocouples (McMaster-Carr 1245N16) are placed inside the boiling chamber to measure liquid and vapor temperatures. The thermocouples are connected to an NI-9210 module for data acquisition.  * Two hydrophones (High Tech HTI-96-Min) are immersed in the liquid pool for acoustic sensing. They are connected to an NI-9230 module. * An acoustic emission sensor (MISTRAS R3a) is mounted to the outer surface of the bottom plate of the boiling chamber. It is connected to a MISTRAS DAQ (1283 USB AE Node) for data collection.  The detailed experimental procedures can be found in [Pandey et al. 2024](https://www.sciencedirect.com/science/article/pii/S1359431123018367), [Pandey et al. 2024](https://www.sciencedirect.com/science/article/pii/S2352340924005493), and [Pandey et al. 2025](https://www.sciencedirect.com/science/article/pii/S1359431124029272). These data are used for training [SeqReg](https://github.com/cldunlap73/SeqReg), a machine learning framework for sequence regression. This dataset is used for acoustic-based heat flux prediction, detailed in this [tutorial](https://colab.research.google.com/drive/13TsHtLGvv9iiV82KZQYsbkmeTUubQO8c?usp=sharing).  ### Files and variables #### File: ned3-005\_Hit2Flux.zip **Description:** This zip includes four folders, i.e., Boiling-124a, Boiling-124b, Boiling-125, and Boiling-126. Each folder includes five files and a subfolder. In each folder, labeled as a generic test ID, Boiling-xxx, * The Boiling-xxx_AE.DTA file is the source file of acoustic emission signals captured using a MISTRAS R3a sensor and a MISTRAS USB AE Node. This file can be opened using the MISTRAS AEWin software.  * The Boiling-xxx_AEHit.txt file is the hit data exported from the DTA file. * The Boiling-xxx_AE_waveform folder contains the waveforms for each hit in csv format, also exported from the DTA file. * The Boiling-xxx_Hydrophone.lvm file includes the continuously sampled acoustic data using two HTI-96-MIN hydrophones with an NI-9230 module. * The Boiling-xxx_Temperature.lvm includes the temperature measurements using probe thermocouples (Omega Engineering Tj36-CPSS-032U-6) in the copper block and pool thermocouples in the water and vapor phases (McMaster-Carr 1245N16). These data are collected using an NI-9210 module. * The Boiling-xxx_Pressure.lvm file includes pressure measurements in the vapor phase using a pressure transducer (Omega Engineering PX409030A5V) and an NI-9239 module. Boiling-124a and Boiling-124b are closed chamber tests, where the Graham condenser is closed after degassing, and the pressure is regulated through the balance between boiling and condensation by tuning the flow rate of chiller water through the copper coil condenser. Boiling-125 and Boiling-126 are open-chamber tests, where the Graham condenser is open throughout the tests, and the vapor pressure inside the chamber is the same as the ambient pressure.  ## Code/software All files except for the DTA files can be opened using programming languages such as Python and MATLAB. They can also be opened using Excel, Notepad, or other sheet or text readers. Python users can follow the procedures documented in the tutorial of [SeqReg](https://github.com/cldunlap73/SeqReg) to read and use the dataset. The DTA file needs to be opened using the MISTRAS AEWin software, but it is not very often needed. For most analyses, the AEHit and waveforms are sufficient. The DTA file is provided in case users may want to export the hit data for different variables or in a different format. 

Abstract: 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. TechnicalInfo: # Data for: A temporal–spatial framework for efficient heat flux monitoring of transient boiling Dataset DOI: [10.5061/dryad.6m905qgc7](10.5061/dryad.6m905qgc7) ## Description of the data and file structure This dataset includes the temperature, acoustic (hydrophone), and videos of transient pool boiling tests of water on flat copper surfaces. The pool boiling test facility includes (a) a heating element that consists of a copper block and cartridge heaters; (b) a closed chamber with flow loops for a chiller (Thermo Scientific Polar ACCEL 500 Low/EA) connecting Graham condenser (Ace glass 5953-106) with an adapter (Ace glass 5838-76) and an in-house built coiled copper condenser; and (c) a synchronized multimodal sensing system. The copper block is submerged in deionized water and heated by nine cartridge heaters (Omega Engineering HDC19102), each with a power rating of 50 W, inserted from the bottom. The cartridge heaters are connected to the DC power supply (MagnaPower SL200-7.5) for controlling the heating power. The copper tube coiled condenser is used to keep the vapor pressure in check during the boiling test. This is done by controlling the flow rate passing through it manually using the flow metering valve with a Vernier handle (Swagelok SS-SS4-VH). Two screw-plug immersion heaters (McMaster-Carr 4668T54) are immersed in the liquid pool for degassing and auxiliary heating. A variable-voltage transformer (McMaster-Carr 6994K17) is used for adjusting the voltage required for the immersion heaters within the given range. The test facility is installed on a Thorlab Nexus optical table (T46V) with passive legs (PTP703) for vibration isolation. ### Files and variables #### File: ned3-006\_BoilingImageSequence.zip **Description:** This zip file includes eleven folders, i.e., Boiling-87, Boiling-89, Boiling-90, Boiling-91, Boiling-92, Boiling-93, Boiling-94, Boiling-96a, Boiling-96b, Boiling-96c, and Boiling-96d. Each folder, labeled with a test ID, Boiling-xxx, includes three files: * The Boiling-xxx_Temperature.lvm includes the temperature measurements using probe thermocouples (Omega Engineering Tj36-CPSS-032U-6) in the copper block and pool thermocouples in the water and vapor phases (McMaster-Carr 1245N16). These data are collected using an NI-9210 module. * The Boiling-xxx_Hydrophone.lvm file includes the continuously sampled acoustic data using two HTI-96-MIN hydrophones with an NI-9230 module. * The Boiling-xxx_video.mp4 file is a video for the boiling test. The original video (.cine) was captured using a Phantom VEO 710L high-speed camera. The .mp4 file was converted from the .cine file using the [Phantom Camera Control (PCC) software](https://www.phantomhighspeed.com/resourcesandsupport/phantomresources/pccsoftware). ## Code/software * The .txt and .lvm files can be opened and analyzed using programming languages such as Python and MATLAB. They can also be opened using Excel, Notepad, or other sheet or text readers. * The .mp4 file can be opened using a video player. 

Abstract: This paper presents BubbleID, a sophisticated deep learning architecture designed to comprehensively identify both static and dynamic attributes of bubbles within sequences of boiling images. By amalgamating segmentation powered by Mask R-CNN with SORT-based tracking techniques, the framework is capable of analyzing each bubble's location, dimensions, interface shape, and velocity over its lifetime and capturing dynamic events such as bubble departure. BubbleID is trained and tested on boiling images across diverse heater surfaces and operational settings. This paper also offers a comparative analysis of bubble interface dynamics prior to and post-critical heat flux conditions. TechnicalInfo: # Data from: BubbleID: A deep learning framework for bubble interface dynamics analysis Dataset DOI: [10.5061/dryad.ksn02v7gx](10.5061/dryad.ksn02v7gx) ## Description of the data and file structure This dataset includes raw bubble images, annotated images, and saved model weights for [BubbleID](https://github.com/cldunlap73/BubbleID), an open-source package for bubble identification, tracking, and analysis, and liquid-vapor interface dynamics analysis. The bubble images are collected using a high-speed camera (Phantom VEO 710L) during pool boiling tests performed using the facility described in [Pandey et al. 2024.](https://www.sciencedirect.com/science/article/pii/S1359431123018367) The bubble images are manually annotated for the liquid-vapor interfaces using the [labelme](https://anaconda.org/conda-forge/labelme) package.  ### Files and variables #### File: ned3-001\_BubbleID.zip **Description:** The zip file includes four folders, i.e., AnnotatedData, Models, TestData, and Tutorials.  The *AnnotatedData* folder includes two subfolders and a readme file. This dataset contains pool boiling image data and annotations made in labelme. * Classes-1: Annotations for liquid-vapor interfaces, including the training set, testing set, and a set from one pool boiling experiment, which does not appear in the other folders (Unseen-Test). - Classes-2: Annotations for bubbles distinguishing between attached and detached bubbles. The *Models* folder includes PyTorch model checkpoint file (.pth) saving the weights of preitrained models, including * seg_model.pth: pre-trained instance segmentation model * class_model.pth: pre-trained classification model The *TestData* includes two subfolders:  * SteadyStateTest for image sequences sampled at 3000 fps.  * TransientSmallTst for image sequences sampled at 150 fps.  The *tutorial* folder includes a Python Notebook script illustrating the steps for using the pretrained model and the BubbleID class for saving and displaying data extracted from bubble images/ videos.  ## Code/software * The images (.jpg) and annotations (.json) can be viewed using the [labelme](https://anaconda.org/conda-forge/labelme "https://anaconda.org/conda-forge/labelme") package.  Yes (and it should be a simple as installing LabelMe and pointing to the directory of the images and JSON files. * The tutorial (.ipynb) can be opened using any Python Notebook packages, e.g., Jupyter Notebook, Google Colab. It can be viewed in Google Colab, but I don't think there is a way (at least not a simple one) to run it in Colab. It can be viewed in Jupyter Notebook and run there if installed correctly * The [PyTorch](https://pytorch.org/ "https://pytorch.org/") package can import the checkpoint file (.pth) in a Python script. This can be done by specifying the path to the weights using the BubbleID class, e.g., *test120=BubbleID.DataAnalysis(imagesfolder,videopath,savefolder,extension,modelweights,"cpu")* 

This paper presents Hit2Flux, a machine learning framework for boiling heat flux prediction using acoustic emission (AE) hits generated through threshold-based transient sampling. Unlike continuously sampled data, AE hits are recorded when the signal exceeds a predefined threshold and are thus discontinuous in nature. Meanwhile, each hit represents a waveform at a high sampling frequency ( 1 MHz). In order to capture the features of both the high-frequency waveforms and the temporal distribution of hits, Hit2Flux involves i) feature extraction by transforming AE hits into the frequency domain and organizing these spectra into sequences using a rolling window to form “sequences-of-sequences,” and ii) heat flux prediction using a long short-term memory (LSTM) network with sequences of sequences. The model is trained on AE hits recorded during pool boiling experiments using an AE sensor attached to the boiling chamber. Continuously sampled acoustic data using a hydrophone were also collected as a reference data set for this study. Results demonstrate that the proposed AE-based method achieves performance comparable to hydrophones, validating its potential for heat flux monitoring. Additionally, it is shown that the inclusion of multiple acoustic emission hits as model inputs leads to higher performance. The Hit2Flux model is also compared to methods pairing various signal preparation techniques with state-of-the-art models. This comparison further highlighted the superior accuracy of the proposed approach. The developed Hi2Flux algorithm can be applied to other transient sampling events, such as structural health monitoring, detection of electromagnetic pulses, among others.