DOI: 10.5061/dryad.6m905qgc7
title: Data for: A temporal–spatial framework for efficient heat flux monitoring of transient boiling

description: 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.
description: # 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.