DOI: 10.5061/dryad.g79cnp628
title: Data for: Hit2flux: A machine learning framework for boiling heat flux prediction using hit-based acoustic emission sensing

description: 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.
description: # 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.