DOI: 10.5061/dryad.q573n5tvq
title: Data for: Nonintrusive heat flux quantification using acoustic emissions during pool boiling

description: Monitoring two-phase cooling systems is crucial to avoid thermal runaways
 and device failures. Nonintrusive monitoring methods using remote sensing,
 e.g., optical and acoustic sensors, are desired to avoid interfering with
 bubble dynamics and ease replacement. Compared to image-based
 technologies, sound-based sensors are cheaper and do not require the same
 environment as cameras. Acoustic signals during pool boiling have been
 used to identify boiling states, but acoustic-based quantitative
 predictions have been challenging. The present work presents a machine
 learning framework to determine the heat flux during pool boiling using
 acoustic signals captured through a hydrophone. This framework
 investigates and compares the performance and computational cost of six
 machine learning models by coupling two feature extraction algorithms
 (fast Fourier transform and convolution) and three different regressors
 (multilayer perceptron, random forest, and Gaussian process regression).
 The fast Fourier transform-Gaussian process regression model is found to
 be the most promising, with high accuracy and the lowest computational
 cost. A parametric study is performed to investigate the effect of the
 temporal length and sampling rates on the model predictions. It is found
 that the model’s performance is improved with increasing temporal lengths
 of the acoustic sequences for all sampling rates. Acoustic features below
 512 Hz are found to be most significant for heat flux predictions. For
 sampling rates beyond 512 Hz, the model performance is dictated by the
 temporal length of the acoustic sequences.
description: # Data for: Nonintrusive heat flux quantification using acoustic emissions
 during pool boiling Dataset DOI:
 [10.5061/dryad.q573n5tvq](10.5061/dryad.q573n5tvq) ## Description of the
 data and file structure This dataset includes temperature profiles,
 hydrophone acoustic data, and bubble images from a transient pool boiling
 test of deionized water on a flat copper surface. This dataset is used to
 train for [SeqReg](https://github.com/cldunlap73/SeqReg), a machine
 learning framework for sequence regression. ### Files and variables ####
 File: ned3-004\_BoilingAcousticsHydrophone.zip **Description:** This
 dataset includes two .lvm files, one .txt file, one spreadsheet, and a
 folder of images.  * The Boiling-32_Hydrophone.lvm file includes the
 continuously sampled acoustic data using two HTI-96-MIN hydrophones with
 an NI-9230 module. * The Boiling-32_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 sound_heat_flux.csv file includes the hydrophone
 acoustic data and the heat flux calculated based on linear regression of
 the temperature profiles. The heat flux is linearly interpolated based on
 the time marks of the acoustic data. Column "r" represents the
 row number of the spreadsheet. Columns "time" and
 "sound" represent the readings from the hydrophone (a.u.) and
 the corresponding time marks (unit: s). Columns "Time (s)" and
 "heat flux" represent the interpolated heat flux (unit: W/cm2)
 and corresponding time marks (unit: s).  * The Boiling-32_Images include
 bubble images captured using a Phantom VEO 710L camera. * The videoInfo
 includes information (e.g., triggering time or the time mark of the first
 frame in the image folder) of the bubble images.  ## Code/software The
 .csv 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 images (.jpg) can be processed using
 Python, MATLAB, or ImageJ.