Data for: Nonintrusive heat flux quantification using acoustic emissions during pool boiling

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 Four..., , # 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 a...,