Data for: Hit2flux: A machine learning framework for boiling heat flux prediction using hit-based acoustic emission sensing

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.