Citation: Loyola-Fuentes, J.;
Pietrasanta, L.; Marengo, M.; Coletti,
F. Machine Learning Algorithms for
Flow Pattern Classification in
Pulsating Heat Pipes. Energies 2022,
15, 1970. https://doi.org/10.3390/
en15061970
Academic Editors: Luis
Hernández-Callejo, Sergio
Nesmachnow, Sara Gallardo
Saavedra and Dmitry Eskin
Received: 9 February 2022
Accepted: 4 March 2022
Published: 8 March 2022
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Article
Machine Learning Algorithms for Flow Pattern Classification in
Pulsating Heat Pipes
Jose Loyola-Fuentes
1
, Luca Pietrasanta
2
, Marco Marengo
2
and Francesco Coletti
1,3,
*
1
Hexxcell Ltd., Foundry Building, 77 Fulham Palace Rd, London W6 8AF, UK; j.loyola@hexxcell.com
2
Advanced Engineering Centre, School of Architecture, Technology and Engineering, University of Brighton,
Lewes Rd, Brighton BN2 4AT, UK; l.pietrasanta2@brighton.ac.uk (L.P.); m.marengo@brighton.ac.uk (M.M.)
3
Department of Chemical Engineering, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK
* Correspondence: f.coletti@hexxcell.com
Abstract:
Owing to their simple construction, cost effectiveness, and high thermal efficiency, pulsating
heat pipes (PHPs) are growing in popularity as cooling devices for electronic equipment. While
PHPs can be very resilient as passive cooling systems, their operation relies on the establishment and
persistence of slug/plug flow as the dominant flow regime. It is, therefore, paramount to predict the
flow regime accurately as a function of various operating parameters and design geometry. Flow
pattern maps that capture flow regimes as a function of nondimensional numbers (e.g., Froude, Weber,
and Bond numbers) have been proposed in the literature. However, the prediction of flow patterns
based on deterministic models is a challenging task that relies on the ability of explaining the very
complex underlying phenomena or the ability to measure parameters, such as the bubble acceleration,
which are very difficult to know beforehand. In contrast, machine learning algorithms require limited
a priori knowledge of the system and offer an alternative approach for classifying flow regimes. In this
work, experimental data collected for two working fluids (ethanol and FC-72) in a PHP at different
gravity and power input levels, were used to train three different classification algorithms (namely
K-nearest neighbors, random forest, and multilayer perceptron). The data were previously labeled
via visual classification using the experimental results. A comparison of the resulting classification
accuracy was carried out via confusion matrices and calculation of accuracy scores. The algorithm
presenting the highest classification performance was selected for the development of a flow pattern
map, which accurately indicated the flow pattern transition boundaries between slug/plug and
annular flows. Results indicate that, once experimental data are available, the proposed machine
learning approach could help in reducing the uncertainty in the classification of flow patterns and
improve the predictions of the flow regimes.
Keywords:
two-phase flow; pulsating heat pipes; flow pattern maps; machine learning; classification
algorithms
1. Introduction
The lifespan and reliability of a wide range of electronic components and electro-
mechanical assemblies are often compromised by the poor performance of the thermal
control system (TCS). Cooling capacity, weight, and cost requirements are becoming very
challenging in high-density PCBs, microprocessors, photovoltaic solar arrays, and actuators,
not only limiting the expected performance [
1
] but also creating safety issues, as in EV
battery systems [
2
]. On the other hand, energy consumption for cooling purposes has
critically increased in recent years. Data centers consume 200 TWh each year worldwide [
3
],
where 38% (76 TWh) is estimated to go toward cooling processes. There are a wide variety of
available cooling processes for electronics. The most common methods based on two-phase
flow are flow boiling [4–10], pool boiling [11–14], and impinging jets [15–18].
Pulsating heat pipes (PHPs) can play a leading role in reducing cooling costs due to
their resulting equivalent thermal conductivity that is several times higher than that of pure
Energies 2022, 15, 1970. https://doi.org/10.3390/en15061970 https://www.mdpi.com/journal/energies
