Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Jose Loyola-Fuentes, Luca Pietrasanta, Marco Marengo and Francesco Coletti
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Jose Loyola-Fuentes: Hexxcell Ltd., Foundry Building, 77 Fulham Palace Rd, London W6 8AF, UK
Luca Pietrasanta: Advanced Engineering Centre, School of Architecture, Technology and Engineering, University of Brighton, Lewes Rd, Brighton BN2 4AT, UK
Marco Marengo: Advanced Engineering Centre, School of Architecture, Technology and Engineering, University of Brighton, Lewes Rd, Brighton BN2 4AT, UK
Francesco Coletti: Hexxcell Ltd., Foundry Building, 77 Fulham Palace Rd, London W6 8AF, UK

Energies, 2022, vol. 15, issue 6, 1-20

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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