Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

被引:16
作者
Loyola-Fuentes, Jose [1 ]
Pietrasanta, Luca [2 ]
Marengo, Marco [2 ]
Coletti, Francesco [1 ,3 ]
机构
[1] Hexxcell Ltd, Foundry Bldg,77 Fulham Palace Rd, London W6 8AF, England
[2] Univ Brighton, Adv Engn Ctr, Sch Architecture Technol & Engn, Lewes Rd, Brighton BN2 4AT, E Sussex, England
[3] Brunel Univ London, Dept Chem Engn, Kingston Lane, Uxbridge UB8 3PH, Middx, England
基金
英国工程与自然科学研究理事会;
关键词
two-phase flow; pulsating heat pipes; flow pattern maps; machine learning; classification algorithms; THERMAL PERFORMANCE; MICROCHANNELS; OPTIMIZATION; SIMULATION; SURFACES; JET;
D O I
10.3390/en15061970
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
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页数:20
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