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Machine learning classification of in-tube condensation flow patterns using visualization
Clean Energy Research Group, Department of Mechanical and Aeronautical Engineering, University of Pretoria,Hatfield (ZAF).
Högskolan Väst, Institutionen för ingenjörsvetenskap, Avdelningen för svetsteknologi (SV). (PTW)ORCID-id: 0000-0002-6102-9021
Clean Energy Research Group, Department of Mechanical and Aeronautical Engineering, University of Pretoria, Hatfield (ZAF).
Clean Energy Research Group, Department of Mechanical and Aeronautical Engineering, University of Pretoria, Hatfield (ZAF).
2021 (engelsk)Inngår i: International Journal of Multiphase Flow, ISSN 0301-9322, E-ISSN 1879-3533, Vol. 143, artikkel-id 103755Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Identifying two-phase flow patterns is fundamental to successfully design and subsequently optimize highprecision heat transfer equipment, given that the heat transfer efficiency and pressure gradients occurring in such thermo-hydraulic systems are dependent on the flow structure of the working fluid. This paper shows that with visualization data and artificial neural networks, the flow pattern images of condensation of R-134a refrigerant in inclined smooth tubes can be classified with more than 98% accuracy. The study considers 10 classes of flow pattern images acquired from previous experimental works for a wide range of flow conditions and the full range of tube inclination angles. Although not the focus of this paper, the use of a Principal Component Analysis allowed feature dimensionality reduction, dataset visualization, and decreased associated computational cost when used together with multilayer perceptron neural networks. In addition, the superior two-dimensional spatial learning capability of convolutional neural networks allowed improved image classification and generalization performance. In both cases, the classification was performed sufficiently fast to enable real-time implementation in two-phase flow systems.

sted, utgiver, år, opplag, sider
Elsevier, 2021. Vol. 143, artikkel-id 103755
Emneord [en]
Condensation flow pattern; Convolutional neural network; Machine learning
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URN: urn:nbn:se:hv:diva-17448DOI: 10.1016/j.ijmultiphaseflow.2021.103755ISI: 000689488400001Scopus ID: 2-s2.0-85111927148OAI: oai:DiVA.org:hv-17448DiVA, id: diva2:1603980
Tilgjengelig fra: 2021-10-18 Laget: 2021-10-18 Sist oppdatert: 2022-03-31bibliografisk kontrollert

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Noori Rahim Abadi, Seyyed Mohammad Ali

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