The prediction of condensation flow patterns by using artificial intelligence (AI) techniques

dc.contributor.advisorMehrabi, Mehdi
dc.contributor.advisorMeyer, Josua P.
dc.contributor.emailu15225250@tuks.co.zaen_ZA
dc.contributor.postgraduateSeal, Michael Kevin
dc.date.accessioned2021-02-08T11:00:31Z
dc.date.available2021-02-08T11:00:31Z
dc.date.created2021-04
dc.date.issued2021
dc.descriptionDissertation (MEng)--University of Pretoria, 2021.en_ZA
dc.description.abstractMultiphase flow provides a solution to the high heat flux and precision required by modern-day gadgets and heat transfer devices as phase change processes make high heat transfer rates achievable at moderate temperature differences. An application of multiphase flow commonly used in industry is the condensation of refrigerants in inclined tubes. The identification of two-phase flow patterns, or flow regimes, is fundamental to the successful design and subsequent optimisation given that the heat transfer efficiency and pressure gradient are dependent on the flow structure of the working fluid. This study showed that with visualisation data and artificial neural networks (ANN), a machine could learn, and subsequently classify the separate flow patterns of condensation of R-134a refrigerant in inclined smooth tubes with more than 98% accuracy. The study considered 10 classes of flow pattern images acquired from previous experimental works that cover a wide range of flow conditions and the full range of tube inclination angles. Two types of classifiers were considered, namely multilayer perceptron (MLP) and convolutional neural networks (CNN). Although not the focus of this study, the use of a principal component analysis (PCA) allowed feature dimensionality reduction, dataset visualisation, and decreased associated computational cost when used together with multilayer perceptron neural networks. The superior two-dimensional spatial learning capability of convolutional neural networks allowed improved image classification and generalisation performance across all 10 flow pattern classes. In both cases, the classification was done sufficiently fast to enable real-time implementation in two-phase flow systems. The analysis sequence led to the development of a predictive tool for the classification of multiphase flow patterns in inclined tubes, with the goal that the features learnt through visualisation would apply to a broad range of flow conditions, fluids, tube geometries and orientations, and would even generalise well to identify adiabatic and boiling two-phase flow patterns. The method was validated by the prediction of flow pattern images found in the existing literature.en_ZA
dc.description.availabilityRestricteden_ZA
dc.description.degreeMEngen_ZA
dc.description.departmentMechanical and Aeronautical Engineeringen_ZA
dc.description.sponsorshipNRFen_ZA
dc.identifier.citation*en_ZA
dc.identifier.otherS2021en_ZA
dc.identifier.urihttp://hdl.handle.net/2263/78303
dc.language.isoenen_ZA
dc.publisherUniversity of Pretoria
dc.rights© 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
dc.subjectConvolutional neural networken_ZA
dc.subjectCondensation flow patternen_ZA
dc.subjectMachine learningen_ZA
dc.subjectUCTD
dc.subject.otherEngineering, built environment and information technology theses SDG-09
dc.subject.otherSDG-09: Industry, innovation and infrastructure
dc.subject.otherEngineering, built environment and information technology theses SDG-07
dc.subject.otherSDG-07: Affordable and clean energy
dc.titleThe prediction of condensation flow patterns by using artificial intelligence (AI) techniquesen_ZA
dc.typeDissertationen_ZA

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