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The prediction of condensation flow patterns by using artificial intelligence (AI) techniques
Dissertation (MEng)--University of Pretoria, 2021.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613641685073920 |
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| access_status_str | Open Access |
| author2 | Mehrabi, Mehdi |
| author_browse | Mehrabi, Mehdi |
| author_facet | Mehrabi, Mehdi |
| collection | Thesis |
| dc_rights_str_mv | © 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. |
| description | Dissertation (MEng)--University of Pretoria, 2021. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/78303 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:22.809Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/78303 The prediction of condensation flow patterns by using artificial intelligence (AI) techniques Mehrabi, Mehdi Meyer, Josua P. u15225250@tuks.co.za Seal, Michael Kevin Convolutional neural network Condensation flow pattern Machine learning UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-07 SDG-07: Affordable and clean energy Dissertation (MEng)--University of Pretoria, 2021. Multiphase 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. NRF mi2025 Mechanical and Aeronautical Engineering MEng Restricted SDG-09: Industry, innovation and infrastructure SDG-07: Affordable and clean energy 2021-02-08T11:00:31Z 2021-02-08T11:00:31Z 2021-04 2021 Dissertation * S2021 http://hdl.handle.net/2263/78303 en © 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. application/pdf University of Pretoria |
| spellingShingle | Convolutional neural network Condensation flow pattern Machine learning UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-07 SDG-07: Affordable and clean energy The prediction of condensation flow patterns by using artificial intelligence (AI) techniques |
| title | The prediction of condensation flow patterns by using artificial intelligence (AI) techniques |
| title_full | The prediction of condensation flow patterns by using artificial intelligence (AI) techniques |
| title_fullStr | The prediction of condensation flow patterns by using artificial intelligence (AI) techniques |
| title_full_unstemmed | The prediction of condensation flow patterns by using artificial intelligence (AI) techniques |
| title_short | The prediction of condensation flow patterns by using artificial intelligence (AI) techniques |
| title_sort | prediction of condensation flow patterns by using artificial intelligence ai techniques |
| topic | Convolutional neural network Condensation flow pattern Machine learning UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-07 SDG-07: Affordable and clean energy |
| url | http://hdl.handle.net/2263/78303 |