Pseudo-image-feature-based identification benchmark for multi-phase flow regimes

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dc.contributor.authorKuang, Boyu
dc.contributor.authorNnabuife, Somtochukwu Godfrey
dc.contributor.authorRana, Zeeshan
dc.date.accessioned2021-01-05T16:48:36Z
dc.date.available2021-01-05T16:48:36Z
dc.date.issued2020-12-08
dc.description.abstractMultiphase flow is a prevalent topic in many disciplines, and flow regime identification is an essential foundation in multiphase flow research. Computer vision and deep learning have achieved numerous excellent models, but many have not demonstrated satisfactory performance in fundamental research, including flow regime identification. This research proposes an advanced pseudo-image feature (PIF) as the flow regime descriptor and a benchmark of multiple deep learning classifiers. The PIF simulates the image format and compactly encodes the flow regime to a pseudo-image, which explicitly displays the implicit flow regime signals. This research further evaluates three proposed and five existing popular deep learning classifiers. The proposed benchmark provides a baseline for applying deep learning in flow regime identification. The proposed fully convolutional network (FCN) classifier achieved state-of-the-art performance, and the testing and verification accuracy respectively reached 99.95% and 99.54%. This research suggests that PIF has an excellent capability for flow regime representation, and the proposed deep learning classifiers achieve superior performance in flow regime identification compared to the existing classifiers. Industries can utilize the proposed multiphase flow identification technology to obtain greater production efficiency, productivity, and financial gainen_UK
dc.identifier.citationKuang B, Nnabuife SG, Rana Z. (2021) Pseudo-image-feature-based identification benchmark for multi-phase flow regimes. Chemical Engineering Journal Advances, Volume 5, March 2021, Article number 100060en_UK
dc.identifier.issn2666-8211
dc.identifier.urihttps://doi.org/10.1016/j.ceja.2020.100060
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16123
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDeep learning classifier benchmarken_UK
dc.subjectPseudo-Image-Featureen_UK
dc.subjectFlow regime identificationen_UK
dc.subjectMultiphase flowen_UK
dc.titlePseudo-image-feature-based identification benchmark for multi-phase flow regimesen_UK
dc.typeArticleen_UK

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