Development of gas-liquid flow regimes identification using a noninvasive ultrasonic sensor, belt-shape features, and convolutional neural network in an S-shaped riser

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dc.contributor.authorNnabuife, Somtochukwu Godfrey
dc.contributor.authorKuang, Boyu
dc.contributor.authorWhidborne, James F.
dc.contributor.authorRana, Zeeshan A.
dc.date.accessioned2021-07-19T14:11:10Z
dc.date.available2021-07-19T14:11:10Z
dc.date.issued2021-07-14
dc.description.abstractThe problem of classifying gas-liquid two-phase flow regimes from ultrasonic signals is considered. A new method, belt-shaped features (BSFs), is proposed for performing feature extraction on the preprocessed data. A convolutional neural network (CNN/ConvNet)-based classifier is then applied to categorize into one of the four flow regimes: 1) annular; 2) churn; 3) slug; or 4) bubbly. The proposed ConvNet classifier includes multiple stages of convolution and pooling layers, which both decrease the dimension and learn the classification features. Using experimental data collected from an industrial-scale multiphase flow facility, the proposed ConvNet classifier achieved 97.40%, 94.57%, and 94.94% accuracy, respectively, for the training set, testing set, and validation set. These results demonstrate the applicability of the BSF features and the ConvNet classifier for flow regime classification in industrial applications.en_UK
dc.identifier.citationNnabuife SG, Kuang B, Whidborne JF, Rana ZA. (2021) Development of gas-liquid flow regimes identification using a noninvasive ultrasonic sensor, belt-shape features, and convolutional neural network in an S-shaped riser. IEEE Transactions on Cybernetics, Available online 14 July 2021en_UK
dc.identifier.issn2168-2267
dc.identifier.urihttps://doi.org/10.1109/TCYB.2021.3084860
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16894
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectultrasonic sensoren_UK
dc.subjectS-shaped riseren_UK
dc.subjectconvolutional neural networks (CNNs)en_UK
dc.subjectBelt-shaped features (BSFs)en_UK
dc.titleDevelopment of gas-liquid flow regimes identification using a noninvasive ultrasonic sensor, belt-shape features, and convolutional neural network in an S-shaped riseren_UK
dc.typeArticleen_UK

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