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Two-phase flow patterns identification in porous media using feature extraction and SVM
Xi An Jiao Tong Univ, State Key Lab Multiphase Flow Power Engn, Xian-ning West Rd 28, Xian 710049, Peoples R China..
Xi An Jiao Tong Univ, State Key Lab Multiphase Flow Power Engn, Xian-ning West Rd 28, Xian 710049, Peoples R China..
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Power Safety.ORCID iD: 0000-0002-8917-7720
Xi An Jiao Tong Univ, State Key Lab Multiphase Flow Power Engn, Xian-ning West Rd 28, Xian 710049, Peoples R China..
2022 (English)In: International Journal of Multiphase Flow, ISSN 0301-9322, E-ISSN 1879-3533, Vol. 156, article id 104222Article in journal (Refereed) Published
Abstract [en]

Rapid and accurate identification of two-phase flow patterns in porous media is of great significance to the chemical industry, petroleum and nuclear engineering, etc. Based on the different pressure signals of gas-liquid two-phase flow in a porous bed, the present work proposes an intelligent recognition method to identify the two-phase flow patterns in porous media by the technologies of feature extraction and support vector machine (SVM). The analysis techniques, including time domain (PDF), frequency domain (PSD) and time-frequency domain (Wavelet), are employed to extract and summarize the corresponding characteristics of differential pressure signals of flow patterns. The intelligent recognition models are developed to identify the two-phase flow patterns in porous media by SVM. The models are trained respectively based on the characteristics of time domain + frequency domain (TF-SVM model), time domain + wavelet (TW-SVM model) and frequency domain + wavelet (FW-SVM model). The overall identification accuracy of the optimal model (TW-SVM model) reaches 96.08%.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 156, article id 104222
Keywords [en]
Flow patterns identification, Porous media, Two-phase flow, Support vector machine, Feature extraction
National Category
Other Chemistry Topics Information Systems
Identifiers
URN: urn:nbn:se:kth:diva-320472DOI: 10.1016/j.ijmultiphaseflow.2022.104222ISI: 000862876000005Scopus ID: 2-s2.0-85135900834OAI: oai:DiVA.org:kth-320472DiVA, id: diva2:1706349
Note

QC 20221026

Available from: 2022-10-26 Created: 2022-10-26 Last updated: 2022-10-26Bibliographically approved

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Ma, Weimin

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