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Two-phase flow patt...
Two-phase flow patterns identification in porous media using feature extraction and SVM
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- Li, Xiangyu (författare)
- Xi An Jiao Tong Univ, State Key Lab Multiphase Flow Power Engn, Xian-ning West Rd 28, Xian 710049, Peoples R China.
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- Li, Liangxing (författare)
- Xi An Jiao Tong Univ, State Key Lab Multiphase Flow Power Engn, Xian-ning West Rd 28, Xian 710049, Peoples R China.
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- Ma, Weimin (författare)
- KTH,Kärnkraftssäkerhet
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- Wang, Wenjie (författare)
- Xi An Jiao Tong Univ, State Key Lab Multiphase Flow Power Engn, Xian-ning West Rd 28, Xian 710049, Peoples R China.
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Xi An Jiao Tong Univ, State Key Lab Multiphase Flow Power Engn, Xian-ning West Rd 28, Xian 710049, Peoples R China Kärnkraftssäkerhet (creator_code:org_t)
- Elsevier BV, 2022
- 2022
- Engelska.
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Ingår i: International Journal of Multiphase Flow. - : Elsevier BV. - 0301-9322 .- 1879-3533. ; 156
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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%.
Ämnesord
- NATURVETENSKAP -- Kemi -- Annan kemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences -- Other Chemistry Topics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
Nyckelord
- Flow patterns identification
- Porous media
- Two-phase flow
- Support vector machine
- Feature extraction
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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