IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v320y2025ics0360544225007832.html

A hybrid deep learning model towards flow pattern identification of gas-liquid two-phase flows in horizontal pipe

Author

Listed:
  • Ma, Tingxia
  • Wang, Tengzan
  • Wang, Lin
  • Tan, Jianying
  • Cao, Yujiao
  • Guo, Junyu

Abstract

Horizontal gas-liquid two-phase flow is common in oilfield gathering and transportation systems, and the flow pattern significantly impacts pipeline operation. To ensure the safety and stability of pipeline operations, it is essential to develop more precise identification techniques for gas-liquid two-phase flow patterns. Traditional deep learning methods have single features, are difficult to capture the dynamic features of the flow pattern, and have poor ability to deal with disturbing information. This paper introduces a novel conductance sensor, with an optimized measurement design to achieve higher accuracy. Gas-liquid two-phase flow experiments were conducted on a horizontal pipe experimental platform with an inner diameter of 50 mm using this sensor, and flow parameters were collected under different flow patterns. The TFN-STFT-CBAM-Anomaly Transformer model was proposed, utilizing the raw data collected by the conductance sensor as input for identifying gas-liquid two-phase flow patterns, thereby enhancing identification efficiency. This model integrates the time-frequency transformation (TFN) network with the Convolutional Block Attention Module (CBAM) attention mechanism and synergizes with the Anomaly Transformer module. The model is effectively able to extract multi-level and multi-scale features to understand the dynamic evolution of the flow pattern in a more comprehensive way, with higher expressive capability and better robustness in coping with anomalous perturbations in practical applications. Additionally, ablation experiments were conducted to validate the effectiveness of the model's components. The superiority of the proposed model was further validated through comparison with the latest methods. The model achieved an accuracy of 97.36 % for flow pattern identification using raw data with different flow patterns.

Suggested Citation

  • Ma, Tingxia & Wang, Tengzan & Wang, Lin & Tan, Jianying & Cao, Yujiao & Guo, Junyu, 2025. "A hybrid deep learning model towards flow pattern identification of gas-liquid two-phase flows in horizontal pipe," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225007832
    DOI: 10.1016/j.energy.2025.135141
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225007832
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135141?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Lifeng & Zhang, Sijia, 2023. "Analysis and identification of gas-liquid two-phase flow pattern based on multi-scale power spectral entropy and pseudo-image encoding," Energy, Elsevier, vol. 282(C).
    2. Du, Ruoyun & Chen, Hongfei & Yu, Min & Li, Wanying & Niu, Dongxiao & Wang, Keke & Zhang, Zuozhong, 2024. "3DTCN-CBAM-LSTM short-term power multi-step prediction model for offshore wind power based on data space and multi-field cluster spatio-temporal correlation," Applied Energy, Elsevier, vol. 376(PA).
    3. Ma, Huimin & Xu, Ying & Huang, Hongbo & Yuan, Chao & Wang, Jinghan & Yang, Yiguang & Wang, Da, 2024. "Intelligent predictions for flow pattern and phase fraction of a horizontal gas-liquid flow," Energy, Elsevier, vol. 303(C).
    4. Li, Chaofan & Song, Yajing & Xu, Long & Zhao, Ning & Wang, Fan & Fang, Lide & Li, Xiaoting, 2022. "Prediction of the interfacial disturbance wave velocity in vertical upward gas-liquid annular flow via ensemble learning," Energy, Elsevier, vol. 242(C).
    5. Wang, Lin & Guo, Wannian & Guo, Junyu & Zheng, Shaocong & Wang, Zhiyuan & Kang, Hooi Siang & Li, He, 2025. "An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    6. Guo, Junyu & Yang, Yulai & Li, He & Wang, Jiang & Tang, Aimin & Shan, Daiwei & Huang, Bangkui, 2024. "A hybrid deep learning model towards fault diagnosis of drilling pump," Applied Energy, Elsevier, vol. 372(C).
    7. Lin, Zi & Liu, Xiaolei & Lao, Liyun & Liu, Hengxu, 2020. "Prediction of two-phase flow patterns in upward inclined pipes via deep learning," Energy, Elsevier, vol. 210(C).
    8. Qu, Jiaqi & Sun, Qiang & Qian, Zheng & Wei, Lu & Zareipour, Hamidreza, 2024. "Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules," Applied Energy, Elsevier, vol. 355(C).
    9. Liu, Liqi & Liu, Yanli, 2022. "Load image inpainting: An improved U-Net based load missing data recovery method," Applied Energy, Elsevier, vol. 327(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Yuwen & Xu, Qiang & Cao, Yeqi & Huang, Bo & Yu, Haiyang & Guo, Liejin, 2025. "Flow pattern transition and void fraction prediction of oil-gas two-phase flow in a pipeline-riser system," Energy, Elsevier, vol. 340(C).
    2. Lang, Yuan Lu & Guo, Hang & Ye, Fang & Chen, Hao, 2025. "A review of X-ray water imaging studies applied to the gas diffusion layer of proton exchange membrane fuel cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 221(C).
    3. Wu, Yan & Xue, Ting & Li, Songlin & Wu, Bin, 2025. "Multi-task learning for gas-liquid two-phase flow through optical carrier microwave interferometry and autoencoder-multilayer perceptron architecture," Energy, Elsevier, vol. 340(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chang, Yingjie & Xu, Qiang & Huang, Bo & Zhang, Xuemei & Yu, Haiyang & Chen, Hao & Thévenin, Dominique & Guo, Liejin, 2025. "Prediction of the severe slugging period in gas-liquid two-phase pipeline-riser systems using an artificial neural network," Energy, Elsevier, vol. 331(C).
    2. Ma, Huimin & Xu, Ying & Huang, Hongbo & Yuan, Chao & Wang, Jinghan & Yang, Yiguang & Wang, Da, 2024. "Intelligent predictions for flow pattern and phase fraction of a horizontal gas-liquid flow," Energy, Elsevier, vol. 303(C).
    3. Zhang, Lifeng & Zhang, Sijia, 2023. "Analysis and identification of gas-liquid two-phase flow pattern based on multi-scale power spectral entropy and pseudo-image encoding," Energy, Elsevier, vol. 282(C).
    4. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    5. Zhang, Huacheng & Umehara, Yutaro & Horiguchi, Naoki & Yoshida, Hiroyuki & Etoh, Atsuroh & Mori, Shoji, 2025. "Experimental simulation of high-temperature and high-pressure annular two-phase flow using an HFC134a–ethanol system: Characterization of disturbance wave flow," Energy, Elsevier, vol. 335(C).
    6. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    7. Xue, Xiaorui & Li, Shaofang & Wang, Xiaonan & Ren, Tingting, 2026. "Enhancing stock market predictions with multivariate signal decomposition and dynamic feature optimization," The North American Journal of Economics and Finance, Elsevier, vol. 81(C).
    8. Li, Xuehan & Wang, Wei & Fang, Fang & Liu, Jizhen & Chen, Zhe, 2025. "Improving active power regulation for wind turbine by phase leading cascaded error-based active disturbance rejection control and multi-objective optimization," Renewable Energy, Elsevier, vol. 243(C).
    9. Tian, Zhongyun & Zheng, Wenke & Guo, Jiwei & Jiang, Yiqiang & Liang, Zhirong & Mi, Xiaoguang, 2024. "Fundamental research on the condensation heat transfer of the hydrocarbon-mixture energy in a spiral tube described by a universal model using flow pattern based and general modes," Energy, Elsevier, vol. 296(C).
    10. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    11. Gu, Jiawei & Yuan, Xiangxiang & Li, Xinming & Wang, Yanxue & Xing, Jinduo, 2026. "Symmetric Radial Vectors for uncertainty-aware rotary machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 266(PA).
    12. Yingrui Chen & Jiarong Shi, 2025. "Broad Random Forest: A Lightweight Prediction Model for Short-Term Wind Power by Fusing Broad Learning and Random Forest," Sustainability, MDPI, vol. 17(11), pages 1-16, May.
    13. Yin, Linfei & Wang, Nannan & Li, Jishen, 2025. "Electricity terminal multi-label recognition with a “one-versus-all” rejection recognition algorithm based on adaptive distillation increment learning and attention MobileNetV2 network for non-invasive load monitoring," Applied Energy, Elsevier, vol. 382(C).
    14. Lu, Na & Fu, Xueqian & Zhang, Pei & Qiu, Dawei & Badihi, Hamed & Abdel-Salam, Mazen & Gu, Haitong, 2026. "Enhancing representative photovoltaic scenario extraction for multiple power stations with a shared-weight and adaptively fused graph clustering method," Applied Energy, Elsevier, vol. 406(C).
    15. Lin, Peijie & Guo, Feng & Lin, Yaohai & Cheng, Shuying & Lu, Xiaoyang & Chen, Zhicong & Wu, Lijun, 2025. "Fault diagnosis of photovoltaic arrays with different degradation levels based on cross-domain adaptive generative adversarial network," Applied Energy, Elsevier, vol. 386(C).
    16. Wei, Jiangxia & Zhang, Weiqiang & Zhang, Wenjie & Ren, Mifeng & Xu, Xinying & Cheng, Lan, 2025. "DBSTN: A dual-branch spatio-temporal network for wind power prediction using multi-modal fusion," Energy, Elsevier, vol. 341(C).
    17. Abrasaldo, Paul Michael B. & Zarrouk, Sadiq J. & Kempa-Liehr, Andreas W., 2024. "A systematic review of data analytics applications in above-ground geothermal energy operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    18. Huang, Maoquan & Yang, Rui & Tang, G.H. & Pu, Jin Huan & Sun, Qie & Du, Mu, 2025. "Quantifying the effects of dust characteristics on the performance of radiative cooling PV systems," Applied Energy, Elsevier, vol. 377(PD).
    19. Wang, Lin & Guo, Wannian & Guo, Junyu & Zheng, Shaocong & Wang, Zhiyuan & Kang, Hooi Siang & Li, He, 2025. "An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    20. Hu, Zhiming & Cai, Baoping & Shao, Xiaoyan, 2026. "Pipeline RUL prediction method that considers the coupling of multiple corrosion factors: Integrating deep learning and stochastic processes," Reliability Engineering and System Safety, Elsevier, vol. 267(PA).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225007832. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.