IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v292y2024ics0360544224002433.html
   My bibliography  Save this article

Spatial and temporal characteristic information parameter measurement of interfacial wave using ultrasonic phased array method

Author

Listed:
  • Jia, Huijun
  • Wen, Jiaqi
  • Xu, Xinrui
  • Liu, Miaomiao
  • Fang, Lide
  • Zhao, Ning

Abstract

Gas-liquid two-phase flow exists in nuclear reactor evaporation, vehicle cooling, chemical production falling film evaporation and other processes, and the dynamic measurement of the interfacial wave is of great significance for industrial process monitoring and production optimization. Accurate identification and characterization of interfacial wave is an important prerequisite for scientific research and engineering practice. In this paper, interfacial wave measurement experiments have been carried out using an ultrasonic phased array system and measurement method to obtain gas-liquid phase interface measurement information in gas-liquid flow system. The liquid film thickness is extracted by fitting the gas-liquid interface through two-dimensional data matrix to ladder, detecting the liquid level data, and constructing a three-dimensional interfacial wave distribution model based on dynamic real-time data. The interfacial wave amplitude prediction correlation is developed using various machine learning regression models, which are optimized by parameters tuning algorithms. Laboratory results indicate that the wave amplitude prediction correlation is highly correlated with five inlet parameters, including superficial gas velocity, superficial liquid velocity, gas Reynolds number, liquid Reynolds number, and Lockhart-Martinelli (L-M) parameter. In the seven typical wave amplitude prediction correlations for stratified and plug flow, Least Squares Boosting (LSBoost) provided the best fit with MAPEs of 11.26 % and 18.42 %. The Gray Wolf Optimizer (GWO) is proposed and used to optimized the wave amplitude prediction correlation of LSBoost, and the MAPEs of optimized model are 4.38 % and 17.26 %. This paper provides a reliable method for accurate measurement and methodological analysis of interfacial wave spatial-temporal characteristic parameters of gas-liquid two-phase flow.

Suggested Citation

  • Jia, Huijun & Wen, Jiaqi & Xu, Xinrui & Liu, Miaomiao & Fang, Lide & Zhao, Ning, 2024. "Spatial and temporal characteristic information parameter measurement of interfacial wave using ultrasonic phased array method," Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:energy:v:292:y:2024:i:c:s0360544224002433
    DOI: 10.1016/j.energy.2024.130472
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224002433
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130472?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Ding, Hongbing & Zhang, Yu & Sun, Chunqian & Yang, Yan & Wen, Chuang, 2022. "Numerical simulation of supersonic condensation flows using Eulerian-Lagrangian and Eulerian wall film models," Energy, Elsevier, vol. 258(C).
    2. Mao, Ning & Azman, Amirah Nabilah & Ding, Guangxin & Jin, Yubo & Kang, Can & Kim, Hyoung-Bum, 2022. "Black-box real-time identification of sub-regime of gas-liquid flow using Ultrasound Doppler Velocimetry with deep learning," Energy, Elsevier, vol. 239(PD).
    3. 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).
    4. Huang, Xin & Chen, Hu & Ling, Xiang & Liu, Lin & Huhe, Taoli, 2022. "Investigation of heat and mass transfer and gas–liquid thermodynamic process paths in a humidifier," Energy, Elsevier, vol. 261(PA).
    5. Tang, Chao & Zhou, Wen & Chen, Zhangxin & Wei, Jiabao, 2023. "Numerical simulation of CO2 sequestration in shale gas reservoirs at reservoir scale coupled with enhanced gas recovery," Energy, Elsevier, vol. 277(C).
    6. Liu, Qinggong & Tao, Yao & Shi, Long & Huang, Yi & Peng, Yuanling & Wang, Yong & Tu, Jiyuan, 2023. "Experimental investigations on the thermal performance of a novel ground heat exchanger under the synergistic effects of shape-stabilized phase change material and nanofluid," Energy, Elsevier, vol. 284(C).
    7. Niu, Wente & Sun, Yuping & Zhang, Xiaowei & Lu, Jialiang & Liu, Hualin & Li, Qiaojing & Mu, Ying, 2023. "An ensemble transfer learning strategy for production prediction of shale gas wells," Energy, Elsevier, vol. 275(C).
    Full references (including those not matched with items on IDEAS)

    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. 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. 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).
    3. Wang, Chongyang & Zhang, Dongming & Liu, Chenxi & Pan, Yisha & Jiang, Zhigang & Yu, Beichen & Lin, Yun, 2023. "Deformation and seepage characteristics of water-saturated shale under true triaxial stress," Energy, Elsevier, vol. 284(C).
    4. 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.
    5. Niu, Wente & Lu, Jialiang & Sun, Yuping & Zhang, Xiaowei & Li, Qiaojing & Cao, Xu & Liang, Pingping & Zhan, Hongming, 2024. "Techno-economic integration evaluation in shale gas development based on ensemble learning," Applied Energy, Elsevier, vol. 357(C).
    6. 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).
    7. Du, Meng & Ren, Fei-fan & Min, Rui & Zhang, Zhen-qian & Gao, Zhong-ke & Grebogi, Celso, 2024. "Detecting non-uniform structures in oil-in-water bubbly flow experiments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    8. Guo, Zixi & Zhao, Jinzhou & You, Zhenjiang & Li, Yongming & Zhang, Shu & Chen, Yiyu, 2021. "Prediction of coalbed methane production based on deep learning," Energy, Elsevier, vol. 230(C).
    9. Wang, Shiwei & Wang, Chao & Ding, Hongbing & Li, Shujuan, 2024. "Evaluation of dynamic behaviors in varied swirling flows for high-pressure offshore natural gas supersonic dehydration," Energy, Elsevier, vol. 300(C).
    10. Ding, Hongbing & Zhang, Yu & Dong, Yuanyuan & Wen, Chuang & Yang, Yan, 2023. "High-pressure supersonic carbon dioxide (CO2) separation benefiting carbon capture, utilisation and storage (CCUS) technology," Applied Energy, Elsevier, vol. 339(C).
    11. Yang, Zhaoming & Liu, Zhe & Zhou, Jing & Song, Chaofan & Xiang, Qi & He, Qian & Hu, Jingjing & Faber, Michael H. & Zio, Enrico & Li, Zhenlin & Su, Huai & Zhang, Jinjun, 2023. "A graph neural network (GNN) method for assigning gas calorific values to natural gas pipeline networks," Energy, Elsevier, vol. 278(C).
    12. Wang, Qiaochu & Chen, Dongxia & Li, Meijun & Li, Sha & Wang, Fuwei & Yang, Zijie & Zhang, Wanrong & Chen, Shumin & Yao, Dongsheng, 2023. "A novel method for petroleum and natural gas resource potential evaluation and prediction by support vector machines (SVM)," Applied Energy, Elsevier, vol. 351(C).
    13. Kim, Sungil & Kim, Tea-Woo & Hong, Yongjun & Kim, Juhyun & Jeong, Hoonyoung, 2024. "Enhancing pressure gradient prediction in multi-phase flow through diverse well geometries of North American shale gas fields using deep learning," Energy, Elsevier, vol. 290(C).
    14. 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.
    15. Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).
    16. Micheal, Marembo & Yu, Hao & Meng, SiWei & Xu, WenLong & Huang, HanWei & Huang, MengCheng & Zhang, HouLin & Liu, He & Wu, HengAn, 2023. "Gas production from shale reservoirs with bifurcating fractures: A modified quadruple-domain model coupling microseismic events," Energy, Elsevier, vol. 278(C).
    17. Mao, Ning & Azman, Amirah Nabilah & Ding, Guangxin & Jin, Yubo & Kang, Can & Kim, Hyoung-Bum, 2022. "Black-box real-time identification of sub-regime of gas-liquid flow using Ultrasound Doppler Velocimetry with deep learning," Energy, Elsevier, vol. 239(PD).
    18. Wang, Shiwei & Wang, Chao & Ding, Hongbing & Zhang, Yu & Dong, Yuanyuan & Wen, Chuang, 2023. "Joule-Thomson effect and flow behavior for energy-efficient dehydration of high-pressure natural gas in supersonic separator," Energy, Elsevier, vol. 279(C).
    19. Ding, Hongbing & Dong, Yuanyuan & Zhang, Yu & Yang, Yan & Wen, Chuang, 2023. "Energy efficiency assessment of hydrogen recirculation ejectors for proton exchange membrane fuel cell (PEMFC) system," Applied Energy, Elsevier, vol. 346(C).
    20. Kasala, Erasto E. & Wang, Jinjie & Lwazi, Hussein M. & Nyakilla, Edwin E. & Kibonye, John S., 2024. "The influence of hydraulic fracture and reservoir parameters on the storage of CO2 and enhancing CH4 recovery in Yanchang formation," Energy, Elsevier, vol. 296(C).

    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:292:y:2024:i:c:s0360544224002433. 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.