IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v242y2022ics0360544221032394.html
   My bibliography  Save this article

Prediction of the interfacial disturbance wave velocity in vertical upward gas-liquid annular flow via ensemble learning

Author

Listed:
  • Li, Chaofan
  • Song, Yajing
  • Xu, Long
  • Zhao, Ning
  • Wang, Fan
  • Fang, Lide
  • Li, Xiaoting

Abstract

Interfacial disturbance wave velocity is an important parameter for the study of momentum transfer between the gas core and the liquid film at the two-phase interface, which directly affects the calculation of frictional pressure drop. Since an exact analytical solution of the interfacial disturbance velocity cannot be derived by the two-phase flow theory equations, an ensemble learning framework for the disturbance wave velocity is constructed and a new model is proposed, which is appropriate for predicting different flow conditions in vertical two-phase flow. The dimensionless velocity-related parameters of interfacial disturbance waves are obtained by feature selection based on the interfacial shear force model, and the grid search method is equipped to tune the important parameters. By comparing the current and literature data prediction results, the extrapolation and applicability of the ensemble learning model are further verified. For the Extra Tree model, the Mean Absolute Percentage Error of the optimized Extra Tree model is less than 20%, and the relative measurement uncertainty is within ±25% for 95.67% of the results. It shows that the proposed ensemble learning framework provides a novel approach in the study of interfacial wave spatiotemporal parameters.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:242:y:2022:i:c:s0360544221032394
    DOI: 10.1016/j.energy.2021.122990
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221032394
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122990?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. Fan, Wenyuan & Cherdantsev, Andrey V. & Anglart, Henryk, 2020. "Experimental and numerical study of formation and development of disturbance waves in annular gas-liquid flow," Energy, Elsevier, vol. 207(C).
    2. Tao, Tao & Liu, Yongqian & Qiao, Yanhui & Gao, Linyue & Lu, Jiaoyang & Zhang, Ce & Wang, Yu, 2021. "Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 1004-1013.
    3. Ni, Dan & Zhang, Ning & Gao, Bo & Li, Zhong & Yang, Minguan, 2020. "Dynamic measurements on unsteady pressure pulsations and flow distributions in a nuclear reactor coolant pump," Energy, Elsevier, vol. 198(C).
    4. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    5. Dong, Xiao-Jian & Shen, Jia-Ni & He, Guo-Xin & Ma, Zi-Feng & He, Yi-Jun, 2021. "A general radial basis function neural network assisted hybrid modeling method for photovoltaic cell operating temperature prediction," Energy, Elsevier, vol. 234(C).
    6. Sun, zexian & Zhao, mingyu & Dong, yan & Cao, xin & Sun, Hexu, 2021. "Hybrid model with secondary decomposition, randomforest algorithm, clustering analysis and long short memory network principal computing for short-term wind power forecasting on multiple scales," Energy, Elsevier, vol. 221(C).
    7. Sukharev, Mikhail G. & Kulalaeva, Maria A., 2021. "Identification of model flow parameters and model coefficients with the help of integrated measurements of pipeline system operation parameters," Energy, Elsevier, vol. 232(C).
    8. Nemati, Nasibeh & Omidvar, Amir & Rosti, Behnam, 2021. "Performance evaluation of a novel hybrid cooling system combining indirect evaporative cooler and earth-air heat exchanger," Energy, Elsevier, vol. 215(PB).
    9. Shan, Baochao & Wang, Runxi & Guo, Zhaoli & Wang, Peng, 2021. "Contribution quantification of nanoscale gas transport in shale based on strongly inhomogeneous kinetic model," Energy, Elsevier, vol. 228(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. 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. Wang, Mi & Liu, Jiegui & Bai, Yuxin & Zheng, Dandan & Fang, Lide, 2024. "Flow rate measurement of gas-liquid annular flow through a combined multimodal ultrasonic and differential pressure sensor," Energy, Elsevier, vol. 288(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. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    2. Fan Cai & Yuesong Jiang & Wanqing Song & Kai-Hung Lu & Tongbo Zhu, 2024. "Short-Term Wind Turbine Blade Icing Wind Power Prediction Based on PCA-fLsm," Energies, MDPI, vol. 17(6), pages 1-15, March.
    3. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    4. Leilei Du & Fankun Zheng & Bo Gao & Mona Gad & Delin Li & Ning Zhang, 2024. "Numerical Investigation of Rotor and Stator Matching Mode on the Complex Flow Field and Pressure Pulsation of a Vaned Centrifugal Pump," Energies, MDPI, vol. 17(10), pages 1-19, May.
    5. He, Xitian & Sun, Bingxiang & Zhang, Weige & Su, Xiaojia & Ma, Shichang & Li, Hao & Ruan, Haijun, 2023. "Inconsistency modeling of lithium-ion battery pack based on variational auto-encoder considering multi-parameter correlation," Energy, Elsevier, vol. 277(C).
    6. Dan Ni & Feifan Wang & Bo Gao & Yang Zhang & Shiyuan Huang, 2022. "Experimental Investigation on the Effect of the Staggered Impeller on the Unsteady Pressure Pulsations Characteristic in a Pump," Energies, MDPI, vol. 15(23), pages 1-15, November.
    7. Chen, Peng & Han, Dezhi, 2022. "Effective wind speed estimation study of the wind turbine based on deep learning," Energy, Elsevier, vol. 247(C).
    8. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Meng, Dean, 2023. "Online diagnosis and prediction of power battery voltage comprehensive faults for electric vehicles based on multi-parameter characterization and improved K-means method," Energy, Elsevier, vol. 283(C).
    9. Mahmoud, Montaser & Alkhedher, Mohammad & Ramadan, Mohamad & Naher, Sumsun & Pullen, Keith, 2022. "An investigation on organic Rankine cycle incorporating a ground-cooled condenser: Working fluid selection and regeneration," Energy, Elsevier, vol. 249(C).
    10. Huang, Yufeng & Tao, Jun & Zhao, Junyi & Sun, Gang & Yin, Kai & Zhai, Junyi, 2023. "Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine," Energy, Elsevier, vol. 283(C).
    11. Chang, Chun & Wang, Qiyue & Jiang, Jiuchun & Jiang, Yan & Wu, Tiezhou, 2023. "Voltage fault diagnosis of a power battery based on wavelet time-frequency diagram," Energy, Elsevier, vol. 278(PB).
    12. Kan, Kan & Binama, Maxime & Chen, Huixiang & Zheng, Yuan & Zhou, Daqing & Su, Wentao & Muhirwa, Alexis, 2022. "Pump as turbine cavitation performance for both conventional and reverse operating modes: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    13. Shen, Dongxu & Lyu, Chao & Yang, Dazhi & Hinds, Gareth & Wang, Lixin, 2023. "Connection fault diagnosis for lithium-ion battery packs in electric vehicles based on mechanical vibration signals and broad belief network," Energy, Elsevier, vol. 274(C).
    14. Fuquan Song & Heying Ding & Yongzheng Wang & Shiming Zhang & Jinbiao Yu, 2023. "A Well Production Prediction Method of Tight Reservoirs Based on a Hybrid Neural Network," Energies, MDPI, vol. 16(6), pages 1-22, March.
    15. Sharma, Abhishek & Jain, Sachin Kumar, 2022. "A novel seasonal segmentation approach for day-ahead load forecasting," Energy, Elsevier, vol. 257(C).
    16. Bao, Zhengyi & Nie, Jiahao & Lin, Huipin & Jiang, Jiahao & He, Zhiwei & Gao, Mingyu, 2023. "A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery," Energy, Elsevier, vol. 282(C).
    17. Ning Zhang & Delin Li & Junxian Jiang & Bo Gao & Dan Ni & Anthony Akurugo Alubokin & Wenbin Zhang, 2023. "Experimental Investigation on Velocity Fluctuation in a Vaned Diffuser Centrifugal Pump Measured by Laser Doppler Anemometry," Energies, MDPI, vol. 16(7), pages 1-17, April.
    18. Tariq, Rasikh & Sheikh, Nadeem Ahmed & Livas-García, A. & Xamán, J. & Bassam, A. & Maisotsenko, Valeriy, 2021. "Projecting global water footprints diminution of a dew-point cooling system: Sustainability approach assisted with energetic and economic assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    19. Mihalakakou, Giouli & Souliotis, Manolis & Papadaki, Maria & Halkos, George & Paravantis, John & Makridis, Sofoklis & Papaefthimiou, Spiros, 2022. "Applications of earth-to-air heat exchangers: A holistic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    20. Hou, Guolian & Fan, Yuzhen & Wang, Junjie, 2024. "Application of a novel dynamic recurrent fuzzy neural network with rule self-adaptation based on chaotic quantum pigeon-inspired optimization in modeling for gas turbine," Energy, Elsevier, vol. 290(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:242:y:2022:i:c:s0360544221032394. 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.