IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v208y2025ics1364032124007974.html
   My bibliography  Save this article

Enhancing wind turbine energy efficiency: Tribo-dynamics modeling and shape modification

Author

Listed:
  • Yin, Jiabao
  • Meng, Xianghui
  • Cheng, Shuai

Abstract

The performance of wind turbine gearbox planetary bearing systems directly influences the turbine's reliability, energy production efficiency, and economy. However, the lack of a comprehensive model to accurately predict the dynamic meshing load of helical gears and temperature effects hinders a deeper understanding of the transient performance of gearbox planetary sliding bearings. Additionally, research on shape modification for gearbox planetary sliding bearings remains inadequate. This study develops a comprehensive tribo-dynamics model that considers the dynamic meshing forces of helical gears and temperature effects. The model's effectiveness is validated by the concordance between experimental data from a full-size test bench and simulation results on oil film thickness and temperature. Transient simulation analysis indicates insufficient lubrication, high-temperature rise, and severe contact that reduce electricity production efficiency and reliability, all of which occur at bearing axial edges. Therefore, three modified bearing designs (super-elliptical, trapezoidal, and quadratic shapes) are introduced, and their improved performance is thoroughly compared. The super-elliptical design increases the minimum oil film thickness by 2.60 μm. The trapezoidal design reduces cumulative friction energy losses by 4.34 %. Each modified design successfully reduces the edge wear and oil temperature. The underlying enhancing mechanisms are revealed to be oil film uniform redistribution and lubrication states' alteration. This work can contribute to the reliability, electricity production efficiency, and economy of global wind turbines and support the achievement of net-zero emission goals.

Suggested Citation

  • Yin, Jiabao & Meng, Xianghui & Cheng, Shuai, 2025. "Enhancing wind turbine energy efficiency: Tribo-dynamics modeling and shape modification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:rensus:v:208:y:2025:i:c:s1364032124007974
    DOI: 10.1016/j.rser.2024.115071
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032124007974
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2024.115071?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. Ghigo, Alberto & Faraggiana, Emilio & Giorgi, Giuseppe & Mattiazzo, Giuliana & Bracco, Giovanni, 2024. "Floating Vertical Axis Wind Turbines for offshore applications among potentialities and challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    2. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    3. Wang, Kechao & Xiao, Wu & He, Tingting & Zhang, Maoxin, 2024. "Remote sensing unveils the explosive growth of global offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    4. Shukla, Vivek & Kaviti, Ajay Kumar, 2017. "Performance evaluation of profile modifications on straight-bladed vertical axis wind turbine by energy and Spalart Allmaras models," Energy, Elsevier, vol. 126(C), pages 766-795.
    5. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
    6. Shanbr, Suliman & Elasha, Faris & Elforjani, Mohamed & Teixeira, Joao, 2018. "Detection of natural crack in wind turbine gearbox," Renewable Energy, Elsevier, vol. 118(C), pages 172-179.
    7. de Azevedo, Henrique Dias Machado & Araújo, Alex Maurício & Bouchonneau, Nadège, 2016. "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 368-379.
    8. Al-Khayat, Mohammad & AL-Rasheedi, Majed, 2024. "A new method for estimating the annual energy production of wind turbines in hot environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
    9. Guo, Yi & Sheng, Shuangwen & Phillips, Caleb & Keller, Jonathan & Veers, Paul & Williams, Lindy, 2020. "A methodology for reliability assessment and prognosis of bearing axial cracking in wind turbine gearboxes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(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. Sun, Min & Peng, Liangchang & Lei, Hongshuai & Zhang, Jialei & Zhang, Zheng & Chen, Qiang & Zhang, Guang & Li, Jiquan, 2025. "A novel small-scale H-type Darrieus vertical axis wind turbine manufactured of carbon fiber reinforced composites," Renewable Energy, Elsevier, vol. 238(C).
    2. Altinpulluk, Nur Banu & Altinpulluk, Deniz & Yildirim, Murat & Zhao, Shijia & Qiu, Feng & Greco, Aaron, 2025. "A survey on degradation modeling, prognosis, and prognostics-driven maintenance in wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 211(C).
    3. Cuesta, Jokin & Leturiondo, Urko & Vidal, Yolanda & Pozo, Francesc, 2025. "A review of prognostics and health management techniques in wind energy," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    4. Wang, Peilin & Liu, Qingsong & Li, Chun & Miao, Weipao & Yue, Minnan & Xu, Zifei, 2022. "Investigation of the aerodynamic characteristics of horizontal axis wind turbine using an active flow control method via boundary layer suction," Renewable Energy, Elsevier, vol. 198(C), pages 1032-1048.
    5. Xu, Xuefang & Li, Bo & Qiao, Zijian & Shi, Peiming & Shao, Huaishuang & Li, Ruixiong, 2023. "Caputo-Fabrizio fractional order derivative stochastic resonance enhanced by ADOF and its application in fault diagnosis of wind turbine drivetrain," Renewable Energy, Elsevier, vol. 219(P1).
    6. Li, Zuobiao & Wen, Fengbo & Wan, Chenxin & Zhao, Zhiyuan & Luo, Yuxi & Wen, Dongsheng, 2024. "A pyramid-style neural network model with alterable input for reconstruction of physics field on turbine blade surface from various sparse measurements," Energy, Elsevier, vol. 308(C).
    7. Liu, Qingsong & Miao, Weipao & Ye, Qi & Li, Chun, 2022. "Performance assessment of an innovative Gurney flap for straight-bladed vertical axis wind turbine," Renewable Energy, Elsevier, vol. 185(C), pages 1124-1138.
    8. Li, Zuobiao & Wen, Fengbo & Liu, Zhongqi & Luo, Yuxi & Zhao, Zhiyuan & Wen, Dongsheng & Wang, Songtao, 2025. "A novel dual attention network for sparse reconstruction of turbine blade surface fields," Energy, Elsevier, vol. 317(C).
    9. Kong, Yun & Han, Qinkai & Chu, Fulei & Qin, Yechen & Dong, Mingming, 2023. "Spectral ensemble sparse representation classification approach for super-robust health diagnostics of wind turbine planetary gearbox," Renewable Energy, Elsevier, vol. 219(P1).
    10. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.
    11. Augusto Bianchini & Jessica Rossi & Lauro Antipodi, 2018. "A procedure for condition-based maintenance and diagnostics of submersible well pumps through vibration monitoring," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 999-1013, October.
    12. He, Tingting & Hu, Yihua & Li, Fashuai & Chen, Yuwei & Zhang, Maoxin & Zheng, Qiming & Jin, Yukan & Ren, He, 2025. "Mapping land- and offshore-based wind turbines in China in 2023 with Sentinel-2 satellite data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
    13. Xia, Tangbin & Dong, Yifan & Pan, Ershun & Zheng, Meimei & Wang, Hao & Xi, Lifeng, 2021. "Fleet-level opportunistic maintenance for large-scale wind farms integrating real-time prognostic updating," Renewable Energy, Elsevier, vol. 163(C), pages 1444-1454.
    14. Kong, Yun & Qin, Zhaoye & Wang, Tianyang & Han, Qinkai & Chu, Fulei, 2021. "An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines," Renewable Energy, Elsevier, vol. 173(C), pages 987-1004.
    15. Thé, Jesse & Yu, Hesheng, 2017. "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods," Energy, Elsevier, vol. 138(C), pages 257-289.
    16. Shen, He & Ruiz, Alexis & Li, Ni, 2023. "Fast online reinforcement learning control of small lift-driven vertical axis wind turbines with an active programmable four bar linkage mechanism," Energy, Elsevier, vol. 262(PA).
    17. Chen, Jian & Pan, Xiong & Wang, Canxing & Hu, Guojun & Xu, Hongtao & Liu, Pengwei, 2019. "Airfoil parameterization evaluation based on a modified PARASEC method for a H-Darrious rotor," Energy, Elsevier, vol. 187(C).
    18. Peyman Mazidi & Mian Du & Lina Bertling Tjernberg & Miguel A Sanz Bobi, 2017. "A health condition model for wind turbine monitoring through neural networks and proportional hazard models," Journal of Risk and Reliability, , vol. 231(5), pages 481-494, October.
    19. Yang, Wenguang & Liu, Chao & Jiang, Dongxiang, 2018. "An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 127(C), pages 230-241.
    20. Nguyen, Thi-Anh-Tuyet & Chou, Shuo-Yan & Yu, Tiffany Hui-Kuang, 2022. "Developing an exhaustive optimal maintenance schedule for offshore wind turbines based on risk-assessment, technical factors and cost-effective evaluation," Energy, Elsevier, vol. 249(C).

    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:rensus:v:208:y:2025:i:c:s1364032124007974. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.