IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v252y2025ics096014812501016x.html

A physics-based leading edge erosion growth prediction model utilizing Bayesian updating and drone-inspection data

Author

Listed:
  • Wu, Wen
  • Naybour, Susie
  • Remenyte-Prescott, Rasa
  • Prescott, Darren

Abstract

Leading edge (LE) erosion causes reduced power output and a reduction in efficiency by impairing the aerodynamics of the blade. In severe cases, it can reduce the structural integrity of the blade. It is preferable and cheaper to repair LE erosion before it reaches the sub-surface. LE erosion is commonly widespread across a wind turbine blade and is monitored using drone inspection with manual review of images, which is time-consuming due to the large number of images generated. In addition, it is hard to forecast the evolution of a LE erosion defect, and to make prioritization of damages across a fleet of wind turbines. The approach proposed in this paper combines Bayesian updating and physics model, with the aid of drone inspection failure data to predict the future evolution of LE erosion. The method, based on the Bayesian updating method, can capture complex interactions in the degradation process. The physics-based approach can reflect physical degradation mechanisms. Fusion of knowledge from physics-based predictive models with information mined from failure databases using Bayesian updating can combine advantages of the two methods, and also make full use of available knowledge. A case study is presented which predicts the evolution of LE erosion using the proposed method. After the application of Bayesian updating method, the uncertainty of material property distribution decreased by 43.43%. The method predicts the future evolution of LE erosion damages across a wind turbine blade, providing more information to an engineer to prioritize which defect to repair first.

Suggested Citation

  • Wu, Wen & Naybour, Susie & Remenyte-Prescott, Rasa & Prescott, Darren, 2025. "A physics-based leading edge erosion growth prediction model utilizing Bayesian updating and drone-inspection data," Renewable Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:renene:v:252:y:2025:i:c:s096014812501016x
    DOI: 10.1016/j.renene.2025.123354
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812501016X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.123354?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. Mishnaevsky, Leon & Hasager, Charlotte Bay & Bak, Christian & Tilg, Anna-Maria & Bech, Jakob I. & Doagou Rad, Saeed & Fæster, Søren, 2021. "Leading edge erosion of wind turbine blades: Understanding, prevention and protection," Renewable Energy, Elsevier, vol. 169(C), pages 953-969.
    2. Kaldellis, John K. & Zafirakis, D., 2011. "The wind energy (r)evolution: A short review of a long history," Renewable Energy, Elsevier, vol. 36(7), pages 1887-1901.
    3. Hoksbergen, T.H. & Akkerman, R. & Baran, I., 2023. "Rain droplet impact stress analysis for leading edge protection coating systems for wind turbine blades," Renewable Energy, Elsevier, vol. 218(C).
    4. Hasager, C. & Vejen, F. & Bech, J.I. & Skrzypiński, W.R. & Tilg, A.-M. & Nielsen, M., 2020. "Assessment of the rain and wind climate with focus on wind turbine blade leading edge erosion rate and expected lifetime in Danish Seas," Renewable Energy, Elsevier, vol. 149(C), pages 91-102.
    5. López, Javier Contreras & Kolios, Athanasios & Wang, Lin & Chiachio, Manuel, 2023. "A wind turbine blade leading edge rain erosion computational framework," Renewable Energy, Elsevier, vol. 203(C), pages 131-141.
    6. Bech, Jakob Ilsted & Johansen, Nicolai Frost-Jensen & Madsen, Martin Bonde & Hannesdóttir, Ásta & Hasager, Charlotte Bay, 2022. "Experimental study on the effect of drop size in rain erosion test and on lifetime prediction of wind turbine blades," Renewable Energy, Elsevier, vol. 197(C), pages 776-789.
    7. Arias Chao, Manuel & Kulkarni, Chetan & Goebel, Kai & Fink, Olga, 2022. "Fusing physics-based and deep learning models for prognostics," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. Herring, Robbie & Dyer, Kirsten & Martin, Ffion & Ward, Carwyn, 2019. "The increasing importance of leading edge erosion and a review of existing protection solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    9. Castorrini, Alessio & Ortolani, Andrea & Campobasso, M. Sergio, 2023. "Assessing the progression of wind turbine energy yield losses due to blade erosion by resolving damage geometries from lab tests and field observations," Renewable Energy, Elsevier, vol. 218(C).
    10. Alkhabbaz, Ali & Yang, Ho-Seong & Weerakoon, A.H Samitha & Lee, Young-Ho, 2021. "A novel linearization approach of chord and twist angle distribution for 10 kW horizontal axis wind turbine," Renewable Energy, Elsevier, vol. 178(C), pages 1398-1420.
    11. Yang, Cong & Liu, Xun & Zhou, Hua & Ke, Yan & See, John, 2023. "Towards accurate image stitching for drone-based wind turbine blade inspection," Renewable Energy, Elsevier, vol. 203(C), pages 267-279.
    12. Verma, Amrit Shankar & Jiang, Zhiyu & Caboni, Marco & Verhoef, Hans & van der Mijle Meijer, Harald & Castro, Saullo G.P. & Teuwen, Julie J.E., 2021. "A probabilistic rainfall model to estimate the leading-edge lifetime of wind turbine blade coating system," Renewable Energy, Elsevier, vol. 178(C), pages 1435-1455.
    13. Li, Zhanhang & Zhou, Jian & Nassif, Hani & Coit, David & Bae, Jinwoo, 2023. "Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    14. Castorrini, Alessio & Barnabei, Valerio F. & Domenech, Luis & Šakalyté, Asta & Sánchez, Fernando & Campobasso, M. Sergio, 2024. "Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades," Renewable Energy, Elsevier, vol. 227(C).
    15. Leimeister, Mareike & Kolios, Athanasios, 2018. "A review of reliability-based methods for risk analysis and their application in the offshore wind industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1065-1076.
    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. López, Javier Contreras & Kolios, Athanasios & Wang, Lin & Chiachio, Manuel, 2023. "A wind turbine blade leading edge rain erosion computational framework," Renewable Energy, Elsevier, vol. 203(C), pages 131-141.
    2. Hoksbergen, T.H. & Akkerman, R. & Baran, I., 2023. "Rain droplet impact stress analysis for leading edge protection coating systems for wind turbine blades," Renewable Energy, Elsevier, vol. 218(C).
    3. Sara C. Pryor & Rebecca J. Barthelmie & Jacob J. Coburn & Xin Zhou & Marianne Rodgers & Heather Norton & M. Sergio Campobasso & Beatriz Méndez López & Charlotte Bay Hasager & Leon Mishnaevsky, 2024. "Prioritizing Research for Enhancing the Technology Readiness Level of Wind Turbine Blade Leading-Edge Erosion Solutions," Energies, MDPI, vol. 17(24), pages 1-29, December.
    4. Lopez, Javier Contreras & Kolios, Athanasios, 2024. "An autonomous decision-making agent for offshore wind turbine blades under leading edge erosion," Renewable Energy, Elsevier, vol. 227(C).
    5. Bech, Jakob Ilsted & Johansen, Nicolai Frost-Jensen & Madsen, Martin Bonde & Hannesdóttir, Ásta & Hasager, Charlotte Bay, 2022. "Experimental study on the effect of drop size in rain erosion test and on lifetime prediction of wind turbine blades," Renewable Energy, Elsevier, vol. 197(C), pages 776-789.
    6. Visbech, Jens & Hasager, Charlotte Bay & Göçmen, Tuhfe & Réthoré, Pierre-Elouan, 2025. "Copula-based joint distributions of rain and wind for leading edge erosion risk atlas," Renewable Energy, Elsevier, vol. 253(C).
    7. Sara C. Pryor & Rebecca J. Barthelmie & Jeremy Cadence & Ebba Dellwik & Charlotte B. Hasager & Stephan T. Kral & Joachim Reuder & Marianne Rodgers & Marijn Veraart, 2022. "Atmospheric Drivers of Wind Turbine Blade Leading Edge Erosion: Review and Recommendations for Future Research," Energies, MDPI, vol. 15(22), pages 1-41, November.
    8. Charlotte Bay Hasager & Flemming Vejen & Witold Robert Skrzypiński & Anna-Maria Tilg, 2021. "Rain Erosion Load and Its Effect on Leading-Edge Lifetime and Potential of Erosion-Safe Mode at Wind Turbines in the North Sea and Baltic Sea," Energies, MDPI, vol. 14(7), pages 1-24, April.
    9. Zengyi Zhang & Zhenru Shu, 2024. "Unmanned Aerial Vehicle (UAV)-Assisted Damage Detection of Wind Turbine Blades: A Review," Energies, MDPI, vol. 17(15), pages 1-31, July.
    10. Castorrini, Alessio & Barnabei, Valerio F. & Domenech, Luis & Šakalyté, Asta & Sánchez, Fernando & Campobasso, M. Sergio, 2024. "Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades," Renewable Energy, Elsevier, vol. 227(C).
    11. Verma, Amrit Shankar & Yan, Jiquan & Hu, Weifei & Jiang, Zhiyu & Shi, Wei & Teuwen, Julie J.E., 2023. "A review of impact loads on composite wind turbine blades: Impact threats and classification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    12. Fang, Jianhao & Hu, Weifei & Liu, Zhenyu & Chen, Weiyi & Tan, Jianrong & Jiang, Zhiyu & Verma, Amrit Shankar, 2022. "Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    13. Leon Mishnaevsky & Antonios Tempelis & Yauheni Belahurau & Nicolai Frost-Jensen Johansen, 2024. "Microplastics Emission from Eroding Wind Turbine Blades: Preliminary Estimations of Volume," Energies, MDPI, vol. 17(24), pages 1-15, December.
    14. Sergio Campobasso, M. & Castorrini, Alessio & Ortolani, Andrea & Minisci, Edmondo, 2023. "Probabilistic analysis of wind turbine performance degradation due to blade erosion accounting for uncertainty of damage geometry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    15. Huang, Xucong & Peng, Zhaoqin & Tang, Diyin & Chen, Juan & Zio, Enrico & Zheng, Zaiping, 2024. "A physics-informed autoencoder for system health state assessment based on energy-oriented system performance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    16. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    17. Alessio Castorrini & Paolo Venturini & Aldo Bonfiglioli, 2022. "Generation of Surface Maps of Erosion Resistance for Wind Turbine Blades under Rain Flows," Energies, MDPI, vol. 15(15), pages 1-14, August.
    18. Xiaohang Wang & Zhenbo Tang & Na Yan & Guojun Zhu, 2022. "Effect of Different Types of Erosion on the Aerodynamic Performance of Wind Turbine Airfoils," Sustainability, MDPI, vol. 14(19), pages 1-13, September.
    19. Verma, Amrit Shankar & Jiang, Zhiyu & Caboni, Marco & Verhoef, Hans & van der Mijle Meijer, Harald & Castro, Saullo G.P. & Teuwen, Julie J.E., 2021. "A probabilistic rainfall model to estimate the leading-edge lifetime of wind turbine blade coating system," Renewable Energy, Elsevier, vol. 178(C), pages 1435-1455.
    20. Wu, Yunna & Liu, Fangtong & Wu, Junhao & He, Jiaming & Xu, Minjia & Zhou, Jianli, 2022. "Barrier identification and analysis framework to the development of offshore wind-to-hydrogen projects," Energy, Elsevier, vol. 239(PB).

    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:renene:v:252:y:2025:i:c:s096014812501016x. 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/renewable-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.