IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v248y2025ics0960148125008134.html

Uncertainty quantification of aerodynamic characteristics of wind turbine blade airfoils

Author

Listed:
  • Hu, Weifei
  • Wang, Shengjun
  • Zhang, Tongzhou
  • Zhang, Yiming
  • Shi, Wei
  • Li, Qingyi
  • Zhang, Fanghong

Abstract

Wind turbines are subject to multiple sources of uncertainties that need to be meticulously considered during the design phase. Among these, the aerodynamic characteristics uncertainty of wind turbine blade airfoils directly impacts the aerodynamic load on the blades. Moreover, the dynamic stall phenomenon exacerbates the variation of aerodynamic characteristics. This paper addresses the impact of airfoil aerodynamic uncertainties on wind turbine blade design, particularly considering the dynamic stall phenomenon. The uncertainty in static aerodynamic characteristics is firstly quantified by parameterizing lift and drag coefficients from wind tunnel test data and treating them as random variables. A statistical Beddoes-Leishman model is then used to incorporate dynamic stall effects, considering its empirical parameters as random variables to account for epistemic uncertainties. Model errors relative to wind tunnel measurements are fitted using a Gaussian process. The uncertainty is used in a reliability analysis of wind turbines using the first-order reliability method to calibrate partial safety factors. Validation with National Renewable Energy Laboratory (NREL) data shows that the predicted confidence intervals align closely with test data, and the method can predict the uncertainty of new airfoils using dynamic stall wind tunnel data from existing airfoils. Additionally, applying the aerodynamic uncertainty to the NREL 5 MW wind turbine enables a 3 % reduction in load safety factors while maintaining reliability, potentially lowering design costs.

Suggested Citation

  • Hu, Weifei & Wang, Shengjun & Zhang, Tongzhou & Zhang, Yiming & Shi, Wei & Li, Qingyi & Zhang, Fanghong, 2025. "Uncertainty quantification of aerodynamic characteristics of wind turbine blade airfoils," Renewable Energy, Elsevier, vol. 248(C).
  • Handle: RePEc:eee:renene:v:248:y:2025:i:c:s0960148125008134
    DOI: 10.1016/j.renene.2025.123151
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125008134
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2025.123151?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. Abdallah, I. & Natarajan, A. & Sørensen, J.D., 2015. "Impact of uncertainty in airfoil characteristics on wind turbine extreme loads," Renewable Energy, Elsevier, vol. 75(C), pages 283-300.
    2. Melani, P.F. & Aryan, N. & Greco, L. & Bianchini, A., 2024. "The Beddoes-Leishman dynamic stall model: Critical aspects in implementation and calibration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    3. Tang, Xinzi & Yuan, Keren & Gu, Nengwei & Li, Pengcheng & Peng, Ruitao, 2022. "An interval quantification-based optimization approach for wind turbine airfoil under uncertainties," Energy, Elsevier, vol. 244(PA).
    4. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).
    5. Ramezani, Mahyar & Choe, Do-Eun & Heydarpour, Khashayar & Koo, Bonjun, 2023. "Uncertainty models for the structural design of floating offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(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. Choe, Do-Eun & Ramezani, Mahyar, 2025. "Fragility estimation for performance-based structural design of floating offshore wind turbine components," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    2. Zeng, Xinmeng & Shao, Yanlin & Feng, Xingya & Xu, Kun & Jin, Ruijia & Li, Huajun, 2024. "Nonlinear hydrodynamics of floating offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    3. Göteman, Malin & Panteli, Mathaios & Rutgersson, Anna & Hayez, Léa & Virtanen, Mikko J. & Anvari, Mehrnaz & Johansson, Jonas, 2025. "Resilience of offshore renewable energy systems to extreme metocean conditions: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 216(C).
    4. Cheng, Yue & Fang, Genshen & Zhao, Lin & Hong, Xu & Ge, Yaojun, 2024. "Uncertainty propagation of flutter analysis for long-span bridges using probability density evolution method," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    5. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    6. Yang, Haoze & Ge, Mingwei & Gu, Bo & Du, Bowen & Liu, Yongqian, 2022. "The effect of swell on marine atmospheric boundary layer and the operation of an offshore wind turbine," Energy, Elsevier, vol. 244(PB).
    7. Shao, Yizhe & Liu, Jie, 2024. "Uncertainty quantification for dynamic responses of offshore wind turbine based on manifold learning," Renewable Energy, Elsevier, vol. 222(C).
    8. Yang, Haoze & Ge, Mingwei & Abkar, Mahdi & Yang, Xiang I.A., 2022. "Large-eddy simulation study of wind turbine array above swell sea," Energy, Elsevier, vol. 256(C).
    9. Han, Fucheng & Wang, Wenhua & Zheng, Xiao-Wei & Han, Xu & Shi, Wei & Li, Xin, 2025. "Investigation of essential parameters for the design of offshore wind turbine based on structural reliability," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    10. Gregory Duthé & Imad Abdallah & Sarah Barber & Eleni Chatzi, 2021. "Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades," Energies, MDPI, vol. 14(21), pages 1-33, November.
    11. Zhang, Jincheng & Zhao, Xiaowei, 2022. "Wind farm wake modeling based on deep convolutional conditional generative adversarial network," Energy, Elsevier, vol. 238(PB).
    12. Salehi, Saeed & Nilsson, Håkan, 2022. "Effects of uncertainties in positioning of PIV plane on validation of CFD results of a high-head Francis turbine model," Renewable Energy, Elsevier, vol. 193(C), pages 57-75.
    13. Toft, Henrik Stensgaard & Svenningsen, Lasse & Sørensen, John Dalsgaard & Moser, Wolfgang & Thøgersen, Morten Lybech, 2016. "Uncertainty in wind climate parameters and their influence on wind turbine fatigue loads," Renewable Energy, Elsevier, vol. 90(C), pages 352-361.
    14. Su, Ouming & Li, Yan & Li, Guoyan & Cui, Yiwen & Li, Haoran & Wang, Bin & Meng, Hang & Li, Yaolong & Liang, Jinfeng, 2024. "Nonlinear harmonic resonant behaviors and bifurcation in a Two Degree-of-Freedom Duffing oscillator coupled system of Tension Leg Platform type Floating Offshore Wind Turbine," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
    15. Zhang, Jincheng & Zhao, Xiaowei, 2020. "A novel dynamic wind farm wake model based on deep learning," Applied Energy, Elsevier, vol. 277(C).
    16. Sun, Jili & Chen, Zheng & Yu, Hao & Gao, Shan & Wang, Bin & Ying, You & Sun, Yong & Qian, Peng & Zhang, Dahai & Si, Yulin, 2022. "Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines," Renewable Energy, Elsevier, vol. 199(C), pages 71-86.
    17. Zhiyu Jiang & Weifei Hu & Wenbin Dong & Zhen Gao & Zhengru Ren, 2017. "Structural Reliability Analysis of Wind Turbines: A Review," Energies, MDPI, vol. 10(12), pages 1-25, December.
    18. Shid-Moosavi, Sina & Di Cioccio, Fabrizio & Haghi, Rad & Tronci, Eleonora Maria & Moaveni, Babak & Liberatore, Sauro & Hines, Eric, 2025. "Modeling and experimentally-driven sensitivity analysis of wake-induced power loss in offshore wind farms: Insights from Block Island Wind Farm," Renewable Energy, Elsevier, vol. 241(C).
    19. Peláez-Zapata, Daniel & Pakrashi, Vikram & Dias, Frédéric, 2025. "Ocean wave measurements for marine renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 219(C).
    20. Tang, Xinzi & Yuan, Keren & Gu, Nengwei & Li, Pengcheng & Peng, Ruitao, 2022. "An interval quantification-based optimization approach for wind turbine airfoil under uncertainties," Energy, Elsevier, vol. 244(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:renene:v:248:y:2025:i:c:s0960148125008134. 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.