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An ML-based wind turbine blade design method considering multi-objective aerodynamic similarity and its experimental validation

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  • Yang, Siyao
  • Lin, Kun
  • Zhou, Annan

Abstract

Model test is an essential technique to study the aerodynamic performance of wind turbines. To overcome the poor aerodynamic performance of scaled models caused by the scaling effect, this study proposes an innovative blade design method for scaled model testing based on machine learning (ML). The method achieves satisfactory similarity between the thrust and power coefficients under multiple operating conditions of the model and prototype. Furthermore, a case study of the NREL 5-MW wind turbine is carried out with wind tunnel tests to validate the effectiveness of the proposed method. Obtained results suggest that the aerodynamic performance of redesigned blade closely mirrors that of the prototype under multiple operating conditions, reaching 97.59 % (thrust) and 97.87 % (power) coefficients of the prototype at the rated operating condition, respectively. With this technique, aerodynamic performance similarities between the redesigned blade and the prototype can be enhanced, contributing to more accurate scale model testing.

Suggested Citation

  • Yang, Siyao & Lin, Kun & Zhou, Annan, 2024. "An ML-based wind turbine blade design method considering multi-objective aerodynamic similarity and its experimental validation," Renewable Energy, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:renene:v:220:y:2024:i:c:s0960148123015409
    DOI: 10.1016/j.renene.2023.119625
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    References listed on IDEAS

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    1. Passon, Patrik, 2015. "Damage equivalent wind–wave correlations on basis of damage contour lines for the fatigue design of offshore wind turbines," Renewable Energy, Elsevier, vol. 81(C), pages 723-736.
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    4. Wen, Binrong & Tian, Xinliang & Dong, Xingjian & Li, Zhanwei & Peng, Zhike & Zhang, Wenming & Wei, Kexiang, 2020. "Design approaches of performance-scaled rotor for wave basin model tests of floating wind turbines," Renewable Energy, Elsevier, vol. 148(C), pages 573-584.
    5. Lin, Kun & Xiao, Shaohui & Zhou, Annan & Liu, Hongjun, 2020. "Experimental study on long-term performance of monopile-supported wind turbines (MWTs) in sand by using wind tunnel," Renewable Energy, Elsevier, vol. 159(C), pages 1199-1214.
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