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Numerically efficient fatigue life prediction of offshore wind turbines using aerodynamic decoupling

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  • Chen, Chao
  • Duffour, Philippe
  • Fromme, Paul
  • Hua, Xugang

Abstract

The fatigue life prediction for offshore wind turbine support structures is computationally demanding, requiring the consideration of a large number of combinations of environmental conditions and load cases. In this study, a computationally efficient methodology combining aerodynamic decoupling and modal reduction techniques is developed for fatigue life prediction. Aerodynamic decoupling is implemented to separate the support structure and rotor-nacelle assembly. The rotor dynamics were modelled using an aerodynamic damping matrix that accurately captures the aerodynamic damping coupling between the fore-aft and side-side motions. Soil-structure interaction is modelled using p-y curves, and wave loading calculated based on linear irregular waves and Morison's equation at a European (North Sea) site. A modal reduction technique is applied to significantly reduce the required number of degrees of freedom, allowing the efficient and accurate calculation of hotspot stresses and fatigue damage accumulation. The modal model was verified against a fully coupled model for a case-study, monopile supported offshore wind turbine in terms of response prediction and fatigue life evaluation. The modal model accurately predicts fatigue life (within 2%) for a range of parameters at a fraction of computational cost (0.5%) compared to the fully coupled model.

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  • Chen, Chao & Duffour, Philippe & Fromme, Paul & Hua, Xugang, 2021. "Numerically efficient fatigue life prediction of offshore wind turbines using aerodynamic decoupling," Renewable Energy, Elsevier, vol. 178(C), pages 1421-1434.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:1421-1434
    DOI: 10.1016/j.renene.2021.06.115
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    References listed on IDEAS

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    Cited by:

    1. Pan, Lin & Xiong, Yong & Zhu, Ze & Wang, Leichong, 2022. "Research on variable pitch control strategy of direct-driven offshore wind turbine using KELM wind speed soft sensor," Renewable Energy, Elsevier, vol. 184(C), pages 1002-1017.
    2. Ren, Chao & Xing, Yihan, 2023. "AK-MDAmax: Maximum fatigue damage assessment of wind turbine towers considering multi-location with an active learning approach," Renewable Energy, Elsevier, vol. 215(C).

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