4.7 Article

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

期刊

RENEWABLE ENERGY
卷 178, 期 -, 页码 1421-1434

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.06.115

关键词

Offshore wind turbines; Fatigue life prediction; Aerodynamic damping; Modal analysis

资金

  1. National Science Fund for Distinguished Young Scholars [52025082]
  2. National Natural Science Foundation of China [51908209, H202194370223]

向作者/读者索取更多资源

This study presents a computationally efficient methodology combining aerodynamic decoupling and modal reduction techniques for predicting the fatigue life of offshore wind turbine support structures. The method accurately predicts fatigue life and significantly reduces computational costs.
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. (c) 2021 Elsevier Ltd. All rights reserved.

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