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Modeling of soil-pile-structure interaction for dynamic response of standalone wind turbines

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  • Yang, Shanghui
  • Deng, Xiaowei
  • Yang, Jun

Abstract

The accurate prediction of the dynamic behavior of the offshore wind turbine plays a significant role in its safe and efficient operation, where special importance should be attached to the foundation modeling of soil-pile-structure interaction. The present study aims to compare the three foundation modeling approaches with special attention to their displacement, acceleration, and internal force response subject to the combined stochastic wind and wave loading. In addition, parametric studies have been conducted on the foundation modeling approaches with the focus on their sensitivity to the variation of the foundation stiffness, pile diameter, thickness, and pile embedded depth. Using the high-fidelity FE model of the soil-pile system as the benchmark, the apparent fixity model underestimates the foundation stiffness remarkably, while the distributed spring model can give a relatively accurate prediction of the foundation stiffness. Furthermore, the FE model of the soil-pile system is more sensitive to the soil densification and the pile embedded depth, while the apparent fixity model exhibits higher sensitivity to the pile diameter and thickness. Compared with the benchmark FE model, the study provides guidance for the applicability of the simplified foundation modeling approaches, the apparent fixity model and distributed spring model, to different foundation stiffness in engineering practice.

Suggested Citation

  • Yang, Shanghui & Deng, Xiaowei & Yang, Jun, 2022. "Modeling of soil-pile-structure interaction for dynamic response of standalone wind turbines," Renewable Energy, Elsevier, vol. 186(C), pages 394-410.
    • Handle: RePEc:eee:renene:v:186:y:2022:i:c:p:394-410
    DOI: 10.1016/j.renene.2021.12.066
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    References listed on IDEAS

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    1. Breton, Simon-Philippe & Moe, Geir, 2009. "Status, plans and technologies for offshore wind turbines in Europe and North America," Renewable Energy, Elsevier, vol. 34(3), pages 646-654.
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    1. Yang, Shanghui & Deng, Xiaowei & Yang, Kun, 2024. "Machine-learning-based wind farm optimization through layout design and yaw control," Renewable Energy, Elsevier, vol. 224(C).

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