Work

Structural performance of offshore wind turbines under wind and seafloor ground motions using a coupled seismic-aerodynamic analysis method

Year: 2026

Type: article

Abstract: This study proposes a fully coupled seismic-aerodynamic analysis method of offshore wind turbines (OWT) through the modular coupling and bidirectional data exchange, which integrates synthesized seafloor ground motions, steel damage evolution model, and soil-structure interaction (SSI) effect. Primarily, for typical offshore seawater-soil-bedrock sites, seafloor seismic transfer functions are derived, and offshore seismic wave time-histories are solved and synthesized. Subsequently, an offshore seismic module is developed and integrated into the OWT finite element model in ANSYS/LS-DYNA, while aerodynamic loads are simultaneously calculated by OpenFAST and transferred to the structural solver through a customized user-defined interface. Through developing the steel material damage model, a fully coupled model of OWTs with SSI under wind-seafloor ground motions is established. Finally, motion responses and damage variations are investigated, and based on the coupled analysis under typical wind-offshore seismic conditions, the seismic dynamic response of OWTs under different wind-earthquake load directions is evaluated. Results demonstrate that wind loads dominate tower top acceleration under normal operation, whereas seafloor ground motions induce pronounced stress redistribution and local concentration at the tower base, and the foundation is identified as the most vulnerable component under overturning failure. Soil shear modulus and layer thickness critically influence the amplitude and frequency characteristics of seafloor ground motions, directly affecting OWT dynamic responses. Furthermore, the misalignment angles between wind and seismic loading exhibit strong directional sensitivity, with peak acceleration and stress responses occurring at specific angles.

Source: Ocean Engineering

DOI: 10.1016/j.oceaneng.2026.128395

Authors: Yingzhou Liu, Zhongxian Liu, Jijian Lian, Jinjun Hu, Xiangyu Yan, Wei Shi