Open Access
Article
21 July 2025Dynamic Analysis of Shared Moorings in Different Wind Farm Layouts
The effects of shared mooring in offshore wind farms are investigated through numerical simulations in the present study. Different farm layouts are modelled and tested in SIMA coupled dynamic analysis software with three and four floaters. The wind turbine and the platform are based on the OC3 project from NREL: a 5-MW wind turbine and a spar floater with a 120-m draft. The water depth is 320 m, and the environmental loads are defined for an average operational condition. Firstly, the static results of the mooring line tension at the fairleads and anchors from the numerical model are compared with the values from the open-source MoorPy code. Then, domain simulations are conducted for three hours, and the dynamic behaviour of the floaters is analysed with a focus on surge and pitch motions. In addition, the dynamic stiffness effects of the polyester in the shared mooring line are considered in the SIMA simulations. The mooring line tensions are analysed, determining the global maximum tension across all systems. Results show that designs with two windward legs have significantly lower anchor mooring line tensions than those with a single windward leg, with no relevant variation in platform surge and pitch. Thus, the former systems are preferable for further investigation.
Open Access
Article
14 August 2025Advanced Study on Structural Health Diagnosis and Maintenance for Floating Wind Turbines Using Computer Vision
Global offshore wind capacity has now surpassed 50 GW and is projected to reach 264 GW by 2050, highlighting the pivotal role of floating wind in the future of clean energy. Given the complexity of marine environments, intelligent diagnostics for floating turbines are crucial for improving operational efficiency, reducing costs, and ensuring robust and sustainable energy production. This paper presents a structural damage detection framework for floating wind turbines, integrating computer vision with advanced artificial intelligence technologies. First, a dataset is constructed through industry collaboration and open-source collection. Then, to optimise the YOLOv7 algorithm, SE attention mechanisms and WISE-IoU loss functions are incorporated, which significantly enhance the accuracy of surface damage detection. Experimental results indicate that the mAP (mean Average Precision) increases from 82.44% to 86.24% compared to the original YOLOv7. Finally, a deployment approach and an example are provided to use the diagnostic framework as a portable application. This enables real–time on–site analysis, enhances detection timeliness, and reduces maintenance costs. It allows for immediate issue identification and adaptation to diverse environments.
