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Structural modeling and optimization design of 15MW semi-submersible floating wind turbine platform based on intelligent algorithms

Author

Listed:
  • Bai, Hong
  • Zhang, Jianhua
  • Ma, Gang
  • Sun, Ke
  • Kang, Won-Hee
  • Lai, Yueqi
  • Chen, Yian
  • Zhu, Ronghua

Abstract

To enhance the operational stability of deep-sea floating wind turbines (FOWT), this study proposes a novel 15 MW semi-submersible quadrilateral floating platform named VolturnX. Aiming to achieve optimal motion stability, the design of the VolturnX platform, incorporating its mooring system, is initially formulated. The heave plate is optimized through an advanced machine learning (ML) framework integrating artificial neural network with heuristic algorithms. The VolturnX platform undergoes comprehensive hydrodynamic analysis. Additionally, the platform's motion responses under combined wind, wave, and current loading are thoroughly investigated. Particular attention is given to load cases involving different incident angles and parked conditions. Finally, the tensions of mooring lines are analyzed to ensure structural safety. The results demonstrate that, compared to the widely used VolturnUS-S platform, the VolturnX platform exhibits a geometrically symmetric distribution, resulting in a smaller overall response amplitude and enhanced adaptability to environmental loads from varying directions. The maximum mooring tension of the VolturnX platform is reduced. The optimized heave plate of the vibration damping device achieves optimal performance at a plate height of 9.50 m and radius of 0.70 m, reducing the average heave by 17.85 % and the maximum heave by 14.95 %. It provides additional protection against excessive vibrations and structural failures that may arise from ill-considered initial platform parameters.

Suggested Citation

  • Bai, Hong & Zhang, Jianhua & Ma, Gang & Sun, Ke & Kang, Won-Hee & Lai, Yueqi & Chen, Yian & Zhu, Ronghua, 2025. "Structural modeling and optimization design of 15MW semi-submersible floating wind turbine platform based on intelligent algorithms," Energy, Elsevier, vol. 335(C).
  • Handle: RePEc:eee:energy:v:335:y:2025:i:c:s0360544225035893
    DOI: 10.1016/j.energy.2025.137947
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    References listed on IDEAS

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