Session: 02-08-02 Reliability of Renewable Energy Devices 2

Submission Number: 182014

Short-Term Extreme Mooring Tension Prediction for a 15 Mw Semi-Submersible Floating Wind Turbine: a Comparison of PINN and MLP Surrogates 

Accurate prediction of short-term extreme responses is essential for the ultimate limit state (ULS) design and reliability assessment of floating offshore wind turbine (FOWT) mooring systems. Environmental contour–based approaches are commonly employed to represent design sea states with equivalent exceedance probability, yet conducting high-fidelity time-domain simulations across all contour points remains computationally demanding. This study develops a Transformer based surrogate modeling framework for efficient prediction of short-term maximum mooring-line tension responses of a 15 MW semi-submersible floating offshore wind turbine . The Transformer architecture is adapted to capture nonlinear interactions among key environmental parameters: wind speed, significant wave height, and spectral peak period, in order to approximate the short-term extreme response surface accurately. The Gumbel extreme value distribution is applied to characterize statistical features of the predicted maxima and evaluate its capability for tail extrapolation under various contour-based sea states. To further assess the model’s robustness, an additional set of contour points corresponding to rarer and more severe sea states (e.g., 100-year environmental contour) is analyzed. The results highlight the potential of deep-learning-based surrogate frameworks to replace computationally expensive time-domain simulations, offering a scalable and physically consistent approach for short-term extreme response estimation and reliability-oriented design of next-generation floating wind systems. x

Presenting Author: Lin He Wuhan University of Technology

Presenting Author Biography: Miss He is a PhD student at Wuhan University of Technology.

Authors:

Lin He Wuhan University of Technology
Wei Chai Wuhan University of Technology
Wei Shi Dalian University of Technology
Zhengshun Cheng Shanghai Jiao Tong University
Linyang Cao Dalian University of Technology
Rukang Wang Wuhan University of Technology