Computational Modeling of a Coupled Aero-Hydro-Structural-Mooring Integrated Dynamic System with Deep Learning for Floating Offshore Wind Turbine Design

Von Mises effective stress field distribution of the offshore wind turbine foundation for the “without cone” case subject to ice loading at different time instants

The proposed project aims to develop a high-fidelity and computationally efficient approach to predict the dynamic response of floating offshore wind turbines (FOWTs) under complex wind loads, stochastic waves, currents, and extreme weather events. This is essential to better understand the dynamic behavior of FOWTs, which requires taking into account the interaction among hydrodynamic and aerodynamic effects, structural and mooring dynamics. The proposed project further develops a short-term early warning technology for FOWTs in extreme sea conditions via the application of deep learning. The proposed method will build on the high-fidelity, open source CFD framework OpenFOAM, to solve significant aerodynamic unsteadiness and complex multi-physics interactions by enhancing the computational efficiency of each physics solver, and by building seamless linkages among solvers. The project will address the accuracy of fully-coupled aero- hydro-structural-mooring simulations for FOWTs, considering the flexibility of the structure, and provide early warning for extreme sea conditions.

Other Researchers

Jeremy Bricker (Civil and Environmental Engineering)

Ayumi Fujisaki-Manome (Cooperative Institute for Great Lakes Research)

Seymour Spence (Civil & Environmental Engineering)

Yulin Pan (Naval Architecture and Marine Engineering)