Data-Driven Modeling of Floating Offshore Wind Turbine Dynamics: An Optimized Artificial Neural Network Approach Using OC5 Experimental Data

The global transition of offshore wind energy into deep-water environments necessitates precise modeling of the complex, nonlinear dynamic responses of floating offshore wind turbines (FOWTs) to stochastic loads. Traditional industry-standard simulation tools often rely on potential flow theory, which neglects critical viscous effects and requires manual, empirical tuning of damping coefficients, reducing model reliability, while CFD modeling demands large computational resources. This paper introduces an application of advanced neural network techniques to model the coupled dynamic response of FOWTs under varied ocean conditions, reducing the simulation time required for training high-fidelity models. The architecture was trained using experimental data from the OC5 semi-submersible platform under the LC4.1 load case and further validated across a matrix of heterogeneous conditions, encompassing steady, turbulent, and irregular wind and wave environments. Results demonstrate exceptional predictive accuracy across coupled degrees of freedom (Heave, Pitch, and Surge), with the model achieving a coefficient of determination (R2>0.9) and maintaining superior phase coherence without discernible time lag. Power spectral density analysis confirms the model’s robust ability to capture resonant frequencies and hydrodynamic restoration across varied sea states. This data-driven framework provides a robust, near-instantaneous alternative for simulating FOWTs global dynamics. By successfully capturing complex nonlinear interactions and inertial effects, the methodology enables rapid decision-making in preliminary design, real-time digital twinning, and accelerated long-term fatigue analysis for safety-critical offshore applications.