Wake prediction of a fully coupled floating wind turbine using dynamic mode decomposition and bidirectional long short-term memory method

Floating offshore wind farms require rapid, accurate wake predictions to support control and layout optimization, yet fully coupled large-eddy simulations (LES) remain prohibitively expensive. This study proposes an integrated combining LES, dynamic mode decomposition (DMD), and bidirectional long short-term memory (BiLSTM) workflow for short-term wake forecasting around floating offshore wind turbines. First, LES coupled with the MoorDyn dynamic-mooring model resolves six degrees of freedom platform motions for the offshore code comparison collaboration continuation-DeepCwind semi-submersible floating wind turbine under combined wind-wave-current loading. DMD compresses the resulting flow fields into a set of spatial modes associated with distinct frequencies, and a BiLSTM network learns the temporal evolution of the leading coefficients. The framework is benchmarked on canonical cylinder flow and then applied to the floating wind turbine. Retaining the first 27 DMD modes captures >98% of wake energy, while the BiLSTM achieves mean absolute and root-mean-square errors below 1% and 1.5%, respectively, over a 5 s prediction horizon (one rotor revolution), with R2 > 0.96. Compared with full LES cases, the surrogate achieves an order-of-magnitude reduction in computational cost without compromising key wake features such as velocity deficit recovery and vortex shedding dynamics. The proposed DMD-BiLSTM model offers a physics-informed, real-time capable tool for wake prediction, providing a physics-informed surrogate that supports advanced control strategies and provides high-fidelity input for dense farm layout optimization.