A novel 6DOF-SG-FOWT model: High-Fidelity wake prediction of floating offshore wind turbine wakes under platform motions

Accurate prediction of wake characteristics for floating offshore wind turbines (FOWTs) is crucial for optimizing wind farm layout and energy yield. However, existing wake models struggle to capture the complex, three-dimensional, and anisotropic wake structures induced by the coupled six-degree-of-freedom (6DOF) platform motions of FOWTs. To bridge this gap, this paper proposes a novel integrated wake model, termed the 6DOF-SG-FOWT model. The key novelty lies in the first unified three-dimensional Super-Gaussian framework that systematically incorporates all six platform motions (surge, sway, heave, roll, pitch, yaw), explicitly addressing motion-induced anisotropic wake deformation and deflection—a critical aspect inadequately modelled in prior studies limited to specific degrees of freedom or quasi-static assumptions. The model is rigorously validated against three independent wind tunnel experiments, demonstrating superior agreement with measurement data (nearly all relative errors below 3%) and outperforming existing benchmark models. Using this model, we then elucidate the distinct impacts of single-degree-of-freedom motions on wake evolution: heave has minimal effect; roll induces a downward wake core shift and vertical flattening; pitch causes asymmetric vertical contraction/expansion and shortens streamwise extension; yaw leads to lateral deflection, widened horizontal diffusion, and reduced streamwise propagation. The proposed 6DOF-SG-FOWT model provides an accurate, computationally efficient tool for analyzing FOWT wake interactions in offshore wind farms under realistic dynamic conditions.