Atmospheric gravity waves (AGWs) are large-scale wave-like flow structures commonly generated when atmospheric flows are vertically perturbed by topographical features or meteorological phenomena. These transient phenomena can significantly affect wind turbine outputs and loads; however, their influence on wake dynamics remains poorly understood, posing challenges for accurate wind farm modeling. In this study, we perform large-eddy simulation of wind turbines operating under an atmospheric condition reconstructed by assimilating lidar measurements of AGWs. Our results show that (i) low-frequency wake meandering becomes more pronounced owing to large-scale AGW flow structures and intensified smaller-scale turbulent structures. This enhanced meandering, combined with stronger turbulent mixing, accelerates mean wake recovery. (ii) The turbulence kinetic energy (TKE) spectrum in the wake region exhibits a peak Strouhal number of approximately 0.3, although the inflow spectrum peaks at significantly lower frequencies. This observation indicates that, under AGW conditions, wake turbulence generation follows a convective instability mechanism. Notably, faster wake recovery reduces wake shear, leading to lower amplification of TKE. Power analysis for two turbines arranged in a streamwise column further highlights the dominant role of convective instabilities. Large-amplitude, low-frequency power fluctuations observed at the most upstream turbine are significantly attenuated for downstream turbines as low-frequency velocity fluctuations are suppressed in the far-wake region. These findings add further insights into wake meandering and turbulence generation, offering guidance for modeling wind turbine and farm flows under non-stationary atmospheric conditions.

Power prediction is a fundamental research topic in wind industry. Offshore wind power prediction mostly relies on either data-driven or physics-based approaches. Few approaches combine physical knowledge and operational data. Nevertheless, there is significant potential for complementarity between these two approaches. In this study, a physics-based Gaussian wake model for wind farms is first constructed, and parameters of the empirical wake model are optimally identified by Particle Swarm Optimization algorithm based on the actual operational data. A purely data-driven power prediction method is constructed through K-means clustering and parallel weighting Long Short-Term Memory with empirical mode decomposition. Based on these methods, an innovative fusion approach combining the physics-based wake model with the data-driven method is constructed using symbolic regression. Taking the real measured data from an offshore wind farm in Jiangsu, China, as a case study, the results show that the accuracy of the proposed approach is 21.67 % higher than that of the data-driven approach and 35.17 % higher than that of the physics-based approach. These results confirm the superiority of the physics-data fusion approach for wind farm power prediction.

Floating wind farms (FOWF) are one of the main forms of wind energy utilization in the deep-sea areas. This study proposes a multi-objective reposition control approach for floating wind farms. Firstly, an imperial wake model of floating wind turbines is constructed considering the effects of wind and wave conditions. A nonlinear model of a catenary mooring line is subsequently constructed. Furthermore, a multi-objective location optimization method is proposed that allows for the tradeoff between the maximum power of the farm and the minimum drift distance of the turbines accounting for the time-varying wind speed and direction. The results of the proposed approach are then compared with those of traditional methods. The findings indicate that time-varying changes in wind have a significant influence on the optimal position of turbines. It can decrease the maximum drift distance by approximately 7 % when considering temporal variations in wind. Furthermore, the proposed reposition control maintains almost the same power output of the wind farm while reducing the total offset distance from the equilibrium point of turbines by approximately 11 %. The impact of mooring orientation, natural length, turbine spacing, and wave speed on the control performance are also elucidated.

Accurately modelling wind turbine wakes is essential for optimising wind farm performance but remains a persistent challenge. While the dynamic wake meandering (DWM) model captures unsteady wake behaviour, it suffers from near-wake inaccuracies due to empirical closures. We propose a symbolic regression-enhanced DWM (SRDWM) framework that achieves equation-level closure by embedding symbolic expressions for volumetric forcing and boundary terms explicitly into governing equations. These physically consistent expressions are discovered from large-eddy simulations (LES) data using symbolic regression guided by a hierarchical, domain-informed decomposition strategy. A revised wake-added turbulence formulation is further introduced to enhance turbulence intensity predictions. Extensive verification across varying inflows shows that SRDWM accurately reproduces both mean wake characteristics and turbulent dynamics, achieving full spatiotemporal resolution with over three orders of magnitude speed-up compared to LES. The results highlight symbolic regression as a bridge between data and physics, enabling interpretable and generalisable modelling.

We perform large-eddy simulation of wind turbine wake flow subjected to atmospheric gravity waves. The mesoscale forcing from atmospheric gravity waves is coupled to microscale turbine simulation using an indirect profile assimilation method. Our analysis will center on examining the effects of atmospheric gravity waves on wake meandering and wake-added turbulence, aiming to enhance understanding of the wake dynamics in the presence of atmospheric gravity waves. This study holds promise for guiding the development of wind farm models under realistic atmospheric conditions.