Wake Steering and Flow Control for Offshore Wind Turbines: Modelling, Experiments, and Data-Driven Prediction

Wake interactions in offshore wind farms lead to substantial power losses and increased structural loading, making their accurate prediction and control a central challenge in wind energy research. Wake steering through yaw misalignment has emerged as a promising strategy to redirect wakes and improve farm-level performance, yet its effectiveness depends on complex flow physics, including turbulence, atmospheric stability, and wake recovery dynamics. Current modelling approaches, ranging from analytical wake models to high-fidelity simulations, often face limitations in balancing accuracy and computational efficiency. Meanwhile, experimental studies, including wind tunnel and field-scale measurements, provide critical validation but remain limited in scope. Recent advances in data-driven methods offer new opportunities to enhance wake prediction and enable adaptive flow control strategies.The primary goal of this Research Topic is to address the gap between the growing need for effective wake control strategies and the limited predictive capability of existing models under realistic offshore conditions. While wake steering via yaw misalignment has shown potential for improving wind farm performance, its reliability remains constrained by uncertainties in wake deflection, turbulence interaction, and atmospheric variability. Recent advances in high-fidelity simulations, controlled experiments, and data-driven modelling have improved understanding of wake dynamics, yet these approaches are often developed in isolation.This Topic aims to integrate these developments by fostering contributions that link physical modelling, experimental validation, and data-driven prediction. Particular emphasis is placed on improving the robustness and transferability of wake models, quantifying uncertainties, and enabling practical flow control.This Research Topic focuses on the fluid mechanics and control of wind turbine wakes, with particular attention to offshore applications. Contributions are invited on topics including, but not limited to: wake modelling and prediction, wake steering and yaw control, turbulence and wake recovery processes, interactions between multiple wakes, and the impact of atmospheric conditions on wake behaviour. Studies involving analytical models, large-eddy simulations, wind tunnel experiments, and field measurements are all within scope.Submissions employing data-driven and machine learning approaches for wake prediction or control are also encouraged, particularly where they complement physical understanding. Both fundamental and applied studies are welcome, provided they offer clear insight into wake dynamics or control mechanisms. Original research articles, review papers, and perspective articles that identify key Topic editors: Ruiyang He, Shaohai Zhang, Qiulei Wang, Guangyao An Source: https://www.frontiersin.org/research-topics/84426/wake-steering-and-flow-control-for-offshore-wind-turbines-modelling-experiments-and-data-driven-prediction