A novel physics-driven framework for yawed wind turbine wake predictions with dual-peak profiles

Wind energy faces significant challenges posed by wake-induced power losses, which results from decreased wind velocity and increased turbulence. To address the critical need for optimizing wind farm yaw angles and mitigating such losses, a three-dimensional dual-cosine shape model for predicting yaw wake velocity and turbulence intensity is proposed in this paper. This physics-based engineering framework is designed to predict wake center offset, velocity fields, and turbulence intensity distributions of yawed wind turbines. The model incorporates dual-peak wake distributions and yaw-induced wake deflection mechanisms, while comprehensively considering the influence of multiple factors on wake flow, including inflow wind conditions, local geographical information, turbine aerodynamic characteristics, and operational conditions. In addition, it eliminates the requirement for parameter fitting while maintaining applicability in both near and far-wake regions. Comprehensive validations against experimental data and high-fidelity computational fluid dynamics simulations demonstrate its superiority over existing models, with particular accuracy in capturing wake center trajectory evolution, wake deficit recovery, and turbulence intensity development throughout the yawed wake region. Quantitative error analyses furthermore confirm the model's accuracy and robustness: the relative root mean square error of the proposed wake deflection model generally remains below 10%, and the predicted wake distribution is improved by 20% relative to several existing yaw wake models in most cases (particularly in the far-wake region). This work provides both an effective analytical model for yawed wake dynamics and a practical tool for facilitating the implementation of yaw control strategies to enhance the energy yield of wind farms.