An elliptic super-Gaussian analytical framework for wind turbine wakes under yawed conditions

Accurate prediction of wind turbine wake effects is essential for optimizing wind farm configurations. This study introduces a novel analytical framework for wake prediction, employing an advanced elliptic super-Gaussian formulation specifically designed for multiple yawed turbines. Unlike conventional Gaussian methods, our approach effectively captures the complex spatial characteristics of wake structures. Key results indicate that the model successfully represents both the top-hat velocity distribution in proximal wake regions and the Gaussian profile in downstream areas, providing a more physically realistic depiction of wake evolution. Validation against field data, wind tunnel measurements, and large eddy simulations demonstrates the model's high predictive accuracy, with peak errors generally constrained within 10%. Comparative assessment with established wake models reveals the superior performance of our framework, particularly in simulating wake behavior near turbine hub height and in near-wake zones. The methodology's computational efficiency and implementation simplicity make it particularly suitable for practical wind farm layout optimization and operational strategy enhancement.