Double-Gaussian model for predictions of the streamwise mean velocity and turbulence intensity in wind-turbine wakes

Wind turbine wakes pose unique challenges for predictions of mean velocity and turbulence intensity due to the high Reynolds numbers and the complex flow physics involved. In this work, the streamwise mean velocity and turbulence intensity obtained from LiDAR measurements of wakes generated by full-scale wind turbines are interrogated to assess the accuracy of existing wake models, such as the Gaussian wake model, and identify opportunities for improvements of wind turbine wake modeling. It is found that a double-Gaussian model provides a higher degree of self-similarity throughout the wake region, specifically in the near-wake, enabling more accurate predictions of the mean velocity than those obtainable with the Gaussian model. The enhanced prediction capabilities of the double-Gaussian model are also confirmed from the analysis of the wake-added turbulence intensity. The predictions of the diagonal streamwise component of the strain-rate tensor obtained with the double-Gaussian model show better agreement with the experimental data, while the Gaussian model generally overestimates the rotor-averaged wake-added turbulence intensity and locates the turbulence-intensity peaks more inwards with respect to their actual positions.