A physics-based model for wind turbine wake expansion in the atmospheric boundary layer. (25th July 2022)

• Record Type: Journal Article
• Authors: Vahidi, Dara; Porté-Agel, Fernando
• Is Part Of: Journal of fluid mechanics. Volume 943(2022)
• Publication Date: 2022-07-25
• Subjects: wakes -- turbulent boundary layers; Fluid mechanics -- Periodicals; 532.005
• DOI: 10.1017/jfm.2022.443
• Languages: English
• ISSNs: 0022-1120

Abstract

Abstract: Abstract : Analytical wind turbine wake models are widely used to predict the wake velocity deficit. In these models, the wake growth rate is a key parameter specified mainly with empirical formulations. In this study, a new physics-based model is proposed and validated to predict the wake expansion downstream of a turbine based on the incoming ambient turbulence and turbine operating conditions. The new model utilises Taylor diffusion theory, the Gaussian wake model, turbulent mixing layer theory and the analogy between wind turbine wake expansion and scalar diffusion. These components ensure that the model conserves mass and momentum in the far wake and accounts for the ambient turbulence and turbine-induced turbulence effects on the wake expansion. To account for the turbulence relevant scales that contribute to the wake expansion, the model uses the root-mean-square of the low-pass filtered radial velocity component. A simplified version that only requires the unfiltered velocity standard deviation and turbulence integral scale is also proposed. In addition, a new relation for the near-wake length is derived. The model performance is validated using large-eddy simulation data of a wind turbine wake under neutral atmospheric conditions with a wide range of incoming turbulence levels. The results show that the proposed model yields reasonable predictions of the wake width, maximum velocity deficit and near-wake length. In the case with a relatively low incoming streamwise turbulence intensity of 0.05, the ambient and turbine-induced terms in the model contribute almost equally to the wake width, rendering them both crucial for reasonable wake predictions.