A New Streamwise Scaling for Wind Turbine Wake Modeling in the Atmospheric Boundary Layer

Dara Vahidi and Fernando Porté-Agel ()
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Dara Vahidi: Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
Fernando Porté-Agel: Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

Energies, 2022, vol. 15, issue 24, 1-18

Abstract: In this study, we aim to investigate if there is a scaling of the streamwise distance from a wind turbine that leads to a collapse of the mean wake velocity deficit under different ambient turbulence levels. For this purpose, we perform large-eddy simulations of the wake of a wind turbine under neutral atmospheric conditions with various turbulence levels. Based on the observation that a higher atmospheric turbulence level leads to faster wake recovery and shorter near-wake length, we propose the use of the near-wake length as an appropriate normalization length scale. By normalizing the streamwise distance by the near-wake length, we obtain a collapse of the normalized wake velocity deficit profiles for different turbulence levels. We then explore the possibility of using the relationship obtained for the normalized maximum wake velocity deficit as a function of the normalized streamwise distance in the context of analytical wake modeling. Specifically, we investigate two approaches: (a) using the new relationship as a stand-alone model to calculate the maximum wake velocity deficit, and (b) using the new relationship to calculate the wake advection velocity within a physics-based wake expansion model. Large-eddy simulation of the wake of a wind turbine under neutral atmospheric conditions is used to evaluate the performance of both approaches. Overall, we observe good agreement between the simulation data and the model predictions, along with considerable savings in terms of the models’ computational costs.

Keywords: wind turbine wake; analytical wake model; near-wake length (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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