08 Nov 2023
 | 08 Nov 2023
Status: this preprint is currently under review for the journal WES.

Improvements to the Dynamic Wake Meandering Model by incorporating the turbulent Schmidt number

Peter Brugger, Corey Markfort, and Fernando Porté-Agel

Abstract. Predictions of the dynamic wake meandering model (DWMM) were compared to flow measurements of a scanning Doppler lidar mounted on the nacelle of a utility-scale wind turbine. We observed that the wake meandering strength of the DWMM agrees better with the observation, if the incoming mean wind speed is used as advection velocity for the downstream transport, while a better temporal agreement is achieved with an advection velocity slower than the incoming mean wind speed. A subsequent investigation of the lateral wake transport revealed differences to the passive tracer assumption of the DWMM in addition to a non-passive downstream transport reported in earlier studies. We propose to include the turbulent Schmidt number in the DWMM to improve (i) the consistency of the model physics and (ii) the prediction quality. Compared to a benchmark, the thus modified DWMM showed a root-mean-square error reduction by 5 % for mean velocity deficit and 7 % for the turbulence intensity, relative to the unmodified DWMM, in addition to better temporal agreement of the dynamics. This is in contrast to an error increase of 64 % and 41 % if only a more accurate downstream transport velocity is used without including the turbulent Schmidt number.

Peter Brugger et al.

Status: open (until 06 Dec 2023)

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Peter Brugger et al.

Peter Brugger et al.


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Short summary
The dynamic wake meandering model (DWMM) assumes that wind turbine wakes are transported like a passive tracer by the large-scale turbulence of the atmospheric boundary layer. We show that both the streamwise transport and the lateral transport have differences to the passive tracer assumption. We then propose to include the turbulent Schmidt number into the DWMM to address the observed differences and show that it improves the quality of the model predictions.