An alternative form of the super-Gaussian wind turbine wake model

Abstract

<jats:p>Abstract. A new analytical wind turbine wake model, based on a super-Gaussian shape function, is presented. The super-Gaussian function evolves from a nearly top-hat shape in the near wake to a Gaussian shape in the far wake, which is consistent with observations and measurements made on wind turbine wakes. Using such a shape function allows to recover the mass and momentum conservation that is violated when applying a near-wake regularization function to the expression of the maximum velocity deficit of the Gaussian wake model. After a brief introduction of the theoretical aspects, an easy-to-implement model with a limited number of parameters is derived. The super-Gaussian model predictions are compared to wind tunnel measurements, full-scale measurements and a LES simulation, showing a good agreement and an improvement compared with predictions based on the Gaussian model. </jats:p>

Wake Statistics of Different-Scale Wind Turbines under Turbulent Boundary Layer Inflow

“…where U is the time-averaged downwind velocity at different downwind locations, U in is the time-averaged incoming downwind velocity (which is taken at 1D upwind the turbine). As in [33], a slight offset of 0.1D is imposed in the negative y direction to compensate for the wake deflection observed in the measured data. It is seen in Figure 4 that the simulation velocity deficit profiles show an overall good agreement with the measurements considering the complex wind and turbine operating conditions in the field and different turbine designs.…”

“…It is also noticed the lateral velocity deficit profiles from the T80 case and the T27 case are very similar at the considered turbine downwind locations. The measured data are digitized from [33]. Details on the measurements can be found in [34].…”

“…Figure 4. Comparison of the simulated velocity deficit profiles with measurements at the SWiFT site.The measured data are digitized from[33]. Details on the measurements can be found in[34].…”

Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2

“…Another issue noted in this article is that the model used for the baseline in all cases is the Gauss model, and we note a tendency toward underpredicting wake losses even when assuming a rather low fixed annual turbulence intensity. A nearwake model, such as presented in Ishihara and Qian (2018) or Blondel and Cathelain (2020), could improve the fit of the closer-spaced turbines without relying on a lower turbulence setting. For the cases of the third turbine in a row, we pro-P. Fleming et al: Continued results from a field campaign of wake steering -Part 2 957 pose that new turbulence models or deep-array models could help increase the accuracy of wake losses in the model even assuming higher turbulence.…”

Continued Results from a Field Campaign of Wake Steering Applied at a Commercial Wind Farm: Part 2

“…There is existing literature on the topic of how yaw misalignment impacts loads (e.g., see Kragh and Hansen (2013) Another issue noted in this article is that the model used for the baseline in all cases is the Gauss model, and we note a tendency toward underpredicting wake losses even when assuming a rather low fixed annual turbulence intensity. A near-wake model, such as presented in Ishihara and Qian (2018) or Blondel and Cathelain (2020), could improve the fit of the closerspaced turbines without relying on a lower turbulence setting. For the cases of the third turbine in a row, we propose that new turbulence models or deep-array models could help increase the accuracy of wake losses in the model even assuming higher turbulence.…”