Quantification of the axial induction exerted by utility-scale wind turbines by coupling LiDAR measurements and RANS simulations

Abstract

Abstract. The axial induction exerted by utility-scale wind turbines for different operative and atmospheric conditions is estimated by coupling ground-based LiDAR measurements and RANS simulations. The LiDAR data are thoroughly post-processed in order to average the wake velocity fields by using as common reference frame their respective wake directions and the turbine hub location. The various LiDAR scans are clustered according to their incoming wind speed at hub height and atmospheric stability regime, namely Bulk Richardson number. Time-averaged velocity fields are then calculated as ensemble averages of the scans belonging to the same cluster. The LiDAR measurements are coupled with RANS simulations in order to estimate the rotor axial induction for each cluster of the LiDAR data. First, a control volume analysis of the streamwise momentum is applied to the time-averaged LiDAR velocity fields to obtain an initial estimate of the axial induction over the rotor disk. The calculated thrust force is imposed as forcing of an axisymmetric RANS simulation in order to estimate pressure, radial velocity and momentum fluxes. The latter are combined with the LiDAR streamwise velocity field in order to refine the estimate of the rotor axial induction through the control volume approach. This process is repeated iteratively until achieving convergence on the rotor axial induction while minimizing difference between LiDAR and RANS streamwise velocity fields. This procedure allows to single out the reduction in thrust load while the blade pitch angle is increased transitioning from region 2 to 3 of the power curve. Furthermore, an enhanced thrust force is observed for a fixed incoming wind speed and transitioning from stable to convective stability regimes. The presented technique is proposed as a data-driven alternative to the blade element momentum theory typically used with current actuator disk models in order to quantify rotor aerodynamic thrust for different operative and atmospheric conditions.

Optimal tuning of engineering wake models through lidar measurements

“…By leveraging the average velocity field of wakes measured with a scanning Doppler wind lidar for different atmospheric-stability regimes and rotor thrust coefficients, we perform optimal tuning of four widely used engineering wake models, namely the Jensen model (Jensen, 1983), the Bastankhah model (Bastankhah and Porté-Agel, 2014), the Larsen model (Larsen, 1988(Larsen, , 2009, and the Ainslie model (Ainslie, 1988). In the following, these models are described, then their parameters are optimally calibrated based on the lidar measurements.…”

“…The pioneering work by Jensen (1983) and Katic et al (1987) assumed a linear wake expansion and a top-hat shape of the wake velocity profile at each downstream location. Despite its simplicity, this model provides a good estimation of the mean kinetic energy content available for downstream turbines.…”

LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework

“…8b, the standard deviation of the streamwise velocity has high values in the very near wake (x/D < 1) in the proximity of the rotor axis, which is most probably connected with the vorticity struc- tures generated in proximity of the rotor hub and their dynamics (Iungo et al, 2013a;Viola et al, 2014;Ashton et al, 2016). Similarly, enhanced values of the velocity standard deviation occur at the wake boundary (r/D ≈ 0.5), which are connected with the formation and dynamics of the helicoidal tip vortices (Ivanell et al, 2010;Debnath et al, 2017c). A peak of u 2 /U ∞ is observed around (x/D ≈ 3), which can be considered as being the formation length of the tip vortices.…”

LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 2: Applications to lidar measurements of wind turbine wakes

“…For the abovementioned technical features of lidars, these remote sensing instruments are now also used for wind resource assessment (Liu et al, 2019), enabling estimates of wind statistics for broad ranges of wind conditions and site typology, such as for flat terrains (Karagali et al, 2018;Sommerfeld et al, 2019;Sanchez-Gomez and Lundquist, 2019), complex terrains (Krishnamurthy et al, 2011(Krishnamurthy et al, , 2013Pauscher et al, 2016;Kim et al, 2016;Vasiljević et al, 2017;Karagali et al, 2018;Risan et al, 2018;Menke et al, 2019;Fernando et al, 2019), and near-shore (Hsuan et al, 2014;Floors et al, 2016;Shimada et al, 2018) and off-shore locations (Pichugina et al, 2012;Koch et al, 2014;Gottschall et al, 2018;Viselli et al, 2019). Lidar scanning strategies for wind resource assessment encompass Doppler beam swinging (DBS; Hsuan et al, 2014;Pauscher et al, 2016;Kim et al, 2016;Shimada et al, 2018;Gottschall et al, 2018;Viselli et al, 2019;Sommerfeld et al, 2019;Sanchez-Gomez and Lundquist, 2019), plan position indicator (PPI) scans (Krishnamurthy et al, 2011(Krishnamurthy et al, , 2013Pauscher et al, 2016;Floors et al, 2016;Vasiljević et al, 2017;Karagali et al, 2018), range height indicator (RHI) scans (Pichugina et al, 2012;Floors et al, 2016;...…”