Browsing by Author "Zhan, Lu"
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Item Lidar Measurements of Wakes Generated by Utility-scale Wind Turbines and Data-driven Optimization of Wind-turbine Wake Models(2020-01-23) Zhan, Lu; Iungo, Giacomo ValerioCurrent utility-scale wind turbines are the largest rotating turbo-machines ever built in the engineering history operating in a heterogeneous and dynamic environment, namely the atmospheric boundary layer. Wind features, such as Reynolds number of the order of several millions, wind shear and veer, daily cycle of atmospheric stability and wind turbulence intensity, topographyinduced wind distortions and wake interactions, among others, make theoretical and numerical predictions highly challenging to achieve accuracy in predictions of the turbine performance and wakes, which is not always sufficient for wind energy applications. These challenges are the motivation to investigate wind turbine operations and wakes through wind LiDAR measurements coupled with meteorological and SCADA data. Two main field campaigns have been performed: one for a wind farm deployed over a relatively flat terrain in Texas and another for a wind power plant installed on a complex terrain located in Colorado. Innovative LiDAR scanning strategies have been developed in order to collect accurate wind data around wind turbines and to probe the intra-wind farm wind field characterized by significant wake interactions. Transformative methodologies have been developed to analyze LiDAR and wind turbine data, such as the wake-cluster analysis and estimation of wake-wind statistics through the Barnes scheme. These detailed investigations have enabled characterizations of wake evolution for broad ranges of wind conditions and operative conditions of the wind turbines. Finally, the collected experimental data have been leveraged to perform diagnostic studies of wind farm performance and to carry-out data-driven tuning of existing wind-turbine wake models in order to provide optimized engineering tools for the wind energy community.Item Quantification of the Axial Induction Exerted by Utility-Scale Wind Turbines by Coupling LiDAR Measurements and RANS Simulations(Institute of Physics Publishing) Iungo, Giacomo V.; Letizia, Stefano; Zhan, Lu; 0000-0002-0990-8133 (Iungo, GV); Iungo, Giacomo V.; Letizia, Stefano; Zhan, LuThe 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. © Published under licence by IOP Publishing Ltd.