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To assess current capabilities for measuring flow within the atmospheric boundary layer, including within wind farms, the U.S. Department of Energy sponsored the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign at the Boulder Atmospheric Observatory (BAO) in spring 2015. Herein, we summarize the XPIA field experiment, highlight novel measurement approaches, and quantify uncertainties associated with these measurement methods. Line-of-sight velocities measured by scanning lidars and radars exhibit close agreement with tower measurements, despite differences in measurement volumes. Virtual towers of wind measurements, from multiple lidars or radars, also agree well with tower and profiling lidar measurements. Estimates of winds over volumes from scanning lidars and radars are in close agreement, enabling the assessment of spatial variability. Strengths of the radar systems used here include high scan rates, large domain coverage, and availability during most precipitation events, but they struggle at times to provide data during periods with limited atmospheric scatterers. In contrast, for the deployment geometry tested here, the lidars have slower scan rates and less range but provide more data during nonprecipitating atmospheric conditions. Microwave radiometers provide temperature profiles with approximately the same uncertainty as radio acoustic sounding systems (RASS). Using a motion platform, we assess motion-compensation algorithms for lidars to be mounted on offshore platforms. Finally, we highlight cases for validation of mesoscale or large-eddy simulations, providing information on accessing the archived dataset. We conclude that modern remote sensing systems provide a generational improvement in observational capabilities, enabling the resolution of finescale processes critical to understanding inhomogeneous boundary layer flows.

Dynamics and instabilities occurring in the near-wake of wind turbines have a crucial role for the wake downstream evolution, and for the onset of far-wake instabilities. Furthermore, wake dynamics significantly affect the intra-wind farm wake flow, wake interactions and potential power losses. Therefore, the physical understanding and predictability of wind turbine wake instabilities become a nodal point for prediction of wind power harvesting and optimization of wind farm layout. This study is focused on the prediction of the hub vortex instability encountered within wind turbine wakes under different operational conditions of the wind turbine. Linear stability analysis of the wake flow is performed by means of a novel approach that enables to take effects of turbulence on wake instabilities into account. Stability analysis is performed by using as base flow the time-averaged wake velocity field at a specific downstream location. The latter is modeled through Carton-McWilliams velocity profiles by mimicking the presence of the hub vortex and helicoidal tip vortices, and matching the wind turbine thrust coefficient predicted through the actuator disc model. The results show that hub vortex instability is promoted by increasing the turbine thrust coefficient. Indeed, a larger aerodynamic load produces an enhanced wake velocity deficit and axial shear, which are considered the main sources for the wake instability. Nonetheless, wake swirl also promotes hub vortex instability, and it can also affect the azimuthal wavenumber of the most unstable mode.

The near wake of wind turbines is characterized by the presence of the hub vortex, which is a coherent vorticity structure generated from the interaction between the root vortices and the boundary layer evolving over the turbine nacelle. Bymoving downstream, the hub vortex undergoes an instability with growth rate, azimuthal and axial wavenumbers determined by the characteristics of the incoming wind and turbine aerodynamics. Thus, a large variability of the hub vortex instability is expected for wind energy applications with consequent effects on wake downstream evolution, wake interactions within a wind farm, power production, and fatigue loads on turbines invested by wakes generated upstream. In order to predict characteristics of the hub vortex instability for different operating conditions, linear stability analysis is carried out by considering different statistics of the incoming wind turbulence, thrust coefficient, tip speed ratio, and blade lift distribution of a wind turbine. Axial and azimuthal wake velocity fields are modeled through Carton-McWilliams velocity profiles by mimicking the presence of the hub vortex, helicoidal tip vortices, and matching the wind turbine thrust coefficient predicted through the actuator disk model. The linear stability analysis shows that hub vortex instability is strongly affected by the wind turbine loading conditions, and specifically it is promoted by a larger thrust coefficient. A higher load of the wind turbines produces an enhanced axial velocity deficit and, in turn, higher shear in the radial direction of the streamwise velocity. The axial velocity shear within the turbine wake is also the main physical mechanism promoting the hub vortex instability when varying the lift distribution over the blade span for a specific loading condition. Cases with a larger velocity deficit in proximity of the wake center and less aerodynamic load towards the blade tip result to be more unstable. Moreover, wake swirl promotes hub vortex instability, and it can also affect the azimuthal wave number of the most unstable mode. Finally, higher Reynolds stresses and turbulent eddy viscosity decrease both growth rate and azimuthal wave number of the most unstable mode.