Defining optimal scanning geometries for scanning lidars for wind energy applications remains an active field of research. This paper evaluates uncertainties associated with arc scan geometries and presents recommendations regarding optimal configurations in the atmospheric boundary layer. The analysis is based on arc scan data from a Doppler wind lidar with one elevation angle and seven azimuth angles spanning 30° and focuses on an estimation of 10-min mean wind speed and direction. When flow is horizontally uniform, this approach can provide accurate wind measurements required for wind resource assessments in part because of its high resampling rate. Retrieved wind velocities at a single range gate exhibit good correlation to data from a sonic anemometer on a nearby meteorological tower, and vertical profiles of horizontal wind speed, though derived from range gates located on a conical surface, match those measured by mast-mounted cup anemometers. Uncertainties in the retrieved wind velocity are related to high turbulent wind fluctuation and an inhomogeneous horizontal wind field. The radial velocity variance is found to be a robust measure of the uncertainty of the retrieved wind speed because of its relationship to turbulence properties. It is further shown that the standard error of wind speed estimates can be minimized by increasing the azimuthal range beyond 30° and using five to seven azimuth angles.

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Assessing potential costs and benefits of siting wind turbines on escarpments is challenging, particularly when the upstream fetch is offshore leading to more persistent wind speeds in power producing classes, but an increased importance of stable stratification under which terrain impacts on the flow may be magnified. In part because of a lack of observational data, critical knowledge gaps remain and there is currently little consensus regarding optimal models for flow characterization and turbine design calculations. We present a unique dataset comprising measurements of flow parameters conducted over a 10–14 m escarpment at turbine relevant heights (from 9 to 200 m) and use them to evaluate model simulations. The results indicate good agreement in terms of the wind speed decrease before the terrain feature and the increase at (and downwind of) the escarpment of ~3–5% at turbine hub‐heights. However, the horizontal extent of the region, in which the impact of the escarpment on the mean flow is evident, is larger in the models than the measurements. A region of high turbulence was indicated close to the escarpment that extended through the nominal rotor plane, but the horizontal extent of this region was narrow (<10 times the escarpment height, H) in both models and observations. Moving onshore the profile of turbulence was more strongly influenced by higher roughness of a small forest. While flow angles close to the escarpment were very complex, by a distance of 10 H, flow angles were <3° and thus well within limits indicated by design standards. Copyright © 2016 John Wiley & Sons, Ltd.

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Understanding the detailed dynamics of wind turbine wakes is critical to predicting the performance and maximizing the efficiency of wind farms. This knowledge requires atmospheric data at a high spatial and temporal resolution, which are not easily obtained from direct measurements. Therefore, research is often based on numerical models, which vary in fidelity and computational cost. The simplest models produce axisymmetric wakes and are only valid beyond the near wake. Higher-fidelity results can be obtained by solving the filtered Navier-Stokes equations at a resolution that is sufficient to resolve the relevant turbulence scales. This work addresses the gap between these two extremes by proposing a stochastic model that produces an unsteady asymmetric wake. The model is developed based on a large-eddy simulation (LES) of an offshore wind farm. Because there are several ways of characterizing wakes, the first part of this work explores different approaches to defining global wake characteristics. From these, a model is developed that captures essential features of a LES-generated wake at a small fraction of the cost. The synthetic wake successfully reproduces the mean characteristics of the original LES wake, including its area and stretching patterns, and statistics of the mean azimuthal radius. The mean and standard deviation of the wake width and height are also reproduced. This preliminary study focuses on reproducing the wake shape, while future work will incorporate velocity deficit and meandering, as well as different stability scenarios. 449A stochastic wind turbine wake model based on new metrics P. Doubrawa et al.equations, using an eddy viscosity model to compute the Reynolds stresses. Both models are only valid beyond the near wake (i.e. approximately 2 rotor diameters downstream). A moving quasi-steady wake can be simulated by adding a stochastic component to the Ainslie model to account for the wake meandering. This dynamic wake meandering (DWM) model 14 treats the wake as a passive tracer that is advected by the large scales in the ambient turbulent flow. Next in the range of fidelity is full three-dimensional computational fluid dynamics using RANS turbulence modeling, either in steady or unsteady form. In the steady form, used with an actuator disk representation of the turbine rotor, a steady wake is formed. This wake need not be axisymmetric and can be affected by shear, terrain and stability. In unsteady form, RANS can be used with a rotating actuator line, and the large-scale unsteady features of wakes can be resolved. Finally, higher fidelity results can be obtained by performing large-eddy simulations (LES), which entail solving the filtered Navier-Stokes equations at a spatial and temporal resolution that is high enough to resolve the relevant turbulence scales, typically O.10 0 / m near the rotor. Different LES codes vary in the treatment of the turbine (e.g. actuator disk versus actuator line 15 ), the turbulence closure used (e.g. Smagorinsky versus mixed-scale models 16 ) and the nu...

The 3D Wind experiment integrates model simulations and measurements from remotesensing, traditional, and unmanned aerial vehicle platforms to quantify wind components over the area of a large wind farm to heights of 200 m.

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