Previous research has revealed the need for a validation study that considers several wake quantities and code types so that decisions on the trade-off between accuracy and computational cost can be well informed and appropriate to the intended application. In addition to guiding code choice and setup, rigorous model validation exercises are needed to identify weaknesses and strengths of specific models and guide future improvements. Here, we consider 13 approaches to simulating wakes observed with a nacelle-mounted lidar at the Scaled Wind Technology Facility (SWiFT) under varying atmospheric conditions. We find that some of the main challenges in wind turbine wake modeling are related to simulating the inflow. In the neutral benchmark, model performance tracked as expected with model fidelity, with large-eddy simulations performing the best. In the more challenging stable case, steady-state Reynolds-averaged Navier-Stokes simulations were found to outperform other model alternatives because they provide the ability to more easily prescribe noncanonical inflows and their low cost allows for simulations to be repeated as needed. Dynamic measurements were only available for the unstable benchmark at a single downstream distance. These dynamic analyses revealed that differences in the performance of time-stepping models come largely from differences in wake meandering. This highlights the need for more validation exercises that take into account wake dynamics and are able to identify where these differences come from: mesh setup, inflow, turbulence models, or wake-meandering parameterizations. In addition to model validation findings, we summarize lessons learned and provide recommendations for future benchmark exercises.
This work presents a new observational wind atlas for the Great Lakes, and proposes a methodology to combine in situ and satellite wind observations for offshore wind resource assessment. Efficient wind energy projects rely on accurate wind resource estimates, which are complex to obtain offshore due to the temporal and spatial sparseness of observations, and the potential for temporal data gaps introduced by the formation of ice during winter months, especially in freshwater lakes. For this study, in situ observations from 70 coastal stations and 20 buoys provide diurnal, seasonal, and interannual wind variability information, with time series that range from 3 to 11 years in duration. Remotely-sensed equivalent neutral winds provide spatial information on the wind climate. NASA QuikSCAT winds are temporally consistent at a 25 km resolution. ESA Synthetic Aperture Radar winds are temporally sparse but at a resolution of 500 m. As an initial step, each data set is processed independently to create a map of 90 m wind speeds. Buoy data are corrected for ice season gaps using ratios of the mean and mean cubed of the Weibull distribution, and reference temporally-complete time series from the North American Regional Reanalysis. Generalized wind climates are obtained for each buoy and coastal site with the wind model WAsP, and combined into a single wind speed estimate for the Great Lakes region. The method of classes is used to account for the temporal sparseness in the SAR data set and combine all scenes into one wind speed map. QuikSCAT winds undergo a seasonal correction due to lack of data during the cold season that is based on its ratio relative to buoy time series. All processing steps reduce the biases of the individual maps relative to the buoy observed wind climates. The remote sensing maps are combined by using QuikSCAT to scale the magnitude of the SAR map. Finally, the in situ predicted wind speeds are incorporated. The mean spatial bias of the final map when compared to buoy time series is 0.1 ms −1 and the RMSE 0.3 ms −1 , which represents an uncertainty reduction of 50% relative to using only SAR, and of 40% to using only SAR and QuikSCAT without in situ observations.
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.
Technology development and design decisions in wind energy are often based on results from simulations performed for individual wind turbines or entire wind plants. It is therefore critical to ensure that the models being used for research and industry applications in wind energy be thoroughly validated against measurements. A full-system validation of wind plant simulations must consider the atmospheric inflow, the response of the wind turbines, and their wakes. This task is complicated by the lack of freely available, quality-controlled, high-quality measurements. Here, such measurements are used to offer a validation exercise that can be used to assess the accuracy of models of any fidelity level. When it comes to real-world measurements, the dataset considered herein is simple in terms of terrain but exhibits pronounced diurnal cycles. Instead of a full-scale wind plant, we consider an individual research-scale, utility wind turbine instrumented for power and loads measurements. Three benchmarks are defined, with increasing levels of complexity: near neutral, slightly unstable, and very stable atmospheric stratification. Through comparisons between observations and simulations, the benchmarks provide complementary information about the model performance and its ability to reproduce mean and dynamic wake characteristics. This article describes the measurements and methodology used to define these benchmarks and provides the information required to perform simulations and conduct the model-measurement comparison. The objective is to provide a robust wake model validation exercise open to anyone, which will serve to minimize uncertainty in model validation practices related to varying methodologies across simulation tools and users.
Recent computational and modeling advances have led a diverse modeling community to experiment with atmospheric boundary layer (ABL) simulations at subkilometer horizontal scales. Accurately parameterizing turbulence at these scales is a complex problem. The modeling solutions proposed to date are still in the development phase and remain largely unvalidated. This work assesses the performance of methods currently available in the Weather Research and Forecasting (WRF) model to represent ABL turbulence at a gray-zone grid spacing of 333 m. We consider three one-dimensional boundary layer parameterizations (MYNN, YSU and Shin-Hong) and coarse large-eddy simulations (LES). The reference dataset consists of five real-case simulations performed with WRF-LES nested down to 25 m. Results reveal that users should refrain from coarse LES and favor the scale-aware, Shin-Hong parameterization over traditional one-dimensional schemes. Overall, the spread in model performance is large for the cellular convection regime corresponding to the majority of our cases, with coarse LES overestimating turbulent energy across scales and YSU underestimating it and failing to reproduce its horizontal structure. Despite yielding the best results, the Shin-Hong scheme overestimates the effect of grid dependence on turbulent transport, highlighting the outstanding need for improved solutions to seamlessly parameterize turbulence across scales.
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...
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