The complexity and associated uncertainties involved with atmospheric-turbine-wake interactions produce challenges for accurate wind farm predictions of generator power and other important quantities of interest (QoIs), even with state-of-the-art high-fidelity atmospheric and turbine models. A comprehensive computational study was undertaken with consideration of simulation methodology, parameter selection, and mesh refinement on atmospheric, turbine, and wake QoIs to identify capability gaps in the validation process. For neutral atmospheric boundary layer conditions, the massively parallel large eddy simulation (LES) code Nalu-Wind was used to produce high-fidelity computations for experimental validation using high-quality meteorological, turbine, and wake measurement data collected at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at Texas Tech University's National Wind Institute. The wake analysis showed the simulated lidar model implemented in Nalu-Wind was successful at capturing wake profile trends observed in the experimental lidar data.
Develop, verify, and document model capabilities sufficient for comparing field wake measurements from SWiFT with synthetic lidar wake measurements from Nalu-Wind (hereafter referred to as `Nalu').
We analyze SpinnerLidar measurements of a single wind turbine wake collected at the SWiFT facility and investigate the wake behaviour under different atmospheric turbulence conditions. The derived wake characteristics include the wake deficit, wake-added turbulence and wake meandering in both lateral and vertical directions. The atmospheric stability at the site is characterized using observations from a sonic anemometer. A wake-tracking technique, based on a bi-variate Gaussian wake shape, is implemented to monitor the wake center dis-placements in time to derive quasi-steady wake deficit and turbulence profiles in a meandering frame of reference. The analysis demonstrates the influence of atmospheric stability on the wake behaviour; a faster wake deficit recovery and a higher level of turbulence mixing are observed under unstable compared to stable atmospheric conditions. We also show that the wake me-andering is driven by large-scale turbulence structures, which are characterized by increasing energy content as the atmosphere becomes more unstable. These results suggest the suitability of the dataset for wake-model calibration and provide statistics of the wake deficit, turbulence levels, and meandering, which are key aspects for load validation studies.
Work has begun towards model validation of wake dynamics for the large-eddy simulation (LES) code Nalu-Wind in the context of research-scale wind turbines in a neutral atmospheric boundary layer (ABL). Interest is particularly directed at the structures and spectra which are influential for wake recovery and downstream turbine loading. This initial work is to determine the feasibility of using nacelle-mounted, continuous-wave lidars to measure and validate wake physics via comparisons of full actuator line simulation results with those obtained from a virtual lidar embedded within the computational domain. Analyses are conducted on the dominant large-scale flow structures via proper orthogonal decomposition (POD) and on the various scales of wake-added turbulence through spectral comparisons. The virtual lidar adequately reproduces spatial structures and energies compared to the full simulation results. Correction of the higher-frequency turbulence spectra for volume-averaging attenuation was most successful at locations where mean gradients were not severe. The results of this work will aid the design of experiments for validation of high-fidelity wake models.
An uncertainty quantification technique for nacelle-mounted lidar is developed that extends conventional error analyses to precisely account for residual uncertainty due to observed non-ideal features in processed Doppler lidar spectra. The technique is applied after quality assurance/quality control (QAQC) processing to quantify residual error, both bias and random, from solid-body interference, shot noise, and any additional uncertainty introduced to the data from the QAQC process itself. The approach follows from the one-time construction of a high-dimensional parametric database of synthetic lidar spectra and subsequent processing with an existing QAQC technique. A model of the correspondence between the spectral shape and the associated residual errors due to non-ideal features is then developed for quantities of interest (QOIs) including the geometric median and spectral standard deviation of line-of-sight velocity. The model is preliminarily implemented within a neural network framework that is then applied in post-processing to sample returns from a DTU SpinnerLidar. The initial analysis uncovers the effects of specific sources of uncertainty in the context of both individual spectra and full-field maps of the measurement domain. The technique is described in terms of application to continuous wave (CW) lidar, though it is also relevant to pulsed lidar.
Nalu-Wind simulations of the neutral inflow Scaled Wind Farm Technology (SWiFT) benchmark were used to analyze which quantities of interest within the wind turbine wake and surrounding control volume are important in performing a momentum deficit analysis of the wind turbine thrust force. The necessary quantities of interest to conduct a full Reynolds-Averaged Navier-Stokes (RANS) formulation analysis were extracted along the control volume surfaces within the Nalu simulation domain over a 10 minute period. The thrust force calculated within the wake from two to eight diameters downstream using the control volume surfaces and the full RANS approach matched the thrust force that the wind turbine applied to the flowfield. A simplified one-dimension momentum analysis was included to determine if the inflow and wake velocities typically acquired during field campaigns would be sufficient to perform a momentum deficit analysis within a wind turbine wake. The one-dimensional analysis resulted in a 70% difference relative to the coefficient of thrust (Ct ) determined by the full RANS method at 2D downstream and a 40% difference from 5D to 8D, where D is the diameter of the turbine. This suggests that the quantities typically captured during field campaigns are insufficient to perform an accurate momentum deficit analysis unless streamwise pressure distribution is acquired, which reduced the relative difference to less than 10% for this particular atmospheric inflow.
Sandia National Laboratories and the National Renewable Energy Laboratory conducted a wake-steering field experiment at the Scaled Wind Farm Technology facility. The campaign included the use of two highly instrumented V27 wind turbines, an upstream meteorological tower, and high-resolution wake measurements of the upstream wind turbine using a customized scanning SpinnerLidar from the Technical University of Denmark. The present work details how the SpinnerLidar data uploaded to the Department of Energy Atmosphere to Electrons Data Archive and Portal was processed, quality controlled and assured to guarantee high data availability with the removal of invalid measurements. A multidimensional approach to processing the SpinnerLidar Doppler spectra was developed based on matching erroneous measurements within the two-dimensional lidar scan with patterns inside the multidimensional lidar Doppler spectra. This method allows image processing techniques to be used to remove regions of the Doppler spectra that are contaminated by hard targets and isolate the velocity field of interest, allowing more accurate line-of-sight velocity measurements and enabling the estimation of the turbulence of the line-of-sight velocities within the probe volume.
Power production of the turbines at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at the Texas Tech University’s National Wind Institute Research Center was measured experimentally and simulated for neutral atmospheric boundary layer operating conditions. Two V27 wind turbines were aligned in series with the dominant wind direction, and the upwind turbine was yawed to investigate the impact of wake steering on the downwind turbine. Two conditions were investigated, including that of the leading turbine operating alone and both turbines operating in series. The field measurements include meteorological evaluation tower (MET) data and light detection and ranging (lidar) data. Computations were performed by coupling large eddy simulations (LES) in the three-dimensional, transient code Nalu-Wind with engineering actuator line models of the turbines from OpenFAST. The simulations consist of a coarse precursor without the turbines to set up an atmospheric boundary layer inflow followed by a simulation with refinement near the turbines. Good agreement between simulations and field data are shown. These results demonstrate that Nalu-Wind holds the promise for the prediction of wind plant power and loads for a range of yaw conditions.
Sandia National Laboratories and the National Renewable Energy Laboratory conducted a wake-steering field campaign at the Scaled Wind Farm Technology facility. The campaign included the use of two highly instrumented V27 wind turbines, an upstream met tower, and high-resolution wake measurements of the upstream wind turbine using a customized scanning lidar from the Technical University of Denmark (DTU). The present work investigates the impact of the upstream wake on the downstream turbine power and blade loads as the wake swept across the rotor in various waked conditions. The wake position was tracked using the DTU SpinnerLidar and synchronized to the met tower and turbine sensors. Fully and partially waked conditions reduced the power output and increased the fatigue loading on the downstream wind turbine. Partial wake impingement was found to result in a 10% increase in fatigue loading over the fully waked condition. Rotational sampling of the blade root bending moments revealed that the fatigue damage accrued during full turbine waking, was primarily caused by turbulence within the wake rather than velocity shear, while the partially waked turbine experienced a large 1-per revolution fatigue due to shear. The development of a power to fatigue load metric curve indicated the wake positions where shifting the wake has the most benefit for the waked turbine.
A method for measuring wake and aerodynamic properties of a wind turbine with reduced error based on simulated lidar measurements is proposed. A scanning lidar measures air velocity scalar projected onto its line of sight. However, line of sight is rarely parallel to the velocities of interest. The line of sight projection correction technique showed reduced axial velocity error for a simple wake model. Next, an analysis based on large-eddy simulations of a 27 m diameter wind turbine was used to more accurately assess the projection correction technique in a turbulent wake. During the simulation, flow behind the turbine is sampled with a nacelle mounted virtual lidar matching the scanning trajectory and sampling frequency of the DTU SpinnerLidar. The axial velocity, axial induction, freestream wind speed, thrust coefficient, and power coefficient are calculated from virtual lidar measurements using two different estimates of the flow: line of sight velocity without correction, and line of sight with projection correction. The flow field is assumed to be constant during one complete scan of the lidar field of view, and the average wind direction is assumed to be equal to the instantaneous wind direction at the lidar measurement location for the projection correction. Despite these assumptions, results indicate that all wake and aerodynamic quantity error is reduced significantly by using the projection correction technique; axial velocity error is reduced on average from 7.4% to 2.8%.
Herges, Thomas H.; Maniaci, D.C.; Naughton, B.T.; Mikkelsen, T.; Sjöholm, M.
High-resolution lidar wake measurements are part of an ongoing field campaign being conducted at the Scaled Wind Farm Technology facility by Sandia National Laboratories and the National Renewable Energy Laboratory using a customized scanning lidar from the Technical University of Denmark. One of the primary objectives is to collect experimental data to improve the predictive capability of wind plant computational models to represent the response of the turbine wake to varying inflow conditions and turbine operating states. The present work summarizes the experimental setup and illustrates several wake measurement example cases. The cases focus on demonstrating the impact of the atmospheric conditions on the wake shape and position, and exhibit a sample of the data that has been made public through the Department of Energy Atmosphere to Electrons Data Archive and Portal.
This report presents the objectives, configuration, procedures, reporting , roles , and responsibilities and subsequent results for the field demonstration of the Sandia Wake Imaging System (SWIS) at the Sandia Scaled Wind Farm Technology (SWiFT) facility near Lubbock, Texas in June and July 2015.