Publications

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A large-scale numerical computation of five wind farms was performed as a part of the American WAKE experimeNt (AWAKEN). This high-fidelity computation used the ExaWind/AMR-Wind LES solver to simulate a 100 km × 100 km domain containing 541 turbines under unstable atmospheric conditions matching previous measurements. The turbines were represented by Joukowski and OpenFAST coupled actuator disk models. Results of this qualitative comparison illustrate the interactions of wind farms with large-scale ABL structures in the flow, as well as the extent of downstream wake penetration in the flow and blockage effects around wind farms.

Wind turbine applications that leverage nacelle-mounted Doppler lidar are hampered by several sources of uncertainty in the lidar measurement, affecting both bias and random errors. Two problems encountered especially for nacelle-mounted lidar are solid interference due to intersection of the line of sight with solid objects behind, within, or in front of the measurement volume and spectral noise due primarily to limited photon capture. These two uncertainties, especially that due to solid interference, can be reduced with high-fidelity retrieval techniques (i.e., including both quality assurance/quality control and subsequent parameter estimation). Our work compares three such techniques, including conventional thresholding, advanced filtering, and a novel application of supervised machine learning with ensemble neural networks, based on their ability to reduce uncertainty introduced by the two observed nonideal spectral features while keeping data availability high. The approach leverages data from a field experiment involving a continuous-wave (CW) SpinnerLidar from the Technical University of Denmark (DTU) that provided scans of a wide range of flows both unwaked and waked by a field turbine. Independent measurements from an adjacent meteorological tower within the sampling volume permit experimental validation of the instantaneous velocity uncertainty remaining after retrieval that stems from solid interference and strong spectral noise, which is a validation that has not been performed previously. All three methods perform similarly for non-interfered returns, but the advanced filtering and machine learning techniques perform better when solid interference is present, which allows them to produce overall standard deviations of error between 0.2 and 0.3ms-1, or a 1%-22% improvement versus the conventional thresholding technique, over the rotor height for the unwaked cases. Between the two improved techniques, the advanced filtering produces 3.5% higher overall data availability, while the machine learning offers a faster runtime (i.e., 1/41s to evaluate) that is therefore more commensurate with the requirements of real-time turbine control. The retrieval techniques are described in terms of application to CW lidar, though they are also relevant to pulsed lidar. Previous work by the authors (Brown and Herges, 2020) explored a novel attempt to quantify uncertainty in the output of a high-fidelity lidar retrieval technique using simulated lidar returns; this article provides true uncertainty quantification versus independent measurement and does so for three techniques rather than one.

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Nalu-Wind is part of the ExaWind code suite.

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In this study we performed detailed comparisons of numerical computations of single turbine wakes with measured data under neutral and stable atmospheric stability conditions. LES of the ABL inflow and turbine wakes are carried out using the ExaWind/Nalu-Wind simulation codes and compared with the equivalent measurements from the SWiFT research facility at wind speeds of 8.7 m/s and 4.8 m/s. The computed ABL inflow profiles and spectra showed good agreement with measured data in both stratification conditions, and the simulated turbine power and rotor speed also agreed with the measured turbine performance. A comparison of the downstream wake deficit profiles and turbulence distributions with lidar observations also showed that the LES computations generally captured the wake evolution in both neutral and stable conditions, with some possible discrepancies due to uncertainty around the turbine thrust and yaw settings. Finally, an examination of the downstream turbulence spectra showed that the peak frequency of the wake added turbulence corresponds to the characteristic wake shedding frequency, and we show that the turbulent integral lengthscale in the wake region also decreases significantly due to the presence of smaller turbulent features.

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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').

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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.

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.

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.

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.

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The American WAKE experimeNt (AWAKEN) is an international multi-institutional wind energy field campaign to better understand wake losses within operational wind farms. Wake interactions are among the least understood physical interactions in wind plants today, leading to unexpected power and profit losses. For example, Ørsted, the world’s largest offshore wind farm developer, recently announced a downward revision in energy estimates across their energy generation portfolio, primarily caused by underprediction of energy losses from wind farm blockage and wakes. In their announcement, they noted that the standard industry models used for their original energy estimates were inaccurate, and this was likely an industry-wide issue. To help further improve and validate wind plant models across scales from individual turbines as well as interfarm interactions between plants, new observations, such as those planned for AWAKEN, are critical. These model improvements will enable both improved layout and more optimal operation of wind farms with greater power production and improved reliability, ultimately leading to lower wind energy costs.

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Uncertainty is present in all wind energy problems of interest, but quantifying its impact for wind energy research, design and analysis applications often requires the collection of large ensembles of numerical simulations. These predictions require a range of model fidelity as predictive models, that include the interaction of atmospheric and wind turbine wake physics, can require weeks or months to solve on institutional high-performance computing systems. The need for these extremely expensive numerical simulations extends the computational resource requirements usually associated with uncertainty quantification analysis. To alleviate the computational burden, we propose here to adopt several Multilevel-Multifidelity sampling strategies that we compare for a realistic test case. A demonstration study was completed using simulations of a V27 turbine at Sandia National Laboratories’ SWiFT facility in a neutral atmospheric boundary layer. The flow was simulated with three models of disparate fidelity. OpenFAST with TurbSim was used stand-alone as the most computationally-efficient, lower-fidelity model. The computational fluid dynamics code Nalu-Wind was used for large eddy simulations with both medium-fidelity actuator disk and high-fidelity actuator line models, with various mesh resolutions. In an uncertainty quantification study, we considered five different turbine properties as random parameters: yaw offset, generator torque constant, collective blade pitch, gearbox efficiency and blade mass. For all quantities of interest, the Multilevel-Multifidelity estimators demonstrated greater efficiency compared to standard and multilevel Monte Carlo estimators.

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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.

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Sandia National Laboratories and the National Renewable Energy Laboratory conducted a field campaign at the Scaled Wind Farm Technology (SWiFT) Facility using a customized scanning lidar from the Technical University of Denmark. The results from this field campaign were used to assess the predictive capability of computational models to capture wake dissipation and wake trajectory downstream of a wind turbine. The present work used large-eddy simulations of the wind turbine wake and a virtual SpinnerLidar to quantify the uncertainty of wind turbine wake position due to the line-of-sight sampling and probe volume averaging effects of the lidar. The LES simulations were of the SWiFT wind turbine at both a 0° and 30° yaw offset with a stable inflow. The wake position extracted from the simulated lidar sampling had an uncertainty of 2.8 m and m as compared to the wake position extracted from the full velocity field with 0° and 30° yaw offset, respectively. The larger uncertainty in calculated wake position of the 30° yaw offset case was due to the increased angle of the wake position relative to the axial flow direction and the resulting decrease in the line-of-sight velocity relative the axial velocity.

A method of wake determination is proposed where a wind turbine rotor wake is defined as the downstream wind velocity deficit region surrounded by increased turbulence creating a local maxima ring of turbulence. This definition creates a non-arbitrary wake boundary and provides criteria for more consistent wake identification and tracking than other methods. The definitive boundary allows for the separation of atmospheric regions and wake regions, facilitating the characterization of wake flow as it differs from atmospheric flow even at high wind turbine yaw offsets and in unstable atmospheric conditions. This definition can also be used to evaluate the effect of wakes on downstream turbines or wake control technologies such as wake steering. The proposed wake tracking method was shown to be robust for measurements at different inflow conditions and matched well with simulated, known wake positions.

To reduce the levelized cost of wind energy, wind plant controllers are being developed to improve overall performance by increasing energy capture. Previous work has shown that increased energy capture is possible by steering the wake around downstream turbines; however, the impact this steering action has on the loading of the turbines continues to need further investigation with operational data to determine overall benefit. In this work, rotor loading data from a wind turbine operating a wake steering wind plant controller at the DOE/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) Facility is evaluated. Rotor loading was estimated from fiber optic strain sensors acquired with a state-of-the-art Micron Optics Hyperion interrogator mounted within the rotor and synchronized to the open-source SWiFT controller. A variety of ground and operational calibrations were performed to produce accurate measurements of rotor blade root strains. Time- and rotational-domain signal processing methods were used to estimate bending moment at the root of the rotor blade. Results indicate a correlation of wake steering angle with: one-perrevolution thrust moment amplitude, two-per-revolution torque phase, and three-perrevolution torque amplitude and phase. Future work is needed to fully explain the correlations observed in this work and study additional multi-variable relationships that may also exist.

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Sandia National Laboratories and the National Renewable Energy Laboratory conducted a field campaign at the Scaled Wind Farm Technology (SWiFT) Facility using a customized scanning lidar from the Technical University of Denmark. The results from this field campaign will support the validation of computational models to predict wake dissipation and wake trajectory offset downstream of a stand-alone wind turbine. In particular, regarding the effect of changes in the atmospheric boundary layer inflow state and turbine yaw offset. A key step in this validation process involves quantifying, and reducing, the uncertainty in the wake measurements. The present work summarizes the process that was used to calibrate the alignment of the lidar in order to reduce this source of uncertainty in the experimental data from the SWiFT field test.

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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.

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The Sandia Wake Imaging System is being developed to improve the spatial and temporal resolution capabilities of velocity measurements within the inflow and wake of wind turbines for the purpose of validating high-fidelity models. Doppler Global Velocimetry has been selected for use by the Sandia Wake Imaging System for its ability to scale to large field of view while still capturing instantaneous coherent structures. A set of field tests have been conducted over a 2 m × 2 m viewing area to investigate how well the system could scale to larger viewing areas applicable to planned wind turbine field testing. Successful velocity measurements of a surrogate 1 m diameter fan flow were achieved which compared favorably to independent sonic anemometer measurements. The system sensitivity limits were analyzed over a range of signal levels to calibrate radiometric modeling used to scale the system for deployment at the Scaled Wind Farm Technology facility operated by Sandia National Laboratories through U.S. Department of Energy funding. Measurement results indicate the system was near the receiver shot noise limit and that an instantaneous velocity measurement with a 1 m/s noise is in all likelihood possible on a 5 m × 5 m viewing region at the Scaled Wind Farm Technology facility.

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