This article introduces the first benchmark study within the International Energy Agency Wind Task 57 framework, focusing on wind plant wakes. Leveraging data from the American WAKE ExperimeNt (AWAKEN), the benchmark aims to assess the accuracy of simulation tools in modeling wind plant wakes and their impact on the downstream flow under diverse inflow conditions. The AWAKEN field campaign, conducted in Oklahoma from 2022 to 2024, provides unprecedented observations of wind plant-atmosphere interactions, thus offering a large dataset to validate numerical models of different complexity. The benchmark will include three phases—code calibration, blind comparison, and iteration—allowing participants to refine their numerical models based on the feedback from the benchmark team. This article describes the benchmark case study selected from observations providing details on atmospheric conditions, wake evidence, and wind turbine operation. The benchmark’s structure and timeline, along with the expected publication of results, are discussed as well. This collaborative effort aims to enhance the accuracy of wind plant wake simulations, thus contributing to the improvement of wind energy production estimates.

Towards the ongoing work of improving the capability of flow modeling within and around wind plants, an onshore model validation benchmark campaign is underway based on a field experiment involving multiple wind plants in Oklahoma, U.S.A. Dual-Doppler radar is being leveraged to provide flowfield information for the benchmarking owing to the unparalleled capability of such radar to capture minute-by-minute horizontal wind fields over a scale of tens of kilometers. However, dual-Doppler radar exhibits sampling artifacts that must be considered during model validation, and these are due to probe-volume averaging, coarse azimuthal/elevational resolution, non-ideal stereo angles, and coarse temporal sampling. Such sources of error in radar-reconstructed flowfields can be quantified using virtual radar sampling in the high-fidelity simulation environment (i.e., large-eddy simulation (LES)) where the true velocity field is known with confidence, and this is the uncertainty quantification approach adopted in this article. We leverage a virtual radar tool designed to replicate the specific sampling strategy of the X-band dual-Doppler instrument installed in the field campaign. This tool is featured in LES of an expansive 100 km by 100 km region of Oklahoma including hundreds of wind turbines modeled as actuator disks. In agreement with the sampling principles of radar, the results show that large-scale flow structures are qualitatively well-resolved by the instrument, though more simulation time and analysis are needed to determine the accuracy of the radar’s integral lengthscale estimates. At the turbine scale, the radar struggles to capture all of the features of the turbine wakes. The process of probe-volume averaging, as well as the subsequent interpolation to a Cartesian grid, biases the reconstruction of the peak near-wake deficit by an average of 2 m/s, or around 20% of the freestream velocity. Errors in this quantity of interest, as well as one characterizing the magnitude of the free-flow wind speed around the wakes, are found to be sensitive to the radar beam-crossing angle and beam range.

This paper provides a summary of planning work for experiments that will be necessary to address the long-term model validation needs required to meet offshore wind energy deployment goals. Conceptual experiments are identified and laid out in a validation hierarchy for both wind turbine and wind plant applications. Instrumentation needs that will be required for the offshore validation experiments to be impactful are then listed. The document concludes with a nominal vision for how these experiments can be accomplished.

Presented in this work is a novel approach to estimate absolute lateral wake center position on the rotor plane of a waked turbine using turbine load and operating state information. The approach formulates the estimation of the absolute lateral wake position as an inverse dynamics problem and utilizes a recurrent neural network to model the inverse mapping between the wake center position and select turbine output channels. The technique is validated on experimental data collected from experiments at the Scaled Wind Farm Technology (SWiFT) facility and numerical simulations of the site in the wind farm simulator FAST.Farm. Estimator performance and analysis of optimal conditions for estimation are discussed.

Wind lidar technology is being extensively used in utility-scale wind turbine wake measurements because of its capabilities to capture the dominant structures of interest in the near-wake region. However, wind lidar devices provide only line-of-sight (LOS) measurements, and retrieval of horizontal wind speed from the LOS measurements of a single lidar in the wind turbine wake could introduce errors due to the assumptions used in the retrieval process. With that in mind, the goals of this paper are to estimate the errors associated with the retrieval processes and provide guidelines for best practices. To achieve these goals, virtual lidar samples are obtained with large-eddy simulations (LES) by sampling the flow field with a model of the Technical University of Denmark (DTU) SpinnerLidar. LES is chosen to simulate the neutral and unstable atmospheric boundary layer (ABL), a benchmark case of the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) Task 31. The precursor ABL simulation is validated with the reference data provided by the IEA Wind Task 31 benchmark case. The data from the precursor simulation is used to drive a simulation of a wind turbine submerged in the ABL. A weighting function is applied to the LES data to mimic the real DTU SpinnerLidar measurements. In the end, the virtual lidar data are processed and compared with the original LES data to estimate the errors. Results show that lidar captures mean ABL profile without any noticeable error, whereas turbulence intensity is underpredicted ≈ 15% at hub height for the neutral case. Volume averaging by the lidar technology has a significant effect in the shear layer of a wind turbine wake.

This paper describes a detailed field-test campaign to investigate yaw-based wake steering. In yaw-based wake steering, an upstream turbine intentionally misaligns its yaw with respect to the inflow to deflect its wake away from a downstream turbine, with the goal of increasing total power production. In the first phase, a nacelle-mounted scanning lidar was used to verify wake deflection of a misaligned turbine and calibrate wake deflection models. In the second phase, these models were used within a yaw controller to achieve a desired wake deflection. This paper details the experimental design and setup. All data collected as part of this field experiment will be archived and made available to the public via the U.S. Department of Energy's Atmosphere to Electrons Data Archive and Portal.

We describe the process of using large-eddy simulations of wind turbine wake flow to help design a wake measurement campaign. The main goal of the experiment is to measure wakes and wake deflection that result from intentional yaw misalignment under a variety of atmospheric conditions at the Scaled Wind Farm Technology facility operated by Sandia National Laboratories in Lubbock, Texas. Prior simulation studies have shown that wake deflection may be used for wind-plant control that maximizes plant power output. In this study, simulations are performed to characterize wake deflection and general behavior before the experiment is performed to ensure better upfront planning. Beyond characterizing the expected wake behavior, we also use the large-eddy simulation to test a virtual version of the lidar we plan to use to measure the wake and better understand our lidar scan strategy options. This work is an excellent example of a "simulation-in-the-loop" measurement campaign.