Publications

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The Department of Energy Atmosphere to Electrons (A2e) initiative has undertaken an experimental planning process for a validation directed program and an experimental planning process directed at improving simulations of wind plant performance. The validation process has been divided into two main sections: Integrated Program Planning, and Integrated Experiment and Model Planning and Execution. This document covers the Integrated Program Planning process in detail as it has been applied to the validation and assessment of models of various fidelity to predict wind plant performance. Three main parts of this process are presented in this document: the Phenomenon Identification and Ranking Table, the Validation Hierarchy, and the Prioritized Phenomenon and Experiment Mapping table. The document concludes with a description of validation program process next steps, which includes the planning and execution of integrated experiment and model campaigns

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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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Annual Milestone (joint NREL/SNL): Create and disseminate documentation that compares the Nalu and SOWFA codes for actuator-line-based wind farm models, including the demonstration of the Windpark Egmond aan Zee (OWEZ). Comparisons will include simulation results for the same cases, assessing computational speed and scalability.

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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.

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One explanation for wind turbine power degradation is insect roughness. Historical studies on insect-induced power degradation have used simulation methods which are either un- representative of actual insect roughness or too costly or time-consuming to be applied to wide-scale testing. Furthermore, the role of airfoil geometry in determining the relations between insect impingement locations and roughness sensitivity has not been studied. To link the effects of airfoil geometry, insect impingement locations, and roughness sensitivity, a simulation code was written to determine representative insect collection patterns for different airfoil shapes. Insect collection pattern data was then used to simulate roughness on an NREL S814 airfoil that was tested in a wind tunnel at Reynolds numbers between 1.6 x 10⁶ and 4.0 x 10⁶. Results are compared to previous tests of a NACA 63₃ -418 airfoil. Increasing roughness height and density results in decreased maximum lift, lift curve slope, and lift-to-drag ratio. Increasing roughness height, density, or Reynolds number results in earlier bypass transition, with critical roughness Reynolds numbers lying within the historical range. Increased roughness sensitivity on the 25% thick NREL S814 is observed compared to the 18% thick NACA 63 3 -418. Blade-element-momentum analysis was used to calculate annual energy production losses of 4.9% and 6.8% for a NACA 63₃ -418 turbine and an NREL S814 turbine, respectively, operating with 200 μm roughness. These compare well to historical field measurements.

The impact of surface roughness on flows over aerodynamically designed surfaces is of interested in a number of different fields. It has long been known the surface roughness will likely accelerate the laminar- turbulent transition process by creating additional disturbances in the boundary layer. However, there are very few tools available to predict the effects surface roughness will have on boundary layer flow. There are numerous implications of the premature appearance of a turbulent boundary layer. Increases in local skin friction, boundary layer thickness, and turbulent mixing can impact global flow properties compounding the effects of surface roughness. With this motivation, an investigation into the effects of surface roughness on boundary layer transition has been conducted. The effort involved both an extensive experimental campaign, and the development of a high fidelity roughness model implemented in a R ANS solver. Vast a mounts of experimental data was generated at the Texas A&M Oran W. Nicks Low Speed Wind Tunnel for the calibration and validation of the roughness model described in this work, as well as future efforts. The present work focuses on the development of the computational model including a description of the calibration process. The primary methodology presented introduces a scalar field variable and associated transport equation that interacts with a correlation based transition model. The additional equation allows for non-local effects of surface roughness to be accounted for downstream of rough wall sections while maintaining a "local" formulation. The scalar field is determined through a boundary condition function that has been calibrated to flat plate cases with sand grain roughness. The model was initially tested on a NACA 0012 airfoil with roughness strips applied to the leading edge. Further calibration of the roughness model was performed using results from the companion experimental study on a NACA 63₃ -418 airfoil. The refined model demonstrates favorable agreement predicting changes to the transition location, as well as drag, for a number of different leading edge roughness configurations on the NACA 63₃-418 airfoil. Additional tests were conducted on a thicker S814 airfoil, with similar roughness configurations to the NACA 63₃-418. Simulations run with the roughness model compare favorably with the results obtained in the experimental study for both airfoils.