Publications

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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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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.

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.

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Wind farm operators observe production deficits as machines age. Quantifying deterioration on individual components is difficult, but one potential explanation is accumulation of blade surface roughness. Historically, wind turbine airfoils were designed for lift to be insensitive to roughness by simulating roughness with trip strips. However, roughness was still shown to negatively affect performance. Furthermore, experiments illustrated distributed roughness is not properly simulated by trip strips. To understand how real-world roughness affects performance, field measurements of turbine-blade roughness were made and simulated on a NACA 63₃-418 airfoil in a wind tunnel. Insect roughness and paint chips were characterized and recreated as distributed roughness and a forward-facing step. Distributed roughness was tested in three heights and five density configurations. The model chord Reynolds number was varied between 0.8 to 4.8 x 10⁶. Measurements of lift, drag, pitching moment, and boundary-layer transition were completed. Results indicate minimal effect from paint-chip roughness. As distributed roughness height and density increase, lift-curve slope, maximum lift, and lift-to-drag ratio decrease. As Reynolds number increases, bypass transition occurs earlier. The critical roughness Reynolds number varies between 178 to 318, within the historical range. Little sensitivity to pressure gradient is observed. At a chord Reynolds number of 3.2 x 10⁶, the maximum lift-to-drag ratio decreases 40% for 140 m roughness, corresponding to a 2.3% loss in annual energy production. Simulated performance loss compares well to measured performance loss on an in-service wind turbine.

A computational investigation has been performed to better understand the impact of surface roughness on the flow over a contaminated surface. This report highlights the implementation and development of the roughness amplication model in the flow solver OVER FLOW-2. The model, originally proposed by Dassler, Kozulovic, and Fiala, introduces an additional scalar field roughness amplification quantity. This value is explicitly set at rough wall boundaries using surface roughness parameters and local flow quantities. The additional transport equation allows non-local effects of surface roughness to be accounted for downstream of rough sections. This roughness amplification variable is coupled with the Langtry-Menter model and used to modify the criteria for transition. Results from at plate test cases show good agreement with experimental transition behavior on the flow over varyings and grain roughness heights. Additional validation studies were performed on a NACA 0012 airfoil with leading edge roughness. The computationally predicted boundary layer development demonstrates good agreement with experimental results. New tests using varying roughness configurations have been carried out at the Texas A&M Oran W. Nicks Low Speed Wind Tunnel to provide further calibration of the roughness amplification method. An overview and preliminary results are provided of this concurrent experimental investigation.

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