Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Many factors that influence the effect of leading edge erosion on annual energy production are uncertain, such as the time to initiation, damage growth rate, the blade design, operational conditions, and atmospheric conditions. In this work, we explore how the uncertain parameters that drive leading edge erosion impact wind turbine power performance using a combination of uncertainty quantification and wind turbine modelling tools, at both low and medium fidelity. Results will include the predicted effect of erosion on several example wind plant sites for representative ranges of wind turbine designs, with a goal of helping wind plant operators better decide mitigation strategies.

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.

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.

Abstract not provided.

Abstract not provided.

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.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Subscale wind turbines can be installed in the field for the development of wind technologies, for which the blade aerodynamics can be designed in a way similar to that of a full-scale wind turbine. However, it is not clear whether the wake of a subscale turbine, which is located closer to the ground and faces different incoming turbulence, is also similar to that of a full-scale wind turbine. In this work we investigate the wakes from a full-scale wind turbine of rotor diameter 80 m and a subscale wind turbine of rotor diameter of 27 m using large-eddy simulation with the turbine blades and nacelle modeled using actuator surface models. The blade aerodynamics of the two turbines are the same. In the simulations, the two turbines also face the same turbulent boundary inflows. The computed results show differences between the two turbines for both velocity deficits and turbine-added turbulence kinetic energy. Such differences are further analyzed by examining the mean kinetic energy equation.

The development of a next generation high-fidelity modeling code for wind plant applications is one of the central focus areas of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative. The code is based on a highly scalable framework, currently called Nalu-Wind. One key aspect of the model development is a coordinated formal validation program undertaken specifically to establish the predictive capability of Nalu-Wind for wind plant applications. The purpose of this document is to define the verification and validation (V&V) plan for the A2e high-fidelity modeling capability. It summarizes the V&V framework, identifies code capability users and use cases, describes model validation needs, and presents a timeline to meet those needs.

Abstract not provided.

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.

Abstract not provided.

Abstract not provided.

Milestone Description: Enhance Nalu-Wind's actuator disc model through hardening, documenting, stress-testing, verifying, and validating. Existing workflows will be improved by reducing the data output stream, and by making the analysis capabilities more modular and generally better. These model capabilities are needed by other A2e areas, namely Wake Dynamics, AWAKEN, and VV&UQ.

Wind energy is stochastic in nature; the prediction of aerodynamic quantities and loads relevant to wind energy applications involves modeling the interaction of a range of physics over many scales for many different cases. These predictions require a range of model fidelity, as predictive models that include the interaction of atmospheric and wind turbine wake physics can take weeks to solve on institutional high performance computing systems. In order to quantify the uncertainty in predictions of wind energy quantities with multiple models, researchers at Sandia National Laboratories have applied Multilevel-Multifidelity methods. A demonstration study was completed using simulations of a NREL 5MW rotor in an atmospheric boundary layer with wake interaction. The flow was simulated with two models of disparate fidelity; an actuator line wind plant large-eddy scale model, Nalu, using several mesh resolutions in combination with a lower fidelity model, OpenFAST. Uncertainties in the flow conditions and actuator forces were propagated through the model using Monte Carlo sampling to estimate the velocity defect in the wake and forces on the rotor. Coarse-mesh simulations were leveraged along with the lower-fidelity flow model to reduce the variance of the estimator, and the resulting Multilevel-Multifidelity strategy demonstrated a substantial improvement in estimator efficiency compared to the standard Monte Carlo method.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

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

Abstract not provided.

Abstract not provided.

Abstract not provided.

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.

Abstract not provided.

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.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

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.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

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.