Do Wind Turbines Pose Roll Hazards to Light Aircraft?

Abstract. Wind energy accounted for 5.6 % of all electricity generation in the United States in 2016. Much of this development has occurred in rural locations, where open spaces favorable for harnessing wind also serve general aviation airports. As such, nearly 40 % of all U.S. wind turbines exist within 10 km of a small airport. Wind turbines generate electricity by extracting momentum from the atmosphere, creating downwind wakes characterized by wind-speed deficits and increased turbulence. Recently, the concern that turbine wakes pose hazards for small aircraft has been used to limit wind farm development. Herein, we assess roll hazards to small aircraft using large-eddy simulations of a utility-scale turbine wake. Wind-generated rolling moments on hypothetical aircraft transecting the wake in stably and neutrally stratified conditions are calculated. In both cases, only 0.001 % of rolling moments experienced by hypothetical aircraft during down-wake and cross-wake transects lead to an increased risk of rolling.

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Do wind turbines pose roll hazards to light aircraft?

Wind Energy Science ◽

10.5194/wes-3-833-2018 ◽

2018 ◽

Vol 3 (2) ◽

pp. 833-843 ◽

Cited By ~ 5

Author(s):

Jessica M. Tomaszewski ◽

Julie K. Lundquist ◽

Matthew J. Churchfield ◽

Patrick J. Moriarty

Keyword(s):

United States ◽

Wind Turbines ◽

Wind Farm ◽

The United States ◽

General Aviation ◽

Case Scenario ◽

Worst Case ◽

Stable Case ◽

Increased Risk ◽

Small Aircraft

Abstract. Wind energy accounted for 5.6 % of all electricity generation in the United States in 2016. Much of this development has occurred in rural locations, where open spaces favorable for harnessing wind also serve general aviation airports. As such, nearly 40 % of all United States wind turbines exist within 10 km of a small airport. Wind turbines generate electricity by extracting momentum from the atmosphere, creating downwind wakes characterized by wind-speed deficits and increased turbulence. Recently, the concern that turbine wakes pose hazards for small aircraft has been used to limit wind-farm development. Herein, we assess roll hazards to small aircraft using large-eddy simulations (LES) of a utility-scale turbine wake. Wind-generated lift forces and subsequent rolling moments are calculated for hypothetical aircraft transecting the wake in various orientations. Stably and neutrally stratified cases are explored, with the stable case presenting a possible worst-case scenario due to longer-persisting wakes permitted by lower ambient turbulence. In both cases, only 0.001 % of rolling moments experienced by hypothetical aircraft during down-wake and cross-wake transects lead to an increased risk of rolling.

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Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks

Energies ◽

10.3390/en15010041 ◽

2021 ◽

Vol 15 (1) ◽

pp. 41

Author(s):

Zexia Zhang ◽

Christian Santoni ◽

Thomas Herges ◽

Fotis Sotiropoulos ◽

Ali Khosronejad

Keyword(s):

Wind Turbines ◽

Wind Farm ◽

Power Production ◽

Velocity Fields ◽

Large Eddy Simulations ◽

Wake Flow ◽

Surface Model ◽

Flow Conditions ◽

Large Eddy ◽

Turbine Wake

A convolutional neural network (CNN) autoencoder model has been developed to generate 3D realizations of time-averaged velocity in the wake of the wind turbines at the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility. Large-eddy simulations (LES) of the SWiFT site are conducted using an actuator surface model to simulate the turbine structures to produce training and validation datasets of the CNN. The simulations are validated using the SpinnerLidar measurements of turbine wakes at the SWiFT site and the instantaneous and time-averaged velocity fields from the training LES are used to train the CNN. The trained CNN is then applied to predict 3D realizations of time-averaged velocity in the wake of the SWiFT turbines under flow conditions different than those for which the CNN was trained. LES results for the validation cases are used to evaluate the performance of the CNN predictions. Comparing the validation LES results and CNN predictions, we show that the developed CNN autoencoder model holds great potential for predicting time-averaged flow fields and the power production of wind turbines while being several orders of magnitude computationally more efficient than LES.

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Utility-Scale Wind Turbine Wake Characterization Using Nacelle-Based Long-Range Scanning Lidar

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-13-00218.1 ◽

2014 ◽

Vol 31 (7) ◽

pp. 1529-1539 ◽

Cited By ~ 25

Author(s):

Matthew L. Aitken ◽

Julie K. Lundquist

Keyword(s):

Wind Turbine ◽

Long Range ◽

Wind Farm ◽

Thrust Coefficient ◽

Velocity Deficit ◽

Scanning Lidar ◽

Range Scanning ◽

Utility Scale ◽

Turbine Wake ◽

Wind Turbine Wake

Abstract To facilitate the optimization of turbine spacing at modern wind farms, computational simulations of wake effects must be validated through comparison with full-scale field measurements of wakes from utility-scale turbines operating in the real atmosphere. Scanning remote sensors are particularly well suited for this objective, as they can sample wind fields over large areas at high temporal and spatial resolutions. Although ground-based systems are useful, the vantage point from the nacelle is favorable in that scans can more consistently transect the central part of the wake. To the best of the authors’ knowledge, the work described here represents the first analysis in the published literature of a utility-scale wind turbine wake using nacelle-based long-range scanning lidar. The results presented are of a field experiment conducted in the fall of 2011 at a wind farm in the western United States, quantifying wake attributes such as the velocity deficit, centerline location, and wake width. Notable findings include a high average velocity deficit, decreasing from 60% at a downwind distance x of 1.8 rotor diameters (D) to 40% at x = 6D, resulting from a low average wind speed and therefore a high average turbine thrust coefficient. Moreover, the wake width was measured to expand from 1.5D at x = 1.8D to 2.5D at x = 6D. Both the wake growth rate and the amplitude of wake meandering were observed to be greater for high ambient turbulence intensity and daytime conditions as compared to low turbulence and nocturnal conditions.

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Wind-farm layout optimisation using a hybrid Jensen–LES approach

10.5194/wes-2016-15 ◽

2016 ◽

Cited By ~ 1

Author(s):

Vahid S. Bokharaie ◽

Pieter Bauweraerts ◽

Johan Meyers

Keyword(s):

Wind Turbines ◽

Energy Production ◽

Wind Farm ◽

Rectangular Domain ◽

Hybrid Combination ◽

Wake Interaction ◽

Large Eddy ◽

Wind Distribution ◽

Online Tuning

Abstract. Given a wind-farm with known dimensions and number of wind-turbines, we try to find the optimum positioning of wind-turbines that maximises wind-farm energy production. In practise, given that optimisation has to be performed for many wind directions and taking into account the yearly wind distribution, such an optimisation is computationally only feasible using fast engineering wake models such as, e.g., the Jensen model. These models are known to have accuracy issues, in particular since their representation of wake interaction is very simple. In the present work, we propose an optimisation approach that is based on a hybrid combination of Large-Eddy Simulations (LES) and the Jensen model, in which optimisation is mainly performed using the Jensen model, and LES is used at a few points only during optimisation for online tuning of the wake-expansion coefficient in the Jensen model, and for validation of the results. An optimisation case study is considered, in which the placement of 30 turbines in a 4 by 3 km rectangular domain is optimised in a neutral atmospheric boundary layer. Both optimisation for single wind direction, and multiple wind directions are discussed.

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Research on the Power Capture and Wake Characteristics of a Wind Turbine Based on a Modified Actuator Line Model

Energies ◽

10.3390/en15010282 ◽

2022 ◽

Vol 15 (1) ◽

pp. 282

Author(s):

Feifei Xue ◽

Heping Duan ◽

Chang Xu ◽

Xingxing Han ◽

Yanqing Shangguan ◽

...

Keyword(s):

Wind Turbine ◽

Output Power ◽

Wind Turbines ◽

Wind Farm ◽

Three Dimensional ◽

Wind Farms ◽

Line Model ◽

Eddy Simulation ◽

Large Eddy ◽

Actuator Line Model

On a wind farm, the wake has an important impact on the performance of the wind turbines. For example, the wake of an upstream wind turbine affects the blade load and output power of the downstream wind turbine. In this paper, a modified actuator line model with blade tips, root loss, and an airfoil three-dimensional delayed stall was revised. This full-scale modified actuator line model with blades, nacelles, and towers, was combined with a Large Eddy Simulation, and then applied and validated based on an analysis of wind turbine wakes in wind farms. The modified actuator line model was verified using an experimental wind turbine. Subsequently, numerical simulations were conducted on two NREL 5 MW wind turbines with different staggered spacing to study the effect of the staggered spacing on the characteristics of wind turbines. The results show that the output power of the upstream turbine stabilized at 5.9 MW, and the output power of the downstream turbine increased. When the staggered spacing is R and 1.5R, both the power and thrust of the downstream turbine are severely reduced. However, the length of the peaks was significantly longer, which resulted in a long-term unstable power output. As the staggered spacing increased, the velocity in the central near wake of the downstream turbine also increased, and the recovery speed at the threshold of the wake slowed down. The modified actuator line model described herein can be used for the numerical simulation of wakes in wind farms.

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Wind-farm layout optimisation using a hybrid Jensen–LES approach

Wind Energy Science ◽

10.5194/wes-1-311-2016 ◽

2016 ◽

Vol 1 (2) ◽

pp. 311-325 ◽

Cited By ~ 7

Author(s):

Vahid S. Bokharaie ◽

Pieter Bauweraerts ◽

Johan Meyers

Keyword(s):

Wind Turbines ◽

Energy Production ◽

Wind Farm ◽

Rectangular Domain ◽

Eddy Simulation ◽

Wake Interaction ◽

Large Eddy ◽

Wind Distribution ◽

Online Tuning

Abstract. Given a wind farm with known dimensions and number of wind turbines, we try to find the optimum positioning of wind turbines that maximises wind-farm energy production. In practice, given that optimisation has to be performed for many wind directions, and taking into account the yearly wind distribution, such an optimisation is computationally only feasible using fast engineering wake models such as the Jensen model. These models are known to have accuracy issues, in particular since their representation of wake interaction is very simple. In the present work, we propose an optimisation approach that is based on a hybrid combination of large-eddy simulation (LES) and the Jensen model; in this approach, optimisation is mainly performed using the Jensen model, and LES is used at a few points only during optimisation for online tuning of the wake-expansion coefficient in the Jensen model, as well as for validation of the results. An optimisation case study is considered, in which the placement of 30 turbines in a 4 km by 3 km rectangular domain is optimised in a neutral atmospheric boundary layer. Optimisation for both a single wind direction and multiple wind directions is discussed.

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A continuously updated, geospatially rectified database of utility-scale wind turbines in the United States

Scientific Data ◽

10.1038/s41597-020-0353-6 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 4

Author(s):

Joseph T. Rand ◽

Louisa A. Kramer ◽

Christopher P. Garrity ◽

Ben D. Hoen ◽

Jay E. Diffendorfer ◽

...

Keyword(s):

United States ◽

Wind Turbines ◽

The United States ◽

Utility Scale

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Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

10.5194/wes-2018-6 ◽

2018 ◽

Cited By ~ 3

Author(s):

Jennifer Annoni ◽

Paul Fleming ◽

Andrew Scholbrock ◽

Jason Roadman ◽

Scott Dana ◽

...

Keyword(s):

Wind Farm ◽

Lidar Data ◽

National Renewable Energy Laboratory ◽

Atmospheric Conditions ◽

Modeling Tools ◽

Lidar Measurements ◽

Utility Scale ◽

The Impact ◽

Turbine Wake ◽

Good Agreement

Abstract. Wind turbines in a wind farm operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Wind farm controls can be used to increase power production or reduce overall structural loads by properly coordinating turbines. One wind farm control strategy that is addressed in literature is known as wake steering, wherein upstream turbines operate in yaw misaligned conditions to redirect their wakes away from downstream turbines. The National Renewable Energy Laboratory (NREL) in Golden, CO conducted a demonstration of wake steering on a single utility-scale turbine. In this campaign, the turbine was operated at various yaw misalignment setpoints while a lidar mounted on the nacelle scanned five downstream distances. The lidar measurements were combined with turbine data, as well as measurements of the inflow made by a highly instrumented meteorological mast upstream. The full-scale measurements are used to validate controls-oriented tools, including wind turbine wake models, used for wind farm controls and optimization. This paper presents a quantitative comparison of the lidar data and controls-oriented wake models under different atmospheric conditions and turbine operation. The results show good agreement between the lidar data and the models under these different conditions.

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Documenting Wind Speed and Power Deficits behind a Utility-Scale Wind Turbine

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-12-0145.1 ◽

2013 ◽

Vol 52 (1) ◽

pp. 39-46 ◽

Cited By ~ 32

Author(s):

Brian D. Hirth ◽

John L. Schroeder

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Farm ◽

Horizontal Wind ◽

Horizontal Wind Speed ◽

Large Variability ◽

Cost Of Energy ◽

The Mean ◽

Utility Scale ◽

Turbine Wake

AbstractHigh-spatial-and-temporal-resolution radial velocity measurements surrounding a single utility-scale wind turbine were collected using the Texas Tech University Ka-band mobile research radars. The measurements were synthesized to construct the first known dual-Doppler analyses of the mean structure and variability of a single turbine wake. The observations revealed a wake length that subjectively exceeded 20 rotor diameters, which far exceeds the typically employed turbine spacing of 7–10 rotor diameters. The mean horizontal wind speed deficits found within the turbine wake region relative to the free streamflow were related to potential reductions in the available power for a downwind turbine. Mean wind speed reductions of 17.4% (14.8%) were found at 7 (10) rotor diameters downwind, corresponding to a potential power output reduction of 43.6% (38.2%). The wind speed deficits found within the wake also exhibit large variability over short time intervals; this variability would have an appreciable impact on the inflow of a downstream turbine. The full understanding and application of these newly collected data have the potential to alter current wind-farm design and layout practices and to affect the cost of energy.

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