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NASA NTRS1996

A Study of the Development of Steady and Periodic Unsteady Turbulent Wakes Through Curved Channels at Positive, Zero, and Negative Streamwise Pressure Gradients, Part 1

The turbomachinery wake flow development is largely influenced by streamline curvature and streamwise pressure gradient. The objective of this investigation is to study the development of the wake under the influence of streamline curvature and streamwise pressure gradient. The experimental investigation is carried out in two phases. The first phase involves the study of the wake behind a stationary circular cylinder (steady wake) in curved channels at positive, zero, and negative streamwise pressure gradients. The mean velocity and Reynolds stress components are measured using a X-hot-film probe. The measured quantities obtained in probe coordinates are transformed to a curvilinear coordinate system along the wake centerline and are presented in similarity coordinates. The results of the steady wakes suggest strong asymmetry in velocity and Reynolds stress components. However, the velocity defect profiles in similarity coordinates are almost symmetrical and follow the same distribution as the zero pressure gradient straight wake. The results of Reynolds stress distributions show higher values on the inner side of the wake than the outer side. Other quantities, including the decay of maximum velocity defect, growth of wake width, and wake integral parameters, are also presented for the three different pressure gradient cases of steady wake. The decay rate of velocity defect is fastest for the negative streamwise pressure gradient case and slowest for the positive pressure gradient case. Conversely, the growth of the wake width is fastest for the positive streamwise pressure gradient case and slowest for the negative streamwise pressure gradient. The second phase studies the development of periodic unsteady wakes generated by the circular cylinders of the rotating wake generator in a curved channel at zero streamwise pressure gradient. Instantaneous velocity components of the periodic unsteady wakes, measured with a stationary X-hot-film probe, are analyzed by the phase averaging techniques. The temporal distribution of velocity and Reynolds stress components obtained in a stationary frame of reference are transformed to a spatial distribution in a relative frame of reference. Profiles of phase-averaged velocity and Reynolds stress distributions in the relative frame of reference and similarity coordinates are presented. The velocity defect and Reynolds stress distributions agree with the results of the wake development behind a stationary cylinder in the curved channel at zero streamwise pressure gradient. The phase-averaged third-order correlations, presented in the relative frame of reference and similarity coordinates, show pronounced asymmetric features.

Schobeiri, M. T.↗
DOE OSTI2023

Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter

Abstract The Large Magellanic Cloud (LMC) will induce a dynamical friction (DF) wake on infall to the Milky Way (MW). The MW’s stellar halo will respond to the gravity of the LMC and the dark matter (DM) wake, forming a stellar counterpart to the DM wake. This provides a novel opportunity to constrain the properties of the DM particle. We present a suite of high-resolution, windtunnel-style simulations of the LMC's DF wake that compare the structure, kinematics, and stellar tracer response of the DM wake in cold DM (CDM), with and without self-gravity, versus fuzzy DM (FDM) with m a = 10 −23 eV. We conclude that the self-gravity of the DM wake cannot be ignored. Its inclusion raises the wake’s density by ∼10%, and holds the wake together over larger distances (∼50 kpc) than if self-gravity is ignored. The DM wake’s mass is comparable to the LMC’s infall mass, meaning the DM wake is a significant perturber to the dynamics of MW halo tracers. An FDM wake is more granular in structure and is ∼20% dynamically colder than a CDM wake, but with comparable density. The granularity of an FDM wake increases the stars’ kinematic response at the percent level compared to CDM, providing a possible avenue of distinguishing a CDM versus FDM wake. This underscores the need for kinematic measurements of stars in the stellar halo at distances of 70–100 kpc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Slow Wake Recovery and Low Turbulence Behind Wind Farms Parameterized in Mesoscale Simulations

Numerical weather prediction (NWP) and climate models equipped with wind-farm parameterizations (WFPs) can simulate cluster wake effects affecting downstream wind farms in both onshore and offshore environments. This study evaluates wake recovery behind a wind farm represented by the NWP-WFP approach in the Weather Research and Forecasting (WRF) model using either the Fitch et al. (2012) or Ma et al. (2022a, b) WFPs. Results are benchmarked against large-eddy simulations (LES) of an idealized offshore wind farm with aligned and staggered layouts under neutral atmospheric stability. Near-farm wake recovery is underestimated in NWP-WFP simulations due to its representation on a coarse mesoscale grid. This limitation leads to slow wake recovery through two interconnected mechanisms: (i) spatial gradients in the wind velocity field are weaker compared to LES and (ii) turbulence kinetic energy (TKE) remains low not because of excessive dissipation but due to insufficient shear production caused by these weakened gradients. For the scenario considered here, a wind-speed bias develops in the near-farm wake and persists into the far wake. Differences between the NWP-WFP simulations and LES emerge within a short distance downstream of the farm exit, where the mesoscale simulations recover too slowly. This reduced recovery contributes approximately 0.15-0.50 m s-1 to the near-farm wind-speed bias. The bias established in this region is not subsequently compensated for downstream but instead propagates into the far wake, where wind-speed differences of approximately 0.4-0.6 m s-1 remain up to 50 km downstream. Higher-resolution mesoscale simulations partially reduce this bias. Increasing turbine-added TKE or including subgrid wake effects provides additional improvement, but neither fully addresses the underlying cause. The slow wake recovery is not caused by limitations of the WFPs themselves, as it also occurs outside their region of influence, and adding subgrid wake effects does not significantly impact recovery. Rather, the slow wake recovery is a consequence of mesoscale flow representation. This behavior is not limited to regions downstream of the wind farm but is less visible within the farm, where wake recovery occurs simultaneously with turbine-induced momentum extraction. These results highlight the need for improved representations of wake recovery both within and downstream of wind farms. While enhanced subgrid modeling, shear-driven TKE production, and refined WFP formulations may improve intra-farm dynamics, accurately capturing near-farm wake recovery downstream remains challenging, as WFPs do not act in this region.

17 WIND ENERGY↗
NASA NTRS2005

Dynamic wake prediction and visualization with uncertainty analysis

A dynamic wake avoidance system utilizes aircraft and atmospheric parameters readily available in flight to model and predict airborne wake vortices in real time. A novel combination of algorithms allows for a relatively simple yet robust wake model to be constructed based on information extracted from a broadcast. The system predicts the location and movement of the wake based on the nominal wake model and correspondingly performs an uncertainty analysis on the wake model to determine a wake hazard zone (no fly zone), which comprises a plurality of wake planes, each moving independently from another. The system selectively adjusts dimensions of each wake plane to minimize spatial and temporal uncertainty, thereby ensuring that the actual wake is within the wake hazard zone. The predicted wake hazard zone is communicated in real time directly to a user via a realistic visual representation. In an example, the wake hazard zone is visualized on a 3-D flight deck display to enable a pilot to visualize or see a neighboring aircraft as well as its wake. The system substantially enhances the pilot's situational awareness and allows for a further safe decrease in spacing, which could alleviate airport and airspace congestion.

Holforty, Wendy L.↗
DOE OSTI2020

Comparison of modular analytical wake models to the Lillgrund wind plant

Efficient and accurate wake models are required for wind plant performance modeling and the suite of engineering processes that support wind plant layout, control, and monitoring. Although many analytical and engineering wake models with low computational costs have been proposed, their ability to represent the power production of large wind plants in a wide range of atmospheric conditions is not completely understood. The following validation study reviews the underlying theory for analytical wake models, outlines quality control procedures for observational data, and compares model results with observational data from the Lillgrund Wind Plant. Lillgrund makes a valuable case study for wake modeling because of its regular arrangement and the relatively close spacing of constituent wind turbines, which lead to regular and significant wake interactions within the wind plant and the development of deep array flow conditions. Formulations for the velocity deficit, wake-added turbulence, and wake superposition methods are considered in a modular sense, yielding many possible configurations to represent wind turbine wakes, of which seven are examined in detail. Velocity deficit models that account for flow conditions in the near wake are better able to reproduce power production for wind turbines in the transitional region of the wind plant, where wind turbines experience as many as five wakes from upstream turbines. Finally, in the deep array, where power production reaches asymptotic values and wake statistics become quasi-periodic, wake superposition schemes become the largest driver in error reduction; using the linear or maximum wake superposition methods can reduce the relative root mean square error by as much as 40% in the deep array.

17 WIND ENERGY↗
DOE OSTI2026

Differences in cluster and internal wake effects from mesoscale and large-eddy simulations off the US East Coast

Mesoscale simulations are increasingly used to estimate wake effects within and between large wind farms, despite limited validation for large-scale wake effects. This study evaluates the capabilities and limitations of mesoscale simulations in capturing wake-induced impacts on wind turbine power production through a direct comparison with large-domain large-eddy simulations (LESs) for three planned offshore wind farms under realistic atmospheric conditions and a range of atmospheric stabilities. We assess mesoscale performance in replicating wake characteristics behind single and multiple turbine clusters and quantify the resulting variability in mean turbine power. Results show that mesoscale Weather Research and Forecasting simulations with the Fitch wind farm parameterization capture key features of the velocity deficit downstream of both single and multiple wind farms, with mean root-mean-square errors near 5 % and good agreement with stability-driven wake behavior. However, in these simulations, the mesoscale Fitch parameterization underestimates power losses from internal wake effects, particularly when turbines align with the prevailing wind direction or under stable stratification. In these conditions, individual wakes persist and dominate downstream power deficits. The coarse resolution of the mesoscale simulations limits their ability to resolve individual wind turbine wakes that drive power fluctuations within wind farms. Nonetheless, mesoscale simulations can yield accurate estimates of combined wake losses from internal and cluster effects across some wind direction sectors, where errors in wake representation may cancel each other out. These findings underscore the strengths of mesoscale simulations for capturing broader wake patterns while highlighting their limitations for modeling turbine-level power losses. Future work should explore hybrid modeling approaches to capture both long-range cluster wake propagation and localized internal wake dynamics.

17 WIND ENERGY↗
DOE OSTI2020

Design and analysis of a wake steering controller with wind direction variability

Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

17 WIND ENERGY↗
DOE OSTI2024

Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production

Abstract. The mid-Atlantic will experience rapid wind plant development due to its promising wind resource located near large population centers. Wind turbines and wind plants create wakes, or regions of reduced wind speed, that may negatively affect downwind turbines and plants. We evaluate wake variability and annual energy production with the first yearlong modeling assessment using the Weather Research and Forecasting model, deploying 12 MW turbines across the domain at a density of 3.14 MW km−2, matching the planned density of 3 MW km−2. Using a series of simulations with no wind plants, one wind plant, and complete build-out of lease areas, we calculate wake effects and distinguish the effect of wakes generated internally within one plant from those generated externally between plants. We also provide a first step towards uncertainty quantification by testing the amount of added turbulence kinetic energy (TKE) by 0 % and 100 %. We provide a sensitivity analysis by additionally comparing 25 % and 50 % for a short case study period. The strongest wakes, propagating 55 km, occur in summertime stable stratification, just when New England's grid demand peaks in summer. The seasonal variability of wakes in this offshore region is much stronger than the diurnal variability of wakes. Overall, yearlong simulated wake impacts reduce power output by a range between 38.2 % and 34.1 % (for 0 %–100 % added TKE). Internal wakes cause greater yearlong power losses, from 29.2 % to 25.7 %, compared to external wakes, from 14.7 % to 13.4 %. The overall impact is different from the linear sum of internal wakes and external wakes due to non-linear processes. Additional simulations quantify wake uncertainty by modifying the added amount of turbulent kinetic energy from wind turbines, introducing power output variability of 3.8 %. Finally, we compare annual energy production to New England grid demand and find that the lease areas can supply 58.8 % to 61.2 % of annual load. We note that the results of this assessment are not intended to make nor are they suitable to make commercial judgments about specific wind projects.

17 WIND ENERGY↗
NASA NTRS2011

Direct Numerical Simulations of Transitional/Turbulent Wakes

The interest in transitional/turbulent wakes spans the spectrum from an intellectual pursuit to understand the complex underlying physics to a critical need in aeronautical engineering and other disciplines to predict component/system performance and reliability. Cylinder wakes have been studied extensively over several decades to gain a better understanding of the basic flow phenomena that are encountered in such flows. Experimental, computational and theoretical means have been employed in this effort. While much has been accomplished there are many important issues that need to be resolved. The physics of the very near wake of the cylinder (less than three diameters downstream) is perhaps the most challenging of them all. This region comprises the two detached shear layers, the recirculation region and wake flow. The interaction amongst these three components is to some extent still a matter of conjecture. Experimental techniques have generated a large percentage of the data that have provided us with the current state of understanding of the subject. More recently computational techniques have been used to simulate cylinder wakes, and the data from such simulations are being used to both refine our understanding of such flows as well as provide new insights. A few large eddy and direct numerical simulations (LES and DNS) of cylinder wakes have appeared in the literature in the recent past. These investigations focus on the low Reynolds number range where the cylinder boundary layer is laminar (sub-critical range). However, from an engineering point of view, there is considerable interest in the situation where the upper and/or lower boundary layer of an airfoil is turbulent, and these turbulent boundary layers separate from the airfoil to contribute to the formation of the wake downstream. In the case of cylinders, this only occurs at relatively large unit Reynolds numbers. However, in the case of airfoils, the boundary layer has the opportunity to transition to turbulence on the airfoil surface at a relatively lower unit Reynolds number because the characteristic length of the airfoil is typically one to two orders of magnitude larger than the trailing edge diameter. This transition to turbulence would occur unless there is a strong favorable pressure gradient that results in the boundary layer remaining laminar or transitional over the surface of the airfoil. This presentation will focus on two direct numerical simulations that have been performed at NASA ARC. The first is of a cylinder wake with laminar separating boundary layers. The second is the wake of a flat plate with a circular trailing edge. The upper and lower plate surface boundary layers are both turbulent and statistically identical. Thus the computed wake is symmetric in a statistical sense. This flow is more representative of airfoil wakes than cylinder wakes. Results from the two simulations including flow visualization and turbulence statistics in the near wake will be presented at the seminar.

wakes↗
DOE OSTI2024

Laser-Doppler Anemometry Measurements of Turbulent Swirling Wakes

This study considers the development and evolution of the swirling turbulent wake and their link to the turbulence present in the wake. A wake generator produces swirling wakes with a minimum of artifacts that would complicate the flow. Four swirling wakes have been studied with swirl numbers ranging from 0.19 to 0.37, and the results are compared to those of a nonswirling wake. Two-component laser-Doppler anemometry has been used to measure mean and turbulence quantities in the wake, and the uncertainty of these quantities has been determined. Careful alignment of the laser-Doppler anemometry system and large numbers of independent samples were used to obtain second-order turbulence statistics with low uncertainty. The results indicate that swirl strength impacts wake behavior, and changes in the behavior are linked to modifications in the turbulence. Faster wake width growth and centerline velocity deficit decay have been observed for wakes with higher swirl strength. The results indicate that a minimum swirl level is required to significantly impact wake behavior, and, at some level of swirl, the enhanced wake evolution observed saturates. For the conditions tested, the swirl number that enhances wake diffusion before saturation has been determined to lie between 0.21 and 0.37.

Engineering↗
NASA NTRS1990

The Importance of the Measurement of the Unsteady Wake of the Aeroassist Flight Experiment Vehicle: A White Paper

The High Frequency Radiometer (HFR) is the only instrument on the Aeroassist Flight Experiment (AFE) with sufficient temporal resolution to discern the frequency of unsteady wake oscillations. Determining both the frequency and amplitude of wake unsteadiness during AFE atmospheric entry is essential for reliably predicting the geometry and motion of the wake of future Aeroassisted Space Transfer Vehicles (ASTV). These parameters directly affect the location and size of the pay- load and the weight of the required afterbody heat protection. The purpose of the AFE is to validate the technologies required for the design of ASTVs, which will be used to exploit Earth-lunar space. This validation will be conducted at a combination of vehicle size, altitude, and velocity not obtainable in ground-based facilities. The AFE will provide the experimental flight data needed to improve our understanding of hypersonic-wake physics and to validate computational predictions of the aerodynamic and heating loads, including afterbody radiative heating loads, on an ASTV. Reliable prediction of ASTV wake flows will ensure that payloads are located within the shear-layer envelope and will determine the amount of thermal protection the payloads require. Specifically, understanding the temporal nature of the wake unsteadiness is important for two reasons. Most importantly, analysis of ground-based experiments suggests that wake unsteadiness results in a variation of as much as +/- 5 deg in the shear-flow turning angle. This angle must be reliably predicted to avoid shear-layer impingement on the vehicle afterbody, which would result in heating rates of about 10 W/sq cm, of the same order as on the forebody stagnation point. Secondly, the energy associated with wake unsteadiness will reduce the static enthalpy of the wake fluid and cause an error of as much as 30% in the amount of predicted wake radiative heating. Therefore, the HFR flight data, which will quantify the frequency and amplitude of the wake unsteadiness, are required for the verification of computational models of ASTV flowfields. Measurement of wake unsteadiness has been endorsed by the Peer Science Steering Group and the AFE Computational Fluid Dynamics (CFD) Working Group. The Peer Science Steering Group has stressed that a reliable measurement of the wake unsteadiness is fundamental because incorporating unsteadiness represents a substantial challenge to the CFD community, and a reliable measurement will raise confidence in the computer simulation. The AFE CFD Working Group has stated that 44 any data which would prove the existence and effects of unsteady flow would be extremely valuable.

Strawa, A. W.↗
NASA NTRS2000

Development of a Wake Vortex Spacing System for Airport Capacity Enhancement and Delay Reduction

The Terminal Area Productivity project has developed the technologies required (weather measurement, wake prediction, and wake measurement) to determine the aircraft spacing needed to prevent wake vortex encounters in various weather conditions. The system performs weather measurements, predicts bounds on wake vortex behavior in those conditions, derives safe wake spacing criteria, and validates the wake predictions with wake vortex measurements. System performance to date indicates that the potential runway arrival rate increase with Aircraft VOrtex Spacing System (AVOSS), considering common path effects and ATC delivery variance, is 5% to 12% depending on the ratio of large and heavy aircraft. The concept demonstration system, using early generation algorithms and minimal optimization, is performing the wake predictions with adequate robustness such that only 4 hard exceedances have been observed in 1235 wake validation cases. This performance demonstrates the feasibility of predicting wake behavior bounds with multiple uncertainties present, including the unknown aircraft weight and speed, weather persistence between the wake prediction and the observations, and the location of the weather sensors several kilometers from the approach location. A concept for the use of the AVOSS system for parallel runway operations has been suggested, and an initial study at the JFK International Airport suggests that a simplified AVOSS system can be successfully operated using only a single lidar as both the weather sensor and the wake validation instrument. Such a selfcontained AVOSS would be suitable for wake separation close to the airport, as is required for parallel approach concepts such as SOIA.

Hinton, David A.↗
NASA NTRS2005

Probabilistic Analysis of Impact of Wake Vortices on Closely-Spaced Parallel Approaches

One of the primary constraints on the capacity of the nation's air transportation system is the landing capacity of its largest airports. Many airports with closely spaced parallel runways suffer a severe runway acceptance rate when the weather conditions do not allow full utilization of these parallel runways. The present requirement for simultaneous independent landings in Instrument Meteorological Conditions, IMC, is at least 4300 feet of lateral runway spacing (as close as 3000 feet for runways with a Precision Runway Monitor). Operations in Visual Meteorological Conditions, VMC, to Closely Spaced Parallel Approaches only require a lateral runway spacing greater than 750 feet. A study by Hardy and Lewis integrated and extended earlier studies and concepts in lateral traffic separation, longitudinal station keeping, wake prediction, wake display, and the concepts of R N P into a preliminary system concept for Closely Spaced Parallel Approaches in IMC. This system allows IMC airport acceptance rates to approach those for VMC. The system concept that was developed, presented traffic and wake information on the NAVigation Display, NAV, and developed operational procedures for a mix of conventional and Runway Independent Aircraft with different approach speeds to Closely Spaced Parallel Runways. This paper first describes some improvements made on the technology needed to better predict and formulate a probabilistic representation for the time-dependent motion and spreading of the hazardous region associated with the lift-generated vortex wakes of preceding aircraft. In this way, the time at which the vortex wakes of leading aircraft intrude into the airspace of adjacent flight-corridor/runway combinations can be more reliably predicted. Such a prediction is needed because it determines restraints to be placed on in-trail separation distances; or, the allowable time intervals between aircraft executing nearly simultaneous landings or takeoffs on very closely-spaced runways. Improved estimates of wake spreading are achieved by inclusion of representations in the equations for wake spreading due to ambient turbulence and due to the long-wave instability of a vortex pair. Wake motion and spreading due to the time-averaged wind and its variations with time, are retained. The more detailed representation of wake spreading presented here permits the development of probabilistically-based uncertainty estimates for wake spreading. Measurements needed within actual aircraft wake vortices to validate and support this analysis are also described. The second part of the paper uses the improvements in the accuracy of the location of wake vortices to extend the preliminary system concept for Closely Spaced Parallel Approaches described earlier with more robust operational procedures. Additionally, improvements in longitudinal station keeping, wake display, and risk assessment methodologies are incorporated and described.

Hardy, Gordon H.↗
DOE OSTI2020

Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models

Abstract. Wake measurements of a scanning Doppler lidar mounted on the nacelle of a full-scale wind turbine during a wake-steering experiment were used for the characterization of the wake flow, the evaluation of the wake-steering set-up, and the validation of analytical wake models. Inflow-scanning Doppler lidars, a meteorological mast, and the supervisory control and data acquisition (SCADA) system of the wind turbine complemented the set-up. Results from the wake-scanning Doppler lidar showed an increase in the wake deflection with the yaw angle and that the wake deflection was not in all cases beneficial for the power output of a downstream turbine due to a bias of the inflow wind direction perceived by the yawed wind turbine and the wake-steering design implemented. Both observations could be reproduced with an analytical model that was initialized with the inflow measurements. Error propagation from the inflow measurements that were used as model input and the power coefficient of a waked wind turbine contributed significantly to the model uncertainty. Lastly, the span-wise cross section of the wake was strongly affected by wind veer, masking the effects of the yawed wind turbine on the wake cross sections.

17 WIND ENERGY↗
DOE OSTI2024

The value of wake steering wind farm flow control in US energy markets

Abstract. Wind farm flow control represents a category of control strategies for achieving wind-plant-level objectives, such as increasing wind plant power production and/or reducing structural loads, by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated – for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm flow control tool with historical electricity price data for 15 existing US wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis dataset. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.4 % and 1.7 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual revenue of production, ARP) 4 % higher than the AEP gains. For most wind plants, ARP gain was found to exceed AEP gain. But the ratio between ARP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool – the region with the highest wind penetration analyzed (31 %) – the increase in ARP from wake steering is 11 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.

17 WIND ENERGY↗
DOE OSTI2025

Comparison of wind-farm control strategies under realistic offshore wind conditions: wake quantities of interest

Wind-farm control strategies aim to increase the efficiency, and therefore lower the levelized cost of energy, of wind farms. This is done by using turbine settings such as the yaw angle, blade pitch angles, or generator torque to manipulate the wake that negatively affects downstream turbines in the farm. Two inherently different wind-farm control methods have been identified in the literature: wake steering (WS) and active wake mixing (AWM). As one of two companion papers focused on understanding practical aspects of these two wind-farm control strategies using large-eddy simulation (LES), we below analyze the wake quantities of interest for a single wind turbine performing WS and AWM, while the companion article (Frederik et al., 2025) focuses on turbine quantities of interest including power and structural loads for the same computational setup and also includes two-turbine arrays with full and partial wake overlap. The simulations, which are based in the LES solver AMR-Wind, are tailored to have inflow conditions representative of measurements from a site off the East Coast of the US, including with strong veer and low turbulence. The turbine, which is modeled in OpenFAST and coupled to the LES, is the IEA 15 MW, an open-source offshore design. After presenting an overview of the wake recovery for the different wake-control cases, the analysis probes the fluid-dynamic causes for the different performance of the arrays reported in the companion article by examining control volumes around the wakes and the budget of the mean-flow kinetic energy (MKE) within these volumes. In the high veer environment considered, the MKE recovery is dominated by mean convection, and this is shown to especially benefit the WS strategy when a neighboring turbine is directly downstream: there is ≈65 % more available power for a downstream turbine than in the baseline case, and this power is gained primarily through mean convection on the left-tip and top-tip faces of the control volume. However, the case with imperfect knowledge of the exact wind direction favors the pulse-type AWM strategy, largely because of ≈9 % increased turbulent entrainment from aloft versus the baseline that could be related to an apparent resistance to skewing in the pulsed wake. The general reduced effectiveness of helix-type and other individual-pitch-based AWM strategies for inflow with high veer and low turbulence as reported in the companion paper is due, in part, to low magnitudes of phase-averaged turbulent entrainment. Two main findings of this study are thus that veer has a significant impact on the effectiveness of different wake-control strategies and that pulse-type AWM may be a useful strategy when the objective is power maximization in realistic, offshore flow environments with imperfect knowledge of the exact wake overlap position on the downstream turbine.

17 WIND ENERGY↗
DOE OSTI2022

A time‐varying formulation of the curled wake model within the FAST.Farm framework

Abstract In this article, we present a time‐varying formulation of the curled wake model that we implemented in FAST.Farm. The curled wake model, originally developed for steady‐state conditions, is used to produce realistic wake profiles behind a wind turbine in yawed (or skewed) conditions. We begin by introducing the key elements of the FAST.Farm framework. Then, after briefly summarizing the original wake dynamics formulation of FAST.Farm based on a polar wake profile, we present the new time‐varying formulation of the curled wake model, compare the two, and highlight the differences with the original curled wake model. After discussing some implementation details, we present different applications with increasing levels of complexity: single turbine with uniform and turbulent inflow, fixed and transient yaw, and multiple turbines. We verify our results using the original FAST.Farm implementation and large‐eddy simulations. The results with the new curled wake model are improved compared to the original implementation, as they include cross‐flow velocities and wake asymmetry. Yet, large‐eddy simulation results show a more pronounced lateral convection of the wake and a stronger concentration of vorticity at the top vortex. The new curled wake implementation in FAST.Farm should enable the calculation of not only generator power but also wind turbine structural loads for applications involving intentional or unintentional skewed flow and wind‐farm control involving wake steering.

17 WIND ENERGY↗
DOE OSTI2021

Evaluation of the potential for wake steering for U.S. land-based wind power plants

The wind power plant-wide control strategy known as wake steering involves the misalignment of upstream turbines with the wind direction to deflect wakes away from downstream turbines, increasing net wind plant power production. In this paper, we evaluate the potential of wake steering for U.S. land-based wind power plants. First, we outline a method for simulating and optimizing wake steering control for existing wind plants by combining the flow redirection and induction in steady state wake steering engineering model with the U.S. Wind Turbine Database and Wind Integration National Dataset Toolkit wind resource dataset. Next, to better understand the potential benefits of wake steering beyond those for existing wind plants, we evaluate the relative impacts of turbine specific power, turbine spacing, and mean wind speed on energy gain and levelized cost of energy (LCOE) using a model land-based wind power plant. For a subset of 60 existing wind plants, assuming a constant turbulence intensity of 8%, wake steering was found to yield an average annual energy production (AEP) gain of 0.80%, equivalent to recovering 13.85% of baseline wake losses. Further, we present a linear approximation between baseline wake losses and AEP gains that can be used to estimate wake steering gains for other wind power plants. Highlighting additional benefits of wake steering, for the model wind power plant we found that energy gains from wake steering enabled an approximate 30% reduction in turbine spacing while keeping LCOE constant.

17 WIND ENERGY↗