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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multi-Scale Simulation of Wind Farm Performance during a Frontal Passage

Predicting the response of wind farms to changing flow conditions is necessary for optimal design and operation. Here in this work, simulation and analysis of a frontal passage through a utility scale wind farm is achieved for the first time using a seamless multi-scale modeling approach. A generalized actuator disk (GAD) wind turbine model is used to represent turbine–flow interaction, and results are compared to novel radar observations during the frontal passage. The Weather Research and Forecasting (WRF) model is employed with a nested grid setup that allows for coupling between multi-scale atmospheric conditions and turbine response. Starting with mesoscale forcing, the atmosphere is dynamically downscaled to the region of interest, where the interaction between turbulent flows and individual wind turbines is simulated with 10 m grid spacing. Several improvements are made to the GAD model to mimic realistic turbine operation, including a yawing capability and a power output calculation. Ultimately, the model is able to capture both the dynamics of the frontal passage and the turbine response; predictions show good agreement with observed background velocity, turbine wake structure, and power output after accounting for a phase shift in the mesoscale forcing. This study demonstrates the utility of the WRF-GAD model framework for simulating wind farm performance under complex atmospheric conditions.

17 WIND ENERGY↗
DOE OSTI2025

High temporal frequency data from a four turbine, blade-resolved wind farm simulation with ExaWind

The data was generated with ExaWind (https://github.com/Exawind) which couples AMR-Wind (https://github.com/Exawind/amr-wind/), Nalu-Wind (https://github.com/Exawind/nalu-wind), TIOGA (https://github.com/Exawind/tioga), and OpenFAST (https://github.com/OpenFAST/openfast). This is a large-scale simulation of a blade-resolved wind farm using the ExaWind software stack. ExaWind couples together a background flow solver, AMR-Wind, and a near-body solver, Nalu-Wind, through an overset technique from the TIOGA application. Another application, OpenFAST, handles the structural dynamics of the turbine blades and towers, which informs the fluid-structure interaction of the wind turbines with the flow solvers. This particular simulation includes four blade-resolved wind turbines operating in a turbulent atmospheric boundary layer. The AMR-Wind solver uses 500 million cells and is being solved on 256 AMD GPUs of the Oakridge Leadership Computing Facility Frontier supercomputer. Each turbine is assigned its own Nalu-Wind solver with over 13 million elements per turbine and solved using 448 CPU cores, for a total of 1792 CPU cores. For each node, 56 cores contain Nalu-Wind, while 8 cores correspond to AMR-Wind operations on the GPUs. Consequently, ExaWind is entirely utilizing the CPUs and the GPUs of the nodes concurrently. The data used in the visualization is full flow field data output from the simulation. It is lossy-compressed to a specific accuracy using ZFP and written to disk every 16 time-steps to enable real-time flow visualization. The flow fields are sampled at a high temporal frequency to enable real-time, 24fps visualization. The flow fields are sampled every 12 simulation time steps (every 0.04132s).

17 WIND ENERGY↗
DOE OSTI2025

Modeling Offshore Wind Farm Performance in Coastal Low-Level Jets Using Coupled Mesoscale-Microscale Large Eddy Simulations

Accurately predicting wind farm reliability under complex offshore atmospheric conditions remains a key challenge, particularly during noncanonical meteorological events such as coastal low-level jets (LLJs). LLJs, characterized by strong nonmonotonic vertical shear and directional veer, depart significantly from the simplified inflow assumptions embedded in conventional design standards, low-fidelity engineering models, and microscale large eddy simulations of the atmospheric boundary layer. In this work, we use the virtual wind farm framework—an exascale, graphics processing unit–accelerated large eddy simulation platform coupled with high-fidelity aeroservoelastic turbine models and advanced mesoscale-microscale coupling via the ExaWind software stack—to investigate turbine responses under realistic LLJ forcing. Simulations are performed over the U.S. North Atlantic offshore domain with the use of meteorological inputs from New York State Energy Research and Development Authority buoy data, focusing on a representative LLJ case impacting the International Energy Agency 15 MW reference turbine. Our results show that LLJs can cause up to 50% power deficits in downstream turbine rows and significantly amplify low-speed shaft and tower loads through nonlinear coupling between complex inflow characteristics and turbine structural dynamics. Two primary mechanisms drive these load amplifications: (1) unique LLJ inflow features—including veer and vertical/lateral shear—and (2) the downstream evolution of the flow under stable thermal stratification, which suppresses turbulence mixing and alters wake recovery. These mechanisms produce streamwise variations in turbine loading not captured by standard hub height–based metrics or existing design load case (DLC) definitions. This study highlights the critical role of rotor-scale flow gradients in driving fatigue and system-level aeroelastic responses, challenging current DLC and control strategies. We advocate the integration of full-flow field, environment-aware wind inputs into load modeling and control algorithms. By leveraging exascale computing to resolve mesoscale-microscale coupling, this work lays the groundwork for next-generation offshore wind turbine design and operation in meteorologically complex marine environments.

17 WIND ENERGY↗
DOE OSTI2024

ExaWind: Open‐source CFD for hybrid‐RANS/LES geometry‐resolved wind turbine simulations in atmospheric flows

Abstract Predictive high‐fidelity modeling of wind turbines with computational fluid dynamics, wherein turbine geometry is resolved in an atmospheric boundary layer, is important to understanding complex flow accounting for design strategies and operational phenomena such as blade erosion, pitch‐control, stall/vortex‐induced vibrations, and aftermarket add‐ons. The biggest challenge with high‐fidelity modeling is the realization of numerical algorithms that can capture the relevant physics in detail through effective use of high‐performance computing. For modern supercomputers, that means relying on GPUs for acceleration. In this paper, we present ExaWind, a GPU‐enabled open‐source incompressible‐flow hybrid‐computational fluid dynamics framework, comprising the near‐body unstructured grid solver Nalu‐Wind, and the off‐body block‐structured‐grid solver AMR‐Wind, which are coupled using the Topology Independent Overset Grid Assembler. Turbine simulations employ either a pure Reynolds‐averaged Navier–Stokes turbulence model or hybrid turbulence modeling wherein Reynolds‐averaged Navier–Stokes is used for near‐body flow and large eddy simulation is used for off‐body flow. Being two‐way coupled through overset grids, the two solvers enable simulation of flows across a huge range of length scales, for example, 10 orders of magnitude going from O(μm) boundary layers along the blades to O(10 km) across a wind farm. In this paper, we describe the numerical algorithms for geometry‐resolved turbine simulations in atmospheric boundary layers using ExaWind. We present verification studies using canonical flow problems. Validation studies are presented using megawatt‐scale turbines established in literature. Additionally presented are demonstration simulations of a small wind farm under atmospheric inflow with different stability states.

17 WIND ENERGY↗
DOE OSTI2023

Blockage and speedup in the proximity of an onshore wind farm: A scanning wind LiDAR experiment

To maximize the profitability of wind power plants, wind farms are often characterized by high wind turbine density leading to operations with reduced turbine spacing. As a consequence, the overall wind farm power capture is hindered by complex flow features associated with flow modifications induced by the various wind turbine rotors. In addition to the generation of wakes, the velocity of the incoming wind field can reduce due to the increased pressure in the proximity of a single turbine rotor (named induction); a similar effect occurs at the wind-farm level (global blockage), which can have a noticeable impact on power production. On the other hand, intra-wind-farm regions featuring increased velocity compared to the freestream (speedups) have also been observed, which can be a source for a potential power boost. To quantify these rotor-induced effects on the incoming wind velocity field, three profiling LiDARs and one scanning wind LiDAR were deployed both before and after the construction of an onshore wind turbine array. The different wind conditions are classified according to the ambient turbulence intensity and streamwise/spanwise spacing among wind turbines. The analysis of the mean velocity field reveals enhanced induction and speedup under stably stratified atmospheric conditions. Additionally, a reduced horizontal area between adjacent turbines has a small impact on the induction zone but increases significantly the speedup between adjacent rotors.

17 WIND ENERGY↗
DOE OSTI2023

Using observational mean-flow data to drive large-eddy simulations of a diurnal cycle at the SWiFT site

Reproducing realistic date- and site-specific unsteady wind conditions in large-eddy simulations is becoming increasingly useful in wind energy. How to run a large-eddy simulation to match observed conditions, however, remains an open research question. One approach that has received considerable attention is mesoscale-to-microscale coupling, in which information about the mesoscale weather, most commonly acquired from a mesoscale numerical weather model, is passed on to a microscale model. In this paper, we demonstrate how the recently developed profile-assimilation technique, a form of mesoscale-to-microscale coupling, can be used to drive large-eddy simulations solely based on observed mean-flow profiles at a single location, bypassing the need for auxiliary mesoscale simulations. The new approach is evaluated for a diurnal cycle at the Scaled Wind Farm Technology site. Observed mean-flow profiles from the ground up to a height of 2 km are reconstructed by aggregating measurements from multiple instruments, and gaps in the data are infilled with natural neighbor interpolation. We perform nine simulations using various forcing approaches to deal with data limitations. The results show that it is indeed possible to drive microscale large-eddy simulation with observations using the profile-assimilation technique, notwithstanding large gaps in virtual potential temperature measurements. However, profile assimilation with vertical smoothing of the error between the desired and actual profiles is required. Without that smoothing, the microscale simulations develop unrealistically high turbulence levels under many situations. Finally, we show that simulated mesoscale data can account for missing observations, although care is needed as both data sources are not necessarily compatible.

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 OSTI2020

The effect of dynamic near-wake modulation on utility-scale wind turbine wake development

High-resolution field-scale experiments using flow visualization with natural snowfall and high-fidelity large eddy simulations are combined to investigate the effect of dynamic turbine operation and atmospheric conditions on wind turbine wake mixing and recovery in the wake of a 2.5 MW wind turbine. Instantaneous near-wake expansion and deflection in response to changes in blade pitch and wind direction, termed dynamic wake modulation, is quantified using both techniques, demonstrating excellent agreement. The simulations are used to extend these results by calculating the energy flux into the wake 7 rotor diameters downstream, showing that dynamic turbine-atmospheric interactions enhance mixing in the far-wake. This finding is exhibited under both uniform and turbulent inflow conditions. Under turbulent flow, a synergistic relationship is also observed between dynamic wake modulation and wake meandering, as wake recovery can be further accelerated when the two phenomena occur together. The results of this study have implications for the development of more realistic far-wake models that include the significant impact of dynamic wake modulation on wake mixing and development. Additionally, the findings from the current study can be used to develop advanced control algorithms to speed up wake breakdown and recovery, further improving wind farm efficiency.

17 WIND ENERGY↗
DOE OSTI2021

The Area Localized Coupled Model for Analytical Mean Flow Prediction in Arbitrary Wind Farm Geometries

This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2021

ExaWind: Predictive Wind Energy Simulations

This presentation describes the ExaWind project and the team's progress in creating a suite of performance-portable codes designed for predictive simulations of wind farms on next-generation exascale-class supercomputers. Such simulations will require the resolution of scales spanning many orders of magnitude, from blade boundary layers to wind farm flow structures. In the U.S., the first exascale systems will be GPU accelerated, and different GPU manufacturers have been chosen for the different systems. At the heart of the ExaWind software is a hybrid-solver approach based on the codes Nalu-Wind and AMR-Wind, which are computational fluid dynamics solvers for the incompressible Navier-Stokes equations. Nalu-Wind is an unstructured-grid code used to resolve wind turbine geometry and blade boundary layers, whereas AMR-Wind is a structured-grid background solver for atmospheric turbulent flow and turbine wake propagation. The models are coupled with overset meshes and global linear systems are approximated through a loose-coupling algorithm. Results will include validation-quality high-fidelity simulations and strong/weak scaling results from the Summit supercomputer.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2026

Assessment of Consensus and Wake Steering Wind Farm Control for the American WAKE ExperimeNt (AWAKEN)

As part of the AmericanWAKE ExperimeNt (AWAKEN), a wind farm control experiment is being conducted at the King Plains wind plant in northern Oklahoma from May 2024 to summer 2025. Two types of wind farm control are being evaluated: 1) wake steering, in which upstream wind turbines are misaligned relative to the wind direction to deflect their wakes away from downstream turbines and increase total wind plant power, and 2) consensus yaw control, whereby each turbine's yaw position is controlled to track a "consensus" weighted average of the wind directions measured at neighboring turbines rather than the turbine's own nacelle wind direction measurement. By replacing the noisy wind direction measured by an individual turbine with the smoother, more slowly varying consensus wind direction, consensus yaw control is intended to reduce yaw activity and increase power capture by improving yaw alignment. To help balance the potential increase in yaw activity for the turbines implementing wake steering, they are also operated using consensus yaw control. In this presentation we highlight the impacts of consensus yaw control and wake steering on both energy production and yaw travel at the wind plant. Results show that the change in energy from wake steering is minor overall, but significant increases in energy are observed for closely spaced turbines. Further, larger increases in energy occur during low turbulence periods. The impact of consensus yaw control on energy production is currently inconclusive, with some energy gains measured for some turbines and losses measured for others. Lastly, consensus yaw control was found to reduce yaw travel significantly, even when combined with wake steering.

17 WIND ENERGY↗
DOE OSTI2021

FAST.Farm load validation for single wake situations at alpha ventus

The main objective of the presented work is the validation of the simulation tool FAST.Farm for the calculation of power and structural loads in single wake situations; the basis for the validation is the measurement database of the operating offshore wind farm alpha ventus. The approach is described in detail and covers the calibration of the aeroelastic turbine model, transfer of environmental conditions to simulations, and comparison between simulations and adequately filtered measurements. It is shown that FAST.Farm accurately predicts power and structural load distributions over wind direction with discrepancies of less than 10 % for most of the cases compared to the measurements. Additionally, the frequency response of the structure is investigated, and it is calculated by FAST.Farm in good agreement with the measurements. In general, the calculation of fatigue loads is improved with a wake-added turbulence model added to FAST.Farm in the course of this study.

17 WIND ENERGY↗
DOE OSTI2024

Analyzing the Effects of Atmospheric Turbulent Fluctuations on the Wake Structure of Wind Turbines and Their Blade Vibrational Dynamics

In recent trends, a rising demand for renewable energy has driven wind turbines to larger proportions, where lighter blade designs are often adopted to reduce the costs associated with logistics and production. This causes modern utility-scale wind turbine blades to be inherently more flexible, and their amplified aeroelastic sensitivity results in complex multi-physics reactions to variant atmospheric conditions, including dynamic patterns of aerodynamic loading at the rotor and vortex structure evolutions within the wake. In this paper, we analyze the influence of inflow variance for wind turbines with large, flexible rotors through simulations of the National Rotor Testbed (NRT) turbine, located at Sandia National Labs’ Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas. The Common Ordinary Differential Equation Framework (CODEF) modeling suite is used to simulate wind turbine aeroelastic oscillatory behavior and wind farm vortex wake interactions for a range of flexible NRT blade variations, operating in differing conditions of variant atmospheric flow. CODEF solutions of turbine operation in Steady-In-The-Average (SITA) wind conditions are compared to SITA wind conditions featuring a controlled gust-like pulse overimposed, to isolate the effects of typical wind fluctuations. Finally, simulations of realistic time-varying wind conditions from SWiFT meteorological tower measurements are compared to the solutions of SITA wind conditions. These increasingly complex atmospheric inflow variations are tested to show the differing effects evoked by various patterns of spatiotemporal atmospheric flow fluctuations. An analysis is presented for solutions of wind turbine aeroelastic response and vortex wake evolution, to elucidate the consequences of variant inflow, which pertain to wind turbine dynamics at an individual and farm-collective scale. The comparisons of simulated farm flow for SITA and measured fluctuating wind conditions show that certain regions of the wake contain up to a 12% difference in normalized axial velocity, due to the introduction of wind fluctuations. The findings of this study prove valuable for practical applications in wind farm control and optimization strategies, with particular significance for modern utility-scale wind power plants operating in variant atmospheric conditions.

Farrell, Alayna (ORCID:000000023555720X)↗
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↗
NASA NTRS1995

Large HAWT wake measurement and analysis

From the theoretical fluid dynamics point of view, the wake region of a large horizontal-axis wind turbine has been defined and described, and numerical models of wake behavior have been developed. Wind tunnel studies of single turbine wakes and turbine array wakes have been used to verify the theory and further refine the numerical models. However, the effects of scaling, rotor solidity, and topography on wake behavior are questions that remain unanswered. In the wind tunnel studies, turbines were represented by anything from scaled models to tea strainers or wire mesh disks whose solidity was equivalent to that of a typical wind turbine. The scale factor compensation for the difference in Reynolds number between the scale model and an actual turbine is complex, and not typically accounted for. Though it is wise to study the simpler case of wakes in flat topography, which can be easily duplicated in the wind tunnel, current indications are that wind turbine farm development is actually occurring in somewhat more complex terrain. Empirical wake studies using large horizontal-axis wind turbines have not been thoroughly composited, and, therefore, the results have not been applied to the well-developed theory of wake structure. The measurement programs have made use of both in situ sensor systems, such as instrumented towers, and remote sensors, such as kites and tethered, balloonborne anemometers. We present a concise overview of the work that has been performed, including our own, which is based on the philosophy that the MOD-2 turbines are probably their own best detector of both the momentum deficit and the induced turbulence effect downwind. Only the momentum deficit aspects of the wake/machine interactions have been addressed. Both turbine power output deficits and wind energy deficits as measured by the onsite meteorological towers have been analyzed from a composite data set. The analysis has also evidenced certain topographic influences on the operation of spatially diverse wind turbines.

Miller, A. H.↗
DOE OSTI2020

Infinite photovoltaic solar arrays: Considering flux of momentum and heat transfer

Large scale solar farms supply an increasing amount of the worlds electricity supply. However, high operation temperatures can strongly reduce efficiency and panel lifetime, negatively affecting the levelized cost of energy. In this work, the convective heat transfer coefficient for a utility-scale solar farm is studied with combined thermal and particle-image-velocimetry measurements in a scaled wind tunnel experiment. The measurements confirm the applicability of the scaled experimental setup to study large solar arrays. The velocity measurements indicate the complex flow structure within the solar array, governed by wakes directed upwards due to the orientation of the solar panels.

14 SOLAR ENERGY↗
DOE OSTI2025

Spectral proper orthogonal decomposition of active wake mixing dynamics in a stable atmospheric boundary layer

Recent advancements in the use of active wake mixing (AWM) to reduce wake effects on downstream turbines open new avenues for increasing power generation in wind farms. However, a better understanding of the fluid dynamics underlying AWM is still needed to make wake mixing a reliable strategy for wind farm flow control. In this work, a spectral proper orthogonal decomposition (SPOD) is used to analyze the dynamics of coherent flow structures that are induced in the wake through blade pitch actuation. The data are generated using the ExaWind software suite to perform large eddy simulations of an NREL 2.8 MW turbine operating in a stable atmospheric boundary layer. SPOD tracks the modal behavior of flow structures from their generation in the turbine induction field through their growth in the near-wake region and to their subsequent evolution and energy transfers in the far wake. SPOD is shown to be a useful tool in the context of AWM because it translates the wavenumber and frequency inputs to the turbine controller to structures in the wake. A decomposition of the radial shear stress flux in the wake is also developed using SPOD to measure the contribution of coherent flow structures to mean flow turbulent entrainment and wake recovery. The effectiveness of AWM is connected to its ability to excite inherent structures in the wake of the turbine that arise using baseline controls. The effects of AWM on blade loading are also analyzed by connecting the axial force along the blade to the SPOD analysis of the turbine induction field. Lastly, the performance of different AWM strategies is demonstrated in a two-turbine array.

17 WIND ENERGY↗
DOE OSTI2026

Wind Plant Flow Physics and Power Performance in Complex Environments: Cooperative Research and Development (Final Report)

Cornell University will partner with NLR on the topic of wind farm wake effects to improve understanding of interactions between complex atmospheric flows, terrain, and wind turbine wakes and plant efficiency. Wind plant flow simulation tools will also be validated. The work performed will help improve wind farm modeling by analyzing data, applying models, designing and performing experiments to acquire additional wind farm data, and develop better models.

17 WIND ENERGY↗