Articles | Volume 10, issue 7
https://doi.org/10.5194/wes-10-1403-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/wes-10-1403-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jul 2025
Research article |
| 18 Jul 2025
| 18 Jul 2025
Modeling the effects of active wake mixing on wake behavior through large-scale coherent structures
Sandia National Laboratories, Livermore, CA, USA
Gopal Yalla
Sandia National Laboratories, Albuquerque, NM, USA
Prakash Mohan
National Renewable Energy Laboratory, Golden, CO, USA
Alan Hsieh
Sandia National Laboratories, Albuquerque, NM, USA
Kenneth Brown
Sandia National Laboratories, Albuquerque, NM, USA
Nathaniel deVelder
Sandia National Laboratories, Albuquerque, NM, USA
Daniel Houck
Sandia National Laboratories, Albuquerque, NM, USA
Marc T. Henry de Frahan
National Renewable Energy Laboratory, Golden, CO, USA
National Renewable Energy Laboratory, Golden, CO, USA
Michael Sprague
National Renewable Energy Laboratory, Golden, CO, USA
Related authors
Joeri A. Frederik, Eric Simley, Kenneth A. Brown, Gopal R. Yalla, Lawrence C. Cheung, and Paul A. Fleming
Wind Energ. Sci., 10, 755–777, https://doi.org/10.5194/wes-10-755-2025, https://doi.org/10.5194/wes-10-755-2025, 2025
Short summary
Short summary
In this paper, we present results from advanced computer simulations to determine the effects of applying different control strategies to a small wind farm. We show that when there is variability in wind direction over height, steering the wake of a turbine away from other turbines is the most effective strategy. When this variability is not present, actively changing the pitch angle of the blades to increase turbulence in the wake could be more effective.
Gopal R. Yalla, Kenneth Brown, Lawrence Cheung, Dan Houck, Nathaniel deVelder, and Nicholas Hamilton
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-14, https://doi.org/10.5194/wes-2025-14, 2025
Revised manuscript under review for WES
Short summary
Short summary
When wind reaches the first set of turbines in a wind farm, energy is extracted, reducing the energy available for downstream turbines. This study examines emerging technologies aimed at re-energizing the wind between turbines in a wind farm to improve overall power production. Optimizing these technologies depends on understanding complex features of the atmosphere and the wakes behind turbines, which is accomplished using high fidelity computer simulations and data analysis techniques.
Kenneth Brown, Gopal Yalla, Lawrence Cheung, Joeri Frederik, Dan Houck, Nate deVelder, Eric Simley, and Paul Fleming
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-191, https://doi.org/10.5194/wes-2024-191, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This paper presents a one half of a companion-paper series that studies strategies to reduce negative aerodynamic interference (i.e., wake effects) between nearby wind turbines in a wind farm. The approach leverages high-fidelity flow simulations of an open-source design for an offshore wind turbine. Complimenting the companion paper’s analysis of the power and loading effects of the wake-control strategies, this article uncovers the underlying fluid-dynamic causes for these effects.
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025, https://doi.org/10.5194/gmd-18-3241-2025, 2025
Short summary
Short summary
Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Joeri A. Frederik, Eric Simley, Kenneth A. Brown, Gopal R. Yalla, Lawrence C. Cheung, and Paul A. Fleming
Wind Energ. Sci., 10, 755–777, https://doi.org/10.5194/wes-10-755-2025, https://doi.org/10.5194/wes-10-755-2025, 2025
Short summary
Short summary
In this paper, we present results from advanced computer simulations to determine the effects of applying different control strategies to a small wind farm. We show that when there is variability in wind direction over height, steering the wake of a turbine away from other turbines is the most effective strategy. When this variability is not present, actively changing the pitch angle of the blades to increase turbulence in the wake could be more effective.
Gopal R. Yalla, Kenneth Brown, Lawrence Cheung, Dan Houck, Nathaniel deVelder, and Nicholas Hamilton
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-14, https://doi.org/10.5194/wes-2025-14, 2025
Revised manuscript under review for WES
Short summary
Short summary
When wind reaches the first set of turbines in a wind farm, energy is extracted, reducing the energy available for downstream turbines. This study examines emerging technologies aimed at re-energizing the wind between turbines in a wind farm to improve overall power production. Optimizing these technologies depends on understanding complex features of the atmosphere and the wakes behind turbines, which is accomplished using high fidelity computer simulations and data analysis techniques.
Kenneth Brown, Gopal Yalla, Lawrence Cheung, Joeri Frederik, Dan Houck, Nate deVelder, Eric Simley, and Paul Fleming
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-191, https://doi.org/10.5194/wes-2024-191, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This paper presents a one half of a companion-paper series that studies strategies to reduce negative aerodynamic interference (i.e., wake effects) between nearby wind turbines in a wind farm. The approach leverages high-fidelity flow simulations of an open-source design for an offshore wind turbine. Complimenting the companion paper’s analysis of the power and loading effects of the wake-control strategies, this article uncovers the underlying fluid-dynamic causes for these effects.
Kenneth Brown, Pietro Bortolotti, Emmanuel Branlard, Mayank Chetan, Scott Dana, Nathaniel deVelder, Paula Doubrawa, Nicholas Hamilton, Hristo Ivanov, Jason Jonkman, Christopher Kelley, and Daniel Zalkind
Wind Energ. Sci., 9, 1791–1810, https://doi.org/10.5194/wes-9-1791-2024, https://doi.org/10.5194/wes-9-1791-2024, 2024
Short summary
Short summary
This paper presents a study of the popular wind turbine design tool OpenFAST. We compare simulation results to measurements obtained from a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate turbulent flow fields through several techniques. We show that successful validation of the tool is not strongly dependent on the inflow generation technique used for mean quantities of interest. The type of inflow assimilation method has a larger effect on fatigue quantities.
Erik K. Fritz, Christopher L. Kelley, and Kenneth A. Brown
Wind Energ. Sci., 9, 1713–1726, https://doi.org/10.5194/wes-9-1713-2024, https://doi.org/10.5194/wes-9-1713-2024, 2024
Short summary
Short summary
This study investigates the benefits of optimizing the spacing of pressure sensors for measurement campaigns on wind turbine blades and airfoils. It is demonstrated that local aerodynamic properties can be estimated considerably more accurately when the sensor layout is optimized compared to commonly used simpler sensor layouts. This has the potential to reduce the number of sensors without losing measurement accuracy and, thus, reduce the instrumentation complexity and experiment cost.
Daniel R. Houck, Nathaniel B. de Velder, David C. Maniaci, and Brent C. Houchens
Wind Energ. Sci., 9, 1189–1209, https://doi.org/10.5194/wes-9-1189-2024, https://doi.org/10.5194/wes-9-1189-2024, 2024
Short summary
Short summary
Experiments offer incredible value to science, but results must come with an uncertainty quantification to be meaningful. We present a method to simulate a proposed experiment, calculate uncertainties, and determine the measurement duration (total time of measurements) and the experiment duration (total time to collect the required measurement data when including condition variability and time when measurement is not occurring) required to produce statistically significant and converged results.
Kenneth A. Brown and Thomas G. Herges
Atmos. Meas. Tech., 15, 7211–7234, https://doi.org/10.5194/amt-15-7211-2022, https://doi.org/10.5194/amt-15-7211-2022, 2022
Short summary
Short summary
The character of the airflow around and within wind farms has a significant impact on the energy output and longevity of the wind turbines in the farm. For both research and control purposes, accurate measurements of the wind speed are required, and these are often accomplished with remote sensing devices. This article pertains to a field experiment of a lidar mounted to a wind turbine and demonstrates three data post-processing techniques with efficacy at extracting useful airflow information.
Srinivas Guntur, Jason Jonkman, Ryan Sievers, Michael A. Sprague, Scott Schreck, and Qi Wang
Wind Energ. Sci., 2, 443–468, https://doi.org/10.5194/wes-2-443-2017, https://doi.org/10.5194/wes-2-443-2017, 2017
Short summary
Short summary
This paper presents a validation and code-to-code verification of the U.S. Dept of Energy/NREL wind turbine aeroelastic code, FAST v8, on a 2.3 MW wind turbine. Model validation is critical to any model-based research and development activity, and validation efforts on large turbines, as the current one, are extremely rare, mainly due to the scale. This paper, which was a collaboration between NREL and Siemens Wind Power, successfully demonstrates and validates the capabilities of FAST.
Related subject area
Thematic area: Fluid mechanics | Topic: Wakes and wind farm aerodynamics
Wake development in floating wind turbines: new insights and an open dataset from wind tunnel experiments
Spatiotemporal behavior of the far wake of a wind turbine model subjected to harmonic motions: phase averaging applied to stereo particle image velocimetry measurements
Spatial development of planar and axisymmetric wakes of porous objects under a pressure gradient: a wind tunnel study
Numerical investigation of regenerative wind farms featuring enhanced vertical energy entrainment
Convergence and efficiency of global bases using proper orthogonal decomposition for capturing wind turbine wake aerodynamics
Direct integration of non-axisymmetric Gaussian wind-turbine wake including yaw and wind-veer effects
Turbine- and farm-scale power losses in wind farms: an alternative to wake and farm blockage losses
Effect of blockage on wind turbine power and wake development
Comparison of wind-farm control strategies under realistic offshore wind conditions: wake quantities of interest
Introduction and comparison of novel deep learning and optimization approaches to analytical wake modeling of a tilted wind turbine
Experimental demonstration of regenerative wind farming using a high-density layout of VAWTs
Proof of concept for multirotor systems with vortex-generating modes for regenerative wind energy: a study based on numerical simulations and experimental data
Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm
Synchronised WindScanner field measurements of the induction zone between two closely spaced wind turbines
Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons
Improvements to the dynamic wake meandering model by incorporating the turbulent Schmidt number
An actuator sector model for wind power applications: a parametric study
Wind tunnel investigations of an individual pitch control strategy for wind farm power optimization
The near-wake development of a wind turbine operating in stalled conditions – Part 1: Assessment of numerical models
Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations
Floating wind turbine motion signature in the far-wake spectral content – a wind tunnel experiment
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 1: Large-eddy-simulation study
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modelling
Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects
A method to correct for the effect of blockage and wakes on power performance measurements
Vortex model of the aerodynamic wake of airborne wind energy systems
A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling
Investigating energy production and wake losses of multi-gigawatt offshore wind farms with atmospheric large-eddy simulation
The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems
Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model
Actuator line model using simplified force calculation methods
Brief communication: A clarification of wake recovery mechanisms
Predictive and stochastic reduced-order modeling of wind turbine wake dynamics
Wind turbine wake simulation with explicit algebraic Reynolds stress modeling
Including realistic upper atmospheres in a wind-farm gravity-wave model
Alessandro Fontanella, Alberto Fusetti, Stefano Cioni, Francesco Papi, Sara Muggiasca, Giacomo Persico, Vincenzo Dossena, Alessandro Bianchini, and Marco Belloli
Wind Energ. Sci., 10, 1369–1387, https://doi.org/10.5194/wes-10-1369-2025, https://doi.org/10.5194/wes-10-1369-2025, 2025
Short summary
Short summary
This paper investigates the impact of large movements allowed by floating wind turbine foundations on their aerodynamics and wake behavior. Wind tunnel tests with a model turbine reveal that platform motions affect wake patterns and turbulence levels. Insights from these experiments are crucial for optimizing large-scale floating wind farms. The dataset obtained from the experiment is published and can aid in developing simulation tools for floating wind turbines.
Antonin Hubert, Boris Conan, and Sandrine Aubrun
Wind Energ. Sci., 10, 1351–1368, https://doi.org/10.5194/wes-10-1351-2025, https://doi.org/10.5194/wes-10-1351-2025, 2025
Short summary
Short summary
The paper aims to study the far wake of a wind turbine under realistic inflow conditions subjected to harmonic floating motions. The present work shows that phase averaging enables the observation of the coherent spatiotemporal wake behaviour in response to the harmonic motions, contrary to previous studies with time averaging, and that the resulting variations in the chosen metrics exhibit an intensity higher than those expected when using basic quasi-steady-state approaches.
Wessel van der Deijl, Martín Obligado, Stéphane Barre, and Christophe Sicot
Wind Energ. Sci., 10, 719–732, https://doi.org/10.5194/wes-10-719-2025, https://doi.org/10.5194/wes-10-719-2025, 2025
Short summary
Short summary
We present a wind tunnel study on the effect of an adverse pressure gradient on wakes from porous discs and cylinders. We quantified the spatial development of turbulent wakes for Reynolds numbers up to 3.9 × 105 and at distances ranging from 1 to 12 diameters downstream, both with and without an adverse pressure gradient. Consistent with previous studies, we find that the pressure gradient has an effect in all cases, resulting in larger velocity deficits and wider wakes.
YuanTso Li, Wei Yu, Andrea Sciacchitano, and Carlos Ferreira
Wind Energ. Sci., 10, 631–659, https://doi.org/10.5194/wes-10-631-2025, https://doi.org/10.5194/wes-10-631-2025, 2025
Short summary
Short summary
A novel wind farm concept, called a regenerative wind farm, is investigated numerically. This concept tackles the significant wake interaction losses among traditional wind farms. Our results show that regenerative wind farms can greatly reduce these losses, boosting power output per unit surface. Unlike traditional farms with three-bladed wind turbines, regenerative farms use multi-rotor systems with lifting devices (MRSLs). This unconventional design effectively reduces wake losses.
Juan Felipe Céspedes Moreno, Juan Pablo Murcia León, and Søren Juhl Andersen
Wind Energ. Sci., 10, 597–611, https://doi.org/10.5194/wes-10-597-2025, https://doi.org/10.5194/wes-10-597-2025, 2025
Short summary
Short summary
Using a global base in a proper orthogonal decomposition provides a common base for analyzing flows, such as wind turbine wakes, across an entire parameter space. This can be used to compare flows with different conditions using the same physical interpretation. This work shows the convergence of the global base, its small error compared to the truncation error in the flow reconstruction, and the insensitivity to which datasets are included for generating the global base.
Karim Ali, Pablo Ouro, and Tim Stallard
Wind Energ. Sci., 10, 511–533, https://doi.org/10.5194/wes-10-511-2025, https://doi.org/10.5194/wes-10-511-2025, 2025
Short summary
Short summary
We introduce an innovative analytical method to better understand and optimise wind-farm performance by accurately calculating how turbine wakes affect each other. Unlike traditional numerical approaches, our method provides a precise way to measure the impact of upstream wakes on downstream turbines. This new approach, validated through numerical comparisons, enhances optimisation strategies, potentially leading to more efficient wind-farm operations and increased power generation.
Andrew Kirby, Takafumi Nishino, Luca Lanzilao, Thomas D. Dunstan, and Johan Meyers
Wind Energ. Sci., 10, 435–450, https://doi.org/10.5194/wes-10-435-2025, https://doi.org/10.5194/wes-10-435-2025, 2025
Short summary
Short summary
Traditionally, the aerodynamic loss of wind farm efficiency is classified into wake loss and farm blockage loss. This study, using high-fidelity simulations, shows that neither of these two losses is well correlated with the overall farm efficiency. We propose new measures called turbine-scale efficiency and farm-scale efficiency to better describe turbine–wake effects and farm–atmosphere interactions. This study suggests the importance of better modelling farm-scale loss in future studies.
Olivier Ndindayino, Augustin Puel, and Johan Meyers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-6, https://doi.org/10.5194/wes-2025-6, 2025
Revised manuscript accepted for WES
Short summary
Short summary
Our aim is to understand the relationship between flow blockage and improved wind farm efficiency using large-eddy simulations, as well as developing an analytical model that shows promise for improving turbine power predictions under blockage. We found that blockage enhances turbine power and thrust by inducing a favourable pressure drop across the turbine row, while simultaneously inducing an unfavourable pressure increase downstream which has minimal direct impact on far wake development.
Kenneth Brown, Gopal Yalla, Lawrence Cheung, Joeri Frederik, Dan Houck, Nate deVelder, Eric Simley, and Paul Fleming
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-191, https://doi.org/10.5194/wes-2024-191, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This paper presents a one half of a companion-paper series that studies strategies to reduce negative aerodynamic interference (i.e., wake effects) between nearby wind turbines in a wind farm. The approach leverages high-fidelity flow simulations of an open-source design for an offshore wind turbine. Complimenting the companion paper’s analysis of the power and loading effects of the wake-control strategies, this article uncovers the underlying fluid-dynamic causes for these effects.
James Cutler, Christopher Bay, and Andrew Ning
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-172, https://doi.org/10.5194/wes-2024-172, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This study compares two methods for modeling wakes from tilted wind turbines. An optimized analytical model improves accuracy but is limited by assumptions about wake shape. In contrast, a deep learning approach captures complex wake patterns without these constraints, matching high-fidelity simulations with minimal computational effort. The comparison highlights the potential of deep learning to transform wake modeling, offering greater accuracy and efficiency for wind energy optimization.
David Bensason, Jayant Mulay, Andrea Sciacchitano, and Carlos Ferreira
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-177, https://doi.org/10.5194/wes-2024-177, 2025
Revised manuscript accepted for WES
Short summary
Short summary
The wake of a scaled vertical-axis wind turbine farm was measured, resulting in the first experimental database of 3D-resovled flowfield measurements. In addition to the baseline operating conditions, two modes of wake control were tested, which involve the passive adjustment of the rotors blade pitch. The results highlight the impacts of these modes adjustments on the trailing vorticity system, wake topology, and affinity towards increasing the rate of wake recovery throughout the farm.
Flavio Avila Correia Martins, Alexander van Zuijlen, and Carlos Simão Ferreira
Wind Energ. Sci., 10, 41–58, https://doi.org/10.5194/wes-10-41-2025, https://doi.org/10.5194/wes-10-41-2025, 2025
Short summary
Short summary
This study examines regenerative wind farming with multirotor systems fitted with atmospheric boundary layer control (ABL-control) wings near the rotor's wake. These wings create vortices that boost vertical momentum transfer and speed up wake recovery. Results show that ABL-control wings can restore 95 % of wind power within six rotor diameters downstream, achieving a recovery rate nearly 10 times faster than that without ABL control.
Diederik van Binsbergen, Pieter-Jan Daems, Timothy Verstraeten, Amir R. Nejad, and Jan Helsen
Wind Energ. Sci., 9, 1507–1526, https://doi.org/10.5194/wes-9-1507-2024, https://doi.org/10.5194/wes-9-1507-2024, 2024
Short summary
Short summary
Wind farm yield assessment often relies on analytical wake models. Calibrating these models can be challenging due to the stochastic nature of wind. We developed a calibration framework that performs a multi-phase optimization on the tuning parameters using time series SCADA data. This yields a parameter distribution that more accurately reflects reality than a single value. Results revealed notable variation in resultant parameter values, influenced by nearby wind farms and coastal effects.
Anantha Padmanabhan Kidambi Sekar, Paul Hulsman, Marijn Floris van Dooren, and Martin Kühn
Wind Energ. Sci., 9, 1483–1505, https://doi.org/10.5194/wes-9-1483-2024, https://doi.org/10.5194/wes-9-1483-2024, 2024
Short summary
Short summary
We present induction zone measurements conducted with two synchronised lidars at a two-turbine wind farm. The induction zone flow was characterised for free, fully waked and partially waked flows. Due to the short turbine spacing, the lidars captured the interaction of the atmospheric boundary layer, induction zone and wake, evidenced by induction asymmetry and induction zone–wake interactions. The measurements will aid the process of further improving existing inflow and wake models.
Kelsey Shaler, Eliot Quon, Hristo Ivanov, and Jason Jonkman
Wind Energ. Sci., 9, 1451–1463, https://doi.org/10.5194/wes-9-1451-2024, https://doi.org/10.5194/wes-9-1451-2024, 2024
Short summary
Short summary
This paper presents a three-way verification and validation between an engineering-fidelity model, a high-fidelity model, and measured data for the wind farm structural response and wake dynamics during an evolving stable boundary layer of a small wind farm, generally with good agreement.
Peter Brugger, Corey D. Markfort, and Fernando Porté-Agel
Wind Energ. Sci., 9, 1363–1379, https://doi.org/10.5194/wes-9-1363-2024, https://doi.org/10.5194/wes-9-1363-2024, 2024
Short summary
Short summary
The dynamic wake meandering model (DWMM) assumes that wind turbine wakes are transported like a passive tracer by the large-scale turbulence of the atmospheric boundary layer. We show that both the downstream transport and the lateral transport of the wake have differences from the passive tracer assumption. We then propose to include the turbulent Schmidt number into the DWMM to account for the less efficient transport of momentum and show that it improves the quality of the model predictions.
Mohammad Mehdi Mohammadi, Hugo Olivares-Espinosa, Gonzalo Pablo Navarro Diaz, and Stefan Ivanell
Wind Energ. Sci., 9, 1305–1321, https://doi.org/10.5194/wes-9-1305-2024, https://doi.org/10.5194/wes-9-1305-2024, 2024
Short summary
Short summary
This paper has put forward a set of recommendations regarding the actuator sector model implementation details to improve the capability of the model to reproduce similar results compared to those obtained by an actuator line model, which is one of the most common ways used for numerical simulations of wind farms, while providing significant computational savings. This includes among others the velocity sampling method and a correction of the sampled velocities to calculate the blade forces.
Franz V. Mühle, Florian M. Heckmeier, Filippo Campagnolo, and Christian Breitsamter
Wind Energ. Sci., 9, 1251–1271, https://doi.org/10.5194/wes-9-1251-2024, https://doi.org/10.5194/wes-9-1251-2024, 2024
Short summary
Short summary
Wind turbines influence each other, and these wake effects limit the power production of downstream turbines. Controlling turbines collectively and not individually can limit such effects. We experimentally investigate a control strategy increasing mixing in the wake. We want to see the potential of this so-called Helix control for power optimization and understand the flow physics. Our study shows that the control technique leads to clearly faster wake recovery and thus higher power production.
Pascal Weihing, Marion Cormier, Thorsten Lutz, and Ewald Krämer
Wind Energ. Sci., 9, 933–962, https://doi.org/10.5194/wes-9-933-2024, https://doi.org/10.5194/wes-9-933-2024, 2024
Short summary
Short summary
This study evaluates different approaches to simulate the near-wake flow of a wind turbine. The test case is in off-design conditions of the wind turbine, where the flow is separated from the blades and therefore very difficult to predict. The evaluation of simulation techniques is key to understand their limitations and to deepen the understanding of the near-wake physics. This knowledge can help to derive new wind farm design methods for yield-optimized farm layouts.
Nikolaos Bempedelis, Filippo Gori, Andrew Wynn, Sylvain Laizet, and Luca Magri
Wind Energ. Sci., 9, 869–882, https://doi.org/10.5194/wes-9-869-2024, https://doi.org/10.5194/wes-9-869-2024, 2024
Short summary
Short summary
This paper proposes a computational method to maximise the power production of wind farms through two strategies: layout optimisation and yaw angle optimisation. The proposed method relies on high-fidelity computational modelling of wind farm flows and is shown to be able to effectively maximise wind farm power production. Performance improvements relative to conventional optimisation strategies based on low-fidelity models can be attained, particularly in scenarios of increased flow complexity.
Benyamin Schliffke, Boris Conan, and Sandrine Aubrun
Wind Energ. Sci., 9, 519–532, https://doi.org/10.5194/wes-9-519-2024, https://doi.org/10.5194/wes-9-519-2024, 2024
Short summary
Short summary
This paper studies the consequences of floater motions for the wake properties of a floating wind turbine. Since wake interactions are responsible for power production loss in wind farms, it is important that we know whether the tools that are used to predict this production loss need to be upgraded to take into account these aspects. Our wind tunnel study shows that the signature of harmonic floating motions can be observed in the far wake of a wind turbine, when motions have strong amplitudes.
Erwan Jézéquel, Frédéric Blondel, and Valéry Masson
Wind Energ. Sci., 9, 97–117, https://doi.org/10.5194/wes-9-97-2024, https://doi.org/10.5194/wes-9-97-2024, 2024
Short summary
Short summary
Wind turbine wakes affect the production and lifecycle of downstream turbines. They can be predicted with the dynamic wake meandering (DWM) method. In this paper, the authors break down the velocity and turbulence in the wake of a wind turbine into several terms. They show that it is implicitly assumed in the DWM that some of these terms are neglected. With high-fidelity simulations, it is shown that this can lead to some errors, in particular for the maximum turbulence added by the wake.
Erwan Jézéquel, Frédéric Blondel, and Valéry Masson
Wind Energ. Sci., 9, 119–139, https://doi.org/10.5194/wes-9-119-2024, https://doi.org/10.5194/wes-9-119-2024, 2024
Short summary
Short summary
Analytical models allow us to quickly compute the decreased power output and lifetime induced by wakes in a wind farm. This is achieved by evaluating the modified velocity and turbulence in the wake. In this work, we present a new model based on the velocity and turbulence breakdowns presented in Part 1. This new model is physically based, allows us to compute the whole turbulence profile (rather than the maximum value) and is built to take atmospheric stability into account.
Maarten J. van den Broek, Delphine De Tavernier, Paul Hulsman, Daan van der Hoek, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1909–1925, https://doi.org/10.5194/wes-8-1909-2023, https://doi.org/10.5194/wes-8-1909-2023, 2023
Short summary
Short summary
As wind turbines produce power, they leave behind wakes of slow-moving air. We analyse three different models to predict the effects of these wakes on downstream wind turbines. The models are validated with experimental data from wind tunnel studies for steady and time-varying conditions. We demonstrate that the models are suitable for optimally controlling wind turbines to improve power production in large wind farms.
Alessandro Sebastiani, James Bleeg, and Alfredo Peña
Wind Energ. Sci., 8, 1795–1808, https://doi.org/10.5194/wes-8-1795-2023, https://doi.org/10.5194/wes-8-1795-2023, 2023
Short summary
Short summary
The power curve of a wind turbine indicates the turbine power output in relation to the wind speed. Therefore, power curves are critically important to estimate the production of future wind farms as well as to assess whether operating wind farms are functioning correctly. Since power curves are often measured in wind farms, they might be affected by the interactions between the turbines. We show that these effects are not negligible and present a method to correct for them.
Filippo Trevisi, Carlo E. D. Riboldi, and Alessandro Croce
Wind Energ. Sci., 8, 999–1016, https://doi.org/10.5194/wes-8-999-2023, https://doi.org/10.5194/wes-8-999-2023, 2023
Short summary
Short summary
Modeling the aerodynamic wake of airborne wind energy systems (AWESs) is crucial to properly estimating power production and to designing such systems. The velocities induced at the AWES from its own wake are studied with a model for the near wake and one for the far wake, using vortex methods. The model is validated with the lifting-line free-vortex wake method implemented in QBlade.
Maarten Paul van der Laan, Oscar García-Santiago, Mark Kelly, Alexander Meyer Forsting, Camille Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, Alfredo Peña, Niels Nørmark Sørensen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 8, 819–848, https://doi.org/10.5194/wes-8-819-2023, https://doi.org/10.5194/wes-8-819-2023, 2023
Short summary
Short summary
Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses. In this work, an efficient numerical method is presented that can be used to estimate these energy losses. The novel method is verified with higher-fidelity numerical models and validated with measurements of an existing wind farm cluster.
Peter Baas, Remco Verzijlbergh, Pim van Dorp, and Harm Jonker
Wind Energ. Sci., 8, 787–805, https://doi.org/10.5194/wes-8-787-2023, https://doi.org/10.5194/wes-8-787-2023, 2023
Short summary
Short summary
This work studies the energy production and wake losses of large offshore wind farms with a large-eddy simulation model. Therefore, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW wind farm scenarios. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Also, a clear impact of atmospheric stability on the energy production is found.
Robert Braunbehrens, Andreas Vad, and Carlo L. Bottasso
Wind Energ. Sci., 8, 691–723, https://doi.org/10.5194/wes-8-691-2023, https://doi.org/10.5194/wes-8-691-2023, 2023
Short summary
Short summary
The paper presents a new method in which wind turbines in a wind farm act as local sensors, in this way detecting the flow that develops within the power plant. Through this technique, we are able to identify effects on the flow generated by the plant itself and by the orography of the terrain. The new method not only delivers a flow model of much improved quality but can also help in understanding phenomena that drive the farm performance.
Tamino Wetz and Norman Wildmann
Wind Energ. Sci., 8, 515–534, https://doi.org/10.5194/wes-8-515-2023, https://doi.org/10.5194/wes-8-515-2023, 2023
Short summary
Short summary
In the present study, for the first time, the SWUF-3D fleet of multirotors is deployed for field measurements on an operating 2 MW wind turbine (WT) in complex terrain. The fleet of multirotors has the potential to fill the meteorological gap of observations in the near wake of WTs with high-temporal and high-spatial-resolution wind vector measurements plus temperature, humidity and pressure. The flow up- and downstream of the WT is measured simultaneously at multiple spatial positions.
Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort
Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, https://doi.org/10.5194/wes-8-401-2023, 2023
Short summary
Short summary
This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.
Gonzalo Pablo Navarro Diaz, Alejandro Daniel Otero, Henrik Asmuth, Jens Nørkær Sørensen, and Stefan Ivanell
Wind Energ. Sci., 8, 363–382, https://doi.org/10.5194/wes-8-363-2023, https://doi.org/10.5194/wes-8-363-2023, 2023
Short summary
Short summary
In this paper, the capacity to simulate transient wind turbine wake interaction problems using limited wind turbine data has been extended. The key novelty is the creation of two new variants of the actuator line technique in which the rotor blade forces are computed locally using generic load data. The analysis covers a partial wake interaction case between two wind turbines for a uniform laminar inflow and for a turbulent neutral atmospheric boundary layer inflow.
Maarten Paul van der Laan, Mads Baungaard, and Mark Kelly
Wind Energ. Sci., 8, 247–254, https://doi.org/10.5194/wes-8-247-2023, https://doi.org/10.5194/wes-8-247-2023, 2023
Short summary
Short summary
Understanding wind turbine wake recovery is important to mitigate energy losses in wind farms. Wake recovery is often assumed or explained to be dependent on the first-order derivative of velocity. In this work we show that wind turbine wakes recover mainly due to the second-order derivative of the velocity, which transport momentum from the freestream towards the wake center. The wake recovery mechanisms and results of a high-fidelity numerical simulation are illustrated using a simple model.
Søren Juhl Andersen and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 2117–2133, https://doi.org/10.5194/wes-7-2117-2022, https://doi.org/10.5194/wes-7-2117-2022, 2022
Short summary
Short summary
Simulating the turbulent flow inside large wind farms is inherently complex and computationally expensive. A new and fast model is developed based on data from high-fidelity simulations. The model captures the flow dynamics with correct statistics for a wide range of flow conditions. The model framework provides physical insights and presents a generalization of high-fidelity simulation results beyond the case-specific scenarios, which has significant potential for future turbulence modeling.
Mads Baungaard, Stefan Wallin, Maarten Paul van der Laan, and Mark Kelly
Wind Energ. Sci., 7, 1975–2002, https://doi.org/10.5194/wes-7-1975-2022, https://doi.org/10.5194/wes-7-1975-2022, 2022
Short summary
Short summary
Wind turbine wakes in the neutral atmospheric surface layer are simulated with Reynolds-averaged Navier–Stokes (RANS) using an explicit algebraic Reynolds stress model. Contrary to standard two-equation turbulence models, it can predict turbulence anisotropy and complex physical phenomena like secondary motions. For the cases considered, it improves Reynolds stress, turbulence intensity, and velocity deficit predictions, although a more top-hat-shaped profile is observed for the latter.
Koen Devesse, Luca Lanzilao, Sebastiaan Jamaer, Nicole van Lipzig, and Johan Meyers
Wind Energ. Sci., 7, 1367–1382, https://doi.org/10.5194/wes-7-1367-2022, https://doi.org/10.5194/wes-7-1367-2022, 2022
Short summary
Short summary
Recent research suggests that offshore wind farms might form such a large obstacle to the wind that it already decelerates before reaching the first turbines. Part of this phenomenon could be explained by gravity waves. Research on these gravity waves triggered by mountains and hills has found that variations in the atmospheric state with altitude can have a large effect on how they behave. This paper is the first to take the impact of those vertical variations into account for wind farms.
Cited articles
Ainslie, J. F.: Calculating the flowfield in the wake of wind turbines, J. Wind Eng. Ind. Aerod., 27, 213–224, 1988. a
Ali, N., Cortina, G., Hamilton, N., Calaf, M., and Cal, R.: Turbulence characteristics of a thermally stratified wind turbine array boundary layer via proper orthogonal decomposition, J. Fluid Mech., 828, 175–195, 2017. a
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine wakes, Renew. Energ., 70, 116–123, 2014. a
Bastine, D., Witha, B., Wächter, M., and Peinke, J.: Towards a simplified dynamic wake model using POD analysis, Energies, 8, 895–920, 2015. a
Brown, K., Yalla, G., Cheung, L., Frederik, J., Houck, D., deVelder, N., Simley, E., and Fleming, P.: Comparison of wind-farm control strategies under realistic offshore wind conditions: wake quantities of interest, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2024-191, accepted, 2025. a, b
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C.: A limited memory algorithm for bound constrained optimization, SIAM J. Sci. Comput., 16, 1190–1208, 1995. a
Cheung, L., Yalla, G., Brown, K., deVelder, N., Houck, D., Herges, T., Maniaci, D., Sakievich, P., and Abraham, A.: Modification of wind turbine wakes by large-scale convective atmospheric boundary layer structures, J. Renew. Sustain. Ener., 16, 063304, https://doi.org/10.1063/5.0211722, 2024b. a, b
Cheung, L. C. and Zaki, T. A.: A nonlinear PSE method for two-fluid shear flows with complex interfacial topology, J. Comput. Phys., 230, 6756–6777, 2011. a
Crow, S. C. and Champagne, F.: Orderly structure in jet turbulence, J. Fluid Mech., 48, 547–591, 1971. a
DNV: NYSERDA Floating LiDAR Buoy Data, Mason, J. Energy Assessment Report. Tech. Rep. 10124962, DNV, 2022. a
Fedeli, L., Huebl, A., Boillod-Cerneux, F., Clark, T., Gott, K., Hillairet, C., Jaure, S., Leblanc, A., Lehe, R., Myers, A., Piechurski, C., Sato, M., Zaim, N., Zhang, W., Vay, J.-L., and Vincenti, H.: Pushing the frontier in the design of laser-based electron accelerators with groundbreaking mesh-refined particle-in-cell simulations on exascale-class supercomputers, in: SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE Computer Society, 13–18 November 2022, Dallas, TX, USA, Los Alamitos, CA, USA, 1–12, https://doi.org/10.1109/SC41404.2022.00008, 2022. a
Frederik, J. A., Doekemeijer, B. M., Mulders, S. P., and van Wingerden, J.-W.: The helix approach: Using dynamic individual pitch control to enhance wake mixing in wind farms, Wind Energy, 23, 1739–1751, 2020a. a
Frederik, J. A., Weber, R., Cacciola, S., Campagnolo, F., Croce, A., Bottasso, C., and van Wingerden, J.-W.: Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments, Wind Energ. Sci., 5, 245–257, https://doi.org/10.5194/wes-5-245-2020, 2020b. a
Fuchs, H. V., Mercker, E., and Michel, U.: Large-scale coherent structures in the wake of axisymmetric bodies, J. Fluid Mech., 93, 185–207, 1979. a
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G., Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Visell, A.: IEA wind TCP task 37: definition of the IEA 15-megawatt offshore reference wind turbine, Tech. rep., National Renewable Energy Laboratory (NREL), Golden, CO (United States), https://docs.nrel.gov/docs/fy20osti/75698.pdf (last access: 16 July 2025), 2020. a
Goit, J. P. and Meyers, J.: Optimal control of energy extraction in wind-farm boundary layers, J. Fluid Mech., 768, 5–50, 2015. a
Gutknecht, J., Becker, M., Muscari, C., Lutz, T., and van Wingerden, J.-W.: Scaling DMD modes for modeling Dynamic Induction Control wakes in various wind speeds, in: 2023 IEEE Conference on Control Technology and Applications (CCTA), IEEE, 574–580, 16–18 August 2023, Bridgetown, Barbados, https://doi.org/10.1109/CCTA54093.2023.10252400, 2023. a
Hack, M. P. and Zaki, T. A.: Modal and non-modal stability of boundary layers forced by spanwise wall oscillations, J. Fluid Mech., 778, 389–427, 2015. a
Hamilton, N., Viggiano, B., Calaf, M., Tutkun, M., and Cal, R. B.: A generalized framework for reduced-order modeling of a wind turbine wake, Wind Energy, 21, 373–390, 2018. a
Henry de Frahan, M. T., Rood, J. S., Day, M. S., Sitaraman, H., Yellapantula, S., Perry, B. A., Grout, R. W., Almgren, A., Zhang, W., Bell, J. B., and Chen, J. H.: PeleC: An adaptive mesh refinement solver for compressible reacting flows, Int. J. High Perform. C., 2022, 109434202211211, https://doi.org/10.1177/10943420221121151, 2022. a
Henry de Frahan, M. T., Esclapez, L., Rood, J., Wimer, N. T., Mullowney, P., Perry, B. A., Owen, L., Sitaraman, H., Yellapantula, S., Hassanaly, M., Rahimi, M. J., Martin, M. J., Doronina, O. A., Sreejith, N. A., Rieth, M., Ge, W., Sankaran, R., Almgren, A. S., Zhang, W., Bell, J. B., Grout, R., Day, M. S., and Chen, J. H.: The Pele simulation suite for reacting flows at exascale, in: Proceedings of the 2024 SIAM Conference on Parallel Processing for Scientific Computing, 5–8 March 2024, Baltimore, MD, USA, 13–25, https://doi.org/10.1137/1.9781611977967.2, 2024. a
Ho, C.-M. and Huerre, P.: Perturbed free shear layers, Annu. Rev. Fluid Mech., 16, 365–424, 1984. a
Hsieh, A. S., Cheung, L. C., Blaylock, M. L., Brown, K. A., Houck, D. R., Herges, T. G., deVelder, N. B., Maniaci, D. C., Yalla, G. R., Sakievich, P. J., Radunz, W. C., Carmo, B. S.: Model intercomparison of the ABL, turbines, and wakes within the AWAKEN wind farms under neutral stability conditions, J. Renew. Sustain. Energ., 17, 023301, https://doi.org/10.1063/5.0211729, 2025. a
Hussain, A. F.: Coherent structures and turbulence, J. Fluid Mech., 173, 303–356, 1986. a
Iqbal, M. and Thomas, F.: Coherent structure in a turbulent jet via a vector implementation of the proper orthogonal decomposition, J. Fluid Mech., 571, 281–326, 2007. a
Jensen, N. O.: A note on wind generator interaction, Risø National Laboratory, Risø-M No. 2411, https://orbit.dtu.dk/files/55857682/ris_m_2411.pdf (last access: July 2025), 1983. a
Jonkman, J. M., Wright, A. D., Hayman, G. J., and Robertson, A. N.: Full-system linearization for floating offshore wind turbines in OpenFAST, in: International Conference on Offshore Mechanics and Arctic Engineering, vol. 51975, American Society of Mechanical Engineers, 17–22 June 2018, Madrid, Spain, https://doi.org/10.1115/IOWTC2018-1025, V001T01A028, 2018. a
Korb, H., Asmuth, H., and Ivanell, S.: The characteristics of helically deflected wind turbine wakes, J. Fluid Mech., 965, A2, https://doi.org/10.1017/jfm.2023.390, 2023. a
Kuhn, M. B., Henry de Frahan, M. T., Mohan, P., Deskos, G., Churchfield, M., Cheung, L., Sharma, A., Almgren, A., Ananthan, S., Brazell, M. J., Martínez-Tossas, L., Thedin, R., Rood, J., Sakievich, P., Vijayakumar, G., Zhang, W., and Sprague, M. A.: AMR-Wind: A performance-portable, high-fidelity flow solver for wind farm simulations, Wind Energy, 28, e70010, https://doi.org/10.1002/we.70010, 2025. a
Kwon, Y., Hutchins, N., and Monty, J.: On the use of the Reynolds decomposition in the intermittent region of turbulent boundary layers, J. Fluid Mech., 794, 5–16, 2016. a
Larsen, G. C., Aagaard Madsen, H., and Bingöl, F.: Dynamic wake meandering modeling, Risoe National Lab., DTU, Roskilde (Denmark), Technical report RISO-R-1607, https://www.osti.gov/etdeweb/servlets/purl/20941220 (last access: 16 July 2025), 2007. a
Li, Z. and Yang, X.: Resolvent-based motion-to-wake modelling of wind turbine wakes under dynamic rotor motion, J. Fluid Mech., 980, A48, https://doi.org/10.1017/jfm.2023.1097, 2024. a
Lumley, J. L.: The structure of inhomogeneous turbulent flows, Atmospheric turbulence and radio wave propagation, Defense Technical Information Center, 166–178, https://cir.nii.ac.jp/crid/1574231874542771712 (last access: 16 July 2025), 1967. a
Madsen, H. A., Larsen, G. C., Larsen, T. J., Troldborg, N., and Mikkelsen, R.: Calibration and validation of the dynamic wake meandering model for implementation in an aeroelastic code, ASME J. Sol. Energy Eng., 132, 041014, https://doi.org/10.1115/1.4002555, 2010. a
Mason, J.: Energy Assessment Report, Tech. Rep. 10124962, DNV, 2022. a
Meyers, J., Bottasso, C., Dykes, K., Fleming, P., Gebraad, P., Giebel, G., Göçmen, T., and van Wingerden, J.-W.: Wind farm flow control: prospects and challenges, Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, 2022. a
Moeng, C.-H.: A large-eddy-simulation model for the study of planetary boundary-layer turbulence, J. Atmos. Sci., 41, 2052–2062, 1984. a
Munters, W. and Meyers, J.: Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and sinusoidal induction control of first-row turbines, Wind Energ. Sci., 3, 409–425, https://doi.org/10.5194/wes-3-409-2018, 2018. a
NREL: OpenFAST Documentation, https://openfast.readthedocs.io (last access: : July 2025), 2023. a
Robinson, S.: Coherent motions in the turbulent boundary layer, Annu. Rev. Fluid Mech., 23, 601–639, 1991. a
Schmid, P. J.: Nonmodal stability theory, Annu. Rev. Fluid Mech., 39, 129–162, 2007. a
Sharma, A., Brazell, M. J., Vijayakumar, G., Ananthan, S., Cheung, L., deVelder, N., Henry de Frahan, M. T., Matula, N., Mullowney, P., Rood, J., Sakievich, P., Almgren, A., Crozier, P. S., and Sprague, M.: ExaWind: Open-source CFD for hybrid-RANS/LES geometry-resolved wind turbine simulations in atmospheric flows, Wind Energy, 27, 225–257, https://doi.org/10.1002/we.2886, 2024. a
Sinner, M. and Fleming, P.: Robust wind farm layout optimization, J. Phys. Conf. Ser., 2767, 032036, https://doi.org/10.1088/1742-6596/2767/3/032036, 2024. a
Sprague, M. A., Ananthan, S., Vijayakumar, G., and Robinson, M.: ExaWind: A multifidelity modeling and simulation environment for wind energy, J. Phys. Conf. Ser., 1452, 012071, https://doi.org/10.1088/1742-6596/1452/1/012071, 2020. a
Towne, A., Schmidt, O. T., and Colonius, T.: Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis, J. Fluid Mech., 847, 821–867, 2018. a
Yalla, G. R., Brown, K., Cheung, L., Houck, D., deVelder, N., and Hamilton, N.: Spectral proper orthogonal decomposition of active wake mixing dynamics in a stable atmospheric boundary layer, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2025-14, accepted, 2025. a, b, c
Zhang, W., Almgren, A., Beckner, V., Bell, J., Blaschke, J., Chan, C., Day, M., Friesen, B., Gott, K., Graves, D., Katz, M., Myers, A., Nguyen, T., Nonaka, A., Rosso, M., Williams, S., and Zingale, M.: AMReX: a framework for block-structured adaptive mesh refinement, Journal of Open Source Software, 4, 1370, https://doi.org/10.21105/joss.01370, 2019. a
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization, ACM T. Math. Software, 23, 550–560, 1997. a
Short summary
Mitigating turbine wakes is an important aspect to maximizing wind farm energy production but is a challenge to model. We demonstrate a new approach to modeling active wake mixing, which re-energizes turbine wake through periodic blade pitching. The new model divides the wake into separate steady, unsteady, and turbulent components and solves for each in a computationally efficient manner. Our results show that the model can reasonably predict the faster wake recovery due to mixing.
Mitigating turbine wakes is an important aspect to maximizing wind farm energy production but is...
Special issue
Altmetrics
Final-revised paper
Preprint