Articles | Volume 10, issue 7
https://doi.org/10.5194/wes-10-1351-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-1351-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jul 2025
Research article |
| 10 Jul 2025
| 10 Jul 2025
Spatiotemporal behavior of the far wake of a wind turbine model subjected to harmonic motions: phase averaging applied to stereo particle image velocimetry measurements
Antonin Hubert
Nantes Université, École Centrale Nantes, CNRS, LHEEA, UMR 6598, 44000 Nantes, France
Boris Conan
Nantes Université, École Centrale Nantes, CNRS, LHEEA, UMR 6598, 44000 Nantes, France
Sandrine Aubrun
CORRESPONDING AUTHOR
Nantes Université, École Centrale Nantes, CNRS, LHEEA, UMR 6598, 44000 Nantes, France
Related authors
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Caroline Braud, Pascal Keravec, Ingrid Neunaber, Sandrine Aubrun, Jean-Luc Attie, Pierre Durand, Philippe Ricaud, Jean-François Georgis, Emmanuel Leclerc, Lise Mourre, and Claire Taymans
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-12, https://doi.org/10.5194/wes-2025-12, 2025
Revised manuscript accepted for WES
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A meteorological 3 year dataset from an operational wind farm of six 2 MW turbines, has been made available. This includes a meteorological mast equipped with sonic anemometers at four different heights and radiometer measurements for atmospheric stability analysis. Simultaneously, supervisory control and data acquisition (SCADA) and the scanned geometry of the turbine blades are provided. This database has been made accessible to the research community (https://awit.aeris-data.fr).
Dimas Alejandro Barile, Roberto Sosa, Sandrine Aubrun, and Alejandro Daniel Otero
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-2, https://doi.org/10.5194/wes-2025-2, 2025
Manuscript not accepted for further review
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This work sets out a novel methodology for the CFD simulation of an ABL wind tunnel flow. Initially, the scheme is well validated against experimental measurements, and then it is applied to the study of a floating offshore wind turbine model under surge motion with varying turbulence intensities and motion frequencies. New insights are gained related to wake recovery of a wind turbine under surge motion, as certain frequency cases exhibit a distinctive behaviour regarding coherence structures.
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
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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.
Boris Conan and Aleksandra Visich
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-141, https://doi.org/10.5194/wes-2023-141, 2023
Revised manuscript not accepted
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The paper describes an original field experiment using a scanning LiDAR set up to measure the wind profile above the sea surface up to an altitude of 500 m. Reaching this height with a good vertical resolution is key for the wind energy sector, especially for wind turbine design, load and fatigue predictions. Observations at the site include low-level jets and extreme wind shear that are observed 15 % and 30 % of the time, respectively.
Stefano Macrí, Sandrine Aubrun, Annie Leroy, and Nicolas Girard
Wind Energ. Sci., 6, 585–599, https://doi.org/10.5194/wes-6-585-2021, https://doi.org/10.5194/wes-6-585-2021, 2021
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This paper investigates the effect of misaligning a wind turbine on its wake deviation response and on the global load variation of a downstream wind turbine during a positive and negative yaw maneuver, representing a misalignment–realignment scenario. Yaw maneuvers could be used to voluntarily misalign wind turbines when wake steering control is targeted. The aim of this wind farm control strategy is to optimize the overall production of the wind farm and its lifetime.
Related subject area
Thematic area: Fluid mechanics | Topic: Wakes and wind farm aerodynamics
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
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
Modeling the effects of active wake mixing on wake behavior through large scale coherent structures
Wake Development in Floating Wind Turbines: New Insights and Open Dataset from Wind Tunnel Experiments
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
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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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.
Lawrence Cheung, Gopal Yalla, Prakash Mohan, Alan Hsieh, Kenneth Brown, Nathaniel deVelder, Daniel Houck, and Marc Henry de Frahan
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-155, https://doi.org/10.5194/wes-2024-155, 2024
Revised manuscript accepted for WES
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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 the model can reasonably predict the faster wake recovery due to mixing.
Alessandro Fontanella, Alberto Fusetti, Stefano Cioni, Francesco Papi, Sara Muggiasca, Giacomo Persico, Vincenzo Dossena, Alessandro Bianchini, and Marco Belloli
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-140, https://doi.org/10.5194/wes-2024-140, 2024
Revised manuscript accepted for WES
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The article 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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
Adrian, R. J. and Westerweel, J.: Particle Image Velocimetry, no. 30 in Cambridge Aerospace Series, Cambridge University Press, ISBN 978-0521440080, 2011. a
Ainslie, J.: Calculating the Flowfield in the Wake of Wind Turbines, J. Wind Eng. Ind. Aerod., 27, 213–224, https://doi.org/10.1016/0167-6105(88)90037-2, 1988. a
Aubrun, S. and Conan, B.: Spatio-temporal behavior of the far-wake of a wind turbine model subjected to harmonic motions: Phase averaging applied to Stereo-PIV measurements, Zenodo [data set], https://doi.org/10.5281/zenodo.15038753, 2025. a
Aubrun, S., Loyer, S., Hancock, P., and Hayden, P.: Wind Turbine Wake Properties: Comparison between a Non-Rotating Simplified Wind Turbine Model and a Rotating Model, J. Wind Eng. Ind. Aerod., 120, 1–8, https://doi.org/10.1016/j.jweia.2013.06.007, 2013. a, b
Aubrun, S., Bastankhah, M., Cal, R., Conan, B., Hearst, R., Hoek, D., Hölling, M., Huang, M., Hur, C., Karlsen, B., Neunaber, I., Obligado, M., Peinke, J., Percin, M., Saetran, L., Schito, P., Schliffke, B., Sims-Williams, D., Uzol, O., Vinnes, M., and Zasso, A.: Round-Robin Tests of Porous Disc Models, J. Phys. Conf. Ser., 1256, 012004, https://doi.org/10.1088/1742-6596/1256/1/012004, 2019. a
Bastankhah, M. and Porté-Agel, F.: A New Analytical Model for Wind-Turbine Wakes, Renew. Energ., 70, 116–123, https://doi.org/10.1016/j.renene.2014.01.002, 2014. a, b, c
Bastankhah, M. and Porté-Agel, F.: A Wind-Tunnel Investigation of Wind-Turbine Wakes in Yawed Conditions, J. Phys. Conf. Ser., 625, 012014, https://doi.org/10.1088/1742-6596/625/1/012014, 2015. a
Bastankhah, M. and Porté-Agel, F.: Experimental and Theoretical Study of Wind Turbine Wakes in Yawed Conditions, J. Fluid Mech., 806, 506–541, https://doi.org/10.1017/jfm.2016.595, 2016. a, b, c
Bayati, I., Belloli, M., Bernini, L., and Zasso, A.: Wind Tunnel Wake Measurements of Floating Offshore Wind Turbines, Enrgy. Proced., 137, 214–222, https://doi.org/10.1016/j.egypro.2017.10.375, 2017. a
Bayati, I., Bernini, L., Zanotti, A., Belloli, M., and Zasso, A.: Experimental Investigation of the Unsteady Aerodynamics of FOWT through PIV and Hot-Wire Wake Measurements, J. Phys. Conf. Ser., 1037, 052024, https://doi.org/10.1088/1742-6596/1037/5/052024, 2018. a
Belvasi, N., Conan, B., Schliffke, B., Perret, L., Desmond, C., Murphy, J., and Aubrun, S.: Far-Wake Meandering of a Wind Turbine Model with Imposed Motions: An Experimental S-PIV Analysis, Energies, 15, 7757, https://doi.org/10.3390/en15207757, 2022. a, b, c
Beucher, S.: Algorithmes sans biais de ligne de partage des eaux, https://www.researchgate.net/publication/265407320_Algorithmes_sans_biais_de_ligne_de_partage_des_eaux (last access: 1 July 2025), 2004. a
Bingöl, F., Mann, J., and Larsen, G. C.: Light Detection and Ranging Mesurements of Wake Dynamics, Wind Energy, 13, 51–61, https://doi.org/10.1002/we.352, 2009. a
Bossuyt, J., Scott, R., Ali, N., and Cal, R. B.: Quantification of Wake Shape Modulation and Deflection for Tilt and Yaw Misaligned Wind Turbines, J. Fluid Mech., 917, A3, https://doi.org/10.1017/jfm.2021.237, 2021. a, b
Choisnet, T.: Report on the Requirements of the Floating Structure, Deliverable 3.1, Floatgen project, EC-GA 295977, IDEOL, https://www.scribd.com/document/455505558/floatgen-d3-1-report-on-the-requirements-of-the-floating-structure (last access: 1 July 2025), 2013. a
Costanzo, G., Brindley, G., and Cole, P.: Wind Energy in Europe – 2022 Statistics and the Outlook for 2023–2027, Tech. rep., WindEurope, https://windeurope.org/intelligence-platform/product/wind-energy-in-europe-2022-statistics-and-the-outlook-for-2023-2027/ (last access: 1 July 2025), 2023. a
Counihan J.: Adiabatic atmospheric boundary layers: A review and analysis of data from the period 1880–1972, Atmos. Environ., 1967, 871–905, https://doi.org/10.1016/0004-6981(75)90088-8, 1975. a, b
Dalla Longa, F., Kober, T., Badger, J., Volker, P., Hoyer-Klick, C., Hidalgo Gonzalez, I., Medarac, H., Nijs, W., Politis, S., Tarvydas, D., and Zucker, A.: Wind potentials for EU and neighbouring countries: Input datasets for the JRC-EU-TIMES Model , EUR 29083 EN, Publications Office of the European Union, Luxembourg, JRC109698, ISBN 978-92-79-77811-7, https://doi.org/10.2760/041705, 2018. a
Duan, L., Sun, Q., He, Z., and Li, G.: Wake Topology and Energy Recovery in Floating Horizontal-Axis Wind Turbines with Harmonic Surge Motion, Energy, 260, 124907, https://doi.org/10.1016/j.energy.2022.124907, 2022. a, b, c
ESDU: Part II: Single Point Data for Strong Winds (Neutral Atmosphere), Tech. Rep. 85020, ESDU International, ISBN 978-0 85679 526 8, 1985. a
España, G., Aubrun, S., Loyer, S., and Devinant, P.: Spatial Study of the Wake Meandering Using Modelled Wind Turbines in a Wind Tunnel, Wind Energy, 14, 923–937, https://doi.org/10.1002/we.515, 2011. a, b, c
Feist, C., Sotiropoulos, F., and Guala, M.: A Quasi-Coupled Wind Wave Experimental Framework for Testing Offshore Wind Turbine Floating Systems, Theoretical and Applied Mechanics Letters, 11, 100294, https://doi.org/10.1016/j.taml.2021.100294, 2021. a
Ferčák, O., Bossuyt, J., Ali, N., and Cal, R. B.: Decoupling Wind–Wave–Wake Interactions in a Fixed-Bottom Offshore Wind Turbine, Appl. Energ., 309, 118358, https://doi.org/10.1016/j.apenergy.2021.118358, 2021. a
Fontanella, A., Bayati, I., Mikkelsen, R., Belloli, M., and Zasso, A.: UNAFLOW: a holistic wind tunnel experiment about the aerodynamic response of floating wind turbines under imposed surge motion, Wind Energ. Sci., 6, 1169–1190, https://doi.org/10.5194/wes-6-1169-2021, 2021. a, b
Fu, S., Jin, Y., Zheng, Y., and Chamorro, L. P.: Wake and Power Fluctuations of a Model Wind Turbine Subjected to Pitch and Roll Oscillations, Appl. Energ., 253, 113605, https://doi.org/10.1016/j.apenergy.2019.113605, 2019. a, b
Fu, S., Zhang, B., Zheng, Y., and Chamorro, L. P.: In-Phase and out-of-Phase Pitch and Roll Oscillations of Model Wind Turbines within Uniform Arrays, Appl. Energ., 269, 114921, https://doi.org/10.1016/j.apenergy.2020.114921, 2020. a
Fu, S., Li, Z., Zhu, W., Han, X., Liang, X., Yang, H., and Shen, W.: Study on Aerodynamic Performance and Wake Characteristics of a Floating Offshore Wind Turbine under Pitch Motion, Renew. Energ., 205, 317–325, https://doi.org/10.1016/j.renene.2023.01.040, 2023. a, b
Hastie, T., Tibshirani, R., and Friedman, J.: Kernel Smoothing Methods, Springer New York, New York, NY, 191–218, https://doi.org/10.1007/978-0-387-84858-7_6, 2009. a
Howland, M. F., Bossuyt, J., Martínez-Tossas, L. A., Meyers, J., and Meneveau, C.: Wake Structure in Actuator Disk Models of Wind Turbines in Yaw under Uniform Inflow Conditions, J. Renew. Sustain. Ener., 8, 043301, https://doi.org/10.1063/1.4955091, 2016. a, b
Jézéquel, E., Blondel, F., and Masson, V.: Analysis of Wake Properties and Meandering under Different Cases of Atmospheric Stability: A Large Eddy Simulation Study, J. Phys. Conf. Ser., 2265, 022067, https://doi.org/10.1088/1742-6596/2265/2/022067, 2022. a
Kaimal, J. C. and Finnigan, J. J.: Atmospheric Boundary Layer Flows: Their Structure and Measurement, Oxford University Press, ISBN 978-0195062397, 1994. a
Kopperstad, K. M., Kumar, R., and Shoele, K.: Aerodynamic Characterization of Barge and Spar Type Floating Offshore Wind Turbines at Different Sea States, Wind Energy, 23, 2087–2112, https://doi.org/10.1002/we.2547, 2020. a, b, c
Larsen, G., Pedersen, A., Hansen, K., Larsen, T., Courtney, M., and Sjöholm, M.: Full-Scale 3D Remote Sensing of Wake Turbulence – a Taster, J. Phys. Conf. Ser., 1256, 012001, https://doi.org/10.1088/1742-6596/1256/1/012001, 2019. a
Larsen, G. C., Madsen Aagaard, H., Bingöl, F., Mann, J., Ott, S., Sørensen, J. N., Okulov, V., Troldborg, N., Nielsen, N. M., Thomsen, K., Larsen, T. J., and Mikkelsen, R.: Dynamic wake meandering modeling, Risø National Laboratory, Denmark, Forskningscenter Risoe, Risoe-R No. 1607(EN), ISBN 978-87-550-3602-4, 2007. a, b
Leimeister, M., Kolios, A., and Collu, M.: Critical Review of Floating Support Structures for Offshore Wind Farm Deployment, J. Phys. Conf. Ser., 1104, 012007, https://doi.org/10.1088/1742-6596/1104/1/012007, 2018. 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, b
Meng, H., Su, H., Qu, T., and Lei, L.: Wind Tunnel Study on the Wake Characteristics of a Wind Turbine Model Subjected to Surge and Sway Motions, J. Renew. Sustain. Ener., 14, 013307, https://doi.org/10.1063/5.0079843, 2022. a, b
Muller, Y.-A., Aubrun, S., and Masson, C.: Determination of Real-Time Predictors of the Wind Turbine Wake Meandering, Exp. Fluids, 56, 53, https://doi.org/10.1007/s00348-015-1923-9, 2015. a
Porchetta, S., Temel, O., Muñoz-Esparza, D., Reuder, J., Monbaliu, J., van Beeck, J., and van Lipzig, N.: A new roughness length parameterization accounting for wind–wave (mis)alignment, Atmos. Chem. Phys., 19, 6681–6700, https://doi.org/10.5194/acp-19-6681-2019, 2019. a, b
Porchetta, S., Muñoz-Esparza, D., Munters, W., van Beeck, J., and van Lipzig, N.: Impact of Ocean Waves on Offshore Wind Farm Power Production, Renew. Energ., 180, 1179–1193, https://doi.org/10.1016/j.renene.2021.08.111, 2021. a
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-Turbine and Wind-Farm Flows: A Review, Bound.-Lay. Meteorol., 174, 1–59, https://doi.org/10.1007/s10546-019-00473-0, 2020. a
Raffel, M., Willert, C., and Kompenhans, J. (Eds.): Particle Image Velocimetry: A Practical Guide, 2nd edn., Springer, Heidelberg, New York, https://doi.org/10.1007/978-3-540-72308-0, 1998. a
Rockel, S., Camp, E., Schmidt, J., Peinke, J., Cal, R., and Hölling, M.: Experimental Study on Influence of Pitch Motion on the Wake of a Floating Wind Turbine Model, Energies, 7, 1954–1985, https://doi.org/10.3390/en7041954, 2014. a
Schliffke, B.: Experimental Characterisation of the Far Wake of a Modelled Floating Wind Turbine as a Function of Incoming Swell, PhD thesis, Centrale Nantes, Nantes, https://theses.hal.science/tel-03722239/ (last access: 1 July 2025), 2022. a
Sciacchitano, A.: Uncertainty Quantification in Particle Image Velocimetry, Meas. Sci. Technol., 30, 092001, https://doi.org/10.1088/1361-6501/ab1db8, 2019. a
Sebastian, T. and Lackner, M.: Characterization of the Unsteady Aerodynamics of Offshore Floating Wind Turbines: Unsteady Aerodynamics of Offshore Floating Wind Turbines, Wind Energy, 16, 339–352, https://doi.org/10.1002/we.545, 2013. a
VDI: Physical Modelling of Flow and Dispersion Processes in the Atmospheric Boundary Layer – Application of Wind Tunnels, Tech. Rep. 12, Engl. VDI/DIN-Kommission Reinhaltung der Luft (KRdL) – Normenausschuss, https://www.vdi.de/en/home/vdi-standards/details/vdi-3783-blatt-12-environmental-meteorology-physical-modelling-of-flow-and-dispersion-processes-in-the-atmospheric-boundary-layer-application-of-wind-tunnels (last access: 1 July 2025), 2000. a, b
Vermeer, L., Sørensen, J., and Crespo, A.: Wind Turbine Wake Aerodynamics, Prog. Aerosp. Sci., 39, 467–510, https://doi.org/10.1016/S0376-0421(03)00078-2, 2003. a, b
Wieneke, B.: PIV Uncertainty Quantification and Beyond, PhD thesis, Delft University of Technology, https://doi.org/10.4233/UUID:4CA8C0B8-0835-47C3-8523-12FC356768F3, 2017. a
Zhang, P., Li, C., Wei, Y., and Wu, W.: Three-Dimensional Analytical Wake Model for Floating Offshore Wind Turbines under Pitch Motion, Ocean Eng., 311, 118935, https://doi.org/10.1016/j.oceaneng.2024.118935, 2024. a, b
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.
The paper aims to study the far wake of a wind turbine under realistic inflow conditions...
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