Articles | Volume 7, issue 5
https://doi.org/10.5194/wes-7-2117-2022
© Author(s) 2022. 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-7-2117-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Oct 2022
Research article |
| 26 Oct 2022
| 26 Oct 2022
Predictive and stochastic reduced-order modeling of wind turbine wake dynamics
Department of Wind and Energy Systems, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kgs Lyngby, Denmark
Juan Pablo Murcia Leon
Department of Wind and Energy Systems, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Related authors
Stefan Ivanell, Warit Chanprasert, Luca Lanzilao, James Bleeg, Johan Meyers, Antoine Mathieu, Søren Juhl Andersen, Rem-Sophia Mouradi, Eric Dupont, Hugo Olivares-Espinosa, and Niels Troldborg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-88, https://doi.org/10.5194/wes-2025-88, 2025
Preprint under review for WES
Short summary
Short summary
This study explores how the height of the atmosphere's boundary layer impacts wind farm performance, focusing on how this factor influences energy output. By simulating different boundary layer heights and conditions, the research reveals that deeper layers promote better energy recovery. The findings highlight the importance of considering atmospheric conditions when simulating wind farms to maximize energy efficiency, offering valuable insights for the wind energy industry.
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.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
Short summary
Short summary
The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
Niels Troldborg, Søren J. Andersen, Emily L. Hodgson, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 1527–1532, https://doi.org/10.5194/wes-7-1527-2022, https://doi.org/10.5194/wes-7-1527-2022, 2022
Short summary
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This article shows that the power performance of a wind turbine may be very different in flat and complex terrain. This is an important finding because it shows that the power output of a given wind turbine is governed by not only the available wind at the position of the turbine but also how the ambient flow develops in the region behind the turbine.
Mark Kelly, Søren Juhl Andersen, and Ásta Hannesdóttir
Wind Energ. Sci., 6, 1227–1245, https://doi.org/10.5194/wes-6-1227-2021, https://doi.org/10.5194/wes-6-1227-2021, 2021
Short summary
Short summary
Via 11 years of measurements, we made a representative ensemble of wind ramps in terms of acceleration, mean speed, and shear. Constrained turbulence and large-eddy simulations were coupled to an aeroelastic model for each ensemble member. Ramp acceleration was found to dominate the maxima of thrust-associated loads, with a ramp-induced increase of 45 %–50 % plus ~ 3 % per 0.1 m/s2 of bulk ramp acceleration magnitude. The LES indicates that the ramps (and such loads) persist through the farm.
Søren Juhl Andersen, Simon-Philippe Breton, Björn Witha, Stefan Ivanell, and Jens Nørkær Sørensen
Wind Energ. Sci., 5, 1689–1703, https://doi.org/10.5194/wes-5-1689-2020, https://doi.org/10.5194/wes-5-1689-2020, 2020
Short summary
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The complexity of wind farm operation increases as the wind farms get larger and larger. Therefore, researchers from three universities have simulated numerous different large wind farms as part of an international benchmark. The study shows how simple engineering models can capture the general trends, but high-fidelity simulations are required in order to quantify the variability and uncertainty associated with power production of the wind farms and hence the potential profitability and risks.
Jaime Liew, Albert M. Urbán, and Søren Juhl Andersen
Wind Energ. Sci., 5, 427–437, https://doi.org/10.5194/wes-5-427-2020, https://doi.org/10.5194/wes-5-427-2020, 2020
Short summary
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In wind farms, the interaction between neighboring turbines can cause notable power losses. The focus of the paper is on how the combination of turbine yaw misalignment and wake effects influences the power loss in a wind turbine. The results of the paper show a more notable power loss due to turbine misalignment when turbines are closely spaced. The presented conclusions enable better predictions of a turbine's power production, which can assist the wind farm design process.
Paul Hulsman, Søren Juhl Andersen, and Tuhfe Göçmen
Wind Energ. Sci., 5, 309–329, https://doi.org/10.5194/wes-5-309-2020, https://doi.org/10.5194/wes-5-309-2020, 2020
Short summary
Short summary
We aim to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion, are built using high-fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°.
Maarten Paul van der Laan, Søren Juhl Andersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 4, 645–651, https://doi.org/10.5194/wes-4-645-2019, https://doi.org/10.5194/wes-4-645-2019, 2019
Short summary
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Wind farm layouts are designed by simple engineering wake models, which are fast to compute but also include a high uncertainty. Higher-fidelity models, such as Reynolds-averaged Navier–Stokes, can be used to verify optimized wind farm layouts, although the computational costs are high due to the large number of cases that are needed to calculate the annual energy production. This article presents a new wind turbine control method to speed up the high-fidelity simulations by a factor of 2–3.
Maarten Paul van der Laan, Søren Juhl Andersen, Néstor Ramos García, Nikolas Angelou, Georg Raimund Pirrung, Søren Ott, Mikael Sjöholm, Kim Hylling Sørensen, Julio Xavier Vianna Neto, Mark Kelly, Torben Krogh Mikkelsen, and Gunner Christian Larsen
Wind Energ. Sci., 4, 251–271, https://doi.org/10.5194/wes-4-251-2019, https://doi.org/10.5194/wes-4-251-2019, 2019
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Over the past few decades, single-rotor wind turbines have increased in size with the blades being extended toward lengths of 100 m. An alternative upscaling of turbines can be achieved by using multi-rotor wind turbines. In this article, measurements and numerical simulations of a utility-scale four-rotor wind turbine show that rotor interaction leads to increased energy production and faster wake recovery; these findings may allow for the design of wind farms with improved energy production.
Jakob Mann, Alfredo Peña, Niels Troldborg, and Søren J. Andersen
Wind Energ. Sci., 3, 293–300, https://doi.org/10.5194/wes-3-293-2018, https://doi.org/10.5194/wes-3-293-2018, 2018
Short summary
Short summary
Turbulence is usually assumed to be unmodified by the stagnation occurring in front of a wind turbine rotor. All manufacturers assume this in their dynamic load calculations. If this assumption is not true it might bias the load calculations and the turbines might not be designed optimally. We investigate the assumption with a Doppler lidar measuring forward from the top of the nacelle and find small but systematic changes in the approaching turbulence that depend on the power curve.
Mads Mølgaard Pedersen, Torben Juul Larsen, Helge Aagaard Madsen, and Søren Juhl Andersen
Wind Energ. Sci., 3, 121–138, https://doi.org/10.5194/wes-3-121-2018, https://doi.org/10.5194/wes-3-121-2018, 2018
Short summary
Short summary
The wind speed measured by a flow sensor mounted on the blade of a wind turbine is disturbed by the turbine. This paper presents a method to obtain the free turbulence inflow by compensating for this disturbance.
The method is tested using numerical simulations and can be used to extract inflow information for accurate aeroelastic load simulations.
Stefan Ivanell, Warit Chanprasert, Luca Lanzilao, James Bleeg, Johan Meyers, Antoine Mathieu, Søren Juhl Andersen, Rem-Sophia Mouradi, Eric Dupont, Hugo Olivares-Espinosa, and Niels Troldborg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-88, https://doi.org/10.5194/wes-2025-88, 2025
Preprint under review for WES
Short summary
Short summary
This study explores how the height of the atmosphere's boundary layer impacts wind farm performance, focusing on how this factor influences energy output. By simulating different boundary layer heights and conditions, the research reveals that deeper layers promote better energy recovery. The findings highlight the importance of considering atmospheric conditions when simulating wind farms to maximize energy efficiency, offering valuable insights for the wind energy industry.
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.
Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das
Wind Energ. Sci., 10, 559–578, https://doi.org/10.5194/wes-10-559-2025, https://doi.org/10.5194/wes-10-559-2025, 2025
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This research develops a new method for assessing hybrid power plant (HPP) profitability, combining wind and battery systems. It addresses the need for an efficient, accurate, and comprehensive operational model by approximating a state-of-the-art energy management system (EMS) for spot market power bidding using machine learning. The approach significantly reduces computational demands while maintaining high accuracy. It thus opens new possibilities in terms of optimizing the design of HPPs.
Juan Pablo Murcia Leon, Hajar Habbou, Mikkel Friis-Møller, Megha Gupta, Rujie Zhu, and Kaushik Das
Wind Energ. Sci., 9, 759–776, https://doi.org/10.5194/wes-9-759-2024, https://doi.org/10.5194/wes-9-759-2024, 2024
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A methodology for an early design of hybrid power plants (wind, solar, PV, and Li-ion battery storage) consisting of a nested optimization that sizes the components and internal operation optimization. Traditional designs that minimize the levelized cost of energy give worse business cases and do not include storage. Optimal operation balances the increasing revenues and faster battery degradation. Battery degradation and replacement costs are needed to estimate the viability of hybrid projects.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
Short summary
Short summary
The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
Niels Troldborg, Søren J. Andersen, Emily L. Hodgson, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 1527–1532, https://doi.org/10.5194/wes-7-1527-2022, https://doi.org/10.5194/wes-7-1527-2022, 2022
Short summary
Short summary
This article shows that the power performance of a wind turbine may be very different in flat and complex terrain. This is an important finding because it shows that the power output of a given wind turbine is governed by not only the available wind at the position of the turbine but also how the ambient flow develops in the region behind the turbine.
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022, https://doi.org/10.5194/wes-7-1069-2022, 2022
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Wind turbines extract kinetic energy from the flow to create electricity. This induces a wake of reduced wind speed downstream of a turbine and consequently downstream of a wind farm. Different types of numerical models have been developed to calculate this effect. In this study, we compared models of different complexity, together with measurements over two wind farms. We found that higher-fidelity models perform better and the considered rapid models cannot fully capture the wake effect.
Mark Kelly, Søren Juhl Andersen, and Ásta Hannesdóttir
Wind Energ. Sci., 6, 1227–1245, https://doi.org/10.5194/wes-6-1227-2021, https://doi.org/10.5194/wes-6-1227-2021, 2021
Short summary
Short summary
Via 11 years of measurements, we made a representative ensemble of wind ramps in terms of acceleration, mean speed, and shear. Constrained turbulence and large-eddy simulations were coupled to an aeroelastic model for each ensemble member. Ramp acceleration was found to dominate the maxima of thrust-associated loads, with a ramp-induced increase of 45 %–50 % plus ~ 3 % per 0.1 m/s2 of bulk ramp acceleration magnitude. The LES indicates that the ramps (and such loads) persist through the farm.
Juan Pablo Murcia Leon, Matti Juhani Koivisto, Poul Sørensen, and Philippe Magnant
Wind Energ. Sci., 6, 461–476, https://doi.org/10.5194/wes-6-461-2021, https://doi.org/10.5194/wes-6-461-2021, 2021
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Detailed wind generation simulations of the 2028 Belgian offshore fleet are performed in order to quantify the distribution and extremes of power fluctuations in several time windows. A model validation with respect to the operational data of the 2018 fleet shows that the methodology presented in this article is able to capture the distribution of wind power and its spatiotemporal characteristics. The results show that the standardized generation ramps are expected to be reduced in the future.
Søren Juhl Andersen, Simon-Philippe Breton, Björn Witha, Stefan Ivanell, and Jens Nørkær Sørensen
Wind Energ. Sci., 5, 1689–1703, https://doi.org/10.5194/wes-5-1689-2020, https://doi.org/10.5194/wes-5-1689-2020, 2020
Short summary
Short summary
The complexity of wind farm operation increases as the wind farms get larger and larger. Therefore, researchers from three universities have simulated numerous different large wind farms as part of an international benchmark. The study shows how simple engineering models can capture the general trends, but high-fidelity simulations are required in order to quantify the variability and uncertainty associated with power production of the wind farms and hence the potential profitability and risks.
Andreas Bechmann, Juan Pablo M. Leon, Bjarke T. Olsen, and Yavor V. Hristov
Wind Energ. Sci., 5, 1679–1688, https://doi.org/10.5194/wes-5-1679-2020, https://doi.org/10.5194/wes-5-1679-2020, 2020
Short summary
Short summary
When assessing wind resources for wind farm development, the first step is to measure the wind from tall meteorological masts. As met masts are expensive, they are not built at every planned wind turbine position but sparsely while trying to minimize the distance. However, this paper shows that it is better to focus on the
similaritybetween the met mast and the wind turbines than the distance. Met masts at similar positions reduce the uncertainty of wind resource assessments significantly.
Jaime Liew, Albert M. Urbán, and Søren Juhl Andersen
Wind Energ. Sci., 5, 427–437, https://doi.org/10.5194/wes-5-427-2020, https://doi.org/10.5194/wes-5-427-2020, 2020
Short summary
Short summary
In wind farms, the interaction between neighboring turbines can cause notable power losses. The focus of the paper is on how the combination of turbine yaw misalignment and wake effects influences the power loss in a wind turbine. The results of the paper show a more notable power loss due to turbine misalignment when turbines are closely spaced. The presented conclusions enable better predictions of a turbine's power production, which can assist the wind farm design process.
Paul Hulsman, Søren Juhl Andersen, and Tuhfe Göçmen
Wind Energ. Sci., 5, 309–329, https://doi.org/10.5194/wes-5-309-2020, https://doi.org/10.5194/wes-5-309-2020, 2020
Short summary
Short summary
We aim to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion, are built using high-fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°.
Maarten Paul van der Laan, Søren Juhl Andersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 4, 645–651, https://doi.org/10.5194/wes-4-645-2019, https://doi.org/10.5194/wes-4-645-2019, 2019
Short summary
Short summary
Wind farm layouts are designed by simple engineering wake models, which are fast to compute but also include a high uncertainty. Higher-fidelity models, such as Reynolds-averaged Navier–Stokes, can be used to verify optimized wind farm layouts, although the computational costs are high due to the large number of cases that are needed to calculate the annual energy production. This article presents a new wind turbine control method to speed up the high-fidelity simulations by a factor of 2–3.
Maarten Paul van der Laan, Søren Juhl Andersen, Néstor Ramos García, Nikolas Angelou, Georg Raimund Pirrung, Søren Ott, Mikael Sjöholm, Kim Hylling Sørensen, Julio Xavier Vianna Neto, Mark Kelly, Torben Krogh Mikkelsen, and Gunner Christian Larsen
Wind Energ. Sci., 4, 251–271, https://doi.org/10.5194/wes-4-251-2019, https://doi.org/10.5194/wes-4-251-2019, 2019
Short summary
Short summary
Over the past few decades, single-rotor wind turbines have increased in size with the blades being extended toward lengths of 100 m. An alternative upscaling of turbines can be achieved by using multi-rotor wind turbines. In this article, measurements and numerical simulations of a utility-scale four-rotor wind turbine show that rotor interaction leads to increased energy production and faster wake recovery; these findings may allow for the design of wind farms with improved energy production.
Jakob Mann, Alfredo Peña, Niels Troldborg, and Søren J. Andersen
Wind Energ. Sci., 3, 293–300, https://doi.org/10.5194/wes-3-293-2018, https://doi.org/10.5194/wes-3-293-2018, 2018
Short summary
Short summary
Turbulence is usually assumed to be unmodified by the stagnation occurring in front of a wind turbine rotor. All manufacturers assume this in their dynamic load calculations. If this assumption is not true it might bias the load calculations and the turbines might not be designed optimally. We investigate the assumption with a Doppler lidar measuring forward from the top of the nacelle and find small but systematic changes in the approaching turbulence that depend on the power curve.
Mads Mølgaard Pedersen, Torben Juul Larsen, Helge Aagaard Madsen, and Søren Juhl Andersen
Wind Energ. Sci., 3, 121–138, https://doi.org/10.5194/wes-3-121-2018, https://doi.org/10.5194/wes-3-121-2018, 2018
Short summary
Short summary
The wind speed measured by a flow sensor mounted on the blade of a wind turbine is disturbed by the turbine. This paper presents a method to obtain the free turbulence inflow by compensating for this disturbance.
The method is tested using numerical simulations and can be used to extract inflow information for accurate aeroelastic load simulations.
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
Spatio-temporal behavior of the far-wake of a wind Turbine model subjected to harmonic motions: Phase averaging applied to Stereo-PIV measurements
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
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
Short summary
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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
Short summary
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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
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
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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
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.
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
Short summary
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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.
Antonin Hubert, Boris Conan, and Sandrine Aubrun
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-95, https://doi.org/10.5194/wes-2024-95, 2024
Revised manuscript accepted for WES
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The paper aims to study the far-wake of a wind turbine under realistic inflow conditions subjected to harmonic floating motions. The present work show that phase-averaging enables the observation of the coherent spatio-temporal wake behaviour in response to the harmonic motions, on contrary to previous studies with time-averaging, and that the resulting variations of the chosen metrics exhibit an intensity higher than those expected by using basic quasi-steady-state approaches.
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.
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
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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
Aagaard Madsen, H., Bak, C., Schmidt Paulsen, U., Gaunaa, M., Fuglsang, P.,
Romblad, J., Olesen, N., Enevoldsen, P., Laursen, J., and Jensen, L.: The
DAN-AERO MW Experiments, Denmark, Forskningscenter Risø, Risø-R,
Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi, https://orbit.dtu.dk/en/publications/the-dan-aero-mw-experiments-final-report (last access: 10 October 2022), 2010.
Ali, N., Calaf, M., and Cal, R. B.: Cluster-based probabilistic structure
dynamical model of wind turbine wake, J. Turbulence, 22, 497–516,
https://doi.org/10.1080/14685248.2021.1925125, 2021. a
Allaerts, D. and Meyers, J.: Gravity Waves and Wind-Farm Efficiency in Neutral and Stable Conditions, Bound.-Lay. Meteorol., 166, 269–299,
https://doi.org/10.1007/s10546-017-0307-5, 2018. a
Andersen, S., Sørensen, J., and Mikkelsen, R.: Reduced order model of the
inherent turbulence of wind turbine wakes inside an infinitely long row of
turbines, J. Phys.: Conf. Ser., 555, 012005, https://doi.org/10.1088/1742-6596/555/1/012005, 2014. a
Andersen, S. J.: Simulation and Prediction of Wakes and Wake Interaction in
Wind Farms, PhD thesis, Technical University of Denmark, Wind Energy,
https://orbit.dtu.dk/en/projects/simulation-and-prediction-of-wakes-and-wake-interaction-in
(last access: 10 October 2022), 2013. a, b
Andersen, S. J., Sørensen, J. N., and Mikkelsen, R.: Simulation of the
inherent turbulence and wake interaction inside an infinitely long row of
wind turbines, J. Turbulence, 14, 1–24, https://doi.org/10.1080/14685248.2013.796085, 2013. a
Andersen, S. J., Sørensen, J. N., and Mikkelsen, R. F.: Turbulence and
entrainment length scales in large wind farms, Philos. T. Roy. Soc. Lond. A, 375, 20160107, https://doi.org/10.1098/rsta.2016.0107, 2017. a, b
Andersen, S. J., Breton, S.-P., Witha, B., Ivanell, S., and Sørensen, J. N.: Global trends in the performance of large wind farms based on high-fidelity simulations, Wind Energ. Sci., 5, 1689–1703, https://doi.org/10.5194/wes-5-1689-2020, 2020. a, b
Bastine, D., Bastine, D., Vollmer, L., Vollmer, L., Wächter, M., Peinke, J., and Peinke, J.: Stochastic wake modelling based on POD analysis, Energies, 11, 612, https://doi.org/10.3390/en11030612, 2018. a, b
Berkooz, G., Holmes, P., and Lumley, J. L.: The proper orthogonal decomposition in the analysis of turbulent flows, Annu. Rev. Fluid Mech., 25, 539–575, https://doi.org/10.1146/annurev.fl.25.010193.002543, 1993. a
Castro, I.: Rough-wall boundary layers: Mean flow universality, J. Fluid Mech., 585, 469–485, https://doi.org/10.1017/S0022112007006921, 2007. a
Christensen, E. A., Brøns, M., and Sørensen, J. N.: Evaluation of Proper Orthogonal Decomposition-Based Decomposition Techniques Applied to
Parameter-Dependent Nonturbulent Flows, SIAM J. Scient. Comput., 21, 1419–1434, https://doi.org/10.1137/S1064827598333181, 1999. a
Cillis, G. D., Cherubini, S., Semeraro, O., Leonardi, S., and Palma, P. D.:
POD analysis of the recovery process in wind turbine wakes, J. Phys.: Conf. Ser., 1618, 062016, https://doi.org/10.1088/1742-6596/1618/6/062016, 2020. a
Deardorff, J.: Stratocumulus-capped mixed layers derived from a
three-dimensional model, Bound.-Lay. Meteorol., 18, 495–527, https://doi.org/10.1007/BF00119502, 1980. a
Debnath, M., Santoni, C., Leonardi, S., and Iungo, G. V.: Towards reduced order modelling for predicting the dynamics of coherent vorticity structures within wind turbine wakes, Philos. T. Roy. Soc. A, 375, 20160108,
https://doi.org/10.1098/rsta.2016.0108, 2017. a
Dimitrov, N.: Surrogate models for parameterized representation of wake-induced loads in wind farms, Wind Energy, 22, 1371–1389, https://doi.org/10.1002/we.2362, 2019. a
Dong, L., Lio, W. H., and Pirrung, G. R.: Analysis and design of an adaptive
turbulence-based controller for wind turbines, Renew. Energy, 178, 730–744, https://doi.org/10.1016/j.renene.2021.06.080, 2021. a
George, W. K.: Insight into the dynamics of coherent structures from a proper
orthogonal decomposition, Proceedings of the International Centre for Heat
and Mass Transfer, Hemisphere Publ. Corp., 469–487,
http://www.turbulence-online.com/Publications/Papers/George88d.pdf
(last access: 10 October 2022), 1988. 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, https://doi.org/10.1002/we.2167, 2018. a
Hinton, G. E. and Salakhutdinov, R. R.: Reducing the dimensionality of data
with neural networks, Science, 313, 504–507, https://doi.org/10.1126/science.1127647,
2006. a
Hodgson, E. L., Andersen, S. J., Troldborg, N., Meyer Forsting, A. R.,
Mikkelsen, R. F., and Sørensen, J. N.: A quantitative comparison of aeroelastic computations using flex5 and actuator methods in les, J. Phys.: Conf. Ser., 1934, https://doi.org/10.1088/1742-6596/1934/1/012014, 2021. a, b
Iqbal, M. O. and Thomas, F. O.: Coherent structure in a turbulent jet via a
vector implementation of the proper orthogonal decomposition, J. Fluid Mech., 571, 281–326, 2007. a
Larsen, G. C., Madsen, H. A., Bingöl, F., Mann, J., Ott, S., Sørensen,
J. N., Okulov, V., Troldborg, N., Nielsen, M., Thomsen, K., Larsen, T. J.,
and Mikkelsen, R.: Dynamic wake meandering modeling, Tech. Rep. R-1607(EN),
Risø-DTU, Roskilde, Denmark, https://orbit.dtu.dk/en/publications/dynamic-wake-meandering-modeling
(last access: 10 October 2022), 2007. a
Larsen, G. C., Madsen, H. A., Thomsen, K., and Larsen, T. J.: Wake Meandering: A Pragmatic Approach, Wind Energy, 11, 377–395, https://doi.org/10.1002/we.267, 2008. a
Larsen, T., Larsen, G., Pedersen, M., Enevoldsen, K., and Madsen, H.:
Validation of the Dynamic Wake Meander model with focus on tower loads, J. Phys.: Conf. Ser. 854, 012027, https://doi.org/10.1088/1742-6596/854/1/012027, 2017. a
Lumley, J. L.: The Structure of Inhomogeneous Turbulence, in: Atmospheric
Turbulence and Wave Propagation, Nauka, Moscow, 166–178, ISBN 9783937655239, 1967. a
Mann, J.: The spatial structure of neutral atmospheric surface-layer
turbulence, J. Fluid Mech., 273, 141–168, https://doi.org/10.1017/S0022112094001886, 1994. a, b
Mann, J.: Wind field simulation, Probabil. Eng. Mech., 13, 269–282,
https://doi.org/10.1016/S0266-8920(97)00036-2, 1998. a
Mann, J., Peña, A., Troldborg, N., and Andersen, S. J.: How does turbulence change approaching a rotor?, Wind Energ. Sci., 3, 293–300,
https://doi.org/10.5194/wes-3-293-2018, 2018. a
Meneveau, C.: Big wind power: seven questions for turbulence research, J. Turbulence, 20, 2–20, https://doi.org/10.1080/14685248.2019.1584664, 2019. a, b
Michelsen, J. A.: Basis 3D – A Platform for Development of Multiblock PDE Solvers, Tech. rep., Danmarks Tekniske Universitet, https://orbit.dtu.dk/files/272917945/Michelsen_J_Basis3D.pdf
(last access: 10 October 2022), 1992. a
Michelsen, J. A.: Block structured Multigrid solution of 2D and 3D elliptic
PDE's, Tech. Rep. AFM 94-06, Technical University of Denmark, 1994. a
Mikkelsen, R.: Actuator Disc Methods Applied to Wind Turbines, PhD thesis, Technical University of Denmark, Mek dept, https://orbit.dtu.dk/en/publications/actuator-disc-methods-applied-to-wind-turbines
(last access: 10 October 2022), 2003. a
Moon, J. S. and Manuel, L.: Toward understanding waked flow fields behind a
wind turbine using proper orthogonal decomposition, J. Renew. Sustain. Energ., 13, 023302, https://doi.org/10.1063/5.0035751, 2021. a
Munters, W., Meneveau, C., and Meyers, J.: Shifted periodic boundary conditions for simulations of wall-bounded turbulent flows, Phys. Fluids, 28,
025112, https://doi.org/10.1063/1.4941912, 2016. a
Newman, A. J., Drew, D. A., and Castillo, L.: Pseudo spectral analysis of the
energy entrainment in a scaled down wind farm, Renew. Energy, 70, 129–141, https://doi.org/10.1016/j.renene.2014.02.003, 2014. a
Øye, S.: Flex4 simulation of wind turbine dynamics, in: Proceedings of 28th IEA Meeting of Experts Concerning State of the Art of Aeroelastic Codes for Wind Turbine Calculations. Available through International Energy Agency,
Danmarks Tekniske Universitet, Lyngby, Denmark, 71–76, 1996. 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, b, c
Qatramez, A. E. and Foti, D.: A reduced-order model for the near wake dynamics of a wind turbine: Model development and uncertainty quantification, J. Renew. Sustain. Energ., 14, 013303, https://doi.org/10.1063/5.0071789, 2022. a
Reinwardt, I., Schilling, L., Steudel, D., Dimitrov, N., Dalhoff, P., and
Breuer, M.: Validation of the dynamic wake meandering model with respect to
loads and power production, Wind Energ. Sci., 6, 441–460,
https://doi.org/10.5194/wes-6-441-2021, 2021. a
Saranyasoontorn, K. and Manuel, L.: Low-Dimensional Representations of Inflow
Turbulence and Wind Turbine Response Using Proper Orthogonal Decomposition,
J. Solar Energ. Eng., 127, 553–562, https://doi.org/10.1115/1.2037108, 2005. a
Shen, W. Z., Michelsen, J. A., Sørensen, N. N., and Sørensen, J. N.: An Improved SIMPLEC Method on Collocated Grids For Steady And Unsteady Flow
Computations, Numer. Heat Trans. Pt. B, 43, 221–239, https://doi.org/10.1080/713836202, 2003. a
Shinozuka, M. and Jan, C.-M.: Digital simulation of random processes and its
applications, J. Sound Vibrat., 25, 111–128, 1972. a
Sirovich, L.: Turbulence and the dynamics of coherent structures. I. Coherent
structures, Quart. Appl. Math., 45, 561–70, https://doi.org/10.1090/qam/910462, 1987. a
Sørensen, J. N., Mikkelsen, R., Henningson, D. S., Ivanell, S., Sarmast, S., and Andersen, S. J.: Simulation of wind turbine wakes using the actuator line technique, Philos. T. Roy. Soc. Lond. A, 373, 20140071,
https://doi.org/10.1098/rsta.2014.0071, 2015. a
Sørensen, N. N.: General Purpose Flow Solver Applied to Flow over Hills ,
PhD thesis, Technical University of Denmark, https://orbit.dtu.dk/en/publications/general-purpose-flow-solver-applied-to-flow-over-hills (last access: 10 October 2022), 1995. a
Sørensen, P., Hansen, A. D., and Rosas, P. A. C.: Wind models for simulation of power fluctuations from wind farms, J. Wind Eng. Indust. Aerodynam., 90, 1381–1402, https://doi.org/10.1016/S0167-6105(02)00260-X, 2002. a, b
Stankiewicz, W., Morzynski, M., Kotecki, K., and Noack, B. R.: On the need of mode interpolation for data-driven Galerkin models of a transient flow around
a sphere, Theor. Comput. Fluid Dynam., 31, 111–126,
https://doi.org/10.1007/s00162-016-0408-7, 2017. a
Stevens, R. J. and Meneveau, C.: Flow Structure and Turbulence in Wind Farms,
Annu. Rev. Fluid Mech., 49, 311–339, https://doi.org/10.1146/annurev-fluid-010816-060206, 2017. a
Troldborg, N. and Meyer Forsting, A.: A simple model of the wind turbine
induction zone derived from numerical simulations, Wind Energy, 20, 2011–2020, https://doi.org/10.1002/we.2137, 2017. a, b
Troldborg, N., Andersen, S. J., Hodgson, E. L., and Meyer Forsting, A.: Brief communication: How does complex terrain change the power curve of a wind turbine?, Wind Energ. Sci., 7, 1527–1532, https://doi.org/10.5194/wes-7-1527-2022, 2022. a
van der Laan, M., Andersen, S., Kelly, M., and Baungaard, M.: Fluid scaling
laws of idealized wind farm simulations, in: vol. 1618, IOP Publishing,
https://doi.org/10.1088/1742-6596/1618/6/062018, 2020. a, b
van der Laan, M. P., Andersen, S. J., and Réthoré, P.-E.: Brief
communication: Wind-speed-independent actuator disk control for faster annual
energy production calculations of wind farms using computational fluid
dynamics, Wind Energ. Sci., 4, 645–651, https://doi.org/10.5194/wes-4-645-2019, 2019. a
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C., Carlson, O., Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki, V., Lundquist, J., Manwell, J., Marquis, M., Meneveau, C., Moriarty, P.,
Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J.,
Robertson, A., Sanz Rodrigo, J., Sempreviva, A., Smith, J., Tuohy, A., and
Wiser, R.: Grand challenges in the science of wind energy, Science, 366, eaau2027, https://doi.org/10.1126/science.aau2027, 2019. a
Veers, P., Bottasso, C., Manuel, L., Naughton, J., Pao, L., Paquette, J., Robertson, A., Robinson, M., Ananthan, S., Barlas, A., Bianchini, A., Bredmose, H., Horcas, S. G., Keller, J., Madsen, H. A., Manwell, J., Moriarty, P., Nolet, S., and Rinker, J.: Grand Challenges in the Design, Manufacture, and Operation of Future Wind Turbine Systems, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-32, in review, 2022. a
VerHulst, C. and Meneveau, C.: Large eddy simulation study of the kinetic
energy entrainment by energetic turbulent flow structures in large wind
farms, Phys. Fluids, 26, 025113, https://doi.org/10.1063/1.4865755, 2014. a
Wu, Y.-T. and Porté-Agel, F.: Simulation of turbulent flow inside and above wind farms: Model validation and layout effects, Bound.-Lay. Meteorol.,
146, 181–205, https://doi.org/10.1007/s10546-012-9757-y, 2013.
a
Xiao, D., Heaney, C. E., Mottet, L., Fang, F., Lin, W., Navon, I. M., Guo, Y., Matar, O. K., Robins, A. G., and Pain, C. C.: A reduced order model for
turbulent flows in the urban environment using machine learning, Build.
Environ., 148, 323–337, https://doi.org/10.1016/j.buildenv.2018.10.035, 2019. a
Zhang, Z., Santoni, C., Herges, T., Sotiropoulos, F., and Khosronejad, A.:
Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder
Convolutional Neural Networks, Energies, 15, 41, https://doi.org/10.3390/en15010041, 2022. a
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.
Simulating the turbulent flow inside large wind farms is inherently complex and computationally...
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