Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2469-2022
© 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-7-2469-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Dec 2022
Research article |
| 15 Dec 2022
| 15 Dec 2022
Comparison of large eddy simulations against measurements from the Lillgrund offshore wind farm
Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium
Elliot Simon
DTU Wind and Energy Systems, Technical University of Denmark, Frederiksborgvej 399,
4000 Roskilde, Denmark
Athanasios Vitsas
Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium
Bart Blockmans
Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium
Gunner C. Larsen
DTU Wind and Energy Systems, Technical University of Denmark, Frederiksborgvej 399,
4000 Roskilde, Denmark
Johan Meyers
Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium
Related authors
Konstanze Kölle, Tuhfe Göçmen, Irene Eguinoa, Leonardo Andrés Alcayaga Román, Maria Aparicio-Sanchez, Ju Feng, Johan Meyers, Vasilis Pettas, and Ishaan Sood
Wind Energ. Sci., 7, 2181–2200, https://doi.org/10.5194/wes-7-2181-2022, https://doi.org/10.5194/wes-7-2181-2022, 2022
Short summary
Short summary
The paper studies wind farm flow control (WFFC) in simulations with variable electricity prices. The results indicate that considering the electricity price in the operational strategy can be beneficial with respect to the gained income compared to focusing on the power gain only. Moreover, revenue maximization by balancing power production and structural load reduction is demonstrated at the example of a single wind turbine.
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.
Frederik Aerts, Koen Devesse, and Johan Meyers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-196, https://doi.org/10.5194/wes-2025-196, 2025
Preprint under review for WES
Short summary
Short summary
This paper presents an improved Bayesian uncertainty quantification framework for calibrating and comparing wind farm flow models. It quantifies the parameter uncertainty in a joint posterior distribution as well as the model uncertainty. Applied to a large-eddy simulation dataset for wind-farm blockage, it compares a standard wake model and an atmospheric perturbation model, showing the latter yields lower model uncertainty. The framework is available in the open-source Python package UMBRA.
Julia Steiner, Emily Louise Hodgson, Maarten Paul van der Laan, Leonardo Alcayaga, Mads Pedersen, Søren Juhl Andersen, Gunner Larsen, and Pierre-Elouan Réthoré
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-200, https://doi.org/10.5194/wes-2025-200, 2025
Preprint under review for WES
Short summary
Short summary
Wake steering is a promising strategy for wind farm optimization, but its success hinges on accurate wake models. We assess models of varying fidelity for the IEA 22 MW turbine, comparing single- and two-turbine cases against LES. All reproduced qualitative trends for power and if applicable loads, but quantitative agreement varied and in general the error increased with increasing yaw angle.
Olivier Ndindayino, Augustin Puel, and Johan Meyers
Wind Energ. Sci., 10, 2079–2098, https://doi.org/10.5194/wes-10-2079-2025, https://doi.org/10.5194/wes-10-2079-2025, 2025
Short summary
Short summary
We study how flow blockage improves wind-farm efficiency using large-eddy simulations and develop an analytical model to better predict turbine power under blockage. We find that blockage enhances turbine power and thrust by creating a favourable pressure drop across the row, reducing near-wake deficit while inducing an unfavourable pressure increase downstream, which has minimal direct impact on far-wake development.
Øyvind Waage Hanssen-Bauer, Paula Doubrawa, Helge Aa. Madsen, Henrik Asmuth, Jason Jonkman, Gunner C. Larsen, Stefan Ivanell, and Roy Stenbro
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-163, https://doi.org/10.5194/wes-2025-163, 2025
Preprint under review for WES
Short summary
Short summary
We studied how different industry-oriented computer models predict the behavior of winds behind turbines in a wind farm. These "wakes" reduce energy output and can affect turbines further down the row. By comparing these three models with more detailed simulations, we found they agree well on overall power but differ in how they capture turbulence and wear on machines. Our results show where the models need improvement to make wind farm computer models more accurate and reliable in the future.
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
Revised manuscript 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.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
Short summary
Short summary
To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Théo Delvaux and Johan Meyers
Wind Energ. Sci., 10, 613–630, https://doi.org/10.5194/wes-10-613-2025, https://doi.org/10.5194/wes-10-613-2025, 2025
Short summary
Short summary
The work explores the potential for wind farm load reduction and power maximization. We carried out a series of high-fidelity large-eddy simulations for a wide range of atmospheric conditions and operating regimes. Because of turbine-scale interactions and large-scale effects, we observed that maximum power extraction is achieved at regimes lower than the Betz operating point. Thus, we proposed three simple approaches with which thrust significantly decreases with only a limited impact on power.
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.
David Onnen, Gunner Christian Larsen, Alan Wai Hou Lio, Paul Hulsman, Martin Kühn, and Vlaho Petrović
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-188, https://doi.org/10.5194/wes-2024-188, 2025
Revised manuscript under review for WES
Short summary
Short summary
Neighbouring wind turbines influence each other, as they leave a complex footprint of reduced wind speed and changed turbulence in the flow, called wake. Modern wind farm control sees the turbines as team players and aims to mitigate the negative effects of such interaction. To do so, the exact flow situation in the wind farm must be known. We show, how to use wind turbines as sensors for waked inflow, test this in the field and compare with independent laser measurements of the flow field.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024, https://doi.org/10.5194/wes-9-2171-2024, 2024
Short summary
Short summary
Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 1695–1711, https://doi.org/10.5194/wes-9-1695-2024, https://doi.org/10.5194/wes-9-1695-2024, 2024
Short summary
Short summary
In our study, we assess how mesoscale weather systems influence wind speed variations and their impact on offshore wind energy production fluctuations. We have observed, for instance, that weather systems originating over land lead to sea wind speed variations. Additionally, we noted that power fluctuations are typically more significant in summer, despite potentially larger winter wind speed variations. These findings are valuable for grid management and optimizing renewable energy deployment.
Ruben Borgers, Marieke Dirksen, Ine L. Wijnant, Andrew Stepek, Ad Stoffelen, Naveed Akhtar, Jérôme Neirynck, Jonas Van de Walle, Johan Meyers, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 697–719, https://doi.org/10.5194/wes-9-697-2024, https://doi.org/10.5194/wes-9-697-2024, 2024
Short summary
Short summary
Wind farms at sea are becoming more densely clustered, which means that next to individual wind turbines interfering with each other in a single wind farm also interference between wind farms becomes important. Using a climate model, this study shows that the efficiency of wind farm clusters and the interference between the wind farms in the cluster depend strongly on the properties of the individual wind farms and are also highly sensitive to the spacing between the wind farms.
Nick Janssens and Johan Meyers
Wind Energ. Sci., 9, 65–95, https://doi.org/10.5194/wes-9-65-2024, https://doi.org/10.5194/wes-9-65-2024, 2024
Short summary
Short summary
Proper wind farm control may vastly contribute to Europe's plan to go carbon neutral. However, current strategies don't account for turbine–wake interactions affecting power extraction. High-fidelity models (e.g., LES) are needed to accurately model this but are considered too slow in practice. By coarsening the resolution, we were able to design an efficient LES-based controller with real-time potential. This may allow us to bridge the gap towards practical wind farm control in the near future.
Jaime Liew, Tuhfe Göçmen, Alan W. H. Lio, and Gunner Chr. Larsen
Wind Energ. Sci., 8, 1387–1402, https://doi.org/10.5194/wes-8-1387-2023, https://doi.org/10.5194/wes-8-1387-2023, 2023
Short summary
Short summary
We present recent research on dynamically modelling wind farm wakes and integrating these enhancements into the wind farm simulator, HAWC2Farm. The simulation methodology is showcased by recreating dynamic scenarios observed in the Lillgrund offshore wind farm. We successfully recreate scenarios with turning winds, turbine shutdown events, and wake deflection events. The research provides opportunities to better identify wake interactions in wind farms, allowing for more reliable designs.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Göçmen, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, https://doi.org/10.5194/wes-7-2271-2022, 2022
Short summary
Short summary
We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
Konstanze Kölle, Tuhfe Göçmen, Irene Eguinoa, Leonardo Andrés Alcayaga Román, Maria Aparicio-Sanchez, Ju Feng, Johan Meyers, Vasilis Pettas, and Ishaan Sood
Wind Energ. Sci., 7, 2181–2200, https://doi.org/10.5194/wes-7-2181-2022, https://doi.org/10.5194/wes-7-2181-2022, 2022
Short summary
Short summary
The paper studies wind farm flow control (WFFC) in simulations with variable electricity prices. The results indicate that considering the electricity price in the operational strategy can be beneficial with respect to the gained income compared to focusing on the power gain only. Moreover, revenue maximization by balancing power production and structural load reduction is demonstrated at the example of a single wind turbine.
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.
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.
Thomas Haas, Jochem De Schutter, Moritz Diehl, and Johan Meyers
Wind Energ. Sci., 7, 1093–1135, https://doi.org/10.5194/wes-7-1093-2022, https://doi.org/10.5194/wes-7-1093-2022, 2022
Short summary
Short summary
In this work, we study parks of large-scale airborne wind energy systems using a virtual flight simulator. The virtual flight simulator combines numerical techniques from flow simulation and kite control. Using advanced control algorithms, the systems can operate efficiently in the park despite turbulent flow conditions. For the three configurations considered in the study, we observe significant wake effects, reducing the power yield of the parks.
Amir R. Nejad, Jonathan Keller, Yi Guo, Shawn Sheng, Henk Polinder, Simon Watson, Jianning Dong, Zian Qin, Amir Ebrahimi, Ralf Schelenz, Francisco Gutiérrez Guzmán, Daniel Cornel, Reza Golafshan, Georg Jacobs, Bart Blockmans, Jelle Bosmans, Bert Pluymers, James Carroll, Sofia Koukoura, Edward Hart, Alasdair McDonald, Anand Natarajan, Jone Torsvik, Farid K. Moghadam, Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, https://doi.org/10.5194/wes-7-387-2022, 2022
Short summary
Short summary
This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the energy conversion systems transferring the kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps.
Luca Lanzilao and Johan Meyers
Wind Energ. Sci., 6, 247–271, https://doi.org/10.5194/wes-6-247-2021, https://doi.org/10.5194/wes-6-247-2021, 2021
Short summary
Short summary
This research paper investigates the potential of thrust set-point optimization in large wind farms for mitigating gravity-wave-induced blockage effects for the first time, with the aim of increasing the wind-farm energy extraction. The optimization tool is applied to almost 2000 different atmospheric states. Overall, power gains above 4 % are observed for 77 % of the cases.
Mads M. Pedersen and Gunner C. Larsen
Wind Energ. Sci., 5, 1551–1566, https://doi.org/10.5194/wes-5-1551-2020, https://doi.org/10.5194/wes-5-1551-2020, 2020
Short summary
Short summary
In this paper, the influence of optimal wind farm control and optimal wind farm layout is investigated in terms of power production. The capabilities of the developed optimization platform is demonstrated on the Swedish offshore wind farm, Lillgrund. It shows that the expected annual energy production can be increased by 4 % by integrating the wind farm control into the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.
Cited articles
Allaerts, D. and Meyers, J.: Large eddy simulation of a large wind-turbine
array in a conventionally neutral atmospheric boundary layer, Phys.
Fluids, 27, 065108, https://doi.org/10.1063/1.4922339, 2015. a, b, c, d
Archer, C. L., Mirzaeisefat, S., and Lee, S.: Quantifying the sensitivity of
wind farm performance to array layout options using large-eddy simulation,
Geophys. Res. Lett., 40, 4963–4970,
https://doi.org/10.1002/grl.50911,
2013. a
Arnold, M., Brüls, O., Arnold, M., Brüls, O., Arnold, M., and
Brüls, O.: Convergence of the generalized-α scheme for
constrained mechanical systems, Multibody System Dynamics, Springer Verlag, 85,
187–202, https://doi.org/10.1007/s11044-007-9084-0, 2007. a
Arya, S.: Comparative Effects of Stability, Baroclinity and the Scale-Height
Ratio on Drag Laws for the Atmospheric Boundary Layer, J.
Atmos. Sci., 35, 40–46,
https://doi.org/10.1175/1520-0469(1978)035<0040:CEOSBA>2.0.CO;2, 1978. a
Arya, S. P. S.: Comments on “Similarity Theory for the Planetary Boundary
Layer of Time-Dependent Height”, J. Atmos. Sci., 32, 839–840, https://doi.org/10.1175/1520-0469(1975)032<0839:COTFTP>2.0.CO;2, 1975. a
Bastankhah, M. and Porté-Agel, F.: A new miniaturewind turbine for wind
tunnel experiments. Part I: Design and performance, Energies, 10, 923,
https://doi.org/10.3390/en10070908, 2017. a
Boersma, S., Doekemeijer, B. M., Siniscalchi-Minna, S., and van Wingerden,
J. W.: A constrained wind farm controller providing secondary frequency
regulation: An LES study, Renew. Energ., 134, 639–652,
https://doi.org/10.1016/j.renene.2018.11.031, 2019. a
Bossanyi, E.: Combining induction control and wake steering for wind farm
energy and fatigue loads optimisation, J. Phys.-Conf.
Ser., 1037, 032011, https://doi.org/10.1088/1742-6596/1037/3/032011, 2018. a
Bou-Zeid, E., Meneveau, C., and Parlange, M.: A scale-dependent Lagrangian
dynamic model for large eddy simulation of complex turbulent flows, Phys.
Fluids, 17, 1–18, https://doi.org/10.1063/1.1839152, 2005. a
Brost, R., Lenschow, D., and Wyngaard, J.: Marine Stratocumulus Layers. Part
1: Mean Conditions., J. Atmos. Sci., 39, 800–817,
https://doi.org/10.1175/1520-0469(1982)039<0800:MSLPMC>2.0.CO;2, 1982. a
Calaf, M., Meneveau, C., and Meyers, J.: Large eddy simulation study of fully
developed wind-turbine array boundary layers, Phys. Fluids, 22,
015110, https://doi.org/10.1063/1.3291077, 2010. a, b, c
Castro, I. P.: Rough-wall boundary layers: Mean flow universality, J.
Fluid Mech., 585, 469–485, https://doi.org/10.1017/S0022112007006921, 2007. a
Churchfield, M. J., Schreck, S., Martínez-Tossas, L. A., Meneveau, C.,
and Spalart, P. R.: An advanced actuator line method for wind energy
applications and beyond, 35th Wind Energy Symposium,
Grapevine, Texas, 9–13 January 2017,
https://doi.org/10.2514/6.2017-1998, 2017. a
Csanady, G. T.: Equilibrium theory of the planetary boundary layer with an
inversion lid, Bound.-Lay. Meteorol., 6, 63–79,
https://doi.org/10.1007/BF00232477, 1974. a
Draper, M., Guggeri, A., and Usera, G.: Validation of the Actuator Line Model
with coarse resolution in atmospheric sheared and turbulent inflow, J.
Phys.-Conf. Ser., 753, 082007, https://doi.org/10.1088/1742-6596/753/8/082007,
2016. a
Frederik, J. A., Doekemeijer, B. M., Mulders, S. P., and van Wingerden, J. W.:
The helix approach: Using dynamic individual pitch control to enhance wake
mixing in wind farms, Wind Energy, 23, 1739–1751, https://doi.org/10.1002/we.2513,
2020. a
Freebury, G. and Musial, W.: Determining equivalent damage loading for
full-scale wind turbine blade fatigue tests, 2000 ASME Wind Energy
Symposium,
Reno, NV, USA, 10–13 January 2000,
287–297, https://doi.org/10.2514/6.2000-50, 2000. a
Ghaisas, N. S. and Archer, C. L.: Geometry-Based Models for Studying the
Effects of Wind Farm Layout, J. Atmos. Ocean. Tech.,
33, 481–501, https://doi.org/10.1175/JTECH-D-14-00199.1, 2016. a
Göçmen, T. and Giebel, G.: Estimation of turbulence intensity
using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind
farms, Renew. Energ., 99, 524–532, https://doi.org/10.1016/j.renene.2016.07.038,
2016. a
Goit, J. P. and Meyers, J.: Optimal control of energy extraction in wind-farm
boundary layers, J. Fluid Mech., 768, 5–50,
https://doi.org/10.1017/jfm.2015.70, 2015. a
Hansen, M. H. and Henriksen, L. C.: Basic DTU Wind Energy controller,
DTU Wind Energy,
https://orbit.dtu.dk/en/publications/basic-dtu-wind-energy-controller
(last access: 1 December 2021), 2013. a
Jiménez, J.: Turbulent flows over rough walls, Annu. Rev. Fluid
Mech., 36, 173–196, https://doi.org/10.1146/annurev.fluid.36.050802.122103, 2004. a
Kunsch, H. R.: The Jackknife and the Bootstrap for General Stationary
Observations, Ann. Stat., 17, 1217–1241,
https://doi.org/10.1214/aos/1176347265, 1989. a
Liang, X.: An Integrating Velocity–Azimuth Process Single-Doppler Radar Wind
Retrieval Method, J. Atmos. Ocean. Tech., 24, 658–665, https://doi.org/10.1175/JTECH2047.1, 2007. a
Lin, M. and Porté-Agel, F.: Large-eddy simulation of yawedwind-turbine
wakes: comparisons withwind tunnel measurements and analyticalwake models,
Energies, 12, 1–18, https://doi.org/10.3390/en12234574, 2019. a
Lu, H. and Porté-Agel, F.: Large-eddy simulation of a very large wind farm in
a stable atmospheric boundary layer, Phys. Fluids, 23, 065101,
https://doi.org/10.1063/1.3589857, 2011. a
Magnusson, M. and Smedman, A.-S.: Influence of Atmospheric Stability on Wind
Turbine Wakes, Wind Eng., 18, 139–152,
http://www.jstor.org/stable/43749538 (last access: 1 December 2021), 1994. a
Martínez-Tossas, L. A., Churchfield, M. J., and Leonardi, S.: Large eddy
simulations of the flow past wind turbines: actuator line and disk modeling,
Wind Energy, 18, 1047–1060, https://doi.org/10.1002/we.1747, 2015. a
Mehta, D., van Zuijlen, A. H., Koren, B., Holierhoek, J. G., and Bijl, H.:
Large Eddy Simulation of wind farm aerodynamics: A review, J. Wind
Eng. Ind. Aerod., 133, 1–17,
https://doi.org/10.1016/j.jweia.2014.07.002, 2014. a
Munters, W. and Meyers, J.: Dynamic strategies for yaw and induction control
of wind farms based on large-eddy simulation and optimization, Energies, 11, 177,
https://doi.org/10.3390/en11010177, 2018. a, b
Munters, W., Meneveau, C., and Meyers, J.: Turbulent Inflow Precursor Method
with Time-Varying Direction for Large-Eddy Simulations and Applications to
Wind Farms, Bound.-Lay. Meteorol., 159, 305–328,
https://doi.org/10.1007/s10546-016-0127-z, 2016. a, b, c
Munters, W., Sood, I., and Meyers, J.: Precursor dataset PDk, Zenodo [data set],
https://doi.org/10.5281/zenodo.2650100, 2019a. a, b, c
Munters, W., Sood, I., and Meyers, J.: Precursor dataset PDkhi, Zenodo [data set],
https://doi.org/10.5281/zenodo.2650102, 2019b. a, b, c
Munters, W., Sood, I., and Meyers, J.: Precursor dataset CNk2, Zenodo [data set],
https://doi.org/10.5281/zenodo.2650096, 2019c. a, b, c
Munters, W., Sood, I., and Meyers, J.: Precursor dataset CNk4, Zenodo [data set],
https://doi.org/10.5281/zenodo.2650098, 2019d. a, b, c
Nilsson, K., Ivanell, S., Hansen, K. S., Mikkelsen, R., Sørensen, J. N.,
Breton, S.-P., and Henningson, D.: Large-eddy simulations of the Lillgrund
wind farm, Wind Energy, 18, 449–467, https://doi.org/10.1002/we.1707, 2014. a
Porté-agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-Turbine and
Wind-Farm Flows: A Review, Bound.-Lay. Meteorol., 44, 1573–1472, https://doi.org/10.1007/s10546-019-00473-0,
2020. a
Salarpour, A. and Khotanlou, H.: An empirical comparison of distance measures
for multivariate time series clustering, International Journal of
Engineering, Transactions B: Applications, 31, 250–262, 2018. a
Sebastiani, A., Castellani, F., Crasto, G., and Segalini, A.: Data analysis and
simulation of the Lillgrund wind farm, Wind Energy, 24, 634–648,
https://doi.org/10.1002/we.2594, 2021. a
Shabana, A. A.: Dynamics of Multibody Systems, Cambridge University Press, 4
edn., https://doi.org/10.1017/CBO9781107337213, 2013. a, b, c, d
Simisiroglou, N., Polatidis, H., and Ivanell, S.: Wind farm power production assessment: a comparative analysis of two actuator disc methods and two analytical wake models, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2018-8, 2018. a, b
Simon, E. and Courtney, M.: A Comparison of sector-scan and dual Doppler wind
measurements at Høvsøre Test Station – one lidar or two?,
Report, https://backend.orbit.dtu.dk/ws/portalfiles/portal/125285452/RUNE_D1.2_ellsim_final.pdf
(last access: 30 November 2021), 2016. a
Socie, D. and Downing, S.: Simple Rainflow Counting Algorithms, Int.
J. Fatigue, 4, 31–40,
https://doi.org/10.1016/0142-1123(82)90018-4,
1982. a
Sood, I.: Comparison of Large Eddy Simulations against measurements from the Lillgrund offshore wind farm – Manuscript data, Zenodo [data set], https://doi.org/10.5281/zenodo.7358841, 2022. a
Sood, I., Meyers, J., and Lanzilao, L.: TotalControl D 1.8 Coupling of
Gaussian wake merging to background ABL model, https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5d2d79f41&appId=PPGMS
(last access: 1 December 2021), 2020a. a
Sood, I., Munters, W., and Meyers, J.: Effect of conventionally neutral
boundary layer height on turbine performance and wake mixing in offshore
windfarms, J. Phys.-Conf. Ser., 1618, 062049,
https://doi.org/10.1088/1742-6596/1618/6/062049, 2020b. a, b
Stevens, R. J., Graham, J., and Meneveau, C.: A concurrent precursor inflow
method for Large Eddy Simulations and applications to finite length wind
farms, Renew. Energ., 68, 46–50, https://doi.org/10.1016/j.renene.2014.01.024,
2014. a, b
Storey, R. C., Norris, S. E., and Cater, J. E.: An actuator sector method for
efficient transient wind turbine simulation, Wind Energy, 18, 699–711,
https://doi.org/10.1002/we.1722, 2015. a
Sutherland: Fatigue analysis of wind turbines. Technical report, Sandia
National Laboratories, Tech. rep., https://doi.org/10.2172/9460, 1999. a
Townsend: The Structure of Turbulent Shear Flow. Cambridge University Press, ISBN 9780521298193,
1976. a
Vasiljević, N., Lea, G., Courtney, M., Cariou, J.-P., Mann, J., and Mikkelsen,
T.: Long-Range WindScanner System, Remote Sensing, 8, 896,
https://doi.org/10.3390/rs8110896, 2016. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng,
Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R.,
Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors:
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,
Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Vitsas, A. and Meyers, J.: Multiscale aeroelastic simulations of large wind
farms in the atmospheric boundary layer, J. Phys.-Conf.
Ser., 753, 082020, https://doi.org/10.1088/1742-6596/753/8/082020, 2016. a, b, c
Wu, Y. T. and Porté-Agel, F.: Large-Eddy Simulation of Wind-Turbine
Wakes: Evaluation of Turbine Parametrisations, Bound.-Lay. Meteorol.,
138, 345–366, https://doi.org/10.1007/s10546-010-9569-x, 2011. 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
Wu, Y. T. and Porté-Agel, F.: Modeling turbine wakes and power losses
within a wind farm using LES: An application to the Horns Rev offshore wind
farm, Renew. Energ., 75, 945–955, https://doi.org/10.1016/j.renene.2014.06.019,
2015. a
Yılmaz, A. E. and Meyers, J.: Optimal dynamic induction control of a pair of
inline wind turbines, Phys. Fluids, 30, 085106, https://doi.org/10.1063/1.5038600, 2018. a
Short summary
In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
In this work, we conduct a validation study to compare a numerical solver against measurements...
Altmetrics
Final-revised paper
Preprint