Articles | Volume 7, issue 3
https://doi.org/10.5194/wes-7-1093-2022
© 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-7-1093-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 May 2022
Research article |
| 24 May 2022
| 24 May 2022
Large-eddy simulation of airborne wind energy farms
Thomas Haas
Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium
Jochem De Schutter
Department of Microsystems Engineering, University of Freiburg, Georges-Köhler-Allee 102, 79110 Freiburg, Germany
Moritz Diehl
Department of Microsystems Engineering, University of Freiburg, Georges-Köhler-Allee 102, 79110 Freiburg, Germany
Department of Mathematics, University of Freiburg, Georges-Köhler-Allee 102, 79110 Freiburg, Germany
Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium
Related authors
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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
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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.
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
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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
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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
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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
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Traditionally, the aerodynamic loss of wind farm efficiency is classified into wake loss and farm blockage loss. This study, using high-fidelity simulations, shows that neither of these two losses is well correlated with the overall farm efficiency. We propose new measures called turbine-scale efficiency and farm-scale efficiency to better describe turbine–wake effects and farm–atmosphere interactions. This study suggests the importance of better modelling farm-scale loss in future studies.
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
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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
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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
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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
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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.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 8, 1153–1178, https://doi.org/10.5194/wes-8-1153-2023, https://doi.org/10.5194/wes-8-1153-2023, 2023
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This study investigates the performance of pumping-mode ground-generation airborne wind energy systems by determining power-optimal flight trajectories based on realistic, k-means clustered, vertical wind velocity profiles. These profiles, derived from mesoscale weather simulations at an offshore and an onshore site in Europe, are incorporated into an optimal control model that maximizes average cycle power by optimizing the kite's trajectory.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
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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.
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
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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
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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
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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.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 7, 1847–1868, https://doi.org/10.5194/wes-7-1847-2022, https://doi.org/10.5194/wes-7-1847-2022, 2022
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This research explores the ground-generation airborne wind energy system (AWES) design space and investigates scaling effects by varying design parameters such as aircraft wing size, aerodynamic efficiency and mass. Therefore, representative simulated onshore and offshore wind data are implemented into an AWES trajectory optimization model. We estimate optimal annual energy production and capacity factors as well as a minimal operational lift-to-weight ratio.
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
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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
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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.
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
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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.
Cited articles
Alemi Ardakani, H. and Bridges, T. J.: Review of the 3-2-1 Euler Angles: a
yaw–pitch–roll sequence, Tech. rep., http://personal.maths.surrey.ac.uk/T.Bridges/SLOSH/3-2-1-Eulerangles.pdf (last access: 19 May 2022), 2010. a
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
Allaerts, D. and Meyers, J.: Boundary-layer development and gravity waves in
conventionally neutral wind farms, J. Fluid Mech., 814, 95–130, 2017. a
Andersson, J. A. E., Gillis, J., Horn, G., Rawlings, J. B., and Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control, Mathematical Programming Computation, Springer, https://doi.org/10.1007/s12532-018-0139-4, 2018. a
Archer, C. L.: An Introduction to Meteorology for Airborne Wind Energy, in:
Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R.,
Springer-Verlag, Berlin, Heidelberg, 81–94, https://doi.org/10.1007/978-3-642-39965-7_5, 2013. a, b
Argatov, I. and Silvennoinen, R.: Efficiency of Traction Power Conversion Based on Crosswind Motion, in: Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R., Springer-Verlag, Berlin, Heidelberg, 65–79,
https://doi.org/10.1007/978-3-642-39965-7_4, 2013. a
Barthelmie, R. J., Pryor, S. C., Frandsen, S. T., Hansen, K. S., Schepers, J. G., Rados, K., Schlez, W., Neubert, A., Jensen, L. E., and Neckelmann, S.:
Quantifying the Impact of Wind Turbine Wakes on Power Output at Offshore Wind
Farms, J. Atmos. Ocean. Tech., 27, 1302–1317, https://doi.org/10.1175/2010JTECHA1398.1, 2010. a
Bauer, F., Kennel, R. M., Hackl, C. M., Campagnolo, F., Patt, M., and Schmehl, R.: Drag power kite with very high lift coefficient, Renew. Energy, 118, 290–305, https://doi.org/10.1016/j.renene.2017.10.073, 2018. a
Bulder, B., Bedon, G., and Bot, E.: Optimal wind farm power density analysis
for future offshore wind farms, Tech. rep., http://resolver.tudelft.nl/uuid:dfe0ce2f-04fb-4db2-80db-52bc02cfb515 (last access: 19 May 2022), 2018. a
Calaf, M., Meneveau, C., and Meyers, J.: Large eddy simulation study of fully
developed wind-turbine array boundary layers, Phys. Fluids, 22, 1–16,
https://doi.org/10.1063/1.862466, 2010. a, b, c, d
Cherubini, A., Papini, A., Vertechy, R., and Fontana, M.: Airborne Wind Energy Systems: A review of the technologies, Renew. Sustain. Energ. Rev., 51, 1461–1476, https://doi.org/10.1016/j.rser.2015.07.053, 2015. a
De Lellis, M., Reginatto, R., Saraiva, R., and Trofino, A.: The Betz limit
applied to Airborne Wind Energy, Renew. Energy, 127, 32–40,
https://doi.org/10.1016/j.renene.2018.04.034, 2018. a
De Schutter, J., Bronnenmeyer, T., Paelinck, R., Diehl, M., and Leuthold, R.:
Optimal control of stacked multi-kite systems for utility-scale airborne wind
energy, in: 2019 IEEE 58th Conference on Decision and Control (CDC), https://doi.org/10.1109/CDC40024.2019.9030026, 2019. a, b
Diehl, M.: Airborne wind energy: Basic concepts and physical foundations, in:
Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R.,
Springer-Verlag, Berlin, Heidelberg, 3–22, https://doi.org/10.1007/978-3-642-39965-7_5, 2013. a, b
Dykes, K.: Optimization of Wind Farm Design for Objectives Beyond LCOE, vol. 1618, IOP Publishing, https://doi.org/10.1088/1742-6596/1618/4/042039, 2020. a
Gaunaa, M., Forsting, A. M., and Trevisi, F.: An engineering model for the
induction of crosswind kite power systems, J. Phys.: Conf. Ser., 1618, 032010, https://doi.org/10.1088/1742-6596/1618/3/032010, 2020. a
Gohl, F. and Luchsinger, R. H.: Simulation Based Wing Design for Kite Power,
in: Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R.,
Springer-Verlag, Berlin, Heidelberg, 325–338, https://doi.org/10.1007/978-3-642-39965-7_5, 2013. a
Gros, S. and Diehl, M.: Modeling of airborne wind energy systems in natural
coordinates, in: Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and
Schmehl, R., Springer-Verlag, Berlin, Heidelberg, 181–203, https://doi.org/10.1007/978-3-642-39965-7_5, 2013. a, b, c
Gros, S. and Zanon, M.: Numerical Optimal Control With Periodicity Constraints in the Presence of Invariants, IEEE T. Automat. Control, 63, 2818–2832, https://doi.org/10.1109/TAC.2017.2772039, 2018. a
Gros, S., Zanon, M., and Diehl, M.: Control of Airborne Wind Energy systems
based on Nonlinear Model Predictive Control and Moving Horizon Estimation,
in: 2013 European Control Conference (ECC), 1017–1022, https://doi.org/10.23919/ECC.2013.6669713, 2013. a
Haas, T. and Meyers, J.: Comparison study between wind turbine and power kite
wakes, J. Phys.: Conf. Ser., 854, 012019, https://doi.org/10.1088/1742-6596/854/1/012019, 2017. a, b
Haas, T. and Meyers, J.: AWESCO Wind Field Datasets, Zenodo [data set],
https://doi.org/10.5281/zenodo.1418676, 2019. a, b
Haas, T. and Meyers, J.: Large-eddy simulation of airborne wind energy farms:
AWES virtual flight data, Zenodo [data set], https://doi.org/10.5281/zenodo.5705563, 2021. a
Haas, T., De Schutter, J., Diehl, M., and Meyers, J.: Wake characteristics of
pumping mode airborne wind energy systems, J. Phys.: Conf. Ser., 1256, 012016, https://doi.org/10.1088/1742-6596/1256/1/012016, 2019b. a
Horn, G., Gros, S., and Diehl, M.: Numerical trajectory optimization for
airborne wind energy systems described by high fidelity aircraft models, in:
Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R.,
Springer-Verlag, Berlin, Heidelberg, 205–218, https://doi.org/10.1007/978-3-642-39965-7_5, 2013. a
Houska, B. and Diehl, M.: Optimal control for power generating kites, in: 2007 European Control Conference (ECC), 3560–3567,
https://doi.org/10.23919/ECC.2007.7068861, 2007. a
HSL: A collection of Fortran codes for large scale scientific computation, http://www.hsl.rl.ac.uk (last access: 19 May 2022), 2011. a
Jeong, J. and Hussain, F.: On the identification of a vortex, J. Fluid Mech., 285, 69–94, https://doi.org/10.1017/S0022112095000462, 1995. a
Jimenez, A., Crespo, A., Migoya, E., and Garcia, J.: Advances in large-eddy
simulation of a wind turbine wake, J. Phys.: Conf. Ser., 75, 012041, https://doi.org/10.1088/1742-6596/75/1/012041, 2007. a
Kaufman-Martin, S., Naclerio, N., May, P., and Luzzatto-Fegiz, P.: An
entrainment-based model for annular wakes, with applications to airborne wind
energy, Wind Energy, 25, 419–431, https://doi.org/10.1002/we.2679, 2022. a
Kheiri, M., Bourgault, F., Saberi Nasrabad, V., and Victor, S.: On the
aerodynamic performance of crosswind kite power systems, J. Wind Eng. Indust. Aerodyn., 181, 1–13, https://doi.org/10.1016/j.jweia.2018.08.006, 2018. a, b
Leuthold, R., Gros, S., and Diehl, M.: Induction in Optimal Control of
Multiple-Kite Airborne Wind Energy Systems, in: Proceedings of 20th IFAC
World Congress, Toulouse, France, https://doi.org/10.1016/j.ifacol.2017.08.026, 2017. a, b
Leuthold, R., De Schutter, J., Malz, E., Licitra, G., Gros, S., and Diehl, M.: Operational Regions of a Multi-Kite AWE System, in: 2018 European Control Conference (ECC), 52–57, https://doi.org/10.23919/ECC.2018.8550199, 2018. a
Leuthold, R., Crawford, C., Gros, S., and Diehl, M.: Engineering Wake Induction Model For Axisymmetric Multi-Kite Systems, J. Phys.: Conf. Ser., 1256, 012009, https://doi.org/10.1088/1742-6596/1256/1/012009, 2019. a
Licitra, G., Sieberling, S., Engelen, S., Williams, P., Ruiterkamp, R., and
Diehl, M.: Optimal control for minimizing power consumption during holding
patterns for airborne wind energy pumping system, in: 2016 European Control Conference (ECC), 1574–1579, https://doi.org/10.1109/ECC.2016.7810515, 2016. a
Licitra, G., Williams, P., Gillis, J., Ghandchi, S., Sieberling, S.,
Ruiterkamp, R., and Diehl, M.: Aerodynamic Parameter Identification for an
Airborne Wind Energy Pumping System, in: 20th IFAC World Congress, IFAC-PapersOnLine, 50, 11951–11958, https://doi.org/10.1016/j.ifacol.2017.08.1038, 2017. a
Licitra, G., Koenemann, J., Bürger, A., Williams, P., Ruiterkamp, R., and
Diehl, M.: Performance assessment of a rigid wing Airborne Wind Energy pumping system, Energy, 173, 569–585, https://doi.org/10.1016/j.energy.2019.02.064, 2019. a
Loyd, M. L.: Crosswind Kite Power, J. Energy, 4, 106–111,
https://doi.org/10.2514/3.48021, 1980. a
Malz, E., Zanon, M., and Gros, S.: A Quantification of the Performance Loss of Power Averaging in Airborne Wind Energy Farms, in: 2018 European Control Conference (ECC), https://doi.org/10.23919/ECC.2018.8550357, 2018. a
Malz, E., Koenemann, J., Sieberling, S., and Gros, S.: A reference model for
airborne wind energy systems for optimization and control, Renew. Energy, 140, 1004–1011, https://doi.org/10.1016/j.renene.2019.03.111, 2019. a, b, c, d
Martinez-Tossas, L. A., Churchfield, M. J., Yilmaz, A. E., Sarlak, H., Johnson, P. L., Sorensen, J. N., Meyers, J., and Meneveau, C.: Comparison of four large-eddy simulation research codes and effects of model coefficient and inflow turbulence in actuator-line-based wind turbine modeling, J. Renew. Sustain. Energ., 10, 033301, https://doi.org/10.1063/1.5004710, 2018. a
Mason, P. J. and Thomson, D. J.: Stochastic backscatter in large-eddy
simulations of boundary layers, J. Fluid Mechan., 242, 51–78,
https://doi.org/10.1017/S0022112092002271, 1992. a
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
Pope, S. B.: Turbulent Flows, Cambridge University Press,
https://doi.org/10.1017/CBO9780511840531, 2000. a
Porte-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-turbine and wind-farm flows: a review, Bound.-Lay. Meteorol., 174, 1–59, 2020. a
Rapp, S., Schmehl, R., Oland, E., and Haas, T.: Cascaded Pumping Cycle Control for Rigid Wing Airborne Wind Energy Systems, J. Guidance Control
Dynam., 42, 2456–2473, https://doi.org/10.2514/1.G004246, 2019. a
Read, R.: Kite Networks for HarvestingWind Energy, in: Airborne Wind Energy:
Advances in Technology Development and Research, edited by: Schmehl, R.,
Springer, Singapore, 515–517, https://doi.org/10.1007/978-981-10-1947-0, 2018. a
Roque, L. A., Paiva, L. T., Fernandes, M. C., Fontes, D. B., and Fontes, F. A.: Layout optimization of an airborne wind energy farm for maximum power
generation, in: the 6th International Conference on Energy and Environment Research – Energy and environment: challenges towards circular economy, Energy Rep., 6, 165–171, https://doi.org/10.1016/j.egyr.2019.08.037, 2020. a
Schelbergen, M. and Schmehl, R.: Validation of the quasi-steady performance
model for pumping airborne wind energy systems, J. Phys.: Conf. Ser., 1618, 32003, https://doi.org/10.1088/1742-6596/1618/3/032003, 2020. a
Sorensen, J. N. and Shen, W. Z.: Numerical Modeling of Wind Turbine Wakes, J. Fluids Eng., 124, 393, https://doi.org/10.1115/1.1471361, 2002. a
Sternberg, J., Goit, J., Gros, S., Meyers, J., and Diehl, M.: Robust and
stable periodic flight of power generating kite systems in a turbulent wind
flow field, IFAC Proceedings Volumes (IFAC-PapersOnline), 45, 140–145,
https://doi.org/10.3182/20120913-4-IT-4027.00009, 2012. 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
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, b
Trevisi, F., Gaunaa, M., and Mcwilliam, M.: The Influence of Tether Sag on
Airborne Wind Energy Generation, J. Phys.: Conf. Ser., 1618, 032006, https://doi.org/10.1088/1742-6596/1618/3/032006, 2020. a
Troen, I. and Lundtang Petersen, E.: European Wind Atlas, Tech. rep., Risø National Laboratory, ISBN 87-550-1482-8, 1989. a
Troldborg, N., Sorensen, J. N., and Mikkelsen, R.: Numerical simulations of
wake characteristics of a wind turbine in uniform inflow, Wind Energy, 13,
86–99, https://doi.org/10.1002/we.345, 2010.
a, b, c
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
Wächter, A.: An Interior Point Algorithm for Large-Scale Nonlinear Optimization with Applications in Process Engineering, PhD thesis, Carnegie Mellon University, http://users.iems.northwestern.edu/~andreasw/pubs/waechter_thesis.pdf (last access: 19 May 2022), 2002. a
XFLR5:
XFLR5: XFLR5 is an analysis tool for airfoils, wings and
planes, https://sourceforge.net/projects/xflr5/ (last access: 19 May 2022), 2021. a
XFOIL:
XFOIL: Subsonic Airfoil Development System,
https://web.mit.edu/drela/Public/web/xfoil/ (last access: 19 May 2022), 2021. a
Zanon, M.: Efficient Nonlinear Model Predictive Control Formulations for
Economic Objectives with Aerospace and Automotive Applications, https://limo.libis.be/primo-explore/fulldisplay?docid=LIRIAS1693005&context=L&vid=Lirias&search_scope=Lirias&tab=default_tab&fromSitemap=1 (last access: 19 May 2022), 2015. a
Zanon, M., Gros, S., Andersson, J., and Diehl, M.: Airborne Wind Energy Based
on Dual Airfoils, IEEE T. Control Syst. Technol., 21, 1215–1222, https://doi.org/10.1109/TCST.2013.2257781, 2013a. a
Zanon, M., Gros, S., and Diehl, M.: Model predictive control of rigid-airfoil
airborne wind energy systems, in: Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R., Springer-Verlag, Berlin, Heidelberg,
219–234, https://doi.org/10.1007/978-3-642-39965-7_5, 2013b. a, b, c
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.
In this work, we study parks of large-scale airborne wind energy systems using a virtual flight...
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