Articles | Volume 11, issue 7
https://doi.org/10.5194/wes-11-2669-2026
https://doi.org/10.5194/wes-11-2669-2026
Research article
 | 
27 Jul 2026
Research article |  | 27 Jul 2026

Large-eddy simulation of airborne wind energy systems flying in turbulent wind using model predictive control

Jean-Baptiste Crismer, Thomas Haas, Matthieu Duponcheel, and Grégoire Winckelmans

Related authors

Improved modeling of flow curvature effects and actuator line method with aerodynamic moment, with application to vertical-axis turbines
Grégoire Winckelmans, Philippe Rochefort, Thierry Villeneuve, François Trigaux, Matthieu Duponcheel, and Guy Dumas
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-283,https://doi.org/10.5194/wes-2025-283, 2025
Revised manuscript under review for WES
Short summary
Investigating wake reproduction of a model-scale wind turbine: experimental measurements versus Large Eddy Simulation with actuator line
Emmanuel Gillyns, Sophia Buckingham, Jeroen van Beeck, and Grégoire Winckelmans
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-242,https://doi.org/10.5194/wes-2025-242, 2025
Revised manuscript under review for WES
Short summary
Aero-servo simulations of an airborne wind energy system using geometry-resolved computational fluid dynamics
Niels Pynaert, Thomas Haas, Jolan Wauters, Guillaume Crevecoeur, and Joris Degroote
Wind Energ. Sci., 10, 2663–2684, https://doi.org/10.5194/wes-10-2663-2025,https://doi.org/10.5194/wes-10-2663-2025, 2025
Short summary
Investigation of blade flexibility effects on the loads and wake of a 15 MW wind turbine using a flexible actuator line method
Francois Trigaux, Philippe Chatelain, and Grégoire Winckelmans
Wind Energ. Sci., 9, 1765–1789, https://doi.org/10.5194/wes-9-1765-2024,https://doi.org/10.5194/wes-9-1765-2024, 2024
Short summary

Cited articles

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, 11, 1–36, https://doi.org/10.1007/s12532-018-0139-4, 2019. a
AWEbox: Modelling and optimal control of single- and multiple-kite systems for airborne wind energy, GitHub [code], https://github.com/awebox (last access: 6 July 2026), 2025. a
Caprace, D.-G., Winckelmans, G., and Chatelain, P.: An immersed lifting and dragging line model for the vortex particle-mesh method, Theor. Comp. Fluid Dyn., 34, 21–48, https://doi.org/10.1007/s00162-019-00510-1, 2020. a
Cocle, R., Bricteux, L., and Winckelmans, G.: Scale dependence and asymptotic very high Reynolds number spectral behavior of multiscale subgrid models, Phys. Fluids, 21, 085101, https://doi.org/10.1063/1.3194302, 2009. a
Coquelet, M., Moens, M., Bricteux, L., Crismer, J.-B., and Chatelain, P.: Performance assessment of wake mitigation strategies, J. Phys. Conf. Ser., 2265, 032078, https://doi.org/10.1088/1742-6596/2265/3/032078, 2022. a
Download
Short summary
Wind energy is key to the energy transition. Airborne wind energy (AWE) is a technology based on kites. It has many advantages. However, their operation in gusts or in farm configurations remains unexplored. This work proposes a tool for studying AWE systems in such conditions. It is used to investigate a two-kite array. It is found that the second kite can avoid the wake of the first kite and stay unperturbed, while in other situations it produces 6 % less energy.
Share
Altmetrics
Final-revised paper
Preprint