Articles | Volume 11, issue 5
https://doi.org/10.5194/wes-11-1631-2026
© Author(s) 2026. 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-11-1631-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 May 2026
Research article |
| 08 May 2026
| 08 May 2026
Dependence of wind-farm-induced gravity waves and wind farm performance on non-dimensional atmospheric parameters and simulation configuration
Delft University of Technology, Delft, the Netherlands
Matthew J. Churchfield
National Laboratory of the Rockies, Golden, Colorado, USA
Simon J. Watson
Delft University of Technology, Delft, the Netherlands
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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
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
Allaerts, D. and Meyers, J.: Sensitivity and feedback of wind-farm-induced gravity waves, J. Fluid Mech., 862, 990–1028, https://doi.org/10.1017/jfm.2018.969, 2019. a
Allaerts, D., Broucke, S. V., Van Lipzig, N., and Meyers, J.: Annual impact of wind-farm gravity waves on the Belgian-Dutch offshore wind-farm cluster, J. Phys. Conf. Ser., 1037, https://doi.org/10.1088/1742-6596/1037/7/072006, 2018. a, b, c
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
Brooke, J. S., Thedin, R., Sharma, A., Branlard, E., Vijayakumar, G., and Michael, A. S.: Effect of the integral length scales of turbulent inflows on wind turbine loads, Renew. Energ., 217, 119-218, https://doi.org/10.1016/j.renene.2023.119218, 2023. a
Churchfield, M. J., Lee, S., Michalakes, J., and Moriarty, P. J.: A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics, J. Turbul., 13, 1–32, https://doi.org/10.1080/14685248.2012.668191, 2012. a
Khan, M. A., Watson, S. J., Allaerts, D. J. N., and Churchfield, M.: Recommendations on setup in simulating atmospheric gravity waves under conventionally neutral boundary layer conditions, J. Phys. Conf. Ser., 2767, 092042, https://doi.org/10.1088/1742-6596/2767/9/092042, 2024. a, b, c, d, e, f, g, h, i, j, k, l
Khan, M. A., Allaerts, D., Watson, S. J., and Churchfield, M. J.: Investigating the relationship between simulation parameters and flow variables in simulating atmospheric gravity waves for wind energy applications, Wind Energ. Sci., 10, 1167–1185, https://doi.org/10.5194/wes-10-1167-2025, 2025. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Khan, M., Churchfield, M., and Watson, S.: Supplementary data to publication: Dependence of wind-farm-induced gravity waves and wind farm performance on non-dimensional atmospheric parameters and simulation configuration (Data & Code), Version 1, 4TU.ResearchData [data set] and [code], https://doi.org/10.4121/d10f3eec-4363-4017-ad00-69136a801657.v1, 2026. a
Lanzilao, L. and Meyers, J.: An improved fringe-region technique for the representation of gravity waves in large-eddy simulation with application to wind farms, Bound.-Lay. Meteorol., 186, 567–593, https://doi.org/10.1007/s10546-022-00772-z, 2023. a, b
Meneveau, C., Lund, T. S., and Cabot, W. H.: A Lagrangian dynamic subgrid-scale model of turbulence, J. Fluid Mech., 319, 353–385, https://doi.org/10.1017/S0022112096007379, 1996. a
Ollier, S. J. and Watson, S. J.: Modelling the impact of trapped lee waves on offshore wind farm power output, Wind Energ. Sci., 8, 1179–1200, https://doi.org/10.5194/wes-8-1179-2023, 2023. a
Queney, P.: The Problem of Air Flow Over Mountains: A Summary of Theoretical Studies, B. Am. Meteorol. Soc., 29, 16–26, https://doi.org/10.1175/1520-0477-29.1.16, 1948. a, b
Sachsperger, J., Serafin, S., and Grubišić, V.: Lee Waves on the Boundary-Layer Inversion and Their Dependence on Free-Atmospheric Stability, Front. Earth Sci., 3, https://doi.org/10.3389/feart.2015.00070, 2015. a, b
Smith, R. B.: The wind farm pressure field, Wind Energ. Sci., 9, 253–261, https://doi.org/10.5194/wes-9-253-2024, 2024. a
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
Stieren, A. and Stevens, R. J.: Impact of wind farm wakes on flow structures in and around downstream wind farms, Flow, 2, E21, https://doi.org/10.1017/flo.2022.15, 2022. a
Stipa, S., Ahmed Khan, M., Allaerts, D., and Brinkerhoff, J.: A large-eddy simulation (LES) model for wind-farm-induced atmospheric gravity wave effects inside conventionally neutral boundary layers, Wind Energ. Sci., 9, 1647–1668, https://doi.org/10.5194/wes-9-1647-2024, 2024a. a, b, c, d, e, f, g, h
Stipa, S., Ajay, A., Allaerts, D., and Brinkerhoff, J.: TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows, Wind Energ. Sci., 9, 297–320, https://doi.org/10.5194/wes-9-297-2024, 2024b. a, b, c, d
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
Short summary
This large-eddy simulation study identifies a realistic set-up for modeling wind-farm–atmosphere interactions, validates a method that minimizes non-physical gravity waves, and examines how real gravity waves and wind farm performance depend on non-dimensional parameters defining atmospheric stability and farm geometry. The results show how realistic and accurate modeling are critical to performance prediction.
This large-eddy simulation study identifies a realistic set-up for modeling wind-farm–atmosphere...
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