Bock, M., Yassin, K., Kassem, H., Theron, J., Lukassen, L. J., and Peinke, J.: Intermittency, an inevitable feature for faster convergence of large eddy simulations, Physics of Fluids, 36,
https://doi.org/10.1063/5.0202514, 2024.
a,
b,
c,
d,
e
Chilla, F., Peinke, J., and Castaing, B.: Multiplicative process in turbulent velocity statistics: A simplified analysis, Journal de Physique II, 6, 455–460,
https://doi.org/10.1051/jp2:1996191, 1996.
a
Churchfield, M. J., Schreck, S. J., Martinez, L. A., Meneveau, C., and Spalart, P. R.: An advanced actuator line method for wind energy applications and beyond, in: 35th Wind Energy Symposium, American Institute of Aeronautics and Astronautics (AIAA), p. 1998,
https://doi.org/10.2514/6.2017-1998, 2017.
a,
b
Davidson, P.: Turbulence: an introduction for scientists and engineers, Oxford University Press, ISBN 9780198722588, 2015. a
Dose, B., Rahimi, H., Herráez, I., Stoevesandt, B., and Peinke, J.: Fluid-structure coupled computations of the NREL 5 MW wind turbine by means of CFD, Renewable Energy, 129, 591–605,
https://doi.org/10.1016/j.renene.2018.05.064, 2018.
a,
b
Ehrich, S., Schwarz, C. M., Rahimi, H., Stoevesandt, B., and Peinke, J.: Comparison of the blade element momentum theory with computational fluid dynamics for wind turbine simulations in turbulent inflow, Applied Sciences, 8, 2513,
https://doi.org/10.3390/app8122513, 2018.
a
Friedrich, J., Moreno, D., Sinhuber, M., Wächter, M., and Peinke, J.: Superstatistical wind fields from pointwise atmospheric turbulence measurements, PRX Energy, 1, 023006,
https://doi.org/10.1103/PRXEnergy.1.023006, 2022.
a
Froude, W.: On the elementary relation between pitch, slip, and propulsive efficiency, Transaction of the Institute of Naval Architects, 19, 47–57, 1878. a
Glauert, H.: Airplane Propellers, in: Aerodynamic Theory, edited by: Durand, W., Springer, Berlin, Heidelberg, Farnborough, England, 169–360,
https://doi.org/10.1007/978-3-642-91487-4_3, 1935.
a,
b
Gritskevich, M. S., Garbaruk, A. V., Schütze, J., and Menter, F. R.: Development of DDES and IDDES formulations for the
k−ω shear stress transport model, Flow, turbulence and combustion, 88, 431–449,
https://doi.org/10.1007/s10494-011-9378-4, 2012.
a
Höning, L., Lukassen, L. J., Stoevesandt, B., and Herráez, I.: Influence of rotor blade flexibility on the near-wake behavior of the NREL 5 MW wind turbine, Wind Energ. Sci., 9, 203–218,
https://doi.org/10.5194/wes-9-203-2024, 2024.
a
Hsu, S., Meindl, E. A., and Gilhousen, D. B.: Determining the power-law wind-profile exponent under near-neutral stability conditions at sea, J. Appl. Meteorol. Clim., 33, 757–765,
https://doi.org/10.1175/1520-0450(1994)033<0757:DTPLWP>2.0.CO;2, 1994.
a
IEC: Wind energy generation systems – Part 1: Design requirements, IEC, Geneva, Switzerland, IEC 61400-1:2019-02, 2019.
a,
b
Jonkman, J.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, National Renewable Energy Laboratory,
https://doi.org/10.2172/947422, 2009.
a
Kaimal, J. C., Wyngaard, J., Izumi, Y., and Coté, O.: Spectral characteristics of surface-layer turbulence, Q. J. Roy. Meteor. Soc., 98, 563–589,
https://doi.org/10.1002/qj.49709841707, 1972.
a
Keck, R.-E., Mikkelsen, R., Troldborg, N., de Maré, M., and Hansen, K. S.: Synthetic atmospheric turbulence and wind shear in large eddy simulations of wind turbine wakes, Wind Energy, 17, 1247–1267,
https://doi.org/10.1002/we.1631, 2014.
a
Kleinhans, D.: Stochastische Modellierung komplexer Systeme: von den theoretischen Grundlagen zur Simulation atmosphärischer Windfelder, PhD thesis, Münster, Univ., Germany, Diss.,
http://d-nb.info/990549984/34 (last access: 19 August 2025), 2008. a
Kolmogorov, A. N.: A refinement of previous hypotheses concerning the local structure of turbulence in a viscous incompressible fluid at high Reynolds number, J. Fluid Mech., 13, 82–85,
https://doi.org/10.1017/S0022112062000518, 1962.
a
Kosović, B., Basu, S., Berg, J., Berg, L. K., Haupt, S. E., Larsén, X. G., Peinke, J., Stevens, R. J. A. M., Veers, P., and Watson, S.: Impact of atmospheric turbulence on performance and loads of wind turbines: Knowledge gaps and research challenges, Wind Energ. Sci. Discuss. [preprint],
https://doi.org/10.5194/wes-2025-42, in review, 2025.
a
Liew, J., Riva, R., and Göçmen, T.: Efficient Mann turbulence generation for offshore wind farms with applications in fatigue load surrogate modelling, Journal of Physics: Conference Series, 2626, 012050,
https://doi.org/10.1088/1742-6596/2626/1/012050, 2023, IOP Publishing.
a
Liu, L., Franceschini, L., Oliveira, D. F., Galeazzo, F. C., Carmo, B. S., and Stevens, R. J.: Evaluating the accuracy of the actuator line model against blade element momentum theory in uniform inflow, Wind Energy, 25, 1046–1059,
https://doi.org/10.1002/we.2714, 2022.
a
Moreno, D., Schubert, C., Friedrich, J., Wächter, M., Schwarte, J., Pokriefke, G., Radons, G., and Peinke, J.: Dynamics of the virtual center of wind pressure: An approach for the estimation of wind turbine loads, Journal of Physics: Conference Series, 2767, 022028,
https://doi.org/10.1088/1742-6596/2767/2/022028, 2024, IOP Publishing.
a,
b
Moreno, D., Friedrich, J., Schubert, C., Wächter, M., Schwarte, J., Pokriefke, G., Radons, G., and Peinke, J.: From the center of wind pressure to loads on the wind turbine: a stochastic approach for the reconstruction of load signals, Wind Energ. Sci., 10, 2729–2754,
https://doi.org/10.5194/wes-10-2729-2025, 2025.
a,
b,
c,
d,
e,
f
Mücke, T., Kleinhans, D., and Peinke, J.: Atmospheric turbulence and its influence on the alternating loads on wind turbines, Wind Energy, 14, 301–316,
https://doi.org/10.1002/we.422, 2011.
a
OpenCFD: OpenFOAM: User Guide v2306, OpenCFD Ltd.,
https://doc.openfoam.com/2306/ (last access: 7 January 2026), 2023. a
Patankar, S. V. and Spalding, D. B.: A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows, in: Numerical prediction of flow, heat transfer, turbulence and combustion, Elsevier, 54–73,
https://doi.org/10.1016/B978-0-08-030937-8.50013-1, 1983.
a
Rankine, W. J. M.: On the mechanical principles of the action of propellers, Transactions of the Institution of Naval Architects, 6, 13–39, 1865. a
Schmidt, J., Peralta, C., and Stoevesandt, B.: Automated generation of structured meshes for wind energy applications, in: Open Source CFD International Conference, London,
https://www.researchgate.net/publication/271754239_Automated_generation_of_structured_meshes_for_wind_energy_applications (last access: 7 January 2026), 2012. a
Schubert, C., Moreno, D., Schwarte, J., Friedrich, J., Wächter, M., Pokriefke, G., Radons, G., and Peinke, J.: Introduction of the Virtual Center of Wind Pressure for correlating large-scale turbulent structures and wind turbine loads, Wind Energ. Sci. Discuss. [preprint],
https://doi.org/10.5194/wes-2025-28, in review, 2025.
a,
b,
c,
d,
e,
f,
g
Spille-Kohoff, A. and Kaltenbach, H.-J.: Generation of turbulent inflow data with a prescribed shear-stress profile, in: DNS/LES Progress and challenges, 319 pp., Greyden Press, Columbus, Ohio, USA, ADP013648, 2001. a
Stoevesandt, B., Schepers, G., Fuglsang, P., and Sun, Y.: Handbook of wind energy aerodynamics, Springer Nature, ISBN 978-3-030-31307-4, 2022. a
Syed, A. H. and Mann, J.: A model for low-frequency, anisotropic wind fluctuations and coherences in the marine atmosphere, Bound.-Lay. Meteorol., 190, 1,
https://doi.org/10.1007/s10546-023-00850-w, 2024a.
a,
b,
c
Taylor, G. I.: The spectrum of turbulence, Proceedings of the Royal Society of London, Series A – Mathematical and Physical Sciences, 164, 476–490,
https://doi.org/10.1098/rspa.1938.0032, 1938.
a
Troldborg, N., Sørensen, J. N., Mikkelsen, R., and Sørensen, N. N.: A simple atmospheric boundary layer model applied to large eddy simulations of wind turbine wakes, Wind Energy, 17, 657–669,
https://doi.org/10.1002/we.1608, 2014.
a
Veers, P.: Modeling stochastic wind loads on vertical axis wind turbines, in: 25th Structures, Structural Dynamics and Materials Conference, 910–928,
https://doi.org/10.2514/6.1984-910, 1984.
a
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O., Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki, V., Lundquist, J. K., 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. M., Smith, J. C., 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
von Kármán, T.: Progress in the statistical theory of turbulence, P. Natl. Acad. Sci. USA, 34, 530–539, 1948. a
Yassin, K., Helms, A., Moreno, D., Kassem, H., Höning, L., and Lukassen, L. J.: Applying a random time mapping to Mann-modeled turbulence for the generation of intermittent wind fields, Wind Energ. Sci., 8, 1133–1152,
https://doi.org/10.5194/wes-8-1133-2023, 2023.
a
