Articles | Volume 7, issue 5
Research article
14 Sep 2022
Research article |  | 14 Sep 2022

A WaveNet-based fully stochastic dynamic stall model

Jan-Philipp Küppers and Tamara Reinicke

Related subject area

Thematic area: Fluid mechanics | Topic: Wind turbine aerodynamics
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Cited articles

Bangga, G., Lutz, T., and Arnold, M.: An improved second-order dynamic stall model for wind turbine airfoils, Wind Energ. Sci., 5, 1037–1058,, 2020. a
Bertagnolio, F., Sørensen, N., and Johansen, J.: Profile catalogue for airfoil sections based on 3D computations, no. 1581(EN) in Denmark, Forskningscenter Risoe, Risoe-R, 2006. a
Bianchini, A., Balduzzi, F., Ferrara, G., and Ferrari, L.: Critical Analysis of Dynamic Stall Models in Low-Order Simulation Models For Vertical-Axis Wind Turbines, Enrgy. Proced., 101, 488–495,, 2016. a
Boilard, J., Gournay, P., and Lefebvre, R.: A literature review of wavenet: theory, application, and optimization, AES Convention: 146, March 2019, Universite de Sherbrooke, Sherbrooke, Quebec, Canada, 10171, (last access: 21 June 2021), 2019. a
Carr, L. W.: Progress in analysis and prediction of dynamic stall, J. Aircraft, 25, 6–17,, 1988. a, b
Short summary
Airfoils play a major role in the technical harnessing of energy from currents such as wind and water. When the angle of attack of a wing changes dynamically, the forces on the wing often change more than would have been assumed from static measurements alone. Since these dynamic forces have a strong influence, e.g., on the performance of airplanes and wind turbines, a neural-network-based model was created that can predict these loads and their stochastic fluctuations.
Final-revised paper