Articles | Volume 5, issue 2
Research article
29 Jun 2020
Research article |  | 29 Jun 2020

Cartographing dynamic stall with machine learning

Matthew Lennie, Johannes Steenbuck, Bernd R. Noack, and Christian Oliver Paschereit

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Cited articles

Abbott, I. H. and Doenhoff, A. E. V.: Theory of Wing Sections, Including a Summary of Airfoil Data, 1st Edn., Dover Publications, Dover, 1959. a, b, c, d, e
Andersen, P. B., Gaunaa, M., Bak, C., and Hansen, M. H.: A Dynamic Stall Model for Airfoils with Deformable Trailing Edges, J. Phys.: Conf. Ser., 75, 012028,, 2007. a
Bak, C., Madsen, H. A., Fuglsang, P., and Rasmussen, F.: Double stall, in: vol. 1043, available at: (last access: 13 September 2019), 1998. a, b
Bak, C., Madsen, H. A., Paulsen, U. S., Gaunaa, M., Fuglsang, P., Romblad, J., Olesen, N. A., Enevoldsen, P., Laursen, J., and Jensen, L.: DAN-AERO MW: Detailed aerodynamic measurements on a full scale MW wind turbine, in: European Wind Energy Conference and Exhibition (EWEC), 20–23 April 2010, Warsaw, Poland, 1–10, 2010. a
Balduzzi, F., Bianchini, A., Church, B., Wegner, F., Ferrari, L., Ferrara, G., and Paschereit, C. O.: Static and Dynamic Analysis of a NACA 0021 Airfoil Section at Low Reynolds Numbers Based on Experiments and Computational Fluid Dynamics, J. Eng. Gas Turb. Power, 141, 1–10,, 2019. a
Short summary
This study presents a marriage of unsteady aerodynamics and machine learning. When airfoils are subjected to high inflow angles, the flow no longer follows the surface and the flow is said to be separated. In this flow regime, the forces experienced by the airfoil are highly unsteady. This study uses a range of machine learning techniques to extract infomation from test data to help us understand the flow regime and makes recomendations on how to model it.