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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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Discussion papers
https://doi.org/10.5194/wes-2019-36
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2019-36
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 01 Jul 2019

Submitted as: research article | 01 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Wind Energy Science (WES).

Cartographing dynamic stall with machine learning

Matthew Lennie1, Johannes Steenbuck1, Bernd R. Noack2, and Christian Oliver Paschereit1 Matthew Lennie et al.
  • 1Technische Universität Berlin, Institut für Strömungsmechanik und Technische Akustik, Berlin, Germany
  • 2LIMSI, CNRS, Université Paris-Saclay, Bât 507, rue du Belvédère, Campus Universitaire, F-91403 Orsay, France

Abstract. Airfoil stall is bad for wind turbines. Once stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge, eventually the shear layer rolls up and then a coherent vortex forms and then sheds downstream with it’s low pressure core causing a lift spike and moment dump. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process, but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analysis cycle to cycle variations. Modern data science/machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures that secondary and tertiary vorticity vary strongly and in static stall with surging flow; the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.

Matthew Lennie et al.
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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Matthew Lennie et al.
Matthew Lennie et al.
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Latest update: 22 Sep 2019
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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.
This study presents a marriage of unsteady aerodynamics and machine learning. When airfoils are...
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