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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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WES | Articles | Volume 5, issue 2
Wind Energ. Sci., 5, 819–838, 2020
https://doi.org/10.5194/wes-5-819-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Wind Energy Science Conference 2019

Wind Energ. Sci., 5, 819–838, 2020
https://doi.org/10.5194/wes-5-819-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 29 Jun 2020

Research article | 29 Jun 2020

Cartographing dynamic stall with machine learning

Matthew Lennie et al.

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Matthew Lennie on behalf of the Authors (11 Nov 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (15 Dec 2019) by Katherine Dykes
RR by Anonymous Referee #3 (30 Dec 2019)
RR by Anonymous Referee #1 (18 Feb 2020)
ED: Publish subject to technical corrections (20 Feb 2020) by Katherine Dykes
ED: Publish subject to technical corrections (20 Feb 2020) by Carlo L. Bottasso(Chief Editor)
ED: Publish subject to minor revisions (review by editor) (23 Feb 2020) by Katherine Dykes
AR by Matthew Lennie on behalf of the Authors (04 Mar 2020)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (10 Apr 2020) by Katherine Dykes
AR by Matthew Lennie on behalf of the Authors (22 Apr 2020)  Author's response    Manuscript
ED: Publish subject to technical corrections (03 May 2020) by Katherine Dykes
ED: Publish subject to technical corrections (05 May 2020) by Joachim Peinke(Chief Editor)
AR by Matthew Lennie on behalf of the Authors (07 May 2020)  Author's response    Manuscript
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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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