Khurana, Parv und Jäckel, Florian und Grabe, Cornelia und Dwight, Richard P. (2022) Data-driven augmentation of a RANS turbulence model for Transonic Flow Prediction. In: 56th 3AF International Conference on Applied Aerodynamics 2022, Seiten 1-11. 56th 3AF International Conference on Applied Aerodynamics, 2022-03-28 - 2022-03-30, Toulouse, Frankreich.
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Offizielle URL: https://www.3af-aerodynamics.com/
Kurzfassung
Purpose: The paper aims to improve Reynolds-Averaged Navier Stokes (RANS) turbulence models using a datadriven approach based on machine learning. A special focus is put on determining the optimal input features used for the machine learning model. Methodology: The Field Inversion and Machine Learning (FIML) approach is applied to the negative Spalart-Allmaras turbulence model for transonic flows over an airfoil where shock-induced separation occurs. Feature selection methods are applied to the results of the field inversion to determine the optimal input features for the machine learning model. Findings: Optimal input features and a machine learning model are developed which improve the existing negative Spalart-Allmaras turbulence model with respect to shock-induced flow separation. Originality: A comprehensive workflow is demonstrated that yields insights on which input features and which machine learning model should be used in the context of the FIML approach
elib-URL des Eintrags: | https://elib.dlr.de/187948/ | ||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||
Titel: | Data-driven augmentation of a RANS turbulence model for Transonic Flow Prediction | ||||||||||||||||||||
Autoren: |
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Datum: | März 2022 | ||||||||||||||||||||
Erschienen in: | 56th 3AF International Conference on Applied Aerodynamics 2022 | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Nein | ||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||
Seitenbereich: | Seiten 1-11 | ||||||||||||||||||||
Herausgeber: |
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Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | RANS turbulence models, data-driven turbulence modeling, machine learning, feature selection, flow separation, transonic flows | ||||||||||||||||||||
Veranstaltungstitel: | 56th 3AF International Conference on Applied Aerodynamics | ||||||||||||||||||||
Veranstaltungsort: | Toulouse, Frankreich | ||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||
Veranstaltungsbeginn: | 28 März 2022 | ||||||||||||||||||||
Veranstaltungsende: | 30 März 2022 | ||||||||||||||||||||
Veranstalter : | 3AF | ||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||
HGF - Programm: | Luftfahrt | ||||||||||||||||||||
HGF - Programmthema: | Effizientes Luftfahrzeug | ||||||||||||||||||||
DLR - Schwerpunkt: | Luftfahrt | ||||||||||||||||||||
DLR - Forschungsgebiet: | L EV - Effizientes Luftfahrzeug | ||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | L - Digitale Technologien | ||||||||||||||||||||
Standort: | Göttingen | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Aerodynamik und Strömungstechnik > CASE, GO | ||||||||||||||||||||
Hinterlegt von: | Jäckel, Florian | ||||||||||||||||||||
Hinterlegt am: | 19 Aug 2022 17:51 | ||||||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:49 |
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