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Titel:

Application of a long short-term memory neural network for modeling transonic buffet aerodynamics

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Zahn, Rebecca; Winter, Maximilian; Zieher, Moritz; Breitsamter, Christian
Abstract:
In the present work, a reduced-order modeling (ROM) framework based on a long short-term memory (LSTM) neural network is applied for the prediction of transonic buffet aerodynamics. This type of network has a high potential for modeling sequential data, which is favorable for capturing the time-delayed effects associated with unsteady aerodynamics. Therefore, the nonlinear identification procedure as well as the generalization of the resulting ROM are presented. Further, a Monte-Carlo-based trai...     »
Stichworte:
Nonlinear system identification; Reduced-order model; Long short-term memory neural network; Buffet aerodynamics; Computational fluid dynamics
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
Aerospace Science and Technology
Jahr:
2021
Band / Volume:
113
Seitenangaben Beitrag:
106652
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.1016/j.ast.2021.106652
Verlag / Institution:
Elsevier BV
E-ISSN:
1270-9638
Hinweise:
The authors would like to thank the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for the funding of the project BR1511/11-1 . Further, the authors thank the Gauss Centre for Supercomputing e.V. ( www.gauss-centre.eu ) for funding this project by providing computing licences and computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Center ( www.lrz.de ).
Eingereicht (bei Zeitschrift):
29.06.2020
Angenommen (von Zeitschrift):
13.03.2021
Publikationsdatum:
01.06.2021
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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