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Titel:

Adaptive Turbulence Model for Leading Edge Vortex Flows Preconditioned by a Hybrid Neural Network

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Zieher, Moritz; Breitsamter, Christian
Abstract:
Eddy-viscosity-based turbulence models provide the most commonly used modeling approach for computational fluid dynamics simulations in the aerospace industry. These models are very accurate at a relatively low cost for many cases but lack accuracy in the case of highly rotational leading edge vortex flows for mid to low aspect-ratio wings. An enhanced adaptive turbulence model based on the one-equation Spalart–Allmaras turbulence model is fundamental to this work. This model employs several add...     »
Stichworte:
turbulence modeling; vortex flows; multiple swept delta wings; machine learning; neural networks
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
Aerospace
Jahr:
2024
Band / Volume:
11
Heft / Issue:
3
Seitenangaben Beitrag:
238
Sprache:
en
Volltext / DOI:
doi:10.3390/aerospace11030238
WWW:
https://www.mdpi.com/2226-4310/11/3/238
Verlag / Institution:
MDPI AG
E-ISSN:
2226-4310
Hinweise:
The funding of the presented work within the Luftfahrtforschungsprogramm VI-1 (LUFO VI-1) project DIGIfly (FKZ: 20X19091) by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) is gratefully acknowledged.
Eingereicht (bei Zeitschrift):
25.01.2024
Angenommen (von Zeitschrift):
13.03.2024
Publikationsdatum:
18.03.2024
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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