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Title:

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

Document type:
Zeitschriftenaufsatz
Author(s):
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...     »
Keywords:
turbulence modeling; vortex flows; multiple swept delta wings; machine learning; neural networks
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Journal title:
Aerospace
Year:
2024
Journal volume:
11
Journal issue:
3
Pages contribution:
238
Language:
en
Fulltext / DOI:
doi:10.3390/aerospace11030238
WWW:
https://www.mdpi.com/2226-4310/11/3/238
Publisher:
MDPI AG
E-ISSN:
2226-4310
Notes:
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.
Submitted:
25.01.2024
Accepted:
13.03.2024
Date of publication:
18.03.2024
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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