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Titel:

Neural-Network-Based Model for Trailing-Edge Flap Loads in Preliminary Aircraft Design

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Stephan, Ralph; Heyen, Cedric; Stumpf, Eike; Ruhland, Johannes; Breitsamter, Christian
Abstract:
Accurately predicting the forces and moments acting on trailing-edge devices under different flight conditions is a critical aspect in the design of the kinematics and actuation for high-lift or variable-camber applications. However, accurate modeling without elaborated computational fluid dynamics (CFD) analyses in the subsonic and transonic regimes needs a sophisticated model. Thus, the objective of this paper is to create such a model that accurately predicts the forces and moments acting on...     »
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
Journal of Aircraft
Jahr:
2024
Band / Volume:
61
Heft / Issue:
4
Seitenangaben Beitrag:
1131-1142
Sprache:
en
Volltext / DOI:
doi:10.2514/1.c037632
WWW:
https://arc.aiaa.org/doi/10.2514/1.C037632
Verlag / Institution:
American Institute of Aeronautics and Astronautics (AIAA)
E-ISSN:
0021-86691533-3868
Hinweise:
Funding text The funding of this investigation within the LUFO VI-2 project H2Avia (FKZ: 20E2106B) by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) is gratefully acknowledged. The data used in this publication was managed using the research data management platform Coscine with storage space granted by the Research Data Storage (RDS) of the DFG and Ministry of Culture and Science of the State of North Rhine-Westphalia (DFG: INST222/1261-1 and MWK: 214-4.06.05.08\u20...     »
Publikationsdatum:
01.07.2024
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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