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Title:

Prediction of Aerodynamic Coefficients for Multi-Swept Delta Wings via a Hybrid Neuronal Network

Document type:
Zeitschriftenaufsatz
Author(s):
MORITZ ZIEHER ; CHRISTIAN BREITSAMTER
Abstract:
In this study, the prediction capabilities of a hybrid neural network with regards to aerodynamic coefficients of multiple swept delta wings are investigated. The quick evaluation of aerodynamic coefficients based on a few geometrical and flow parameters instead of cost-consuming computational fluid dynamics simulations or wind tunnel experiments could save time and costs during early aircraft design phases. The training data is based on the results of wind tunnel measurements for a number o...     »
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Journal title:
Proceedings of the Aerospace Europe Conference - EUCASS - CEAS - 2023
Year:
2023
Language:
en
Fulltext / DOI:
doi:10.13009/EUCASS2023-383
WWW:
https://www.eucass.eu/doi/EUCASS2023-383.pdf
Publisher:
Proceedings of the Aerospace Europe Conference - EUCASS - CEAS - 2023
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 acknowl- edged.
Date of publication:
01.01.0202
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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