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Titel:

WING BUFFET PRESSURE LOAD PREDICTION BASED ON A HYBRID DEEP LEARNING MODEL

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Zahn, Rebecca; Weiner, Andre; Breitsamter, Christian
Abstract:
In the present study, a hybrid deep learning reduced order model (ROM) is applied for unsteady transonic wing buffet load prediction. The hybrid model is defined by the combination of a convolutional variational autoencoder (CNN-VAR-AE) and a long short-term memory (LSTM) neural network. In the first step, the CNN-VAR-AE is trained using experimental buffet data. Thereby, the high-dimensional buffet flow field is reduced into a low-dimensional latent space. In the second step, the LSTM is traine...     »
Stichworte:
Convolutional Autoencoder; Deep Learning; Long Short-Term Memory Neural Network; Transonic Wing Buffet Aerodynamics
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongress- / Buchtitel:
33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022
Band / Teilband / Volume:
3
Datum der Konferenz:
4 September 2022through 9 September 2022
Verlag / Institution:
International Council of the Aeronautical Sciences
Jahr:
2022
Seiten:
1892 – 1908
Nachgewiesen in:
Scopus
Print-ISBN:
978-171387116-3
Sprache:
en
WWW:
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159712696&partnerID=40&md5=b6adfee67e4b1551edca3980bf4b0d4a
Hinweise:
Funding text The authors gratefully acknowledge the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for funding this work in the framework of the research unit FOR 2895 (Unsteady flow and interaction phenomena at high speed stall conditions), subproject TP7, grant number BR1511/14-1. Further, the authors would like to thank the Helmholtz Gemeinschaft HGF (Helmholtz Association), Deutsches Zentrum für Luft - und Raumfahrt DLR (German Aerospace Center) and Airbus for providing t...     »
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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