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Titel:

Race Car Flow Field Analysis using Autoencoders and Clustering

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Reck, Michaela; Hilhorst, René; Hilbert, Marc; Indinger, Thomas
Abstract:
The aerodynamic development process of a racing car involves the generation of a great amount of data from numerical investigations. A Convolutional Autoencoder (CAE) architecture is applied to optimize the aerodynamic analysis workflow. In this study, flow fields obtained from Reynolds Averaged Navier Stokes (RANS) simulations serve as input for dimensionality reduction and clustering methods. The objective is to relate variations in flow topology to changes of corresponding performance metrics...     »
Stichworte:
aerodynamic performance; computational fluid dynamics; heat + fluid; neural networks [D1]; steady aerodynamics
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
International Journal of Automotive Engineering
Jahr:
2023
Band / Volume:
14
Heft / Issue:
2
Seitenangaben Beitrag:
35-42
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.20485/jsaeijae.14.2_35
Verlag / Institution:
Society of Automotive Engineers of Japan, Inc.
E-ISSN:
2185-09842185-0992
Hinweise:
Funding text We would like to thank our industrial partner Toyota GAZOO Racing Europe for their financial support. We wish to extend our special thanks to Dr. R. Hilhorst, who pushed to conduct research. This work is supported by TUM School of Engineering and Design.
Publikationsdatum:
01.01.2023
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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