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Titel:

Comparison of Deep Learning Architectures for Dimensionality Reduction of 3D Flow Fields of a Racing Car

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Reck, Michaela; Hilbert, Marc; Hilhorst, René; Indinger, Thomas
Abstract:
In motorsports, aerodynamic development processes target to achieve gains in performance. This requires a comprehensive understanding of the prevailing aerodynamics and the capability of analysing large quantities of numerical data. However, manual analysis of a significant amount of Computational Fluid Dynamics (CFD) data is time consuming and complex. The motivation is to optimize the aerodynamic analysis workflow with the use of deep learning architectures. In this research, variants of 3D de...     »
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongress- / Buchtitel:
SAE Technical Paper Series
Verlag / Institution:
SAE International
Publikationsdatum:
11.04.2023
Jahr:
2023
Nachgewiesen in:
Scopus
Print-ISBN:
0148-719
E-ISBN:
2688-3627
Sprache:
en
Volltext / DOI:
doi:10.4271/2023-01-0862
WWW:
https://www.sae.org/publications/technical-papers/content/2023-01-0862/
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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