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Titel:

GAN-based generation of realistic compressible-flow samples from incomplete data

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Abaidi, R.; Adams, N.A.
Abstract:
Predictive sampling of compressible flows is an important aspect of aerodynamic design, analysis, and optimization. The process is usually done by generating flow fields from computational fluid dynamics (CFD) simulations and solving governing evolution equations, from which quantities of interest (QoI) are obtained by post-processing. With this study, we propose a data-driven approach for the predictive sampling of compressible flows around airfoils. This paper demonstrates the potential of gen...     »
Stichworte:
Compressible flowFlow fieldGenerative adversarial networksImage generation
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
Computers & Fluids
Jahr:
2024
Band / Volume:
269
Seitenangaben Beitrag:
106113
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.1016/j.compfluid.2023.106113
WWW:
https://www.sciencedirect.com/science/article/pii/S0045793023003389#d1e3700
Verlag / Institution:
Elsevier BV
Print-ISSN:
0045-7930
E-ISSN:
1879-0747
Hinweise:
Acknowledgments This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant 461278652.
Eingereicht (bei Zeitschrift):
08.12.2022
Angenommen (von Zeitschrift):
08.11.2023
Publikationsdatum:
01.01.2024
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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