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Title:

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

Document type:
Zeitschriftenaufsatz
Author(s):
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...     »
Keywords:
Compressible flowFlow fieldGenerative adversarial networksImage generation
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Journal title:
Computers & Fluids
Year:
2024
Journal volume:
269
Pages contribution:
106113
Covered by:
Scopus
Language:
en
Fulltext / DOI:
doi:10.1016/j.compfluid.2023.106113
WWW:
https://www.sciencedirect.com/science/article/pii/S0045793023003389#d1e3700
Publisher:
Elsevier BV
Print-ISSN:
0045-7930
E-ISSN:
1879-0747
Notes:
Acknowledgments This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant 461278652.
Submitted:
08.12.2022
Accepted:
08.11.2023
Date of publication:
01.01.2024
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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