Benutzer: Gast  Login
Titel:

Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Chen, Li-Wei; Cakal, Berkay A.; Hu, Xiangyu; Thuerey, Nils
Abstract:
Efficiently predicting the flow field and load in aerodynamic shape optimisation remains a highly challenging and relevant task. Deep learning methods have been of particular interest for such problems due to their success in solving inverse problems in other fields. In the present study U-net-based deep neural network (DNN) models are trained with high-fidelity datasets to infer flow fields and then employed as surrogate models to carry out the shape optimisation problem i.e.To find a minimal d...     »
Stichworte:
computational methods; general fluid mechanics; Navier-Stokes equations
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Horizon 2020:
Eurpoean Research Council, grants 637014, 838342
Zeitschriftentitel:
Journal of Fluid Mechanics
Jahr:
2021
Band / Volume:
919
Nachgewiesen in:
Scopus
Volltext / DOI:
doi:10.1017/jfm.2021.398
WWW:
https://www.cambridge.org/core/journals/journal-of-fluid-mechanics/article/numerical-investigation-of-minimum-drag-profiles-in-laminar-flow-using-deep-learning-surrogates/8BFCFCD2123B4569A8B0ACF4BE880CDF
Verlag / Institution:
Cambridge University Press (CUP)
E-ISSN:
0022-11201469-7645
Publikationsdatum:
01.06.2021
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
 BibTeX