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Surrogate Estimations of Complete Flow Fields of Fan Stage Designs via Deep Neural Networks

Aulich, Marcel and Küppers, Fabian and Schmitz, Andreas and Voß, Christian (2019) Surrogate Estimations of Complete Flow Fields of Fan Stage Designs via Deep Neural Networks. In: Proceedings of the ASME Turbo Expo. ASME Turbo Expo 2019, 17.-21. Jun. 2019, Phoenix, Arizona, USA.

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Item URL in elib:https://elib.dlr.de/133429/
Document Type:Conference or Workshop Item (Speech)
Title:Surrogate Estimations of Complete Flow Fields of Fan Stage Designs via Deep Neural Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Aulich, MarcelMarcel.Aulich (at) dlr.deUNSPECIFIED
Küppers, FabianFabian.Kueppers (at) dlr.deUNSPECIFIED
Schmitz, AndreasAndreas.Schmitz (at) dlr.deUNSPECIFIED
Voß, ChristianChristian.Voss (at) dlr.deUNSPECIFIED
Date:June 2019
Journal or Publication Title:Proceedings of the ASME Turbo Expo
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Status:Published
Keywords:Surrogate Models, Deep Learning, Artificial Neural Networks, Fan Design, Turbomachinery Optimisation, 3D Flow Field Estimation, Convolutional Layers
Event Title:ASME Turbo Expo 2019
Event Location:Phoenix, Arizona, USA
Event Type:international Conference
Event Dates:17.-21. Jun. 2019
Organizer:The American Society of Mechanical Engineers
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:other
DLR - Research area:Aeronautics
DLR - Program:L - no assignment
DLR - Research theme (Project):L - no assignment
Location: Köln-Porz
Institutes and Institutions:Institute of Propulsion Technology > Fan and Compressor
Deposited By: Aulich, Marcel
Deposited On:13 Jan 2020 09:31
Last Modified:13 Jan 2020 09:31

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