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.

Full text not available from this repository.

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:

Authors	Institution or Email of Authors	Author's ORCID iD
Aulich, Marcel	UNSPECIFIED	UNSPECIFIED
Küppers, Fabian	UNSPECIFIED	UNSPECIFIED
Schmitz, Andreas	UNSPECIFIED	UNSPECIFIED
Voß, Christian	UNSPECIFIED	UNSPECIFIED

Date:

June 2019

Journal or Publication Title:

Proceedings of the ASME Turbo Expo

Refereed publication:

Yes

Open Access:

Gold Open Access:

In SCOPUS:

Yes

In ISI Web of Science:

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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