Contribution to a conference proceedings/Contribution to a book

FZJ-2019-00776

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning

Bode, M. ; Gauding, M. (Corresponding author) ; Göbbert, J. H.FZJ* ; Liao, B. ; Jitsev, J.FZJ* ; Pitsch, H.

2018
Springer International Publishing Cham
ISBN: 978-3-030-02464-2 (print), 978-3-030-02465-9 (electronic)

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Please use a persistent id in citations: doi:10.1007/978-3-030-02465-9_44

Abstract: In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various generative adversarial networks (GANs) are discussed with respect to their suitability for understanding and modeling turbulence. Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence. Highly resolved direct numerical simulation (DNS) turbulent data is used for training the WGANs and the effect of network parameters, such as learning rate and loss function, is studied. Qualitatively good agreement between DNS input data and generated turbulent structures is shown. A quantitative statistical assessment of the predicted turbulent fields is performed.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)
2. Using deep learning to predict statistics of turbulent flows at high Reynolds numbers (jhpc55_20180501) (jhpc55_20180501)

Appears in the scientific report 2018

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Database coverage:
Nationallizenz ; SCOPUS

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