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Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning

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High Performance Computing (ISC High Performance 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11203))

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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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Acknowledgment

The authors gratefully acknowledge the computing time granted for the project JHPC55 by the JARA-HPC Vergabegremium and provided on the JARA-HPC Partition part of the supercomputer JURECA at Forschungszentrum Jülich. Also, the computing time granted for the projects HFG00/HFG02 on the supercomputer JUQUEEN [8] at Forschungszentrum Jülich is acknowledged. MG acknowledges financial support by Labex EMC3, under the grant VAVIDEN.

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Authors and Affiliations

  1. Institute for Combustion Technology, RWTH Aachen University, Templergraben 64, 52062, Aachen, Germany

    Mathis Bode, Baohao Liao & Heinz Pitsch

  2. CORIA – CNRS UMR 6614, Saint Etienne du Rouvray, France

    Michael Gauding

  3. Jülich Supercomputing Centre, FZ Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany

    Jens Henrik Göbbert & Jenia Jitsev

Authors
  1. Mathis Bode
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  2. Michael Gauding
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  3. Jens Henrik Göbbert
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  4. Baohao Liao
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  5. Jenia Jitsev
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  6. Heinz Pitsch
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Corresponding author

Correspondence to Michael Gauding .

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Editors and Affiliations

  1. Tokyo Institute of Technology, Tokyo, Japan

    Rio Yokota

  2. University of Edinburgh, Edinburgh, UK

    Michèle Weiland

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    John Shalf

  4. Swiss National Supercomputing Centre, Lugano, Switzerland

    Sadaf Alam

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Bode, M., Gauding, M., Göbbert, J.H., Liao, B., Jitsev, J., Pitsch, H. (2018). Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning. In: Yokota, R., Weiland, M., Shalf, J., Alam, S. (eds) High Performance Computing. ISC High Performance 2018. Lecture Notes in Computer Science(), vol 11203. Springer, Cham. https://doi.org/10.1007/978-3-030-02465-9_44

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  • DOI: https://doi.org/10.1007/978-3-030-02465-9_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02464-2

  • Online ISBN: 978-3-030-02465-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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