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Deep Learning at Scale for Subgrid Modeling in Turbulent Flows: Regression and Reconstruction

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High Performance Computing (ISC High Performance 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11887))

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  • The original version of this chapter was revised: It has been changed to non-open access and the copyright holder is now “Springer Nature Switzerland AG”. The correction to this chapter is available at https://doi.org/10.1007/978-3-030-34356-9_50

Abstract

Modeling of turbulent flows is still challenging. One way to deal with the large scale separation due to turbulence is to simulate only the large scales and model the unresolved contributions as done in large-eddy simulation (LES). This paper focuses on two deep learning (DL) strategies, regression and reconstruction, which are data-driven and promising alternatives to classical modeling concepts. Using three-dimensional (3-D) forced turbulence direct numerical simulation (DNS) data, subgrid models are evaluated, which predict the unresolved part of quantities based on the resolved solution. For regression, it is shown that feedforward artificial neural networks (ANNs) are able to predict the fully-resolved scalar dissipation rate using filtered input data. It was found that a combination of a large-scale quantity, such as the filtered passive scalar itself, and a small-scale quantity, such as the filtered energy dissipation rate, gives the best agreement with the actual DNS data. Furthermore, a DL network motivated by enhanced super-resolution generative adversarial networks (ESRGANs) was used to reconstruct fully-resolved 3-D velocity fields from filtered velocity fields. The energy spectrum shows very good agreement. As size of scientific data is often in the order of terabytes or more, DL needs to be combined with high performance computing (HPC). Necessary code improvements for HPC-DL are discussed with respect to the supercomputer JURECA. After optimizing the training code, 396.2 TFLOPS were achieved.

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Change history

  • 08 January 2020

    In the original version of this LNCS volume, four papers were erroneously released as open access papers. This has been corrected to only two papers – papers 5 and 7.

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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 financial support by the Cluster of Excellence “The Fuel Science Center”, which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – Exzellenzcluster 2186 “The Fuel Science Center” ID: 390919832, and from of the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under grant agreement No. 695747 is acknowledged. MG acknowledges financial support provided under the grant EMCO2RE. Furthermore, the authors want to thank Jenia Jitsev, Zeyu Lian, and Mayur Vikas Joshi for their help.

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Authors and Affiliations

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

    Mathis Bode, Konstantin Kleinheinz & Heinz Pitsch

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

    Michael Gauding

Authors
  1. Mathis Bode
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  2. Michael Gauding
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  3. Konstantin Kleinheinz
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  4. Heinz Pitsch
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Corresponding author

Correspondence to Mathis Bode .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Michèle Weiland

  2. Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Sachsen, Germany

    Guido Juckeland

  3. Swiss National Supercomputing Centre, Lugano, Ticino, Switzerland

    Sadaf Alam

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Heike Jagode

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Bode, M., Gauding, M., Kleinheinz, K., Pitsch, H. (2019). Deep Learning at Scale for Subgrid Modeling in Turbulent Flows: Regression and Reconstruction. In: Weiland, M., Juckeland, G., Alam, S., Jagode, H. (eds) High Performance Computing. ISC High Performance 2019. Lecture Notes in Computer Science(), vol 11887. Springer, Cham. https://doi.org/10.1007/978-3-030-34356-9_41

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  • DOI: https://doi.org/10.1007/978-3-030-34356-9_41

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