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Machine Learning of Combustion LES Models from Reacting Direct Numerical Simulation

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Data Analysis for Direct Numerical Simulations of Turbulent Combustion

Abstract

In this chapter we demonstrate how supervised deep learning techniques can be used to construct models for the filtered progress variable source term necessary for large eddy simulation (LES). The source data for the model is a direct numerical simulation (DNS) of a reacting flow in a low swirl burner configuration. Filtered quantities taken from the DNS data are used to train a deep neural network (DNN)-based model. An efficient data sampling strategy was devised to ensure that a uniform representation of all the states observed in the filtered DNS data are equally present in the training dataset. A-priori testing of the DNN-based model highlights the representative power of DNN to accurately reproduce the filtered reaction progress variable source term over a range of scales and various flame regimes as seen in an industrial burner.

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Acknowledgements

This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding was provided by U.S. Department of Energy Office of Science and National Nuclear Security Administration. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

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Authors and Affiliations

  1. High Performance Algorithms and Complex Fluids, Computational Science Center, National Renewable Energy Laboratory, 15013 Denver W Pkwy, ESIF301, Golden, CO, 80401, USA

    Shashank Yellapantula, Marc T. Henry de Frahan, Ryan King & Ray Grout

  2. Center for Computational Sciences and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

    Marc Day

Authors
  1. Shashank Yellapantula
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  2. Marc T. Henry de Frahan
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  3. Ryan King
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  4. Marc Day
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Correspondence to Shashank Yellapantula .

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Editors and Affiliations

  1. Institute for Combustion Technology, RWTH Aachen University, Aachen, Germany

    Heinz Pitsch

  2. Institute for Combustion Technology, RWTH Aachen University, Aachen, Germany

    Antonio Attili

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Yellapantula, S., de Frahan, M.T.H., King, R., Day, M., Grout, R. (2020). Machine Learning of Combustion LES Models from Reacting Direct Numerical Simulation. In: Pitsch, H., Attili, A. (eds) Data Analysis for Direct Numerical Simulations of Turbulent Combustion. Springer, Cham. https://doi.org/10.1007/978-3-030-44718-2_14

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