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Machine Learning of Combustion LES Models from Reacting Direct Numerical Simulation

In: Data Analysis for Direct Numerical Simulations of Turbulent Combustion

Author

Listed:
  • Shashank Yellapantula

    (National Renewable Energy Laboratory, High Performance Algorithms and Complex Fluids, Computational Science Center)

  • Marc T. Henry de Frahan

    (National Renewable Energy Laboratory, High Performance Algorithms and Complex Fluids, Computational Science Center)

  • Ryan King

    (National Renewable Energy Laboratory, High Performance Algorithms and Complex Fluids, Computational Science Center)

  • Marc Day

    (Lawrence Berkeley National Laboratory, Center for Computational Sciences and Engineering)

  • Ray Grout

    (National Renewable Energy Laboratory, High Performance Algorithms and Complex Fluids, Computational Science Center)

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.

Suggested Citation

  • Shashank Yellapantula & Marc T. Henry de Frahan & Ryan King & Marc Day & Ray Grout, 2020. "Machine Learning of Combustion LES Models from Reacting Direct Numerical Simulation," Springer Books, in: Heinz Pitsch & Antonio Attili (ed.), Data Analysis for Direct Numerical Simulations of Turbulent Combustion, chapter 0, pages 273-292, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-44718-2_14
    DOI: 10.1007/978-3-030-44718-2_14
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