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Deep learning of turbulent scalar mixing

Maziar Raissi

Hessam Babaee and Peyman Givi

  • Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912, USA

  • Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA

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Phys. Rev. Fluids 4, 124501 – Published 2 December, 2019

DOI: https://doi.org/10.1103/PhysRevFluids.4.124501

Abstract

Based on recent developments in physics-informed deep learning and deep hidden physics models, we put forth a framework for discovering turbulence models from scattered and potentially noisy spatiotemporal measurements of the probability density function (PDF). The models are for the conditional expected diffusion and the conditional expected dissipation of a Fickian scalar described by its transported single-point PDF equation. The discovered models are appraised against an exact solution derived by the amplitude mapping closure (AMC)–Johnson-Edgeworth translation (JET) model of binary scalar mixing in homogeneous turbulence.

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Phys. Rev. Fluids 4, 124501– Published 2 December, 2019
  • Received 15 November 2018
Article part of CHORUS
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