Deep Learning of Turbulent Scalar Mixing

2018/11/17 by Maziar Raissi, Hessam Babaee, Raissi, Maziar +3 · 2 citations
Computer Science · Physics and Astronomy · #Computational Engineering #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Fluid Dynamics (physics.flu-dyn) #and Science (cs.CE) #cs.CE #physics.comp-ph #physics.flu-dyn

paper · pdf · doi:10.48550/arxiv.1811.07095

arXiv admin note: text overlap with arXiv:1808.04327, arXiv:1808.08952

arxiv created 2018/11/17 · arxiv updated 2018/11/20

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 spatio-temporal 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 model are appraised against exact solution derived by the amplitude mapping closure (AMC)/ Johnsohn-Edgeworth translation (JET) model of binary scalar mixing in homogeneous turbulence.

Citations

• Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data

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