Deep Learning of Turbulent Scalar Mixing
Fluid Dynamics
2018-11-20 v1 Computational Engineering, Finance, and Science
Computational Physics
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.
Keywords
Cite
@article{arxiv.1811.07095,
title = {Deep Learning of Turbulent Scalar Mixing},
author = {Maziar Raissi and Hessam Babaee and Peyman Givi},
journal= {arXiv preprint arXiv:1811.07095},
year = {2018}
}
Comments
arXiv admin note: text overlap with arXiv:1808.04327, arXiv:1808.08952