Deep learning of turbulent scalar mixing
Abstract
Based on recent developments in physicsinformed 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 singlepoint PDF equation. The discovered models are appraised against an exact solution derived by the amplitude mapping closure (AMC)JohnsonEdgeworth translation (JET) model of binary scalar mixing in homogeneous turbulence.
 Publication:

Physical Review Fluids
 Pub Date:
 December 2019
 DOI:
 10.1103/PhysRevFluids.4.124501
 arXiv:
 arXiv:1811.07095
 Bibcode:
 2019PhRvF...4l4501R
 Keywords:

 Physics  Fluid Dynamics;
 Computer Science  Computational Engineering;
 Finance;
 and Science;
 Physics  Computational Physics
 EPrint:
 arXiv admin note: text overlap with arXiv:1808.04327, arXiv:1808.08952