Deep learning of turbulent scalar mixing
Copyright policy )Deep learning of turbulent scalar mixing
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.
arXiv admin note: text overlap with arXiv:1808.04327, arXiv:1808.08952
- Brown University United States
- Division of Applied Mathematics Brown University United States
- University of Pittsburgh United States
- Brown University United States
Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, Physics - Fluid Dynamics, Computational Physics (physics.comp-ph), Computer Science - Computational Engineering, Finance, and Science, Physics - Computational Physics
Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, Physics - Fluid Dynamics, Computational Physics (physics.comp-ph), Computer Science - Computational Engineering, Finance, and Science, Physics - Computational Physics
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