Recent progress in augmenting turbulence models with physics-informed machine learning

In view of the long stagnation in traditional turbulence modeling, researchers have attempted using machine learning to augment turbulence models. This paper presents some of the recent progresses in our group on augmenting turbulence models with physics-informed machine learning. We also discuss ou...

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Bibliographic Details
Published in:	Journal of hydrodynamics. Series B Vol. 31; no. 6; pp. 1153 - 1158
Main Authors:	Zhang, Xinlei, Wu, Jinlong, Coutier-Delgosha, Olivier, Xiao, Heng
Format:	Journal Article
Language:	English
Published:	Singapore Springer Singapore 01.12.2019
Subjects:	Engineering Engineering Fluid Dynamics Hydrology/Water Resources Numerical and Computational Physics Simulation Special Column on 30th NCHD turbulence modeling model uncertainty data-driven modeling Machine learning
ISSN:	1001-6058, 1878-0342
Online Access:	Get full text
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Summary:	In view of the long stagnation in traditional turbulence modeling, researchers have attempted using machine learning to augment turbulence models. This paper presents some of the recent progresses in our group on augmenting turbulence models with physics-informed machine learning. We also discuss our works on ensemble-based field inversion to provide training data for constructing machine learning models. Future and on-going research efforts are introduced.
ISSN:	1001-6058 1878-0342
DOI:	10.1007/s42241-019-0089-y