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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Vydáno v:Journal of hydrodynamics. Series B Ročník 31; číslo 6; s. 1153 - 1158
Hlavní autoři: Zhang, Xinlei, Wu, Jinlong, Coutier-Delgosha, Olivier, Xiao, Heng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Singapore 01.12.2019
Témata:
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
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Shrnutí: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