Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from...

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Veröffentlicht in:Journal of fluid mechanics Jg. 807; S. 155 - 166
Hauptverfasser: Ling, Julia, Kurzawski, Andrew, Templeton, Jeremy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge, UK Cambridge University Press 25.11.2016
Schlagworte:
Anisotropy
CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
Fluid mechanics
Navier-Stokes equations
Reynolds number
Turbulence
Viscosity
turbulent flows
turbulence modelling
turbulence theory
ISSN:0022-1120, 1469-7645
Online-Zugang:Volltext
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Zusammenfassung:There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. The Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
AC04-94AL85000
USDOE National Nuclear Security Administration (NNSA)
SAND-2016-7345J
ISSN:0022-1120
1469-7645
DOI:10.1017/jfm.2016.615