skip to main content

DOE PAGESDOE PAGES

Title: 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 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. Furthermore, 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.
Authors:
 [1] ;  [2] ;  [1]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Univ. of Texas at Austin, Austin, TX (United States)
Publication Date:
Report Number(s):
SAND-2016-7345J
Journal ID: ISSN 0022-1120; applab; PII: S0022112016006157; TRN: US1700127
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
Journal of Fluid Mechanics
Additional Journal Information:
Journal Volume: 807; Journal ID: ISSN 0022-1120
Publisher:
Cambridge University Press
Research Org:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
OSTI Identifier:
1333570

Ling, Julia, Kurzawski, Andrew, and Templeton, Jeremy. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. United States: N. p., Web. doi:10.1017/jfm.2016.615.
Ling, Julia, Kurzawski, Andrew, & Templeton, Jeremy. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. United States. doi:10.1017/jfm.2016.615.
Ling, Julia, Kurzawski, Andrew, and Templeton, Jeremy. 2016. "Reynolds averaged turbulence modelling using deep neural networks with embedded invariance". United States. doi:10.1017/jfm.2016.615. https://www.osti.gov/servlets/purl/1333570.
@article{osti_1333570,
title = {Reynolds averaged turbulence modelling using deep neural networks with embedded invariance},
author = {Ling, Julia and Kurzawski, Andrew and Templeton, Jeremy},
abstractNote = {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. Furthermore, 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.},
doi = {10.1017/jfm.2016.615},
journal = {Journal of Fluid Mechanics},
number = ,
volume = 807,
place = {United States},
year = {2016},
month = {10}
}