Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Sandia National Laboratories California · The University of Texas at Austin
Abstract
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…
Citation impact
- FWCI
- 63.27
- Percentile
- 100%
- References
- 31
Authors
3Topics & keywords
- Reynolds-averaged Navier–Stokes equations
- Turbulence modeling
- Reynolds stress
- Turbulence
- Reynolds stress equation model
- Artificial neural network
- Invariant (physics)
- K-epsilon turbulence model