Convolutional Neural Network Models and Interpretability for the Anisotropic Reynolds Stress Tensor in Turbulent One-dimensional Flows

The Reynolds-averaged Navier-Stokes (RANS) equations are widely used in turbulence applications. They require accurately modeling the anisotropic Reynolds stress tensor, for which traditional Reynolds stress closure models only yield reliable results in some flow configurations. In the last few year...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-06
Main Authors: Haitz Sáez de Ocáriz Borde, Sondak, David, Protopapas, Pavlos
Format: Article
Language:English
Subjects:
Artificial neural networks
Channel flow
Computational fluid dynamics
Mathematical analysis
Modelling
Neural networks
One dimensional flow
Reynolds averaged Navier-Stokes method
Reynolds stress
Tensors
Turbulence
Turbulent flow
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The Reynolds-averaged Navier-Stokes (RANS) equations are widely used in turbulence applications. They require accurately modeling the anisotropic Reynolds stress tensor, for which traditional Reynolds stress closure models only yield reliable results in some flow configurations. In the last few years, there has been a surge of work aiming at using data-driven approaches to tackle this problem. The majority of previous work has focused on the development of fully-connected networks for modeling the anisotropic Reynolds stress tensor. In this paper, we expand upon recent work for turbulent channel flow and develop new convolutional neural network (CNN) models that are able to accurately predict the normalized anisotropic Reynolds stress tensor. We apply the new CNN model to a number of one-dimensional turbulent flows. Additionally, we present interpretability techniques that help drive the model design and provide guidance on the model behavior in relation to the underlying physics.
ISSN:2331-8422
DOI:10.48550/arxiv.2106.15757