Reynolds Stress Modeling Using Data Driven Machine Learning Algorithms

TL;DR

This paper demonstrates the first application of machine learning algorithms to develop Reynolds Stress Models for turbulence, aiming to improve accuracy and generalization over traditional models.

Contribution

It introduces a data-driven approach to Reynolds Stress Modeling, assessing various machine learning methods for pressure strain correlation in turbulence.

Findings

ML models can predict flow fields with reasonable accuracy for unseen flows.

ML-based turbulence models can potentially overcome limitations of traditional models.

Assessment of different ML approaches for turbulence closure modeling.

Abstract

Fluid turbulence is an important problem for physics and engineering. Turbulence modeling deals with the development of simplified models that can act as surrogates for representing the effects of turbulence on flow evolution. Such models correspond to a range of different fidelities, from simple eddy-viscosity-based closures to Reynolds Stress Models. Till now the focus of the data-driven turbulence modeling efforts has focused on Machine Learning augmented eddy-viscosity models. In this communication, we illustrate the manner in which the eddy-viscosity framework delimits the efficacy and performance of Machine learning algorithms. Based on this foundation we carry out the first application of Machine learning algorithms for developing improved Reynolds Stress Modeling-based closures for turbulence. Different machine learning approaches are assessed for modeling the pressure strain correlation in turbulence, a longstanding problem of singular importance. We evaluate the performance of these algorithms in the learning dataset, as well as their ability to generalize to different flow cases where the inherent physical processes may vary. This explores the assertion that ML-based data-driven turbulence models can overcome the modeling limitations associated with the traditional turbulence models and ML models trained with large amounts of data with different classes of flows can predict flow field with reasonable accuracy for unknown flows with similar flow physics.