Sub-grid modelling for two-dimensional turbulence using neural networks

TL;DR

This paper introduces a neural network-based data-driven framework for sub-grid modeling in 2D turbulence, effectively predicting turbulence source terms using high-fidelity data and local flow information.

Contribution

It presents a novel neural network approach that maps local flow features and eddy-viscosity kernels to sub-grid vorticity forcing, advancing turbulence closure modeling.

Findings

Successful prediction of sub-grid vorticity forcing.

Effective flow evolution modeling in 2D turbulence.

Validation through kinetic energy spectra analysis.

Abstract

In this investigation, a data-driven turbulence closure framework is introduced and deployed for the sub-grid modelling of Kraichnan turbulence. The novelty of the proposed method lies in the fact that snapshots from high-fidelity numerical data are used to inform artificial neural networks for predicting the turbulence source term through localized grid-resolved information. In particular, our proposed methodology successfully establishes a map between inputs given by stencils of the vorticity and the streamfunction along with information from two well-known eddy-viscosity kernels. Through this we predict the sub-grid vorticity forcing in a temporally and spatially dynamic fashion. Our study is both a-priori and a-posteriori in nature. In the former, we present an extensive hyper-parameter optimization analysis in addition to learning quantification through probability density function based validation of sub-grid predictions. In the latter, we analyse the performance of our framework for flow evolution in a classical decaying two-dimensional turbulence test case in the presence of errors related to temporal and spatial discretization. Statistical assessments in the form of angle-averaged kinetic energy spectra demonstrate the promise of the proposed methodology for sub-grid quantity inference. In addition, it is also observed that some measure of a-posteriori error must be considered during optimal model selection for greater accuracy. The results in this article thus represent a promising development in the formalization of a framework for generation of heuristic-free turbulence closures from data.