Deep Neural Networks for Data-Driven Turbulence Models

TL;DR

This paper introduces a neural network-based data-driven turbulence model for Large Eddy Simulation, demonstrating promising generalization and accuracy in predicting closure terms from coarse grid data.

Contribution

It presents a novel neural network approach to turbulence modeling that learns closure terms directly from data without prior assumptions, improving LES accuracy.

Findings

Neural networks achieved up to 73% cross correlation in predictions.

Including both primitive variables and LES operators as inputs improved results.

The learned model produced a stable, accurate LES scheme comparable to state-of-the-art methods.

Abstract

In this work, we present a novel data-based approach to turbulence modelling for Large Eddy Simulation (LES) by artificial neural networks. We define the exact closure terms including the discretization operators and generate training data from direct numerical simulations of decaying homogeneous isotropic turbulence. We design and train artificial neural networks based on local convolution filters to predict the underlying unknown non-linear mapping from the coarse grid quantities to the closure terms without a priori assumptions. All investigated networks are able to generalize from the data and learn approximations with a cross correlation of up to 47% and even 73% for the inner elements, leading to the conclusion that the current training success is data-bound. We further show that selecting both the coarse grid primitive variables as well as the coarse grid LES operator as input features significantly improves training results. Finally, we construct a stable and accurate LES model from the learned closure terms. Therefore, we translate the model predictions into a data-adaptive, pointwise eddy viscosity closure and show that the resulting LES scheme performs well compared to current state of the art approaches. This work represents the starting point for further research into data-driven, universal turbulence models.