Neural-network-based mixed subgrid-scale model for turbulent flow

TL;DR

This paper develops a neural-network-based subgrid-scale model for turbulent flow that improves prediction accuracy and generalizes well across different turbulence conditions, outperforming traditional models in simulations.

Contribution

The paper introduces a neural-network model that predicts subgrid-scale stresses more accurately and generalizes across various turbulence conditions, enhancing turbulence modeling techniques.

Findings

Neural network with stress tensor input improves subgrid-scale stress prediction.

Model generalizes well across different Reynolds numbers and grid resolutions.

Outperforms traditional dynamic models in accuracy and computational efficiency.

Abstract

An artificial neural-network-based subgrid-scale model using the resolved stress, which is capable of predicting untrained decaying isotropic turbulence, is developed. Providing the grid-scale strain-rate tensor alone as input leads the model to predict a subgrid-scale stress tensor aligns with the strain-rate tensor, and the model performs similar to the dynamic Smagorinsky model. On the other hand, providing the resolved stress tensor as input in addition to the strain-rate tensor is found to significantly improve the model in terms of the energy spectra and probability density function of subgrid-scale dissipation. In an attempt to apply the neural-network-based model trained for forced homogeneous isotropic turbulence to decaying homogeneous isotropic turbulence, special attention is given to the normalisation of the input and output tensors. It is found that successful generalisation of the model to turbulence at various untrained conditions is possible if the distributions of the normalised inputs and outputs of the neural-network remain unchanged as Reynolds numbers and grid resolution of the turbulence vary. In a posteriori tests of the forced and the decaying homogeneous isotropic turbulence, the developed neural-network model is found to predict turbulence statistics more accurately and to be computationally more efficient than the conventional dynamic models.