Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates

TL;DR

This paper introduces a CNN-based framework for estimating sub-grid scale reaction rates in turbulent combustion, demonstrating improved accuracy over classical models by leveraging data-driven topological insights.

Contribution

The work presents a novel CNN architecture inspired by U-Net for sub-grid flame surface density estimation, trained on DNS data, and validated on unsteady turbulent flames.

Findings

CNN outperforms classical algebraic models in predicting subgrid wrinkling.

The approach effectively captures the topological features of turbulent flames.

The method extends dynamic formulations with data-driven topological information.

Abstract

This work presents a new approach for premixed turbulent combustion modeling based on convolutional neural networks (CNN). We first propose a framework to reformulate the problem of subgrid flame surface density estimation as a machine learning task. Data needed to train the CNN is produced by direct numerical simulations (DNS) of a premixed turbulent flame stabilized in a slot-burner configuration. A CNN inspired from a U-Net architecture is designed and trained on the DNS fields to estimate sub-grid scale wrinkling. It is then tested on an unsteady turbulent flame where the mean inlet velocity is increased for a short time and the flame must react to a varying turbulent incoming flow. The CNN is found to efficiently extract the topological nature of the flame and predict subgrid scale wrinkling, outperforming classical algebraic models. This method can be seen as a data-driven extension of dynamic formulations, where topological information was extracted in a hand-designed fashion.