Skip to Main content Skip to Navigation
Journal articles

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

Abstract : This work presents a new approach for premixed turbulent combustion modeling based on convolutional neural networks (CNN).1 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 subgrid-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.
Complete list of metadatas

Cited literature [48 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02072920
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Tuesday, March 19, 2019 - 3:09:15 PM
Last modification on : Friday, July 3, 2020 - 9:28:04 AM
Long-term archiving on: : Thursday, June 20, 2019 - 2:28:16 PM

File

Lapeyre_23414.pdf
Files produced by the author(s)

Identifiers

Citation

Corentin J. Lapeyre, Antony Misdariis, Nicolas Cazard, Denis Veynante, Thierry Poinsot. Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates. Combustion and Flame, Elsevier, 2019, 203, pp.255-264. ⟨10.1016/j.combustflame.2019.02.019⟩. ⟨hal-02072920⟩

Share

Metrics

Record views

142

Files downloads

119