Machine learning for detailed chemistry reduction in DNS of a syngas turbulent oxy-flame with side-wall effects

Kaidi Wan (1) , Camille Barnaud (1) , Luc Vervisch (1) , Pascale Domingo (1)

1 CORIA - Complexe de recherche interprofessionnel en aérothermochimie

A novel chemistry reduction strategy based on convolutional neural networks (CNNs) is developed and applied to direct numerical simulation (DNS) of a turbulent non-premixed flame interacting with a cooled wall. The fuel syngas mixture is burning in pure oxygen. The training and the subsequent application of the CNN rely on the processing of two-dimensional (2D) images built from species mass fractions and temperature (CNN input), to predict the corresponding chemical sources at the center of the image (CNN output). This image-type treatment of chemistry is found to efficiently capture intermediate radicals species highly sensitive to the local flame topology. To reduce the CPU cost, a simplified 2D DNS database with detailed chemistry serves as reference and is used for training and testing the neural network. Comparisons are also made a posteriori against the same 2D DNS with a reduced chemical scheme specialized for syngas. Then, three-dimensional (3D) DNS are conducted either with CNN or the reduced chemistry for more a posteriori tests. The CNN reduced chemistry outperforms the reduced Arrhenius based mechanism in the prediction of radical species, such as monoatomic hydrogen, and also in terms of CPU cost.

Mots clés: Convolutional neural network; Chemistry reduction; Direct numerical simulation; Syngas

Domaines: Sciences de l'ingénieur [physics]

Kaidi Wan, Camille Barnaud, Luc Vervisch, Pascale Domingo. Machine learning for detailed chemistry reduction in DNS of a syngas turbulent oxy-flame with side-wall effects. Proceedings of the Combustion Institute, 2020, ⟨10.1016/j.proci.2020.06.047⟩. ⟨hal-03041429⟩