期刊
COMBUSTION AND FLAME
卷 203, 期 -, 页码 255-264出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.combustflame.2019.02.019
关键词
Turbulent combustion; Deep learning; Flame surface density; Direct numerical simulation
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. (C) 2019 The Combustion Institute. Published by Elsevier Inc. All rights reserved.
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