Deep mechanism reduction (DeePMR) method for fuel chemical kinetics

Wang，Zhiwei1; Zhang，Yaoyu1,2; Lin，Pengxiao1; Zhao，Enhan5,6; E，Weinan3,4; Zhang，Tianhan5; Xu，Zhi Qin John1

2024-03-01

10.1016/j.combustflame.2023.113286

Combustion and Flame   影响因子和分区

0010-2180

1556-2921

Fuel chemistry represents a typical complex system involving thousands of intermediate species and elementary reactions. Traditional mechanism reduction methods, such as sensitivity analysis and graph-based approaches, fail to explore global correlations of the sub-systems, thereby compromising their efficiency and accuracy. A novel machine learning-based approach called deep mechanism reduction (DeePMR) has been developed to address this issue. The current method transforms mechanism reduction into an optimization problem in the combinatorial space of chemical species while mitigating the curse of dimensionality inherent in the high-dimensional space. We propose an iterative sampling–training–predicting strategy combining deep neural networks with genetic algorithms to learn the landscape of the combinatorial space and locate the targeted subspace. Applying DeePMR to fuel chemistry mechanisms has led to much more compact mechanisms than traditional methods, including directed relation graph (DRG) or path flux analysis (PFA) methods, with three to four orders of magnitude acceleration in numerical simulation. In addition, reduced mechanisms by DeePMR indicate a principal-satellite formulation for constructing chemical reaction mechanisms, providing a straightforward yet effective alternative to hierarchy-based construction methods. The DeePMR method provides a general framework for model reduction across various fields.

Chemical kinetics Deep neural network Machine learning Mechanism reduction Skeletal mechanism

SCI ; EI

英语

通讯

ENGINEERING

2-s2.0-85181778454

Scopus

1.Institute of Natural Sciences,School of Mathematical Sciences,MOE-LSC and Qing Yuan Research Institute,Shanghai Jiao Tong University,Shanghai,200240,China
2.Shanghai Center for Brain Science and Brain-Inspired Technology,Shanghai,200240,China
3.AI for Science Institute,Beijing,100080,China
4.Center for Machine Learning Research,School of Mathematical Sciences,Peking University,Beijing,100871,China
5.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Guangdong,518055,China
6.School of Electronics Engineering and Computer Science,Peking University,Beijing,100871,China

Wang，Zhiwei,Zhang，Yaoyu,Lin，Pengxiao,et al. Deep mechanism reduction (DeePMR) method for fuel chemical kinetics[J]. Combustion and Flame,2024,261.

Wang，Zhiwei.,Zhang，Yaoyu.,Lin，Pengxiao.,Zhao，Enhan.,E，Weinan.,...&Xu，Zhi Qin John.(2024).Deep mechanism reduction (DeePMR) method for fuel chemical kinetics.Combustion and Flame,261.

Wang，Zhiwei,et al."Deep mechanism reduction (DeePMR) method for fuel chemical kinetics".Combustion and Flame 261(2024).