A new skeletal mechanism for simulating MILD combustion optimized using Artificial Neural Network

This work develops a new skeletal mechanism of methane MILD combustion by a joint method of Directed Relation Graph (DRG), Computational Singular Perturbation (CSP) and Artificial Neural Network (ANN) (abbreviated as DRG-CSP-ANN method), where DRG and CSP are used for mechanism reduction and ANN for optimization. The detailed mechanism GRI-3.0, containing 53 species and 325 elementary reactions, is simplified to a skeletal mechanism with only 13 species and 35 reactions, named as Reduced-ANN. In addition, the mechanism reduced by DRG-CSP without ANN optimization, called Reduced-Ori, is also considered for comparison. Subsequently, the Reduced-ANN is validated by comparing its performance with those of other skeletal mechanisms, against that of GRI-3.0, in the auto ignition time, one-dimensional premixed flame propagation speed and different computational-fluid dynamics (CFD) simulations (i.e., CH4/H-2 jet flame in hot coflow, premixed and non-premixed in furnace MILD combustion). Results show that Reduced-ANN performs significantly better than all the other skeletal mechanisms including Reduced-Ori. For instance, the use of Reduced-ANN lessens the errors of predicting autoignition time and flame propagation speed from 7-18 % to 1.4 % and 16 % to 4 %, respectively. Therefore, the DRG-CSP-ANN method is qualified as a very promising way for mechanism reduction. In addition, the unsatisfying performance of Reduced-Ori demonstrates the necessity of mechanism optimization in reduction work, so that better predictions of specific quantities can be made to match those by the detailed mechanism. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This work developed a new skeletal mechanism of methane MILD combustion using the DRG-CSP-ANN method, which significantly outperformed other skeletal mechanisms in predicting autoignition time and flame propagation speed. The study highlighted the importance of mechanism optimization in reduction work for better predictions matching those by the detailed mechanism.