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

Jicang Si, Guochang Wang, Pengfei Li and Jianchun Mi

Energy, 2021, vol. 237, issue C

Abstract: 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/H2 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.

Keywords: Mechanism reduction; Skeletal mechanism; MILD combustion; Artificial Neural Network (ANN) (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:237:y:2021:i:c:s036054422101851x

DOI: 10.1016/j.energy.2021.121603

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