Statistical degree screening method for combustion mechanism reduction

Abstract From a statistical perspective, large complex reaction networks have the power-law degree distributions, which means a small fraction of hub nodes are responsible for most of the network function, while the remaining large amount of low degree nodes give negligible contributions. Based on this, a statistical degree screening (SDS) combustion mechanism reduction method was proposed and verified in 42 popular mechanisms of different size and developed by various institutes. It is confirmed that most large combustion networks obey the power-law form degree distribution with limited fitting error. Then a reduced model could be obtained by setting a degree threshold according to the degree distribution and prediction error requirements to distinguish the important species to keep. Intensive validation results in the comparison with the directed relation graph (DRG) method-reduced mechanisms prove that despite the SDS method requires considerably less input information and processing time, it can come up with significantly reduced models with comparable or even better prediction ability over a broad parameter range. The good reduction application results of SDS shown here indicates a brand-new angle for large combustion mechanism analysis and reduction, i.e., from the statistical network structure properties aspect. It is of practical significance for the swift analysis and reduction of large combustion mechanisms and can considerably avoid the influence coming from the dynamical parameters that introduce great uncertainty.