Optimization of the Global Reaction Mechanism for MILD Combustion of Methane Using Artificial Neural Network
Jicang Si, Guochang Wang, Pengfei Li, Jianchun Mi
Abstract
The present work optimizes the global chemical mechanism of methane MILD combustion from Jones and Lindstedt (Combust. Flame 1988, 73, 233–249), named “JL”, using artificial neural network (ANN) for computational fluid dynamics (CFD) simulations. Such an optimized JL mechanism, abbreviated as “JL-ANN”, is obtained by ANN searching for the optimal reaction parameters that lead to the results matching those from GRI-Mech 3.0, the detailed mechanism for burning methane in a plug flow reactor. This JL-ANN mechanism is then checked by comparing its performance with that of GRI-Mech 3.0 and that of previous JL mechanisms whose reaction parameters were refined in various CFD simulations against experimental measurements available for reference. Results demonstrate that JL-ANN performs significantly better than all the previous JL mechanisms for numerical simulations of both a nonpremixed methane-jet flame in hot coflow and in-furnace MILD combustion. Therefore, the ANN method can be considered as a promising tool in optimizing various global mechanisms of combustion chemistry for CFD simulations of MILD combustion or any mode of combustion.