A Combustion Kinetic Model Reduction Method based on Particle Swarm Optimization(PSO)
To reduce the instability and uncertainty of current linear methods for combustion ki-netic model reduction,a new nonlinear method based on artificial intelligence algorithm is proposed to simplify chemical models.This method is applied to reduce the USC-Mech Ⅱ and JetSurF 2.0 mechanisms,so that the reduced mechanisms can accurately predict the ignition delay time of ethy-lene and n-decane respectively.The results show that the new reduction method based on particle swarm optimization(PSO)can obtain more compact reduced models.The 22-species ethylene mech-anism and the 45-species n-decane combustion mechanism are obtained,which are more compact than those obtained by other reduction methods under similar operating conditions.
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维普
