摘要
针对目前线性简化方法在简化详细机理过程中出现的不稳定性及预测不确定性较大的现象,本文提出一种基于智能算法的新型燃烧反应动力学非线性简化手段,并应用此方法对USC-Mech II和JetSurF 2.0详细机理进行简化,从而分别构建乙烯和正癸烷燃烧简化机理。结果表明,基于非线性方式的粒子群简化方法可以获得更加精简的简化机理;最终形成22组分的乙烯燃烧机理和45组分的正癸烷燃烧机理;在相似验证范围下,粒子群算法获得的组分数均少于其他简化方法所获得的组分数。
To reduce the instability and uncertainty of current linear methods for combustion kinetic 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 ethylene 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 mechanism 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.
作者
林圣强
张现发
吴悠
朱锦娇
易婷
熊永莲
李春生
孙嬿
杨斌
LIN Shengqiang;ZHANG Xianfa;WU You;ZHU Jinjiao;YI Ting;XIONG Yonglian;LI Chunsheng;SUN Yan;YANG Bin(School of Automotive Engineering,Yancheng Institute of Technology,Yancheng 224051,China;Center for Combustion Energy,Department of Energy and Power Engineering,Tsinghua University,Beijing 100084,China;School of Chemistry and Life Sciences,Suzhou University of Science and Technology,Suzhou 215009,China;School of Chemistry and Life Sciences,Suzhou University of Science and Technology,China Key Laboratory of Solar Cell Electrode Materials for Petroleum and Chemical Industry,Suzhou 215009,China)
出处
《工程热物理学报》
EI
CAS
CSCD
北大核心
2024年第9期2861-2866,共6页
Journal of Engineering Thermophysics
基金
国家自然科学基金资助项目(No.52076116)。
关键词
粒子群算法
人工智能算法
机理简化
非线性简化手段
particle swarm optimization(PSO)
artificial intelligence algorithm
mechanism reduction
non-linear reduction method
