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An experimental and modeling study on norbornane pyrolysis aided by chemical information from neural network-assisted molecular dynamics

文献类型：期刊论文

作者Xiao, Hang1; Chu, Zhaohan1; Chen, Haodong1; Zhang TC(张泰昌)2; Lu, Jinghui3; Wang, Changyang3; Zhao, Long3; Yang, Bin1
刊名COMBUSTION AND FLAME
出版日期2025-04-01
卷号274
关键词Norbornane Pyrolysis Flow tube reactor Deep potential molecular dynamics Detailed kinetic models
ISSN号0010-2180
DOI10.1016/j.combustflame.2025.114039
英文摘要Norbornane has been reported in recent years as a diesel additive that can improve soot characteristics or as the backbone of new jet fuels to increase fuel density and the net heat of combustion. However, there is still a lack of experimental and modeling studies on norbornane pyrolysis, which limits its further application. This work uses a flow tube reactor to conduct experiments at 30 torr, 923 K similar to 1373 K, with a photoionization molecular-beam mass spectrometer to identify and quantify the pyrolysis species. At the same time, an attempt is made to apply high-precision deep potential molecular dynamics (DPMD) to assist in kinetic model construction. Species analysis on MD simulations provides additional chemical information on key pyrolysis species in the norbornane pyrolysis system, which agrees with experimental results. Therefore, the reactions appearing in MD simulations are supposed to play a nonnegligible role in the experimental system, so that high-frequency reactions of selected are added to the pyrolysis kinetic model. Further comparison of experimental and modeling results shows that the modeling results can accurately predict experimental concentrations for most species. By analyzing the rate of production of all species in the system, we highlight their primary production and consumption pathways, especially the pathways from norbornane to benzene in the system. When conducting sensitivity analysis, it is found that the initial decomposition reactions of the fuel have large sensitivity coefficients on the experimental results, especially the concentration of norbornane; it is also noteworthy that the reactions extracted in the MD results have an essential impact on the concentration of 1,3-cyclohexadiene.
分类号一类/力学重要期刊
WOS研究方向Thermodynamics ; Energy & Fuels ; Engineering, Multidisciplinary ; Engineering, Chemical ; Engineering, Mechanical ; Engineering
语种英语
WOS记录号WOS:001428575900001
资助机构This work is funded by the National Natural Science Foundation of China (No. 52425605) and the National Key R & D Program of China (No. 2022YFB4003901-1) .
其他责任者Yang, Bin
源URL[http://dspace.imech.ac.cn/handle/311007/101433]  
专题力学研究所_高温气体动力学国家重点实验室
作者单位1.Tsinghua University;
2.Institute of Mechanics, CAS;
3.University of Science & Technology of China, CAS;
4.University of Science & Technology of China, CAS
推荐引用方式
GB/T 7714		 Xiao, Hang,Chu, Zhaohan,Chen, Haodong,et al. An experimental and modeling study on norbornane pyrolysis aided by chemical information from neural network-assisted molecular dynamics[J]. COMBUSTION AND FLAME,2025,274.
APA Xiao, Hang.,Chu, Zhaohan.,Chen, Haodong.,张泰昌.,Lu, Jinghui.,...&Yang, Bin.(2025).An experimental and modeling study on norbornane pyrolysis aided by chemical information from neural network-assisted molecular dynamics.COMBUSTION AND FLAME,274.
MLA Xiao, Hang,et al."An experimental and modeling study on norbornane pyrolysis aided by chemical information from neural network-assisted molecular dynamics".COMBUSTION AND FLAME 274(2025).

入库方式： OAI收割

来源：力学研究所

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