For internal combustion engines, the determination of combustion characteristics and subsequent emissions formation relies heavily on the fuel injection process. With the increasing demand for enhanced fuel efficiency and reduced emissions, it becomes vital to develop fundamental understanding of the physical process involved in the fuel injection process. In this study, an optimal numerical approach to predict the high-pressure liquid injection process in the context of industrial computations has been investigated. In particular, this study focuses on the respective performance of the partially averaged Navier-Stokes (PANS) and large eddy simulation models to predict turbulent igniting sprays. Both approaches are coupled with the widely used Lagrangian discrete droplet method for spray modeling. The results are validated against well -established engine combustion network (ECN) spray A case in reactive and nonreactive conditions. For reacting conditions, the flameletgenerated manifold (FGM) combustion model is employed in the present work. Comparative study and validation against experimental data have shown that the PANS turbulence model allows for coarser grids while still maintaining accurate results.
机构:
Univ Lille Sci & Technol, Ecole Polytech Lille, LGCgE EA Lille Nord France 4515, F-59655 Villeneuve Dascq, FranceUniv Lille Sci & Technol, Ecole Polytech Lille, LGCgE EA Lille Nord France 4515, F-59655 Villeneuve Dascq, France
机构:
Southwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R ChinaSouthwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R China
Li, Yi-Long
Chen, Chang-Hao
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Southwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R ChinaSouthwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R China
Chen, Chang-Hao
Li, Xiao-Ping
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Southwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R ChinaSouthwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R China
Li, Xiao-Ping
Zhang, Ji-Qiang
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CNOOC China Ltd, Hainan Branch, Haikou 570312, Hainan, Peoples R ChinaSouthwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R China
Zhang, Ji-Qiang
Lu, Rui-Bin
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CNOOC China Ltd, Hainan Branch, Haikou 570312, Hainan, Peoples R ChinaSouthwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R China
Lu, Rui-Bin
Tan, Xiao-Hua
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Southwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R ChinaSouthwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R China
Tan, Xiao-Hua
Yang, Hao
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Southwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R ChinaSouthwest Petr Univ, State Key Lab Oil Gas Reservoir Geol & Exploitat, Chengdu, Peoples R China