Through comprehensive experimental and modeling efforts, this work unravels the underlying mechanisms governing flame development and misfire at advanced engine conditions that are representative of low-load and lean blow-out operations. Toward this, preliminary heat release, autoignition, and flame developing patterns are characterized, via a case study of n-heptane, at ultra-lean conditions in a well-controlled optical engine under various combustion modes including homogeneous charge compression ignition (HCCI), partially premixed combustion (PPC), and reactivity-controlled compression ignition (RCCI). Changes in preliminary heat release and flame developing patterns at three overall equivalence ratios (0.12, 0.18, and 0.24) are first characterized under the PPC mode. Flame development characteristics including flame areas and number of initial flame kernels at close-to-misfire conditions are further extracted and compared across the HCCI, RCCI, and three PPC modes, with two distinctive and one transition regimes identified. Further analyses indicate that sustainable flame development and misfire are largely controlled by the spatial distribution of local equivalence ratio (phi) and local temperature in the mixture, which dictate the initial flame kernel generation and the subsequent flame propagation through localized preliminary heat release and autoignition. Chemical kinetic modeling is also undertaken, using a recently updated gasoline chemistry model, in conjunction with a backpropagation neural network, where the predicted ignition delay map well captures the different regions of flame development. Further kinetic analysis and heat rate of production per reaction analysis corroborate the CH2O planar laser-induced fluorescence experiments and highlight the important chemical kinetics that govern the initial flame development patterns.
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May 2024
Research Article|
May 29 2024
Comprehending flame development and misfire at advanced engine conditions: Detailed experimental characterizations and machine learning-assisted kinetic analyses
Yanqing Cui (崔雁清)
;
Yanqing Cui (崔雁清)
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing)
1
State Key Laboratory of Engines, Tianjin University
, Tianjin 300072, China
2
Department of Mechanical Engineering, The Hong Kong Polytechnic University
, Hong Kong, China
3
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University
, Hong Kong, China
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Haifeng Liu (刘海峰)
;
Haifeng Liu (刘海峰)
a)
(Investigation, Resources, Supervision)
1
State Key Laboratory of Engines, Tianjin University
, Tianjin 300072, China
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Mingsheng Wen (文铭升)
;
Mingsheng Wen (文铭升)
(Software, Validation)
1
State Key Laboratory of Engines, Tianjin University
, Tianjin 300072, China
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Zhenyang Ming (明镇洋);
Zhenyang Ming (明镇洋)
(Software, Validation)
1
State Key Laboratory of Engines, Tianjin University
, Tianjin 300072, China
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Zunqing Zheng (郑尊清)
;
Zunqing Zheng (郑尊清)
(Funding acquisition, Validation)
1
State Key Laboratory of Engines, Tianjin University
, Tianjin 300072, China
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Yu Han (韩禹)
;
Yu Han (韩禹)
(Validation)
2
Department of Mechanical Engineering, The Hong Kong Polytechnic University
, Hong Kong, China
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Song Cheng (成松)
;
Song Cheng (成松)
a)
(Funding acquisition, Software, Writing – review & editing)
2
Department of Mechanical Engineering, The Hong Kong Polytechnic University
, Hong Kong, China
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Mingfa Yao (尧命发)
Mingfa Yao (尧命发)
(Funding acquisition, Supervision)
1
State Key Laboratory of Engines, Tianjin University
, Tianjin 300072, China
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Physics of Fluids 36, 055161 (2024)
Article history
Received:
April 02 2024
Accepted:
May 10 2024
Citation
Yanqing Cui, Haifeng Liu, Mingsheng Wen, Zhenyang Ming, Zunqing Zheng, Yu Han, Song Cheng, Mingfa Yao; Comprehending flame development and misfire at advanced engine conditions: Detailed experimental characterizations and machine learning-assisted kinetic analyses. Physics of Fluids 1 May 2024; 36 (5): 055161. https://doi.org/10.1063/5.0211783
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