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Ammonia fuel piston trajectory combustion optimal control based on genetic algorithm
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Jiaqi Li1, Chen Zhang2, Jin Xiao1, Zhen Huang1, 2
Transactions of CSICE | 2025, 43(1) : 11 - 19
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Transactions of CSICE | 2025, 43(1): 11-19
Ammonia fuel piston trajectory combustion optimal control based on genetic algorithm
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Jiaqi Li1, Chen Zhang2, Jin Xiao1, Zhen Huang1, 2
Affiliations
  • 1.Key Laboratory for Power Machinery and Engineering of Ministry of Education,Shanghai Jiao Tong University,Shanghai 200240,China
  • 2.College of Smart Energy,Shanghai Jiao Tong University,Shanghai 200240,China
Published: 2025-01-25 doi: 10.16236/j.cnki.nrjxb.202501002
Outline
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Utilizing a repetitive control-based proactive controller known as the‘virtual crankshaft’, diverse piston trajectory operating characteristics under the same compression ratio(represented by parameter Ω, which is the ratio between the minor axis and the major axis of the ellipse generating trajectories)were achieved in a free piston engine. Through an analysis considering characteristics such as indicated thermal efficiency, peak pressure location, ignition delay, nitrogen oxide emissions, etc., an optimal piston trajectory based on parameter Ω was obtained. Using this as the initial condition for optimization parameters, a global optimization design of the piston trajectory was performed based on a genetic algorithm, with indicated thermal efficiency as the optimization objective. The research results demonstrate that changes in the piston trajectory affect ammonia HCCI combustion processes. The optimized trajectory obtained through the design optimization process shows a 3.35% improvement in indicated thermal efficiency compared to traditional internal combustion engines, along with a 42.93% reduction in ignition delay time.

free piston engine  /  ammonia  /  combustion control  /  genetic algorithm
李家齐, 张宸, 肖进, 黄震. 基于遗传算法的氨燃料活塞轨迹燃烧优化控制. 内燃机学报, 2025 , 43 (1) : 11 -19 . DOI: 10.16236/j.cnki.nrjxb.202501002
Jiaqi Li, Chen Zhang, Jin Xiao, Zhen Huang. Ammonia fuel piston trajectory combustion optimal control based on genetic algorithm[J]. Transactions of CSICE, 2025 , 43 (1) : 11 -19 . DOI: 10.16236/j.cnki.nrjxb.202501002
Year 2025 volume 43 Issue 1
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Article Info
doi: 10.16236/j.cnki.nrjxb.202501002
  • Receive Date:2024-03-19
  • Online Date:2026-11-06
  • Published:2025-01-25
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  • Received:2024-03-19
  • Revised:2024-09-11
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Affiliations
    1.Key Laboratory for Power Machinery and Engineering of Ministry of Education,Shanghai Jiao Tong University,Shanghai 200240,China
    2.College of Smart Energy,Shanghai Jiao Tong University,Shanghai 200240,China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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