压缩比
燃烧
平均有效压力
均质压燃
燃烧室
内燃机
甲醇
废气再循环
发动机效率
材料科学
汽车工程
发动机爆震
核工程
巴(单位)
汽油机
分数(化学)
化学
环境科学
热效率
燃油喷射
内燃机
废气
辛烷值
燃气发动机
废物管理
喷气发动机
热机
化油器压燃式模型发动机
压缩(物理)
热力学
焓
自然吸气发动机
作者
Patrick Burkardt,Bastian Lehrheuer,Stefan Pischinger
标识
DOI:10.1177/14680874251382661
摘要
The application of active pre-chamber (PC) combustion systems offers potential increases of the indicated efficiency of spark-ignition (SI) engines by increasing the lean operating limit. In conventional SI operation, methanol revealed promising engine performance without substantial knock limitation. In PC operation with methanol under lean conditions, the PC jets can fail to ignite the charge in the main combustion chamber (MC) due to methanol’s high enthalpy of vaporization. The PC jet characteristics in turn depend on the applied PC fuel. In this study, it was assumed that the application of alternative liquid PC fuels might extend the lean operating limit of an engine that was operated with methanol as MC fuel. To this end, methanol, ethanol, 2-butanone, ethyl acetate, 1,3-dioxolane, and diethoxymethane were investigated as PC fuels in this study, while the MC was fueled with methanol. The fuels were experimentally investigated on a thermodynamic SI single-cylinder engine equipped with an active PC and a compression ratio of 16.4. Variations of the relative air/fuel ratio in the exhaust gas at net indicated mean effective pressures of 6 bar and 15 bar were performed. The observed differences in the engine performance between the investigated PC fuels were minor because a large fraction of the PC mixture consisted of the MC fuel methanol that was forced into the PC during the compression stroke. However, measured PC-driven HC emissions depended on the applied PC fuel and increased toward the engine’s lean operating limit at low engine load. At high engine load, this behavior was not observed. In PC operation, a maximum net indicated efficiency of 47.3% was observed, which was 0.8%-points higher than in conventional SI operation.
科研通智能强力驱动
Strongly Powered by AbleSci AI