A reduced and optimized kinetic mechanism was built for coke oven gas (COG) as a clean alternative vehicle fuel. This mechanism was constructed by combining a reduced methane mechanism, an optimized H2/CO mechanism, and a reduced NOx formation mechanism based on the mechanism structure for simple hydrocarbon fuels. The key reactions for combustion were investigated by a sensitivity analysis model, and the kinetic parameters of these reactions were optimized within the uncertainty range by an optimization model based on particle swarm optimization (PSO). The ignition delay time and laminar flame speed were simulated using the optimized mechanism with the software of CHEMKIN, and the results agreed well with the relevant experimental data. A computational fluid dynamics (CFD) model coupled with the optimized mechanism was established using KIVA-CHEMKIN software, and the in-cylinder combustion process was simulated. The simulation results (in-cylinder pressure and NOx emission) showed good agreement with the engine bench test results.
Abstract
目的
化学反应机理在内燃机计算流体动力学(CFD)仿真中起关键作用。基于敏感性分析与粒子群 寻优算法,本文旨在提出适用于内燃机CFD仿真的焦炉气化学反应机理,为焦炉气在内燃机上的应用研究提供条件。
创新点
1. 结合敏感性分析与离子群寻优算法,对化学反应机理参数进行了优化;2. 建立了焦炉气 化学反应机理,可准确仿真滞燃期、层流火焰速度、缸内压力变化和NOx生成。
方法
1. 根据简单碳氢燃料机理结构,搭建焦炉气化学反应机理(图1);2. 通过敏感性分析,获得在燃烧中起关键作用的化 学反应(图2和3);3. 通过粒子群寻优算法,对上述关键化学反应的动力学参数进行优化(图4和5);4. 通过数 值仿真,验证机理的准确性(图6~13、16和17)
结论
1. 根据敏感性定义,搭建的敏感性分析模型可准确地识别在燃烧过程中起关键作用的化学反应;2. 基于粒子 群寻优算法搭建的优化模型可对化学反应的动力学参数进行合理优化;3. 优化后得到的焦炉气化学反应机理可准确预测滞燃 期与层流火焰速度以及模拟内燃机缸内压力变化与NOx 生成。
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Project supported by the Major Project of Guizhou Province (No. 2012(6001)), China
ORCID: Hai-bin HE, http://orcid.org/0000-0002-1225-0272; Dong-wei YAO, http://orcid.org/0000-0001-7698-514X
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He, HB., Yao, DW. & Wu, F. A reduced and optimized kinetic mechanism for coke oven gas as a clean alternative vehicle fuel. J. Zhejiang Univ. Sci. A 18, 511–530 (2017). https://doi.org/10.1631/jzus.A1600636
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DOI: https://doi.org/10.1631/jzus.A1600636
Keywords
- Coke oven gas (COG)
- Kinetic mechanism
- Sensitivity analysis
- Particle swarm optimization (PSO)
- Spark-ignition (SI) engine
- Computational fluid dynamics (CFD) simulation