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Experimental Investigation of the Influence of Ignition System Parameters on Combustion in a Rapid Compression-Expansion Machine
Technical Paper
2020-01-1122
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Lean burn combustion concepts with high mean effective pressures are being pursued for large gas engines in order to meet future stringent emission limits while maintaining high engine efficiencies. Since severe boundary conditions for the ignition process are encountered with these combustion concepts, the processes of spark ignition and flame initiation are important topics of applied research, which aims to avoid misfiring and to keep cycle-to-cycle combustion variability within reasonable limits. This paper focuses on the fundamental investigation of early flame kernel development using different ignition system settings. The investigations are carried out on a rapid compression-expansion machine in which the spark ignition process can be observed under engine-like pressure and excess air ratio conditions while low flow velocities are maintained. The schlieren setup for high-speed optical investigations of the area of the spark plug electrodes is described and a suitable post-processing routine is introduced. The influence of different spark current durations on early flame kernel formation is investigated using a modulated capacitive discharge ignition (MCDI) system. The outcomes reveal that a short spark current duration results in a slower increase and higher standard deviation of the flame area during the early phase of combustion. Moreover, stable flame initiation appears to require a minimum spark current duration. The methodology introduced in this paper will be applied in detailed investigations of other spark plug geometries and ignition settings in order to shed more light on the ignition of lean mixtures.
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Kiesling, C., Pirker, G., Tilz, A., Oppl, T. et al., "Experimental Investigation of the Influence of Ignition System Parameters on Combustion in a Rapid Compression-Expansion Machine," SAE Technical Paper 2020-01-1122, 2020, https://doi.org/10.4271/2020-01-1122.Data Sets - Support Documents
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