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Variation in Squish Length and Swirl to Reach Higher Levels of EGR in a CNG Engine
Technical Paper
2019-01-0081
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Gaseous methane fuel for internal combustion engines have proved to be a competitive source of propulsion energy for heavy duty truck engines. Using biogas can even reduce the carbon footprint of the truck to near-zero levels, creating fully environmentally friendly transport. Gas engines have already been on the market and proved to be a popular alternative for buses and waste transport. However, for long haulage these gas engines have not been on par with the equivalent diesel engines. To improve the power and efficiency of EURO VI gas engines running stoichiometrically, a direct way forward is adding more boost pressure and spark advance in combination with more EGR to mitigate knock. Using in-cylinder turbulence to achieve higher mixing rate, the fuel can still be combusted efficiently despite the increased fraction of inert gases. In this paper, previous findings on in-cylinder air flows for diesel engine simulations are investigated for the applicability on to stoichiometric gas combustion. Two key parameters were identified, swirl and squish. By varying the levels of swirl with different squish lengths in the piston design, the in-cylinder flow motion is altered to investigate its effect on stoichiometric gas combustion. The testing was performed on a single cylinder research engine operated in the equivalent multi cylinder engine operating points. The results show that previous modelling findings are verified on the pre-mixed gas combustion studied. By choosing swirl and squish for the design of the gas engine, it is possible to increase the combustion speed and thus the fraction of EGR in the combustion charge, without the latter having a negative impact on the combustion.
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Adlercreutz, L., Cronhjort, A., and Stenlaas, O., "Variation in Squish Length and Swirl to Reach Higher Levels of EGR in a CNG Engine," SAE Technical Paper 2019-01-0081, 2019, https://doi.org/10.4271/2019-01-0081.Data Sets - Support Documents
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