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Particulate Emissions in a GDI with an Upstream Fuel Source
Technical Paper
2019-01-1180
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Public health risk and resulting stringent emission regulations for internal combustion engines pose a need for solutions to reduce particle emissions (PN). Current PN control approaches include increasing fuel injection pressure, optimizing spray targeting, multiple injection strategies, and the use of tumble flaps together with gasoline particulate filters (GPF).
Experiments were performed using a single-cylinder spark-ignited GDI engine equipped with a custom inlet manifold and a port fuel injector located 500 mm upstream. Particulate emissions were measured during stationary medium/high load operation to evaluate the effect of varying the mass split between the direct and upstream injectors. Mixing quality is improved substantially by upstream injection and can thus be controlled by altering the mass split between the injectors. Additional particulate measurements were performed using a thermodenuder and a catalyst to remove major part of the volatile organic compounds (VOCs) from raw emissions. This made it possible to determine particle numbers (PN) both raw emissions and solid particulates, and the size distribution of the solid particulate emissions.
Upstream fuel source was found to reduce PN emissions by almost a factor of 10 under optimal conditions, and significant reductions were achieved even when only 10% of the fuel mass was injected upstream. At a fixed load, as mass percentage from PFI increases, PN decreases. However, the PN reduction due to PFI is load-dependent and can be sensitive to engine speed. Solid PN decreased almost linearly with the PFI mass percentage, independently of engine speed. This implies that upstream injection improved mixing and thus reduced rich zone formation and/or wall-wetting compared to exclusive direct injection.
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Etikyala, S., Koopmans, L., and Dahlander, P., "Particulate Emissions in a GDI with an Upstream Fuel Source," SAE Technical Paper 2019-01-1180, 2019, https://doi.org/10.4271/2019-01-1180.Data Sets - Support Documents
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