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Effect of Lubricant Formulation on Characteristics of Particle Emission from Engine Fed with a Hydrogen-Rich Fuel
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 15, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
This study investigates the particle engine emission characteristics including particle-bound metals for different lubricants used in a direct injection (DI) engine fed with the hydrogen-rich reformate containing 75% mol. H2 and 25% mol. CO2. The particle number concentration, size distribution and content of trace metals in the emitted particles are measured, analyzed, and compared for the baseline gasoline-fed engine and the reformate-fed engine.
The results show that for all tested lubricants the particle number and mass emission from the reformate-fueled engine are significantly higher than from the baseline gasoline-fed counterpart. Also, an ICP analysis performed on PM demonstrated that the content of trace elements from the lubricant are higher for the reformate fuel. This indicates that an excessive lubricant involvement in combustion is the reason of these findings. Furthermore, the particle measurement results suggest that the engine using synthetic lubricant shows lower total particle number concentration and metal content in PM than it does with the mineral counterpart. However, particle number and mass distribution show more particles in the accumulation mode for the synthetic lubricant compared to the mineral one. The trace metals analysis shows that additives such as Ca, Na, P, Si, and Zn, present in relatively high amounts in the particle matter for each investigated lubricant. Si with its high boiling temperature clearly acts as a precursor of vapor nucleation with subsequent particles formation.
CitationThawko, A., Yadav, H., Shapiro, M., and Tartakovsky, L., "Effect of Lubricant Formulation on Characteristics of Particle Emission from Engine Fed with a Hydrogen-Rich Fuel," SAE Technical Paper 2020-01-2200, 2020, https://doi.org/10.4271/2020-01-2200.
Data Sets - Support Documents
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