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New Methodology for Real-time and Quantitative Measurement of Oil Emissions on I.C. Engines
Technical Paper
2020-01-2193
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
New environmental regulations and stringent emissions standards have lowered the limits for Particulate Matter (PM) mass and Particle Number (PN) emissions requiring further optimization of gasoline direct injection (GDI) engines. Partially burned and unburned lubricating oil contributes significantly to black carbon and soot particle emissions which are not neglectable compared to fuel generated particles. Oil emissions are influenced by engine speed and load conditions but also by engine oil properties and formulation. Minimizing oil consumption has become a fundamental concern and there is a real need for better understanding associated sources and mechanisms. Therefore, researchers and engineers need appropriate tools and methodologies to solve these new problems. A reliable oil consumption measurement methodology able to quantify oil emissions (all oil emissions forms) and only oil emissions is a prerequisite. Recent developments of a methodology based on the use of radiotracer, the Germanium-69, allow very fast and quantitative results with the possibility to determine the exact origin of engine oil consumption: engine block, ventilation circuit or turbocharger. New technical challenges are, on one side, related to the impact of fuel dilution due to direct injection, modern after-treatment systems and/or bio-fuels. On the other side, fast accelerations from cold start conditions, which correspond to hybrid operating mode, need a fast response methodology to study oil consumption during transient operating phases. The paper presents the latest results obtained on current GDI engines using the Germanium-69 radiotracer to label lubricating oil and precisely meter oil emissions under these new situations.
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Delvigne, T. and Courtois, O., "New Methodology for Real-time and Quantitative Measurement of Oil Emissions on I.C. Engines," SAE Technical Paper 2020-01-2193, 2020, https://doi.org/10.4271/2020-01-2193.Data Sets - Support Documents
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References
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