Investigation of the Impact of Fuel Properties on Particulate Number Emission of a Modern Gasoline Direct Injection Engine [electronic resource]

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 621.43 Internal-combustion engines

Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2018

Mô tả vật lý: Size: 3.0 MB : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 266474

 Gasoline Direct Injection (GDI) has become the preferred technology for spark-ignition engines resulting in greater specific power output and lower fuel consumption, and consequently reduction in CO2 emission. However, GDI engines face a substantial challenge in meeting new and future emission limits, especially the stringent particle number (PN) emissions recently introduced in Europe and China. Studies have shown that the fuel used by a vehicle has a significant impact on engine out emissions. In this study, nine fuels with varying chemical composition and physical properties were tested on a modern turbo-charged side-mounted GDI engine with design changes to reduce particulate emissions. The fuels tested included four fuels meeting US certification requirements
  two fuels meeting European certification requirements
  and one fuel meeting China 6 certification requirements being proposed at the time of this work. Two risk safeguard fuels (RSG), representing the properties of worst case market fuels in Europe and China, were also included. The particle number concentration of the solid particulates was measured in the engine-out exhaust flow at steady state engine operations with load and speed sweeps, and semi-transient load steps. The test results showed a factor of 6 PN emission difference among all certification fuels tested. Combined with detailed fuel analyses, this study evaluated important factors (such as oxygenates, carbon chain length and thermo-physical properties) that cause PN emissions which were not included in PMI index. A linear regression was performed to develop a PN predictive model which showed improved fitting quality than using PMI.
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