Journal Article


A combined experimental and computational fluid dynamics investigation of particulate matter emissions from a wall-guided gasoline direct injection engine

Abstract

The latest generation of high-efficiency gasoline direct injection (GDI) engines continues to be a significant source of dangerous ultra-fine particulate matter (PM) emissions. The forthcoming advent in the 2017–2020 timeframe of the real driving emission (RDE) standards affords little time for the identification of viable solutions. The present research work aims to contribute towards a much-needed improved understanding of the process of PM formation in theoretically-homogeneous stoichiometric spark-ignition combustion. Experimental measurements of engine-out PM have been taken from a wall-guided GDI engine operated at part-load; through parallel computational fluid dynamics (CFD) simulations of the test-engine, the process of mixture preparation was investigated. About 80% of the total particle number is emitted on average in the 5–50 nm range, with the vast majority being below the regulated lower limit of 23 nm. The results suggest that both improved charge homogeneity and lower peak combustion temperature contribute to lower particle number density (PNDen) and larger particle size, as engine speed and load increase. The effect of engine load is stronger and results from greater injection pressure through better fuel droplet atomisation. Increases in pre-combustion homogeneity of 6% are associated with one order of magnitude reductions of PNDen. A simplified two-equation functional model was developed, which returns satisfactory qualitative predictions of PNDen as a function of basic engine control variables.

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Authors

Sciortino, Davide D.
Bonatesta, Fabrizio
Hopkins, Edward
Yang, Changho
Morrey, Denise

Oxford Brookes departments

Faculty of Technology, Design and Environment\Department of Mechanical Engineering and Mathematical Sciences

Dates

Year of publication: 2017
Date of RADAR deposit: 2017-09-20


Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License


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