Abstract
In order to clarify details of the aggregation process of Gasoline Direct Injection (GDI) in-cylinder soot and thus to provide comprehensive quantitative experimental database required for development and validation of the PM/PN prediction models, Diffused Back Illumination (DBI) laser extinction measurements, crank-angle-resolved total in-cylinder gas/particles sampling, filter gravimetry, High-Resolution Transmission Electron Microscopy (HR-TEM) observation, and analysis of morphology and nanostructure of in-cylinder soot particles using a Rapid Compression and Expansion Machine (RCEM) were conducted. Crank-angle-resolved total mass, aggregate size distribution, primary particle size distribution, fractal dimension, nanostructure, and porosity of in-cylinder soot particles were successfully obtained. The in-cylinder soot mass rapidly increased right after the ignition, gradually increased after cylinder pressure peaked, and stabilized around 0.2 mg under the present experimental condition. The amount and size of large aggregates increased with the progress of combustion, soot formation, and growth. The primary soot particle size increased in the early combustion phase and did not exhibit significant change thereafter. The fractal dimension of the aggregates gradually decreased with the progress of combustion, soot formation, and growth. The porosity of soot particles derived from the HR-TEM nanostructure analysis exhibited relatively high value for initially formed young soot particles and decreased with the progress of combustion. Secondary-agglomeration-assisted sampling was found to effectively facilitate the identification of very sparsely scattered young soot particles deposited onto the TEM grids in low-magnification wide-field TEM observations and the selection of primary particles that do not overlap with the lacey carbon on the TEM grid in high-magnification nanostructure observations. The crank-angle-resolved quantitative data on the aggregation process of GDI in-cylinder soot particles obtained in the present study are expected to facilitate the development and validation of PM/PN prediction models.
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