CFD Modelling of the Mixture Preparation in a Modern Gasoline Direct Injection Engine and Correlations with Experimental PN Emissions

A detailed 3D CFD analysis of a modern gasoline direct injection (GDI) engine is carried out to reveal the connections between pre-combustion mixture indicators and PN emissions. Firstly, a novel calibration methodology is introduced to accurately predict the widely used characteristics of the high-pressure fuel spray. The methodology utilised the Siemens STAR-CD 3D CFD software environment and employed a combination of statistical and optimization methods supported by experimental data. The calibration process identified dominant factors influencing spray properties and established their optimal levels. The two most used models for fuel atomisation were investigated. The Kelvin–Helmholtz/Rayleigh–Taylor (KH–RT) and Reitz–Diwakar (RD) break-up models were calibrated in conjunction with the Rosin–Rammler (RR) mono-modal droplet size distribution. RD outperformed KH–RT in terms of prediction when comparing numerical spray tip penetration and droplet size characteristics to the experimental counterparts. Then, the modelling protocol incorporated droplet-wall interaction models and a multi-component surrogate fuel blend model. The comprehensive digital model was validated using published data and applied to a modern small-capacity GDI engine. The study explored various engine operating conditions and highlights the contribution of fuel mal-distribution and liquid film retention at spark timing to Particle Number (PN) emissions. Finally, a novel surrogate model was developed to predict the engine-out PN. An extensive CFD analysis was conducted considering part-load operating conditions and variations of engine control variables. The PN surrogate model was developed using an Elastic Net (EN) regression technique, establishing relationships between experimental PN emission levels and modelled, pre-combustion, air-fuel mixture quality indicators. The approach enabled the reliable prediction of engine sooting tendencies without relying on complex measurements of combustion characteristics. These research efforts aim to enhance engine efficiency, reduce emissions, and contribute to the development of a reliable and cost-effective digital toolset for engine development and diagnostics.

Grantor dc:publisher

Oxford Brookes University

Year dc:date

2024

Author dc:creator

• Sciortino, Davide Domenico

Contributors dc:contributor

• Bonatesta, Fabrizio
• Bell, Daniel
• Yang, Changho

Statement dc:rights

• All rights reserved

Language dc:language

en

DOI dc:identifier

https://doi.org/10.24384/836y-at47

OAI identifier oai:identifier

tle:74642356-ecd1-43c9-a0d4-4b7e51919653:d6bd9758-527a-46cd-bfe2-c433766e8fca:1