Abstract

This paper presents the details of a Computational Fluid Dynamics methodology to accurately model the process of mixture preparation in modern Gasoline Direct Injection engines, with particular emphasis on liquid film as one of the main causes of Particulate Matter formation. The proposed modelling protocol, centred on the Bai-Onera approach of droplets-wall interaction and on multi-component surrogate fuel blend models, is validated against relevant published data and then applied to a modern small-capacity GDI engine, featuring centrally-mounted spray-guided injection system. The work covers a range of part-load, stoichiometric and theoretically-homogeneous operating conditions, for which experimental engine data and engine-out Particle Number measurements were available. The results, based on the parametric variation of start of injection timing and injection pressure, demonstrate how both fuel mal-distribution and liquid film retained at spark timing, may contribute to PN emissions, whilst their relative importance vary depending on operating conditions and engine control strategy. Control of PN emissions and compliance with future, more stringent regulations remain large challenges for the engine industry. Renewed and disruptive approaches, which also consider the sustainability of the sector, appear to be essential. This work, developed using Siemens Simcenter CFD software as part of the Ford-led APC6 DYNAMO project, aims to contribute to the development of a reliable and cost-effective digital toolset, which supports engine development and diagnostics through a more fundamental assessment of engine operation and emissions formation.

Highlights

  • Internal combustion engine designers and developers are faced everyday with new challenges to improve efficiency and cut down emissions, in order to comply with ever more stringent regulations.[1,2] Downsizing and turbo-charging have become common practice, leading to the production of families of small-capacity Gasoline Direct Injection (GDI) engines, which reach high power densities

  • The following concluding remarks can be drawn from the analysis: available piston telemetry measurements indicate that The study establishes the use of multi-component the piston surface temperature in Case C is on average surrogate blends, which capture the distillation and 20°C hotter, and this may explain the lower levels of saturation properties of real gasoline fuels, as an liquid film retained at spark timing

  • The results suggest that both charge maldistribution and the amount of liquid film retained at spark timing can be associated to engine-out particle number density (PN), and the relative importance of their impact depends on engine conditions, wall temperature and enacted control strategy

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Summary

Introduction

Internal combustion engine designers and developers are faced everyday with new challenges to improve efficiency and cut down emissions, in order to comply with ever more stringent regulations.[1,2] Downsizing and turbo-charging have become common practice, leading to the production of families of small-capacity Gasoline Direct Injection (GDI) engines, which reach high power densities. The relation between SOI and local wall temperatures was investigated using advanced piston surface temperature telemetry in another study by Kopple et al.[29] The experiments showed localised surface cooling in the impingement region, with the spray footprint temperature reducing temporarily by up to 40 K compared to the average piston crown In light of this finding, Giovannoni et al.[35] developed a methodology integrating CFD flow modelling and Conjugate Heat Transfer simulation of the engine piston, in order to account for realistic levels of piston surface temperatures. Part 2, of forthcoming publication, expands on the application of the approach presented here, to further explore the correlation between piston/wall temperature and liquid film, and the strong influence this exerts on soot formation due to piston cooling and/or transient engine operation

Methodology
Experimental setup and data collection
Findings
Concluding remarks
Full Text
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