Fabrizio Bonatesta

Piston surface temperature is an important factor in the reduction of harmful emissions in modern gasoline direct injection (GDI) engines. In transient operation, the piston surface temperature can change rapidly, increasing the risk of fuel puddling. The prediction of the piston surface temperature can provide the means to significantly improve multiple-pulse fuel injection control strategies through the avoidance of fuel puddling. It could also be used to intelligently control the piston cooling jet (PCJ), which is common in modern engines. Considerable research has been undertaken to identify generalized engine heat transfer correlations and to predict piston and cylinder wall surface temperatures during operation. Most of these correlations require in-cylinder combustion pressure as an input, as well as the identification of numerous model parameters. These requirements render such an approach impractical. In this study, the authors have developed a thermodynamic model of piston surface temperature based on the global energy balance (GEB) methodology, which includes the effect of PCJ activation. The advantages are a simple structure and no requirement for in-cylinder pressure data, and only limited experimental tests are needed for model parameter identification. Moreover, the proposed model works well during engine transient operation, with maximum average error of 6.68% during rapid transients. A detailed identification procedure is given. This and the model performance have been demonstrated using experimental piston crown surface temperature data from a prototype 1-liter 3-cylinder turbocharged GDI engine, operated in both engine steady-state and transient conditions with an oil jet used for piston cooling turned both on and off.

The high specific properties of aluminium based nanostructured alloys have attracted significant attention due to their promise for structural applications especially at elevated temperatures such as pistons for internal combustion engines. Several types of aluminium-based nanostructured alloys have been developed with microstructures of nanometre-sized particles embedded in the aluminium matrix. In this work a newly developed aluminium based nanostructured alloy is studied to understand its microstructure formation, stability and mechanical properties at elevated temperatures. The microstructure was characterised by means of X-ray diffraction, light and scanning electron microscopies. Heat treatments were carried out to determine the T6 condition properties and the microstructural stability at elevated temperatures for long periods of exposure. The hardness of the new alloy at T6 was 30% higher than the corresponding to Al-4032 which is the commonly used alloy for piston application. The work also compared the mechanical properties of the new alloy with two conventional aluminium alloys used in piston applications. The new alloy has 1.3–4.7 times higher strengths than Al-4032.

Road vehicles are a large contributor to nitrogen oxides (NOx) pollution. The routine roadside monitoring stations, however, may underrepresent the severity of personal exposure in urban areas because long-term average readings cannot capture the effects of momentary, high peaks of air pollution. While numerical modelling tools historically have been used to propose an improved distribution of monitoring stations, ultra-high resolution Computational Fluid Dynamics models can further assist the relevant stakeholders in understanding the important details of pollutant dispersion and exposure at a local level. This study deploys a 10-cm-resolution CFD model to evaluate actual high peaks of personal exposure to NOx from traffic by tracking the gases emitted from the tailpipe of moving vehicles being dispersed towards the roadside. The investigation shows that a set of four Euro 5-rated diesel vehicles travelling at a constant speed may generate momentary roadside concentrations of NOx as high as 1.25 mg/m³, with a 25% expected increase for doubling the number of vehicles and approximately 50% reduction when considering Euro 6-rated vehicles. The paper demonstrates how the numerical tool can be used to identify the impact of measures to reduce personal exposure, such as protective urban furniture, as traffic patterns and environmental conditions change.

Road vehicles are a large contributor to Nitrogen Oxides (NOx) pollution. The routine road-side monitoring stations, however, may underrepresent the severity of personal exposure in urban areas, because long-term average readings cannot capture the effects of momentary, high peaks of air pollution. While numerical modelling tools historically have been used to propose an improved distribution of monitoring stations, ultra-high resolution Computational Fluid Dynamics models can further assist the relevant stakeholders in understanding the important details of pollutant dispersion and exposure at local level. This study deploys a 10 cm-resolution CFD model to evaluate actual high peaks of personal exposure to NOx from traffic, by tracking the gases emitted from the tailpipe of moving vehicles being dispersed towards the roadside. The investigation shows that a set of four Euro 5-rated diesel vehicles travelling at constant speed may generate momentary road-side concentrations of NOx as high as 1.25 mg/m3, with 25% expected increase for doubling the number of vehicles and approximately 50% reduction when considering Euro 6-rated vehicles. The paper demonstrates how the numerical tool can be used to identify the impact of measures to reduce personal exposure, such as protective urban furniture, as traffic patterns and environmental conditions change.

A novel calibration methodology is presented to accurately predict the fundamental characteristics of high-pressure fuel sprays for Gasoline Direct Injection (GDI) applications. The model was developed within the Siemens Simcenter STAR-CD 3D CFD software environment and used the Lagrangian–Eulerian solution scheme. The simulations were carried out based on a quiescent, constant volume, computational vessel to reproduce the real spray testing environment. A combination of statistic and optimisation methods was used for spray model selection and calibration and the process was supported by a wide range of experimental data. A comparative study was conducted between the two most commonly used models for fuel atomisation: Kelvin–Helmholtz/Rayleigh–Taylor (KH–RT) and Reitz–Diwakar (RD) break-up models. The Rosin–Rammler (RR) mono-modal droplet size distribution was tuned to assign initial spray characteristics at the critical nozzle exit location. A half factorial design was used to reveal how the various model calibration factors influence the spray properties, leading to the selection of the dominant ones. Numerical simulations of the injection process were carried out based on space-filling Design of Experiment (DoE) schedules, which used the dominant factors as input variables. Statistical regression and nested optimisation procedures were then applied to define the optimal levels of the model calibration factors. The method aims to give an alternative to the widely used trial-and-error approach and unveils the correlation between calibration factors and spray characteristics. The results show the importance of the initial droplet size distribution and secondary break-up coefficients to accurately calibrate the entire spray process. RD outperformed KH–RT in terms of prediction when comparing numerical spray tip penetration and droplet size characteristics to the experimental counterparts. The calibrated spray model was able to correctly predict the spray properties over a wide range of injection pressure. The work presented in this paper is part of the APC6 DYNAMO project led by Ford Motor Company.

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.

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 (PN_Den) 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 PN_Den. A simplified two-equation functional model was developed, which returns satisfactory qualitative predictions of PN_Den as a function of basic engine control variables.