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School of Engineering, Computing and Mathematics
Faculty of Technology, Design and Environment
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 STARCD 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.
A novel surrogate model is presented, which predicts the engine-out Particle Number (PN) emissions of a light-duty, spray-guided, turbo-charged, GDI engine. The model is developed through extensive CFD analysis, carried out using the Siemens Simcenter STAR-CD, and considers a range of part-load operating conditions and single-variable sweeps where control parameters such as start of injection and injection pressure are varied in isolation. The work is attached to the Ford-led APC6 DYNAMO project, which aims to improve efficiency and reduce harmful emissions from the next generation of gasoline engines.The CFD work focused on the air exchange, fuel spray and mixture preparation stages of the engine cycle. A combined Rosin-Rammler and Reitz-Diwakar model, calibrated over a wide range of injection pressure, is used to model fuel atomization and secondary droplets break-up. A validated approach, based on the Bai-Onera model of droplet-wall interaction, is used to capture the details of liquid film formation. A multi-component surrogate fuel blend model reproduces the relevant characteristics of the E5 95RON gasoline used in parallel experiments. A fixed, but region-specific, wall temperature scheme is used for the in-cylinder simulations, based on available experimental data.An Elastic Net (EN) regression technique was used to construct a novel PN surrogate model, through the identification of relevant relationships between experimental engine-out PN emission levels and modelled air-fuel mixture quality indicators. To maximize model usefulness and applicability, these indicators are then correlated through sub-models to engine control parameters and easily-accessible measurements. The sub-models are obtained via Radial Basis Function (RFB) or a combination of RBF and EN regression. Within limits, engine sooting tendencies can be reliably predicted without reliance on combustion characteristics, which are complex to measure in real time.