Synthetic fuels from renewable energy sources can be a significant contribution on the roadmap to sustainable mobility. Porsche sees electro-mobility as the top priority, but eFuels produced by renewable electricity are an effective addition to support the defossilization of the transportation sector.
In addition to the sustainability aspect, the composition and properties of eFuels can be optimized via the synthetic fuel production path. The use of optimized fuel formulations has a direct influence on combustion and emission behavior. The latter is one focus of the development of internal combustion engines in the wake of constantly tightening emissions legislation. The increasing restrictions on vehicles with internal combustion engines require the reduction of emissions. Particulate matter emissions are among others the focus of criticism. The composition and properties of fuels can reduce particulate emissions and the formation of unburned hydrocarbons to a high degree. The emission formation of these is highly dependent on the spray and mixture formation.
In this work, the investigations focus on the influence of fuel properties on emission formation. For this purpose, statistical models were trained using experimental data from a Porsche research single-cylinder engine to identify the most important components and properties. In addition, the developed models can be used to predict emission behavior. The influence of fuel properties on spray and mixture formation behavior was experimentally investigated in a constant volume chamber through high-speed imaging and PDA tools. The experimental data were analyzed to understand the fuel behavior and they were used for the calibration of an injection model, which was finally adopted in a 3D-CFD simulation of a single-cylinder engine for the evaluation of the mixture formation, combustion quality and knock sensitivity. A detailed description of the calibration process of the injection and engine simulations is given by Rossi et al. [1].
The experimental spray analysis shows minor differences between the two tested fuels. Good model and simulation quality were achieved using the developed methods. The acquired understanding of sensitivity and correlations can be applied to the optimization of fuel formulations with respect to combustion and emission improvement.