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A stochastic reactor model has been employed to aid the development of a new highly efficient and compact opposing piston, barrel engine. It is desirable to utilize the engine across a broad range of applications and the designers have identified the use of low calorific value fuels derived from low grade biomass gasification in HCCI mode as one possible end use. Biogas from solid fuel gasification can vary largely in composition of main components depending on feedstock and gasification method. Hence, in order to address the engines applicability to run on biogas in general terms, identifying a simple two-component surrogate fuel which can be varied under testing is of great importance. A stochastic reactor model in the form of a commercially available software, LOGEsoft, has been used to examine suitable surrogate gas mixtures which could be used to best simulate the biogas during initial engine testing and development.

Development of numerical tools for quantitatively assessing biofuel combustion in Internal Combustion Engines and facilitating the identification of optimum operating parameters and emission strategy are challenges of engine combustion research. Biofuels obtained through e.g. a Fischer-Tropsch process (FT) are complex mixtures of wide ranges of high molecular weight hydrocarbons in the diesel and naphtha boiling range dominated by C10-C18 hydrocarbons in n-alkane, iso-alkane, alkenes, aromatic and oxygenate classes. In this paper modeling of combustion in a rapid compression machine has been performed using model compounds from a given FT biofuel distribution as surrogate fuels. Furthermore, the detailed mechanism has been reduced by applying an automatic necessity analysis removing redundant species from the detailed model.

In internal combustion engines, fuel injection timing, injection rate and pressure is optimized to ensure suitable combustion and reduce emissions. Injectors are complex systems where mechanical, electromagnetic, and fluid dynamics interact together. Numerical model development of injectors allows for investigating different conditions, as well as optimization of the system in a safe manner. In this study, a 1-dimensional (1D) mathematical model of a direct gasoline injector (GDI) is presented, supported by computational fluid dynamics (CFD) in-nozzle flow simulations. The described system is a commercially available injector where the internal geometry was captured using silicone molds of the nozzle. The model includes the representation and interaction of the different components across several domains using the bond graph methodology. In the injector, the needle is magnetic and is lifted when an electromagnetic field is activated.