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Ammonia (NH3), as a carbon-free fuel, has a higher optimization potential to power internal combustion engines (ICEs) compared to hydrogen due to its relatively high energy density (7.1MJ/L), with an established transportation network and high flexibility. However, the NH3 is still far underdeveloped as fuel for ICE application because of its completely different chemical and physical properties compared with hydrocarbon fuels. Among all uncertainties, the dynamics of the NH3 spray at engine conditions is one of the most important factors that should be clarified for optimizing the fuel-air mixing. To characterize the evolution and evaporation process of NH3 spray, a high-speed Z-type schlieren imaging technique is employed to estimate the spray characteristics under different injection pressure and air densities in a constant volume chamber.

Distillation properties of gasoline are regulated to ensure the safe and efficient operation of SI-engines. Blending various gasoline components affects the distillation values in a non-linear fashion, making the prediction of these properties challenging. Furthermore, the rise of renewable components necessitates the development of new property prediction methods. In this work, a variety of Machine Learning models were created to predict the distillation points of gasoline blends based on the blending recipe. As input data, real industrial data from a refinery was used together with data from blends created for R&D purposes. The predicted properties were the evaporated volume at the 70 and 100 °C distillation points (E70 and E100). Altogether four different machine learning models were trained, cross-validated and tested using seven different pre-processing methods. It was found that Support Vector Regression (SVR) was the most effective at predicting the distillation points.