The Fit for 55 package introduced by the European Union aims to achieve a 55% reduction in greenhouse gas emissions by 2030. In parallel, increasingly stringent exhaust gas regulations have intensified research into alternative fuels. Ethanol presents a promising option due to its compatibility with gasoline, higher octane rating, and lower exhaust emissions compared to conventional gasoline. Additionally, ethanol can be derived from agricultural waste, further enhancing its sustainability. This study examines the impact of two ethanol–gasoline blends (E10, E20) on emissions and performance in a turbocharged gasoline direct injection (TGDI) spark-ignition (SI) engine. The investigation is conducted using three-dimensional computational fluid dynamics (3D CFD) simulations to minimize development time and costs. This paper details the model development process and presents the initial results. The boundary conditions for the simulations are derived from one-dimensional (1D) simulations, which have been validated against experimental data. Subsequently, the simulated performance and emissions results are compared with experimental measurements. The E10 simulations correlated well with experimental measurements, with the largest deviation in cylinder pressure being an RMSE of 1.42. In terms of emissions, HC was underpredicted, while CO was overpredicted compared to the experimental data. For E20, the IMEP was slightly higher at some operating points; however, the deviations were negligible. Regarding emissions, HC and CO emissions were higher with E20, whereas NOx and CO2 emissions were lower.

Mobility is the engine of our society in the third millennium. Rapid technical development makes it possible to increase cognitive ability in various fields; mobility is one of the most affected. Mobility has become a multidimensional concept in our interpretation; in addition to transportation, it also appears in the digital space, among other dimensions. The 21st century has brought unprecedented challenges, such as the covid-19 virus and the Russian war in Ukraine. These highlight bottlenecks in mobility systems and prompted us to explore the concept of cognitive mobility. This paper intends to refine the CogMob approach, which co-manages human and machine capabilities in mobility. New achievements within CogMob's domain match the new challenges of war and viruses. This article aims to outline the field of cognitive mobility. It presents CogMob's definition and examples that clarify the combinations of cognitive levels of different mobilities.