This study explores the performance and emissions characteristics of a dual-fuel internal combustion engine operating on a blend of hydrogen and gasoline. This research began with a baseline simulation of a conventional gasoline engine, which was subsequently validated through experimental testing on an AVL testbed. The simulation results closely matched the testbed data, confirming the accuracy of the model, with deviations within 5%. Building on this validated model, a hydrogen–gasoline dual-fuel engine simulation was developed. The predictive simulation revealed an approximately 5% increase in overall engine efficiency at the optimal operating point, primarily due to hydrogen’s combustion properties. Additionally, the injected gasoline mass and CO₂ emissions were reduced by around 30% across the RPM range. However, the introduction of hydrogen also resulted in a slight reduction (~10%) in torque, attributed to the lower volumetric efficiency caused by hydrogen displacing intake air. While CO emissions were significantly reduced, NO_x emissions nearly doubled due to the higher combustion temperatures associated with hydrogen. This research demonstrates the potential of hydrogen–gasoline dual-fuel systems in reducing carbon emissions, while highlighting the need for further optimization to balance performance with environmental impact.

In the automotive sector, the zero emissions area has been dominated by battery electric vehicles. However, prospective users cite charging times, large batteries, and the deployment of charging stations as a counter-argument. Hydrogen will offer a solution to these areas, in the future. This research focuses on the development of a prototype three-wheeled vehicle that is named Neumann H2. It integrates state-of-the-art energy storage systems, demonstrating the benefits of solar-, battery-, and hydrogen-powered drives. Of crucial importance for the R&D platform is the system’s ability to record its internal states in a time-synchronous format, providing valuable data for researchers and developers. Given that the platform is equipped with the ROS2 Open-Source interface, the data are recorded in a standardized format. Energy management is supported by artificial intelligence of the “Reinforcement Learning” type, which selects the optimal energy source for operation based on different layers of high-fidelity maps. In addition to powertrain control, the vehicle also uses artificial intelligence to detect the environment. The vehicle’s environment-sensing system is essentially designed to detect, distinguish, and select environmental elements through image segmentation using camera images and then to provide feedback to the user via displays.

During the injection molding process, the melt travels with a flow due to friction. As the velocity of the layers next to the wall is less than that of those flowing in the middle of the channel, a fountain flow is formed at the melt front. The temperature of the polymer surface decreases from the melt temperature to the contact temperature after contacting the mold surface. Based on all this, a complex shell–core structure is formed in injection-molded products, which can be influenced by the processing parameters and the surface of the tool insert. This paper focuses on investigating the effect of the microstructures replicated from the insert to the polymer product on its mechanical properties. During the research, two microstructured surfaces were created, with different effects on the melt flow formed by the femtosecond laser. These were compared with a ground insert to analyze the effects. For examining the effect of technological variables on the mechanical properties, an experimental design was used. The structure created by the femtosecond laser on the surface of the tool influenced the mechanical properties of the polymer products. Recognizing the effect of microstructures on the melt front and, through this, the change in mechanical properties, a predefined polymer product property can be achieved.

Hydrogen–gasoline dual-fuel spark ignition (SI) engines represent a promising transitional solution toward cleaner combustion and reduced carbon emissions. In a previous study, a predictive engine model was developed to simulate the performance and combustion characteristics of such systems; however, its accuracy was constrained by the use of estimated combustion parameters. This study presents an experimental validation based on high-resolution in-cylinder pressure measurements performed on a naturally aspirated SI engine operating with a 20% hydrogen energy share. The objectives are twofold: (1) to refine the combustion model using empirically derived combustion metrics, and (2) to evaluate the feasibility of moderate hydrogen enrichment in a stock engine configuration. To facilitate a more accurate understanding of how key combustion parameters evolve under different operating conditions, Vibe function was fitted to the ensemble-averaged heat release rate curves computed from 100 consecutive engine cycles at each static full-load operating point. This approach enabled the extraction of stable and representative metrics, including the mass fraction burned at 50% (MFB50) and combustion duration, which were then used to recalibrate the predictive combustion model. In addition, cycle-to-cycle variation and combustion duration were also investigated in the dual-fuel mode. The combustion duration exhibited a consistent and substantial reduction across all of the examined operating points when compared to pure gasoline operation. Furthermore, the cycle-to-cycle variation difference remained statistically insignificant, indicating that the introduction of 20% hydrogen did not adversely affect combustion stability. In addition to improving model accuracy, this work investigates the occurrence of abnormal combustion phenomena—including backfiring, auto-ignition, and knock—under enriched conditions. The results confirm that 20% hydrogen blends can be safely utilized in standard engine architectures, yielding faster combustion and reduced burn durations. The validated model offers a reliable foundation for further dual-fuel optimization and supports the broader integration of hydrogen into conventional internal combustion platforms.

During the research, functional microstructures were created on the cavity surface of the injection moulding tool using femtosecond laser technology. Automotive-grade polypropylene (PP), as well as its recycled and carbon nanotube-reinforced composites, were used as the raw materials. The replicated structures were examined using confocal microscopy. It is expected that by optimizing the process parameters, the filling of the structured cavity surface with nanocomposite materials can reach a quality level comparable to plastic specimens made from non-reinforced raw materials. The aim is to provide results of scientific and industrial value to demonstrate the influence of the modified mould surface on the flow of the polymer melt and thus on the filling of the injection moulded products.

The subject of this research is the machining of Ni-based super alloys using indexable end mills. The cutting ability of these materials is known to be difficult, even challenging with modern tools, so our goal is to create an efficient technology recommendation on an experimental basis. To this end, we have developed an experimental design from which results are used to determine the optimal technological parameters. This research took place at John Von Neumann University, Department of Vehicle Technology of GAMF Faculty.