A turbulent jet ignition engine enables operation with lean mixtures, decreasing nitrogen oxide (NO_X) emissions up to 92%, while the engine efficiency can be increased compared to conventional spark-ignition engines. The geometry of the pre-chamber and engine operating parameters play the most important role in the performance of turbulent jet ignition engines and, therefore, must be optimized. The initial experimental and 3D CFD results of a single-cylinder engine fueled by gasoline were used for the calibration of a 0D/1D simulation model. The 0D/1D simulation model was upgraded to capture the effects of multiple flame propagations, and the evolution of the turbulence level was described by the new K-k-ε turbulence model, which considers the strong turbulent jets occurring in the main chamber. The optimization of the pre-chamber volume, the orifice diameter, the injected fuel mass in the pre-chamber and the spark timing was made over 9 different operating points covering the variation in engine speed and load with the objective of minimizing the fuel consumption while avoiding knock. Two optimization methods using 0D/1D simulations were presented: an individual optimization method for each operating point and a simultaneous optimization method over 9 operating points. It was found that the optimal pre-chamber volume at each operating point was around 5% of the clearance volume, while the favorable orifice diameters depended on engine load, with optimal values around 2.5 mm and 1.2 mm at stoichiometric mixtures and lean mixtures, respectively. Simultaneous optimization of the pre-chamber geometry for all considered operating points resulted in a pre-chamber volume equal to 5.14% of the clearance volume and an orifice diameter of 1.1 mm.

This paper presented an experimental and numerical study of pre-chamber volume, number of orifices and orifice diameter influence on engine performance and emissions. All the measurements were performed on a single cylinder test engine at fixed engine speed of 1600 rpm, while engine load was varied by a change of the excess air ratio in the main chamber from a stochiometric mixture to a lean limit. The total of nine pre-chamber variants comprised three different pre-chamber volumes, two orifice number combinations (six and four orifices) and nine different orifice diameters. It was observed that the pre-chamber volume affects the indicated efficiency in a trend which is mostly independent of excess air ratio, with the efficiency gain between the best and worst results ranging from 1 to 4.4%. While keeping the same pre-chamber volume and the total cross-sectional area of the orifices, the larger number of orifices show better performance on two out of three investigated pre-chamber volumes, with the efficiency gains more pronounced at higher excess air ratios. Finally, on a fixed pre-chamber volume and number of orifices, the variation of orifice diameter leads to a trend in efficiency gains which favor larger orifice diameter. The comparison of the obtained efficiencies between all pre-chamber variants identified two pre-chambers, differing in each of the varied geometrical parameters, that show the best performance depending on excess air ratio range. On the other hand, a single variant which showed the worst performance on each excess ratio was identified. An additional investigation was performed by the application of the cycle-simulation model to quantify the share of emissions which are formed in the pre-chamber. The presented results showed that when PC volume is lowered, PC emission shares of NO_X and CO grow larger. The influence of orifice number and size has a minor effect on the pre-chamber emissions shares. The maximum PC emission shares of 54.8% and 80.6% are achieved at lean limit (λ = 2.2) for NO_X and CO, respectively. THC emission share, on the other hand, is not affected in a significant manner by either the pre-chamber geometry or operating conditions.

A comprehensive thermodynamic model of the marine diesel engine in combination with the operating cost assessment is used in the decision-making process regarding the selection of the most favorable slow steaming speed. The influence of the number of cylinders and sailing speed on exhaust emissions, fuel consumption and operating costs is analyzed for the case of a containership sailing on a Trans-Pacific route. The engine simulation model was used for the calculation of engine fuel consumption, NO_X and soot emissions. The operating costs and annual income were calculated through a fuel consumption correlation. The benefit of slow steaming is shown through the comparison of calculated data with the data calculated for the six-cylinder engine and the design speed of 23 knots. The highest reduction of 67.2% in CO₂ and 93.3% in NO_X emissions is achieved with the seven-cylinder engine at 15 knots, but the six-cylinder engine yields the highest increase in income per route of 6.2%. To comply with the proposed regulations for GHG emissions, the sailing speed should be reduced by at least 26%, which results in a decrease in the annual income by 24% compared to the design speed.

In this study, a multi-objective optimization procedure with embedded topology optimization was presented. The procedure simultaneously optimizes the spatial arrangement and topology of bodies in a multi-body system. The multi-objective algorithm determines the locations of supports, joints, active loads, reactions, and load magnitudes, which serve as inputs for the topology optimization of each body. The multi-objective algorithm dynamically adjusts domain size, support locations, and load magnitudes during optimization. Due to repeated topology optimization calls within the genetic algorithm, the computational cost is significant. To address this, two reduction strategies are proposed: (I) using a coarser mesh and (II) reducing the number of iterations during the initial generations. As optimization progresses, Strategy I gradually refines the mesh, while Strategy II increases the maximum allowable iteration count. The effectiveness of both strategies is evaluated against a baseline (Reference) without reductions. By the 25th generation, all approaches achieve similar hypervolume values (Reference: 2.181; I: 2.112; II: 2.133). The computation time is substantially reduced (Reference: 42,226 s; I: 16,814 s; II: 21,674 s), demonstrating that both strategies effectively accelerate optimization without compromising solution quality.

This paper compares train energy consumption of hybrid diesel-electric multiple unit (HDEMU) to hydrogen fuel-cell multiple unit (HFCMU). In the simulation, the parameters of the DMU HŽ7022 train were used for the train model created in Matlab/Simuli...