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The paper presents a 3D model of the processes taking place in the cylinder of a large 4-stroke marine engine. The model is based on CFD calculations performed on the moving mesh. The modelling range includes the full duty cycle (720° crankshaft position) and the complete geometry of the cylinder with inlet and exhaust ducts. The input data, boundary conditions and validation data were obtained by direct measurements on the real object. Fuel injection characteristics were obtained by Mie scattering measurements in a fixed-volume chamber. The modelling results have been validated in terms of the pressure characteristics of the engine's cylinder within the entire range of its loads. The mean error did not exceed 1.42% for the maximum combustion pressure and 1.13% for the MIP (Mean Indicated Pressure). The model was also positively validated in terms of the O2 and NOx content of the exhaust gas. The mean error in this case was 1.2% for NOx fractions in the exhaust gas and 0.4% for O2 fractions. The complete model data has been made available in the research data repository on an open access basis. [ABSTRACT FROM AUTHOR]

The rising human population is causing the utilization of enormous amounts of fossil fuels to fulfill energy needs. Various renewable sources are used as fossil fuels however those resources are not powerful in supplanting customary non-renewable energy sources like gasoline in vehicles. The depletion of conventional fossil fuel utilized in a vehicle contributes to an increased portion of air contamination and is a danger to human well-being. Also, to maintain the supply demand, many active types of research have been carried out in mixing a higher percentage of ethanol over gasoline and further moving towards flex-fuel vehicles. But there arises a problem of knocking and higher CO, and HC emissions from the engine. To overcome the above problem, ethanol could be mixed in a higher percentage over gasoline with the help of hydrogen assistance and can completely avoid the problem of knocking and reducing CO and HC emissions. In this research, the combustion, emission, and performance characteristics of a variable-speed gasoline engine fuelled with ethanol-blended gasoline along with hydrogen assistance are taken for investigation at variable speeds like 1800, 1600, 1400, and 1200 rpm. Hydrogen is added to blended fuel (E30) which has better combustion, emission, and performance than other blended fuels. Hydrogen addition is done at 2, 3, and 4 ms respectively. The outcomes showed that the E30 + H 2 at 3 ms has better combustion, emission, and performance, still, the emission of NOx is higher in comparison with all the other blends due to complete combustion. Thus, a two-stage analysis has been done, one is making a comparison among various blends of ethanol, and the second one is the comparison among the various energy shares of hydrogen. • Hydrogen injection timing variation at 2, 3, and 4 ms over the E30 fuel blend. • Hydrogen injecting strategy enhancing the usage of flex fuels in SI engine. • Split hydrogen and E30 fuel over port injection at the inlet manifold. • BTE was highest with E30 + H 2 at 3 ms and also the emissions were optimum. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW)-to-energy systems have gained significant attention in recent years for their potential to produce renewable energy from waste. These systems involve the conversion of MSW into electricity, heat or fuel. One of the most promising applications of MSW-to-energy systems is the production of hydrogen, which is considered a clean and sustainable fuel. Machine learning algorithms have the potential to revolutionize the way MSW-to-energy systems are managed. The integration of machine learning into MSW-to-energy systems has the potential to significantly improve the sustainability and profitability of this industry. In this study, a novel integrated MSW-to-energy system is modeled to produce hydrogen, power, and oxygen and with capacities of heating water and air. Hydrogen production, power production, oxygen storage, hot water, hot air, and system emission are predicted using machine learning algorithms based on regression models with high validity and R2 values more than 99.8% having errors smaller than 1%. The reduced regression models are developed by eliminating the insignificant variables from the full algorithms using the analysis of variance. The findings reveal high accuracy for the reduced regression models while their errors slightly decrease to 2%. This suggests that the machine learning algorithms can also be used as an effective tool to further improve MSW-to-energy systems. [Display omitted] • A novel municipal solid waste-to-energy system is modeled. • Hydrogen and oxygen are produced as well as electrical power, hot water and air. • Machine learning algorithms are developed using regression models. • Reduced regression models are developed using analysis of variance. • Machine learning algorithms show a high accuracy with errors less than 1%. [ABSTRACT FROM AUTHOR]