Road vehicles, particularly cars, are one of the primary sources of CO₂ emissions in the transport sector. Shifting to unconventional energy sources such as solar and wind power may reduce their carbon footprints considerably. Consequently, using ammonia as a fuel due to its potential benefits, such as its high energy density, being a carbon-free fuel, and its versatility during storage and transportation, has now grabbed the attention of researchers. However, its slow combustion speed, larger combustion chamber requirements, ignition difficulties, and limited combustion stability are still major challenges. Therefore, authors tried to analyze the combustion pressure of ammonia in a constant-volume combustion chamber across different equivalence ratios by adopting a machine learning approach. While conducting the analysis, the experimental values were assessed and subsequently utilized to predict the induced combustion pressure in a constant-volume combustion chamber across various equivalence ratios. In this research, a two-step prediction process was employed. In the initial step, the Random Forest algorithm was applied to assess the combustion pressure. Subsequently, in the second step, artificial neural network machine learning algorithms were employed to pinpoint the most effective algorithm with a lower root-mean-square error and R². Finally, Linear Regression illustrated the lowest error in both steps with a value of 1.0, followed by Random Forest.

Injection characteristics play an important role in the emission and overall thermal efficiency of an engine. Several methods have been proposed for analyzing different fuel injection characteristics. This study focused on the interferometric laser imaging for droplet sizing (ILIDS) technique to investigate the effects of droplet size and velocity under different conditions of water-glycerin mixtures. These effects were evaluated using intermittent spray flows in both ambient and pressurized constant volume spray chamber conditions. The initial results were compared to those reported by previous studies and used to determine the Sauter mean diameter (SMD), arithmetic mean diameter (AMD), droplet velocity, and probability density function of the spray droplet size. SMD and AMD tended to decrease as the plate temperature, injection pressure, and viscosity were increased at specific observation areas. The average velocity of the droplet increased with higher plate temperature and injection pressure at specific observation areas. The distribution of the smaller droplet increased with higher plate temperature and injection pressure. For the water-glycerin mixture, as the glycerin ratio increased, more viscous droplets were created. This was followed in higher nozzle shear force at the outlet of the fuel injector, which decreased the particle size and generated more atomized fuel sprays. This result can enable the reduction in hydrocarbon and carbon monoxide emissions from internal combustion engines.

The push for decarbonization of internal combustion engines (ICEs) has spurred interest in alternative fuels, such as hydrogen and ammonia. To optimize combustion efficiency and reduce emissions, a closer look at the intake system and in-cylinder flows is crucial, especially when a hard-to-burn fuel, such as ammonia is utilized. In port fuel injection ICEs, airflow within cylinders profoundly affects combustion and emissions by influencing the air–fuel mixing phenomenon. Adjusting intake port openings is an important factor in controlling the in-cylinder airflow. In previous experiments with a transparent cylinder, tangential and helical ports demonstrated that varying the helical port’s opening significantly impacts flow velocities, swirl ratios, and swirl center positions (SCPs). In this study, we used a particle image velocimetry technique to investigate how the tangential port’s opening affects intake and in-cylinder flows. Flow velocities were assessed at different planes near the cylinder head, evaluating streamline maps, turbulent kinetic energy (TKE), and SCPs. Under the given experimental conditions, swirl flows were successfully generated early in the compression stroke when the tangential port opening exceeded 25%. Our findings emphasize the importance of minimizing TKE and SCP variation for successful swirl flow generation in engine cylinders equipped with both tangential and helical ports.

Carbon fiber reinforced plastic (CFRP) is a composite material with high specific strength and is applied to transportation and aviation equipment. However, conventional processing methods require large-scale production apparatus or a high level of dexterity that only comes with extensive experience which makes it difficult to achieve high processing efficiency. The objective of this study is to develop a novel method for forming thermos-plastic CFRP (CFRTP) preforms implementing a 3D printer for press molding. Applying this method offers the advantage that continuous carbon fibers can be formed on a free-form surface. It also reduces the manufacturing time and operator skill required. The goal of this research is to establish a method for molding a free-form surface composed of continuous fibers by employing a 3D-printed preform designed to match the unfolded polygonised diagram of the free-form surface. Previous research introduced an unfolding approach for converting a three-dimensional shape to a plane surface based on a computer-aided design and manufacturing (CAD/CAM) system, enabling the generation of an unfolding diagram that maintains the continuity of fiber tow. Furthermore, the validity of unfolded diagram was confirmed by reproducing the objective three-dimensional shape from the unfolded diagram using thermos-setting CPRP (CFRTS) tow prepreg. In this study, the viability of the proposed molding process using CFRTP preform fabricated by a 3D printer was verified and an assessment of the formability of the molded parts was conducted.

In the past two decades, internal combustion engines have been required to improve their thermal efficiency in order to limit hazardous gas emissions. For further improvement of the thermal efficiency, it is required to predict the mass of intake air into cylinders in order to control the auto-ignition timing for CI engines. For an accurate prediction of intake air mass, it is necessary to model the heat transfer phenomena at the intake manifold. From this intention, an empirical equation was developed based on Colburn equation. Two new arguments were presented in the derived formula. The first argument was the addition of Graetz number, where it characterized the entrance region thermal boundary layer development and its effect on the heat transfer inside the intake manifold. As the second argument, Strouhal number was included in order to represent intake valve effect on heat transfer. This study compared experimental data with the present empirical equation, and average error was estimated to be 3.1%, which was significantly improved in comparison with the Colburn equation. Furthermore, derived empirical heat transfer equation was implemented to the intake manifold model of a diesel engine in 1-D engine simulation. The study confirmed the influence of the heat transfer phenomena, and its importance to intake air. At IVC, temperature difference between Colburn equation and derived equation was calculated to be 3.8 K. This corresponded to an advanced auto-ignition timing by 0.78 deg. CA, which gives an estimated improvement of 0.22% when evaluating both the thermal efficiency and CO₂ emission.