This paper presents research on the combustion and emission characteristics of a four-stroke Natural gas–Diesel dual-fuel marine engine at full load. The AVL FIRE R2018a (AVL List GmbH, Graz, Austria) simulation software was used to conduct three-dimensional simulations of the combustion process and emission formations inside the engine cylinder in both diesel and dual-fuel mode to analyze the in-cylinder pressure, temperature, and emission characteristics. The simulation results were then compared and showed a good agreement with the measured values reported in the engine’s shop test technical data. The simulation results showed reductions in the in-cylinder pressure and temperature peaks by 1.7% and 6.75%, while NO, soot, CO, and CO₂ emissions were reduced up to 96%, 96%, 86%, and 15.9%, respectively, in the dual-fuel mode in comparison with the diesel mode. The results also show better and more uniform combustion at the late stage of the combustions inside the cylinder when operating the engine in the dual-fuel mode. Analyzing the emission characteristics and the engine performance when the injection timing varies shows that, operating the engine in the dual-fuel mode with an injection timing of 12 crank angle degrees before the top dead center is the best solution to reduce emissions while keeping the optimal engine power.

Piston bowl geometry plays an important role in the fuel/air mixing quality which has a direct influence on the performance and emission characteristics of the engine. This work studied the effects of piston bowl geometry on the combustion and emissions of a four-stroke direct injection (DI) heavy-duty diesel marine engine. Three types of piston bowls with various parameters were numerically investigated, targeting to improve engine performance, fuel oil consumption, and emission characteristics. The results pointed out that the ω-type and re-entrant piston bowls enhanced cylinder power by 1.26% and 2.67% while reducing the indicated specific fuel oil consumption (ISFOC) of the engine by 1.06% and 1.60%, respectively, compared to the U-type bowl. Especially, the ω-type and re-entrant piston bowls reduced NO (nitric oxide) emission by up to 25.61% and 46.09%, respectively, owing to lower peak temperatures in comparison to the U-type piston bowl. The results also pointed out that changing the piston bowl type has negligible effects on the engine-out soot and CO₂ emission characteristics of the engine. Based on this study, it is highly recommended to utilize the re-entrant piston bowl for four-stroke DI diesel engines to improve both engine performance and fuel oil consumption while reducing NO emission simultaneously.

This work studied the effect of the injector spray angle (SA) on the combustion and emissions of a 4-stroke port-injection natural gas-diesel dual-fuel (NG-Diesel DF) marine engine to determine the optimal SA for the fuel injector, aiming to reduce exhaust gas emissions while keeping the engine performance. Three-dimensional (3D) simulations of the combustion process and emission formations occurring in the engine cylinder in both diesel and DF modes were carried out using the AVL FIRE R2018a code. The engine’s in-cylinder temperature, pressure, and emission characteristics were analyzed. To clarify the effect of the injector SA on the combustion and emission characteristics of the engine, only the injector SA has been varied from 145 to 160°. Meanwhile, all other boundary conditions for the simulations and operating conditions of the engine have remained unchanged. The simulation results have been compared and showed a good agreement with the engine experimental results. The study has successfully investigated the effects of the injector SA on the combustion and emission characteristics of the engine. A better SA for the fuel injector, to reduce the NO emissions (145°) or soot and CO₂ emissions (150°), while keeping the engine power almost unchanged, without the use of any exhaust gas post-treatment equipment, has also been suggested.

Methanol is a promising alternative marine fuel due to its favorable combustion characteristics and potential to reduce exhaust emissions under increasingly stringent International Maritime Organization (IMO) regulations. This study presents a three-dimensional computational fluid dynamics (CFD) analysis of a four-stroke, medium-speed marine engine operating in methanol–diesel dual-fuel (DF) mode. Simulations were performed using AVL FIRE for a MAN B&W 6H35DF engine, covering the in-cylinder process from intake valve closing to exhaust valve opening. Nine operating cases were investigated, including seven methanol–diesel DF cases with equivalence ratios (Φ) from 0.18 to 0.30, one methane–diesel DF case (Φ = 0.22), and one pure diesel baseline. A power-matched condition (IMEP ≈ 20 bar) enabled consistent comparison among fueling strategies. The results show that methanol–diesel DF operation reduces peak in-cylinder pressure, heat-release rate, turbulent kinetic energy, and wall heat losses compared with diesel operation. At low to moderate Φ, methanol DF combustion significantly suppresses nitric oxide (NO), soot, and carbon monoxide (CO emissions), while carbon dioxide (CO₂) emissions increase with Φ and approach diesel levels under power-matched conditions. These results highlight methanol’s potential as a viable low-carbon fuel for marine engines.

In the ongoing search for an alternative fuel for diesel engines, biogas is an attractive option. Biogas can be used in dual-fuel mode with diesel as pilot fuel. This work investigates the modeling of injecting strategies for a waste-derived biogas-powered dual-fuel engine. Engine performance and emissions were projected using supervised machine learning methods including random forest, lasso regression, and support vector machines (SVM). Mean Squared Error (MSE), R-squared (R²), and Mean Absolute Percentage Error (MAPE) were among the criteria used in evaluations of the models. Random Forest has shown better performance for Brake Thermal Efficiency (BTE) with a test R² of 0.9938 and a low test MAPE of 3.0741%. Random Forest once more exceeded other models with a test R² of 0.9715 and a test MAPE of 4.2242% in estimating Brake Specific Energy Consumption (BSEC). With a test R² of 0.9821 and a test MAPE of 2.5801% Random Forest emerged as the most accurate model according to carbon dioxide (CO₂) emission modeling. Analogous results for the carbon monoxide (CO) prediction model based on Random Forest obtained a test R² of 0.8339 with a test MAPE of 3.6099%. Random Forest outperformed Linear Regression with a test R² of 0.9756% and a test MAPE of 7.2056% in the case of nitrogen oxide (NOx) emissions. Random Forest showed the most constant performance overall criteria. This paper emphasizes how well machine learning models especially Random Forest can prognosticate the performance of biogas dual-fuel engines.