Multi-objective optimization and numerical analysis of ammonia-diesel dual-fuel engine combustion and emissions based on RSM-NSGA-Ⅲ-TOPSIS approach

Due to environmental protection and the pursuit of lower-carbon operation, ammonia-diesel dual-fuel (ADDF) engines have attracted increasing research attention. In this study, an intelligent optimization framework integrating response surface methodology (RSM), Non-dominated Sorting Genetic Algorithm III (NSGA-III), and technique for order preference by similarity to an ideal solution (TOPSIS) was developed to improve the combustion and emissions performance of an ADDF engine. First, a CFD model of the diesel engine was established and validated using experimental data. Based on the validated model, the influence of diesel injection hole diameter (DIHD) on the combustion and emissions of the ADDF engine was investigated. The ammonia energy ratio cases were 15%, 30%, 45%, and 60%. Finally, unburned NH 3 emissions, CO 2 emissions, and peak in-cylinder pressure (PCP) were selected as the optimization objectives, and the RSM-NSGA-III-TOPSIS method was applied to optimize the engine accordingly. The results showed that as DIHD increases, in-cylinder pressure (CP), heat release rate (HRR) and in-cylinder temperature (CT) are decreased by a maximum of 9.15 %, 11.14 % and 57.78 %, respectively. In addition, the artificial intelligence optimization strategy of RSM-NSGA-III-TOPSIS was achieved by combining the ammonia energy ratio (AER), DIHD, and swirl ratio. The optimal combination obtained was: AER of 22.29%, swirl ratio of 1.78, DIHD of 0.28 mm. The corresponding CO 2 , unburned NH 3 emissions, and PCP values were 64147.33 ppm, 2356.34 ppm, and 8.32 MPa, respectively. These results indicate that the proposed RSM-NSGA-III-TOPSIS framework can effectively balance combustion performance and emission reduction for the ADDF engine under the investigated operating condition.