Multi-Objective Optimization of Fuel Injection Pattern for a Light-Duty Diesel Engine through Numerical Simulation
ISSN: 1946-3936, e-ISSN: 1946-3944
Published April 03, 2018 by SAE International in United States
Citation: Piano, A., Millo, F., Sapio, F., and Pesce, F., "Multi-Objective Optimization of Fuel Injection Pattern for a Light-Duty Diesel Engine through Numerical Simulation," SAE Int. J. Engines 11(6):1093-1107, 2018, https://doi.org/10.4271/2018-01-1124.
Development trends in modern common rail fuel injection systems (FIS) show dramatically increasing capabilities in terms of optimization of the fuel injection strategy through a constantly increasing number of injection events per engine cycle as well as through the modulation and shaping of the injection rate. In order to fully exploit the potential of the abovementioned fuel injection strategy optimization, numerical simulation can play a fundamental role by allowing the creation of a kind of a virtual test rig, where the input is the fuel injection rate and the optimization targets are the combustion outputs, such as the burn rate, the pollutant emissions, and the combustion noise (CN). Starting from a previously developed 1D-CFD (Computational Fluid Dynamics) virtual test rig, obtained coupling a 1.6L, 4-cylinder turbocharged diesel engine model with a 1D-CFD injector model, this article presents a methodology for optimizing the fuel injection strategy aiming to minimize brake-specific fuel consumption (BSFC) and CN without exceeding the brake-specific NOx (BSNOx) baseline value. The Non-dominated Sorting Genetic Algorithm (NSGA-III) was used in GT-SUITE environment for Pareto optimization in the BSFC-CN space, for three different engine operating conditions in the low-medium speed and low-medium load range. The proposed approach highlighted that significant improvements in terms of BSFC and CN can be achieved by adopting digitalized close pilot events with respect to the Design of Experiments (DoE) analysis previously presented in , also highlighting relevant computational time savings for the optimization process.