An Application of Multi-Criteria Genetic Algorithms to the Optimization of a Common-Rail Injector

The aim of the present investigation is to optimize the injection profile and the time response of a high pressure common rail injection system by adjusting the geometric and dynamic characteristics of an electronically controlled injector. The optimization method is based on the use of genetic algorithms which include the operators of crossover, mutation and elitist reproduction. As evaluation function for the GA a 1D simulation code of the injection systems, developed and extensively tested by the authors, has been used. The 1D model is based on the concentrated volume method and includes the effect of friction on the dynamics of the movable parts. Cavitation is also taken into account by the code as well as the effect on pressure wave propagation of the air and vapor mass fraction in the fuel. Conservation equations are integrated by using the characteristic method. The electromagnetic force generated by the solenoid on the head of the injector is simulated with an empirical function obtained by fitting experimental data. The optimized injection profile was defined by evaluating the predicted performance of a small bore DI diesel engine in terms of Brake Mean Effective Pressure (BMEP), soot and NO_x emissions using a modified version of the KIVA-3V code. For the definition of the best injection profile injected quantity and start of injection were kept constants and only single-injection strategies were considered. The geometric and dynamic parameters (i.e. spring stiffness) of a commercial five holes VCO injector were used as baseline case. An optimized configuration of the selected parameters has been found. The optimized combinations of the investigated parameters are compared with the original values of the commercial injector as well as their predicted performance on the engine application.