Optimization of High Pressure Common Rail Electro-injector using Genetic Algorithms

The aim of the present investigation is the implementation of an innovative procedure to optimise the design of a high pressure common rail electroinjector. The optimization method is based on the use of genetic programming, a search procedure developed by John Holland at the University of Michigan. A genetic algorithm (GA) creates a random population which evolves combining the genetic code of the most capable individual of the previous generation. For the present investigation an algorithm which includes the operators of crossover, mutation and elitist reproduction has been developed. This genetic algorithm allows the optimization of both single and multicriteria problems. For the determination of the multi-objective fitness function, the concept of Pareto optimality has been implemented. The performance of the multiobjective genetic algorithm was examined by using appropriate mathematical functions and was compared with the single objective one. The proposed genetic algorithm was used to define the geometrical and dynamic characteristics of high pressure injectors that optimize the injection profile and the time response of the system. As evaluation function for the GA a 1D simulation code of injection systems, already 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. The conservation equations were integrated by using the characteristic method. The electromagnetic force on the anchor of the injector has been simulated with an empirical function obtained by fitting experimental data. For the optimization the geometrical and dynamical data of a commercial five holes VCO injector were used as baseline case and the best combination of different groups of parameter has been found. The optimized combinations of the investigated parameters were compared with the original values of the commercial injector. Finally a feasibility analysis of the optimized parameters was performed.