Comparing Polynomials and Neural Network to Modelling Injection Dosages in Modern CI Engines

Abstract

The article discusses the possibility of using computational methods for modelling the size of the injection doses. Polynomial and artificial intelligence methods were used for prediction. The aim of the research was to analyze whether it is possible to model the operating parameters of the fuel injector without knowing its internal dimensions and tribological associations. The black box method was used to make the model. This method is based on the analysis of input and output parameters and their correlation. The paper proposes a mathematical model determined on the basis of a polynomial and a neural network based on input and output parameters. The above models make it possible to predict the amount of fuel injection doses on the basis of their operating parameters. Modelling was performed in the Matlab environment. Calculating methods could support the diagnosis processes of fuel injectors. Fuel injection characteristic is non-linear. Study shows that it is possible to predict injection characteristic with high matching using polynomial and neural network. That way accelerates fuel injector work parameters research process. Fuel injector test basis on known its work areas. Mathematical modelling can calculate all injection area using few parameters. To modelling fuel injection dosages by neural network have been used back propagation and Levenberg—Marquardt algorithms.