A Statistical and Computational Predictive Model of the Direct Fuel Injection System in Diesel Engines

In recent years, the high emission standards have grown the development of different strategies focused on the reduction of pollutants produced by combustion processes in energy transfer systems. For that reason, different studies have been developed to minimize fuel consumption and elevate internal combustion performance under different operating modes. Internal combustion engines are widely studied currently by means of advanced theories of thermal and fluid mechanics sciences with the aim to improve the energy transfer processes needed to transform the chemical energy generated in work during the complex fuel combustion process into the combustion chamber. Inexpensive methods have been developed to improve the internal combustion engine performance based on the understanding of chemical reactions and physical processes of mass and energy transfer.  Mathematical and experimental models are employed to approximate the real working conditions, the physical phenomenon of the fuel flow injected into the combustion chamber of the internal combustion engine. Therefore, this paper proposes a predictive model that relates the fuel injection system with the combustion process and the heat transfer into the walls of the combustion chamber. External forces are considered during the internal combustion engine operation under real working conditions taking into account the dependent variables of the partial differential equations system that describes the internal combustion engine performance. A good agreement was reached between the experimental and predictive approaches. The results showed an error rate of less than 3 percent, considering a multiple linear regression model adjusted to the characterized internal combustion engine.