Akademik Veri Yönetim Sistemi
NEURAL COMPUTING & APPLICATIONS, cilt.22, ss.1713-1725, 2013 (SCI-Expanded)
Efficiency, reliability and emission demands on fuel consumptions have directed us to develop a microcontroller-based electromechanical educational platform that emulates the basic injection process of common four-stroke type diesel engines. Modeling of a system provides rapid programming and implementation capabilities. This study focuses on modeling and simulation of the platform in order to observe the results of novel methods and development strategies. The model determines the injection time (IT) and injection order (IO) of the related pistons. Determination of the IO has standard steps, where of IT which directly affects the fuel consumption lets novel optimization methods. In traditional applications, IT is assigned by a lookup table, whose inputs are crankshaft speed (CS) and manifold absolute pressure (MAP) values. In this study, an alternative relation surface created by feedforward artificial neural networks (ANNs) is suggested to determine the IT. The novel method could interpolate precise intermediate values of IT which bring about optimization in fuel consumption. Performances of the traditional method and the ANNs method are compared.