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MODELLING ANALYSIS OF MULTIPLE DIESEL INJECTION STRATEGIES WITH ONE-DIMENSIONAL SIMULATION COUPLED WITH ARTIFICIAL NEURAL NETWORKS

ABSTRACT
In the modern Diesel injection systems the phasing of injection in the same cycle gives a high flexibility to engineers from the perspective of engines performance and emission optimization. Basically, the injection is separated in to three phases: the pilot, main, and post injection phases. The focus of this study is based on pilot injection strategy implementation, which can be used for emission control effectively. In this work, reference main and pilot + main injection strategy experiments were realized in a modern Diesel engine. The logged data groups were used to model the engine at 1-D thermodynamic simulation AVL BOOST. In the second stage of this work, the engine operating points which are not realized at test bench are made run at BOOST programme. The new model parameters of simulation are identified with artificial neural network technique. The results showed that the implementation of appropriate mass of pilot injection at the appropriate injection advance will reduce the NOx emissions compared to reference main injection strategy. For reducing CO emissions the pilot injection mass should also be kept in the same range with higher injection pressure that can be achieved. Usage of 1-D simulation programme coupled with artificial neural network was found useful up to a certain extent especially for parametric analyses and optimization problems via with validation of calibration parameters at a huge experimental data.
KEYWORDS
PAPER SUBMITTED: 2016-05-04
PAPER REVISED: 2016-08-02
PAPER ACCEPTED: 2016-08-24
PUBLISHED ONLINE: 2016-09-05
DOI REFERENCE: https://doi.org/10.2298/TSCI160504223O
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2017, VOLUME 21, ISSUE 1, PAGES [413 - 425]
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© 2017 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence