International Scientific Journal

External Links


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.
PAPER REVISED: 2016-08-02
PAPER ACCEPTED: 2016-08-24
CITATION EXPORT: view in browser or download as text file
  1. Zhixia H., et al., Study on effect of fuel injection strategy on combustion noise and exhaust emission of diesel engine, Thermal Science, 17 (2012) 81-90.
  2. European Commission Emission Regulations, in, Brussels, 2008.
  3. Yüksek L., et al., Modelling the effect of injection pressure on heat release parameters and nitrogen oxides in direct injection diesel engines, Thermal Science, 18 (2014) 155-168.
  4. Emission Regulations for Heavy Duty Vehicles- European Union in, Brussels, 1988.
  5. Özkan, M., Comparative Study of the Effect of Biodiesel and Diesel Fuel on a Compression Ignition Engine's Performance, Emissions, and Its Cycle by Cycle Variations, Energy & Fuels, 21 (2007) 3627-3636.
  6. Ferguson, C. R., Kirkpatrick, A. T., Internal Combustion Engines,, Second ed., John Wiley&Sons, 2001.
  7. Ozkan, M., et al., Comparison of EGR ratios determined by four different methods for electronic recirculation gate control, International Journal of Environment and Pollution, 23 (2005) 223-231.
  8. Herfatmanesh, M. R., et al., Experimental investigation into the effects of two-stage injection on fuel injection quantity, combustion and emissions in a high-speed optical common rail diesel engine, Fuel, 109 (2013) 137-147.
  9. Zhang, L., A Study of Pilot Injection in a DI Diesel Engine, Society of Automotive Engineers, SAE Paper No, (1999) 1999-1901-3493.
  10. Payri, F., et al., Investigation of Diesel combustion using multiple injection strategies for idling after cold start of passenger-car engines, Experimental Thermal and Fluid Science, 34 (2010) 857-865.
  11. Zheng, M., Kumar, R., Implementation of multiple-pulse injection strategies to enhance the homogeneity for simultaneous low-NOx and -soot diesel combustion, International Journal of Thermal Sciences, 48 (2009) 1829-1841.
  12. Suh, H. K., Investigations of multiple injection strategies for the improvement of combustion and exhaust emissions characteristics in a low compression ratio (CR) engine, Applied Energy, 88 (2011) 5013-5019.
  13. AVl List Gmbh, AVL BOOST, in, 2012.
  14. Technologies, G., GT POWER, in, USA.
  15. C., R., Wave, in, UK.
  16. Nikzadfar, K., Shamekhi, A. H., Investigating the relative contribution of operational parameters on performance and emissions of a common-rail diesel engine using neural network, Fuel, 125 (2014) 116-128.
  17. Lešnik, L., et al., Numerical and experimental study of combustion, performance and emission characteristics of a heavy-duty DI diesel engine running on diesel, biodiesel and their blends, Energy Conversion and Management, 81 (2014) 534-546.
  18. Kozarac, D., et al., Analysis of benefits of using internal exhaust gas recirculation in biogas-fueled HCCI engines, Energy Conversion and Management, 87 (2014) 1186-1194.
  19. He, Y., Rutland, C. J., Application of artificial neural networks in engine modelling, International Journal of Engine Research, 5 (2004) 281-296.
  20. Oğuz, H., et al., Prediction of diesel engine performance using biofuels with artificial neural network, Expert Systems with Applications, 37 (2010) 6579-6586.
  21. Chmela, F., et al., Die Vorausberechnung des Bennverlaufs von Dieselmotoren mit direkter Einspritzung auf der Basis des Einspritzverlaufs, MTZ 59 (1998) 7-8.
  22. Chmela, F., Orthaber, G., Rate of Heat Release Prediction for Direct Injection Diesel Engines Based on Purely Mixing Controlled Combustion, Society of Automotive Engineers, SAE Paper No, (1999) 1999-1901-0186.
  23. AVL, BOOST Theory, (2011).
  24. Andree, A., Pachernegg, S. J., Ignition Conditions in Diesel Engines, Society of Automotive Engineers, SAE Paper No, (1969) 690253.
  25. Sitkei, G., Kraftstoffausbreitung und Verbrennung bei Dieselmotoren, Springer Verlag, Munich, 1964.
  26. Technologies, A., ATI Vision Engine Calibration Software, in, 2011.
  27. Holman, J. P., Experimental Methods for Engineers, Mc Graw Hill, New York, 2012.
  28. Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice Hall, New Jersey, 1998.
  29. O Özener, L. Y., M Özkan, Artificial neural network approach to predicting engine-out emissions and performance parameters of a turbo charged diesel engine, Thermal Science, 17 (2013) 153-166.
  30. Öztemel, E., Artificial Neural Networks, Papatya Publishing, İstanbul, 2003.
  31. Uzun, A., A parametric study for specific fuel consumption of an intercooled diesel engine using a neural network, Fuel, 93 (2012) 189-199.
  32. Canakci, M., et al., Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil, Expert Systems with Applications, 36 (2009) 9268-9280.
  33. Parlak, A., et al., Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine, Applied Thermal Engineering, 26 (2006) 824-828.
  34. Yuanwang, D., et al., An analysis for effect of cetane number on exhaust emissions from engine with the neural network, Fuel, 81 (2002) 1963-1970.
  35. Beale, H. M., et al., Matlab Neural Network Toolbox User Guide, in, TheMathworks Inc., Massachusetts, 2010.
  36. Pattas, K., Häfner, G., Stickoxidbildung bei der ottomotorischen Verbrennung, MTZ, 12 (1973) 397-404.
  37. Onorati, A., et al., 1D Unsteady Flows with Chemical Reactions in the Exhaust Duct-System of S.I. Engines: Predictions and Experiments, Society of Automotive Engineers, SAE Paper No, (2001) 2001- 2001-0939.
  38. Schubiger R.A., et al., Rußbildung und Oxidation bei der dieselmotorischen Verbrennung, MTZ 5(2002).
  39. Dürnholz, M., et al., Preinjection A Measure to Optimize the Emission Behavior of DI-Diesel Engine, Society of Automotive Engineers, SAE Paper No, (1994) 940674.
  40. Okude, K., et al., Effects of Multiple Injections on Diesel Emission and Combustion Characteristics, Society of Automotive Engineers, SAE Paper No, (2007) 2007-2001-4178.
  41. Abdullah, N. R., et al., Effect of Injection Pressure with Split Injection in a V6 Diesel Engine, Society of Automotive Engineers, SAE Paper No, (2009) 2009-2024-0049.

© 2019 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