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Control-Oriented Modeling of Turbocharged Diesel Engines Transient Combustion Using Neural Networks
Technical Paper
2014-01-1093
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Study and modeling of diesel combustion during transient operations is an important scientific objective. This is partially due to the fact that emissions under transient operations have aroused increasing attention by control groups during recent decades. The objective of this paper is to develop a combustion model to predict the peculiarities of transient combustion for developing and testing control strategies. To by-pass the complicated principles of transient combustion, the Neural Networks are applied to link the coefficients in an empirical combustion model with engine operating parameters. Finally, the Neural Networks combustion model would not only reflect the influence of turbocharge lag on combustion process during transient event, which cannot be predicted by its interpolation alternative, but also shown great potential for analyzing combustion characteristics during load increase transient event or other transient operations.
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Authors
- Taotao Wu - Beijing Institute of Technology
- Changlu Zhao - Beijing Institute of Technology
- Kai Han - Beijing Institute of Technology
- Bolan Liu - Beijing Institute of Technology
- Zhenxia Zhu - Beijing Institute of Technology
- Yangyang Liu - Beijing Institute of Technology
- Xiaokang Ma - Beijing Institute of Technology
- Guoliang Luo - Beijing Institute of Technology
Citation
Wu, T., Zhao, C., Han, K., Liu, B. et al., "Control-Oriented Modeling of Turbocharged Diesel Engines Transient Combustion Using Neural Networks," SAE Technical Paper 2014-01-1093, 2014, https://doi.org/10.4271/2014-01-1093.Also In
References
- Galindo , J. , Luján , J.M. , Serrano , J.R. , and Hernández , L. Combustion Simulation of Turbocharger HSDI Diesel Engines During Transient Operation Using Neural Networks Applied Thermal Engineering 25 877 898 2005
- Serrano R. , Climent H. , Guardiola C. , Piqueras P. Methodology for characterisation and simulation of turbocharged diesel engines combustion during transient operation. Part 2: Phenomenological combustion simulation Applied Thermal Engineering 29 2009 150 158
- Galindo , J. , Bermúdez , V. , Serrano , J. and López , J. Cycle to Cycle Diesel Combustion Characterisation During Engine Transient Operation SAE Technical Paper 2001-01-3262 2001 10.4271/2001-01-3262
- Assanis , D. , Filipi , Z. , Fiveland , S. , and Syrimis , M. A Methodology for Cycle-By-Cycle Transient Heat Release Analysis in a Turbocharged Direct Injection Diesel Engine SAE Technical Paper 2000-01-1185 2000 10.4271/2000-01-1185
- Tauzia Xavier , Maiboom Alain , Chesse Pascal , Thouvenel Nicolas A new phenomenological heat release model for thermo-dynamical simulation of modern turbocharged heavy duty Diesel engines Applied Thermal Engineering 26 2006 1851 1857
- Amsdem A.A. , Butler T.D. , O'Rourke P.J. , Ramshaw J.D. KIVA-a comprehensive model for 2-D and 3-D engine simulations SAE Technical Paper 850554 1985 10.4271/850554
- Wiebe I. Halbempirische Formel für die Verbrennungs-ges-chwindigkeit Verlag der Akademie der Wissenschaften der Ud SSR Moscow 1956
- Heywood , J.B. Internal Combustion Engine Fundamentals McGraw-Hill New York 1988
- Rakopoulos , C. and Giakoumis , E. Review of Thermodynamic Diesel Engine Simulations under Transient Operating Conditions SAE Technical Paper 2006-01-0884 2006 10.4271/2006-01-0884
- Rakopoulos , C. , Mavropoulos , G. , and Hountalas , D. Modeling the Structural Thermal Response of an Air-Cooled Diesel Engine under Transient Operation Including a Detailed Thermodynamic Description of Boundary Conditions SAE Technical Paper 981024 1998 10.4271/981024
- Thompson G.J. , Atkinson C.M. , Clark N.N. , Long T.W. , Hanzevack E. Neural network modeling of the emissions and performance of a heavy-duty Diesel engine Prc Instn Mech Engrs 214 Part D, Journal of Automobile Engineering 2000 111 126
- Lin W. , Wu M.H. , Duan S. Engine test data modelling by evolutionary radial basis function networks Prc Instn Mech Engrs 217 Part D, Journal of Automobile Engineering 2003 489 497
- Assanis D.N. , Nelson S.A. II , Filipi Z.S. The Use of Neural Nets for Matching Fixed or Variable Geometry Compressors with Diesel Engines, Journal of Engineering of Gas Turbines and Power Transaction of the ASME 125 2003 572 579
- Lenz , U. and Schroeder , D. Artificial Intelligence for Combustion Engine Control SAE Technical Paper 960328 1996 10.4271/960328
- Benajes J. , Molina S. , López J.J. , Hernández L. Neural network application for NOx prediction in Diesel engines EAEC Congress Proceedings Bratislava 01176 2001
- Desantes , J. , López , J. , García , J. , and Hernández , L. Application of Neural Networks for Prediction and Optimization of Exhaust Emissions in a H.D. Diesel Engine SAE Technical Paper 2002-01-1144 2002 10.4271/2002-01-1144
- Woschni , G. and Anisits , F. Experimental Investigation and Mathematical Presentation of Rate of Heat Release in Diesel Engines Dependent upon Engine Operating Conditions SAE Technical Paper 740086 1974 10.4271/740086
- Wu Taotao , Han Kai , Zhao Changlu and Zhu Zhenxia Control-oriented Characterization and Modeling of Turbocharged Diesel Engines Combustion 2013 International Conference on Frontiers of Environment, Energy and Bioscience 2013
- Cui , Y. , Deng , K. and Wu , J. A Modeling and Experimental Study of Transient NOx Emissions in Turbocharged Direct Injection Diesel Engines Proceedings of the Institution of Mechanical Engineers, Part D, Journal of Automobile Engineering 218 535 541 2004
- Gamma Technologies GT-POWER Users” Manual Version 7.1 2010
- Maiboom A. , Tauzia X. , Hetet J.F. , Cormerais M. A 5-zones phenomenological combustion model for DI diesel engine for a wide range of operating conditions FISITA Congress 22 27 October Yokohama, Japan 2006
- Papadimitriou , I. , Warner , M. , Silvestri , J. , Lennblad , J. et al. Neural Network Based Fast-Running Engine Models for Control-Oriented Applications SAE Technical Paper 2005-01-0072 2005 10.4271/2005-01-0072