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The ANN approximation of the CH4 combustion model : the mixture composition

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EN
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EN
The calculation of the changing of the combustion mixture composition during the combustion process of the CH4 is presented of the paper. Correct calculation results of the mixture composition during the combustion process in combustion chambers of internal combustion engines is important to define the heat release calculation, modeling and simulation of the combustion phenomena. The paper presents results of calculations for the GriMech 3 kinetic mechanism of the methane combustion for different thermodynamic parameters and the composition of the combusted mixture. Results of the kinetic calculation of combustion process are qualitatively consistent with the data available in literature. The second purpose of research was the approximation of obtained results with the trained artificial neural network. Input data needed to approximate mole fractions of considered in the GriMech 3 mechanism combustion process chemical species consisted of 52 mole fractions of initial chemical species and temperature and pressure process. For all considered chemical species the mean square error did not exceed a value of 1-10-2 %, but the maximum error for a single value of 43 species excess even more than 100% of the value of mole fraction values taken from kinetic calculations. Single values of errors disqualify the neural network application for modeling of mole fractions of chemical species.
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autor
  • Gdynia Maritime University, Department of Engineering Sciences Morska Street 81-87, 81-225 Gdynia, Poland tel:+48 58 6901484, fax: +48 58 6901399, jerzy95@am.gdynia.pl
Bibliografia
  • [1] Bowman, C. T, et. al, http://www.me.berkeley.edu/gri_mech/.
  • [2] Bradley, A., Williams, A., Pasternack, L., The effect of nitric oxide premixed flames of CH4, C2H6, C2H4 and C2H2, Combustion and flame, Elsevier Science Inc, Vol. 111, pp. 87-110, 1997.
  • [3] Chopey, N. P., Handbook of chemical engineering calculations 3-rd edition, McGraw-Hill, 2004.
  • [4] Demirbas, A., Biodiesel – a realistic fuel alternative for diesel engines. Springer-Verlag, 2008.
  • [5] Ghobadian, B., Rahimi, H., Nikbakht, A. M., Najafi, G., Yusaf, T. F., Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network, Renewable Energy, Elsevier Science Inc, Vol. 34, 2009.
  • [6] Heywood, J. B., Internal Combustion Engine Fundamentals, McGraw-Hill, 1988.
  • [7] Kowalski, J., Tarełko, W., NOx emission from a two-stroke ship engine. Part 1: Modeling aspect, Applied Thermal Engineering, Elsevier Science Inc, Vol. 29 No. 11-12, pp. 2153-2159, 2009.
  • [8] Kowalski, J., The ANN approximation of the CH4 combustion model: the heat release, Journal of KONES, Vol. 17, Warsaw 2010.
  • [9] Masters, T., Practical neural network recipes in C++, Academic Press Inc, 1993.
  • [10] Svozil, D., Kvasnicka, V., Pospichal, J., Introduction to multi-layer feed-forward neural networks, Chemometrics and intelligent laboratory systems, Elsevier Science Inc, Vol. 39, 1997.
  • [11]Winterbone, D. E., Advanced Thermodynamics for Engineers, Wiley & Sons, 1997.
  • [12] Zienkiewicz, O. C., Taylor, R. L., Zhu, J. Z., The Finite element method. 6-th edition, McGraw-Hill, 2005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ7-0016-0059
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