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Journal of Combustion
Volume 2012 (2012), Article ID 854393, 11 pages
http://dx.doi.org/10.1155/2012/854393
Research Article

HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm

1Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309-0427, USA
2Department of Mechanical Engineering, University of California, Berkeley, CA 94720-1740, USA
3Department of Aerospace Engineering, Sharif University of Technology, Tehran 8639-11365, Iran

Received 18 December 2011; Accepted 27 February 2012

Academic Editor: Constantine D. Rakopoulos

Copyright © 2012 AbdoulAhad Validi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A Dynamic model of Homogeneous Charge Compression Ignition (HCCI), based on chemical kinetics principles and artificial intelligence, is developed. The model can rapidly predict the combustion probability, thermochemistry properties, and exact timing of the Start of Combustion (SOC). A realization function is developed on the basis of the Sandia National Laboratory chemical kinetics model, and GRI3.0 methane chemical mechanism. The inlet conditions are optimized by Genetic Algorithm (GA), so that combustion initiates and SOC timing posits in the desired crank angle. The best SOC timing to achieve higher performance and efficiency in HCCI engines is between 5 and 15 degrees crank angle (CAD) after top dead center (TDC). To achieve this SOC timing, in the first case, the inlet temperature and equivalence ratio are optimized simultaneously and in the second case, compression ratio is optimized by GA. The model’s results are validated with previous works. The SOC timing can be predicted in less than 0.01 second and the CPU time savings are encouraging. This model can successfully be used for real engine control applications.