HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm
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 f...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Journal of Combustion |
| Online Access: | http://dx.doi.org/10.1155/2012/854393 |
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| _version_ | 1832567754529439744 |
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| author | AbdoulAhad Validi Jyh-Yuan Chen Akbar Ghafourian |
| author_facet | AbdoulAhad Validi Jyh-Yuan Chen Akbar Ghafourian |
| author_sort | AbdoulAhad Validi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c41b8e3f1d1d42b99b8e88b2936508d0 |
| institution | Kabale University |
| issn | 2090-1968 2090-1976 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Combustion |
| spelling | doaj-art-c41b8e3f1d1d42b99b8e88b2936508d02025-02-03T01:00:40ZengWileyJournal of Combustion2090-19682090-19762012-01-01201210.1155/2012/854393854393HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic AlgorithmAbdoulAhad Validi0Jyh-Yuan Chen1Akbar Ghafourian2Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309-0427, USADepartment of Mechanical Engineering, University of California, Berkeley, CA 94720-1740, USADepartment of Aerospace Engineering, Sharif University of Technology, Tehran 8639-11365, Iran 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.http://dx.doi.org/10.1155/2012/854393 |
| spellingShingle | AbdoulAhad Validi Jyh-Yuan Chen Akbar Ghafourian HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm Journal of Combustion |
| title | HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm |
| title_full | HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm |
| title_fullStr | HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm |
| title_full_unstemmed | HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm |
| title_short | HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm |
| title_sort | hcci intelligent rapid modeling by artificial neural network and genetic algorithm |
| url | http://dx.doi.org/10.1155/2012/854393 |
| work_keys_str_mv | AT abdoulahadvalidi hcciintelligentrapidmodelingbyartificialneuralnetworkandgeneticalgorithm AT jyhyuanchen hcciintelligentrapidmodelingbyartificialneuralnetworkandgeneticalgorithm AT akbarghafourian hcciintelligentrapidmodelingbyartificialneuralnetworkandgeneticalgorithm |