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An ANN-GA Framework for Optimal Engine Modeling
Joint Authors
Barghash, Mahmoud
Tahboub, Khaldoun K.
Arafeh, Mazen
Ghazal, Osama
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-03-13
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Internal combustion engines are a main power source for vehicles.
Improving the engine power is important which involved optimizing combustion timing and quantity of fuel.
Variable valve timing (VVT) can be used in this respect to increase peak torque and power.
In this work Artificial Neural Network (ANN) is used to model the effect of the VVT on the power and genetic algorithm (GA) as an optimization technique to find the optimal power setting.
The same proposed technique can be used to improve fuel economy or a balanced combination of both fuel and power.
Based on the findings of this work, it was noticed that the VVT setting is more important at high speed.
It was also noticed that optimal power can be obtained by changing the VVT settings as a function of speed.
Also to reduce computational time in obtaining the optimal VVT setting, an ANN was successfully used to model the optimal setting as a function of speed.
American Psychological Association (APA)
Tahboub, Khaldoun K.& Barghash, Mahmoud& Arafeh, Mazen& Ghazal, Osama. 2016. An ANN-GA Framework for Optimal Engine Modeling. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1112420
Modern Language Association (MLA)
Tahboub, Khaldoun K.…[et al.]. An ANN-GA Framework for Optimal Engine Modeling. Mathematical Problems in Engineering No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1112420
American Medical Association (AMA)
Tahboub, Khaldoun K.& Barghash, Mahmoud& Arafeh, Mazen& Ghazal, Osama. An ANN-GA Framework for Optimal Engine Modeling. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1112420
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112420