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Optimization of a High Speed Gasoline Engine Using Genetic Algorithm
Technical Paper
2013-01-1626
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In order to improve the torque of engine full load characteristics, especially for the engine torque at high speed, the genetic algorithm combining with the weighted sum method is adopted to optimize the performance of a high-speed gasoline engine. Firstly the simulation model is built by software GT-power. The simulated values are contrasted with the tested values at the same operating condition. The results show the correspondence of the calculated values with tested values. So it proves that the simulation model is reliable. According to the importance of torque at each speed, a corresponding weight coefficient is got. Using the weighted sum method to construct an evaluation function, the multi-objective optimization problem is transformed into a single one. The resonant cavity volume intake V, the intake manifold length L and diameter D, intake advance Angle θi and exhaust advance Angle θe are selected as the optimized variables, and the weighted average torque is selected as optimized object. At last the simulation model is optimized by genetic algorithm. The results show that the method of weighted sums combined with genetic algorithm can quickly search the optimal solution and realize the multi-objective optimization. Through correlation analysis, we can understand linear correlation between the optimization variables and optimization target. At the same time, the simulation model is also optimized by the use of the traditional single variable optimization method. Then the results optimized by the two different methods are contrasted.
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Citation
Yang, J., Zhang, Z., chen, L., and Wang, Y., "Optimization of a High Speed Gasoline Engine Using Genetic Algorithm," SAE Technical Paper 2013-01-1626, 2013, https://doi.org/10.4271/2013-01-1626.Also In
References
- Longbao , Zhou Internal combustion engine learning Beijing mechanical industry press 2005 16
- Liu Biao , Huang Ming , Yang Xiaolong Based on multi-objective genetic algorithm optimization engine intake and exhaust system Journal of human university: natural science edition 33 2010 31 35
- Shi Laihua Based on the GT - power car gasoline engine power performance optimization MSME diss. Hunan university machinery and vehicle engineering institute 2008
- Luan YanLong Natural gas engine air fuel ratio and ignition advance Angle simulation optimization MSME diss. Chongqing traffic institute 2005
- Tang Kaiyuan , OuYang Guangyao Higher internal combustion engine learning Beijing national defense industry press 2008 383
- Daniel C. Use of Half-normal plots in interpreting factorial two-level experiments Technimetrics 1 311 341
- Daniel C. Applications of statistics to industrial experiments Wiley New York