Engine Operating Parameter Optimization using Genetic Algorithm

1. Engine Operating Parameter Optimization using Genetic Algorithm ECE 539 –Introduction to Artificial Neural Networks and Fuzzy Systems Final Project, Fall 2005 Yong Sun

2. Introduction Future diesel engine technologies will need to incorporate advanced combustion strategies with optimized engine operating parameters for achieving low emissions while maintaining fuel economy and power density Genetic algorithms (GA) are being used by engine researchers to optimize engine design and operating parameters to achieve these goals

3. Outline A micro-Genetic Algorithm (μGA) code was coupled with a 3D engine Computational Fluid Dynamic (CFD) code KIVA to optimize six engine operating parameters The results were used as inputs for the development of a multi-layer perceptron (MLP) configuration The MLP network can be used to predict the engine performance based on the engine operating parameters

4. Genetic Algorithm • Classification • Simple Genetic Algorithm (SGA) – large population • Micro-Genetic Algorithm (μGA) -- small population • Micro-Genetic Algorithm • μpopulation of five individuals • fewer number of total function evaluations compared to SGAs • Gen4μGA code

5. Gen4μGA code By Carroll (1996)

6. Coding

7. Coding Example Coding example – parameter F

8. Cost Function Cost (merit) function X- NOx emissions, Y- Unburned Hydrocarbon (HC) Z- Break Specific Fuel Consumption (BSFC) Several penalty functions have also been applied to the merit function based on the peak in-cylinder pressure, exhaust temperature and pressure, maximum rate of pressure rise, soot emissions misfire and the wall-film amount at Exhaust Valve Opening (EVO)

9. Reproduction • The present generation is first “mixed up” such that the order of individuals is completely random. • The fitness of individual 1 is compared with the fitness of individual 2. The individual with the higher fitness is chosen as “parent 1.” • The fitness of individual 3 is compared with the fitness of individual 4. The individual with the higher fitness is chosen as “parent 2.” • Parents 1 and 2 are used in the crossover operation. • “elitist approach” (Goldberg 1989)

10. Crossover • Single-point crossover • Multi-point crossover • Uniform corssover

11. Convergence and restarting • Convergence is defined as the progression towards chromosome uniformity • A gene may be considered to be converged, if 95% of that particular gene in the entire population shares the same value • A population is then converged when all of the genes are converged • Process can be restarted without any loss of information

12. Results and Discussion (1) • 351 generation was run to get the ‘optimum’ case • 16×16×16×32×32×8=33,554,432 possible combinations of the six parameters • GA was able to find a satisfying case with great improvement compared with the baseline case and the maximum merit was observed to not change for more than 200 generations. It is considered that an ‘optimum’ case was found

13. Results and Discussion (2) Maximum merit as a function of generation number

14. Results and Discussion (3) Comparison between the baseline and optimum case Significant improvements have been achieved

15. Multi Layer Perceptron (1) Multi-Layer Perceptron (MLP) network is setup to correlate the six inputs and three outputs 30% of the data was selected randomly and reserved for testing. Only 70% of the data was used for training 6-8-3 MLP network structure The final training error is 0.0090 and the testing error is 0.0127

16. Multi Layer Perceptron (2) • The weight matrix of the hidden layer: -0.0928 0.9782 -0.7673 0.3791 5.9811 1.4702 -1.3212 1.5729 0.3189 -0.3103 -1.8411 -4.5168 1.1668 0.0312 1.8331 -0.5381 2.1315 1.1248 -0.1184 0.4692 -0.2042 2.9580 -0.0267 -0.5908 -0.2677 3.3509 0.0241 1.0066 4.4803 1.4442 -3.8054 -0.5408 0.7064 0.6601 0.1934 -2.0629 0.7693 -2.3483 0.0138 -0.5446 -0.0094 1.7161 1.4502 -0.2288 -1.4001 -0.7772 4.8072 0.0577 -2.5117 -2.1469 2.2675 2.4541 3.2225 0.1221 -2.6403 -0.6212 The weight matrix of the output layer: 0.2365 0.3723 0.1336 0.3715 -0.4441 -0.1610 -0.1951 0.0984 -0.0809 1.3772 0.4357 -0.5307 0.0127 -0.8271 -0.3923 0.5913 0.1959 0.4667 0.5020 0.0197 -0.1090 0.0477 -0.4352 -0.1570 0.1923 0.0680 0.7139

17. Conclusion Genetic algorithm is a global optimization tool and can be used for engine design and operating parameter optimization effectively and efficiently. Optimization results can be used to train the MLP network, and the developed network can be used to predict engine performance based on the inputs of the six engine operating parameters.

18. References Carroll, D. L., “Chemical Laser Modeling with Genetic Algorithms,” AIAA Journal, 34, 338, 1996. Krishnakumar, K., “Micro-Genetic Algorithms for Stationary and Non-Stationary Function Optimization,” SPIE 1196, Intelligent Control and Adaptive Systems, 1989. Senecal, P.K., and Reitz, R.D., “Simultaneous Reduction of Engine Emissions and Fuel Consumption Using Genetic Algorithms and Multi-Dimensional Spray and Combustion Modeling,” SAE 2000-01-1890, 2000. Senecal, P.K., “Numerical Optimization Using the Gen4 Micro-Genetic Algorithm Code”, ERC Document, 2000 Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989. Homaifar, A., Lai H. Y. and McCormick, E., “System Optimization of Turbofan Engines using Genetic Algorithms,” Appl. Math. Modelling, 18, 1994.