论文信息 - Improving Genetic Algorithm Efficiency with an Artificial Neural Network for Optimization of Low Reynolds Number Airfoils

Improving Genetic Algorithm Efficiency with an Artificial Neural Network for Optimization of Low Reynolds Number Airfoils

In this paper, we employ a genetic algorithm (GA) for shape optimization of low Reynolds number airfoils for generating maximum lift for Micro-Air-Vehicle (MAV) applications. The computational efficiency of GA is significantly enhanced with an artificial neural network (ANN). The commercially available software FLUENT is used for calculation of the flow field. It is shown that the combined GA/ANN optimization technique is capable of accurately and efficiently finding globally optimal airfoils.

[1] D. E. Goldberg,et al. Genetic Algorithms in Search , 1989 .

[2] Carl Häggmark,et al. Investigations of disturbances developing in a laminar separation bubble flow , 2000 .

[3] Antony Jameson,et al. Aerodynamic design via control theory , 1988, J. Sci. Comput..

[4] Ethem Alpaydin,et al. Introduction to machine learning , 2004, Adaptive computation and machine learning.

[5] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6] Stephen Grossberg,et al. Contour Enhancement, Short Term Memory, and Constancies in Reverberating Neural Networks , 1973 .

[7] Thomas J. Mueller,et al. Fixed and Flapping Wing Aerodynamics for Micro Air Vehicle Applications , 2001 .

[8] J. Anderson,et al. Fundamentals of Aerodynamics , 1984 .