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Application of a Genetic/Artificial Neural Network Algorithm for Optimization of Low Reynolds Number Airfoils

M. BELLMAN, B. MORGAN, R. AGARWAL

Abstract


In this paper, we employ a genetic algorithm (GA) for shape optimization of low Reynolds number airfoils for generating maximum lift for Unmanned-Air-Vehicle (UAV) 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.

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