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AIRFOIL DESIGN USING A GENETIC ALGORITHM AND AN INVERSE METHOD

AIRFOIL DESIGN USING A GENETIC ALGORITHM AND AN INVERSE METHOD

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AIAA 2003–0043 AIRFOIL DESIGN USING A GENETIC ALGORITHM AND AN INVERSE METHOD B. Allen Gardner and Michael S. Selig Department of Aeronautical and Astronautical Engineering University of Illinois at Urbana–Champaign Urbana, Illinois 61801 41st Aerospace Sciences Meeting and Exhibit 6–9 January 2003 Reno, Nevada For permission to copy or republish, contact the American Institute of Aeronautics and Astronautics 1801 Alexander Bell Drive, Suite 500, Reston, VA 20191–4344 41st Aerospace Sciences Meeting and Exhibit 6-9 January 2003, Reno, Nevada AIAA 2003-43 Copyright © 2003 by B. Allen Gardner and Michael S. Selig. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
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Airfoil Design Using a Genetic Algorithm and an Inverse Method B. Allen Gardner * and Michael S. Selig Department of Aeronautical and Astronautical Engineering University of Illinois at Urbana–Champaign Urbana, Illinois 61801 Abstract In this paper, optimal airfoil shapes are found through manipulation of the velocity distribution by a genetic algorithm. The airfoil geometries are gener- ated by an inverse method from velocity distribution parameters, and a viscous-flow analysis code is used to determine proper fitness values for candidate airfoils based on preset performance criteria. The method is compared with the more traditional approach of direct geometry manipulation for a simple single-objective aerodynamic optimization problem for a symmetric airfoil. The inverse and direct approaches are com- pared using a simple genetic algorithm and a hybrid genetic algorithm, where the hybrid method is formed by combining a simple genetic algorithm and a special- ized local search method. Finally, the method is used to design a cambered airfoil that outperforms the ex- isting state-of-the-art. Results indicate that using the design variables defining the velocity distribution in the inverse method has great potential for increasing the efficiency of airfoil shape optimization using ge- netic algorithms. Introduction In the past, several researchers have developed op- timization methods that directly adjust airfoil shapes by way of spline supports, orthogonal shape functions, linear combinations of known airfoils, or geometry per- turbations of an airfoil that is known to be close to an optimum. These direct-design approaches have been used within a wide range of optimization algorithms including classical methods 1,2 and genetic algorithms (GAs). 3–6 In their GA-based optimization method, Holst and Pulliam 3 used the PARSEC 7 method to parameter- ize airfoil geometries using 10 control variables that represent typical geometry characteristics. Using this method, broad geometry constraints can be met by * Graduate Research Assistant, 306 Talbot Laboratory. Stu- dent Member AIAA. [email protected] Associate Professor, 306 Talbot Laboratory. Senior Member AIAA. [email protected] Copyright (c) 2003 by B. Allen Gardner and Michael S.
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