Genetic Algorithms In Aerodynamic Optimization Design Study

Genetic algorithm is a global search algorithm based upon the mechanics of natural evolution. It shows its robustness in handling some complicated optimization problems. However, genetic algorithm requires a large number of evaluations of the objective function which normally involves executing a numerical solver of the governing equations. Therefore aerodynamic optimization design using genetic algorithm is computationally inefficient, even infeasible because of excessive computation cost. The purpose of the research here is to improve the performances of genetic algorithm especially when it is used in aerodynamic optimization design. The following work is performed: 1. The standard genetic algorithm is modified to form the genetic optimization model base on real number encoding. In the model established here, nonlinear ranking selection and dynamic penalty strategy are introduced. 2. Two types of hybrid genetic algorithms, loose hybrid genetic algorithm and tight hybrid genetic algorithm, are established by combining genetic algorithm with flexible tolerance polyhedron method. Due to their high computation efficiency, the hybrid genetic algorithms are suitable for dealing with aerodynamic optimization design with complex configuration. 3. Pareto genetic algorithm is formed to handle multi-objective optimization problems by combining genetic algorithm with pareto strategy. Compared with conventional approach, pareto genetic optimization are capable of dealing with multi-objective optimization problems more conveniently and more efficiently. 4. 2-D and 3-D Euler equations solvers are taken as the aerodynamic analysis tools for airfoil and wing. Axelson抯 engineering Abstract evaluation method is taken as the aerodynamic analysis tool for aircraft. 5. To analyze quantitatively the performances of genetic algorithms used in aerodynamic optimization design and the effects of the skills introduced, the numerical test planform for genetic optimization models is set up on Microsoft Fortran Powerstation. As applications, several aerodynamic optimization designs for airfoil, wing and aircraft are carried out to testify the adaptation of genetic optimization models. It can be concluded that the genetic algorithms developed in this paper have better performances when it is used in aerodynamic optimization designs of airfoil, wing and aircraft. Moreover, much higher efficiency will make genetic algorithms suitable for aerodynamic optimization design with more complex configuration.