Application of genetic algorithms and neural networks to unsteady flow control optimization

Evolutionary algorithms have been successfully used as a design optimization tool in several engineering optimization problems. Here, a genetic algorithm is linked with a computational fluid dynamics code in a GA-CFD system to optimize the configuration of a dual synthetic jet arrangement. The test problem is a two-dimensional NACA 0012 at a high angle of attack. The optimal configuration significantly reduces the separation region over the airfoil, yielding higher lift and lower drag. The data generated from this evolution are also used to test a possible neural network replacement for CFD computations which if successful could significantly accelerate the GA-CFD process for these types of optimizations.