ABSTRACT
An augmented genetic algorithm with artificial neural network is introduced as a new aerodynamic design and optimization technique. With the purpose of getting a faster algorithm, a neural network and a real coded genetic algorithm are hybridized in a new way. In this way, instead of predicting the computational fluid dynamics calculation of a candidate airfoil, a properly trained neural network is used for predicting the candidate itself. At each step of the genetic process, using the target pressure distribution as an input to the trained neural network produces an airfoil that is a candidate solution of the inverse design problem. The proposed algorithm is tested for the inverse airfoil design problem in the transonic flow case. The results indicate that the computational efficiency of the implemented algorithm is tremendously high.