Optimization of Blowing and Suction Control on NACA0012 Airfoil using Genetic Algorithm with Diversity Control

Active control of the flow over an airfoil is an area of heightened interest in the aerospace community. Previous research on flow control design processes heavily depended on trial and error and the designers ’ knowledge and intuition. Such an approach cannot always meet the growing demands of higher design quality in less time. Successful application of computational fluid dynamics (CFD) to this kind of control problem critically depends on an efficient searching algorithm for design optimization. CFD in conjunction with Genetic Algorithms (GA) potentially offers an efficient and robust optimization method and is a promising solution for current flow control designs. But the traditional binary GA and its operators need to be transformed or re-defined to meet the requirements of real world engineering problems. Current research has combined different existing GA techniques and proposed a real-coded “Explicit Adaptive Range Normal Distribution ” (EARND) genetic algorithm with diversity control to solve the convergence problems. First, a traditional binary-coded GA is replaced by a real-coded algorithm in which the corresponding design variables are encoded into a vector of real numbers that is conceptually closest to the real design space