Date Available


Year of Publication


Document Type





Mechanical Engineering

First Advisor

Raymond P. Lebeau

Second Advisor

George P. Huang


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 realcoded 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. Second, to address the convergence speed problem, an additional normal distribution scheme is added into the basic GA in order to monitor the global optimization process; meanwhile, design parameters boundaries are explicitly updated to eliminate unnecessary evaluations (computation) in un-promising areas to balance the workload between the global and local searching process. Third, during the initial 20% evolution (search process), the diversity of the individuals within each generation are controlled by a formula in order to conquer the problem of preliminary convergence to the local optimum. In order to better understand the two-jet control optimization results and process, at first, a single jet with a width of 2.5% the chord length is placed on a NACA 0012 airfoils upper surface simulating the blowing and suction control under Re=500,000 and angle of attack 18 degree. Nearly 300 numerical simulations are conducted over a range of parameters (jet location, amplitude and angle). The physical mechanisms that govern suction and blowing flow control are determined and analyzed, and the critical values of suction and blowing locations, amplitudes, and angles are discussed. Moreover, based on the results of single suction/blowing jet control on a NACA 0012 airfoil, the design parameters of a two-jet system are proposed. Our proposed algorithm is built on top of the CFD code, guiding the movement of two jets along the airfoils upper surface. The reasonable optimum control values are determined within the control parameter range. The current study of Genetic Algorithms on airfoil flow control has been demonstrated to be a successful optimization application.



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