Application of evolutionary algorithms to small jet arrays for flow control

Computational Fluid Dynamics (CFD) in conjunction with an evolutionary search algorithm like a genetic algorithm (GA) potentially offers a more efficient and robust optimization method for current flow control designs. As the parameter space under investigation increases in complexity, the performance of evolutionary search algorithms should remain high and become increasingly effective compared to gradient-based methods. Based on previous work optimizing a two-jet system, this paper evaluates the performance of the EARND genetic algorithm on small arrays of blowing and suction jets on a NACA 0012 airfoil. In addition to the flow control results, emphasis is placed on the development of an efficient algorithm and efficient CFD computation to address the high computational costs associated with these problems. Possible techniques for reducing the computational cost are examined and the prospects for the evaluation of larger arrays are assessed in light of preliminary simulations of a four jet array.