Abstract
In this paper, a robust design optimization framework is proposed with a variable fidelity Kriging model. By the use of the variable fidelity Kriging model approach, an accurate surrogate model can be constructed efficiently by the absolute values of a high-fidelity function as well as the trends obtained by low-fidelity function values. The high- and low-fidelity levels can be defined by utilizing different physical models, computational meshes and so on. The robustness of a candidate design is efficiently evaluated by a Monte Carlo simulation which is executed on the variable fidelity Kriging model. The efficiencies of robust design optimization approaches are investigated in a 2D airfoil drag minimization problem. In this problem, free-stream Mach number as well as target lift coefficient are supposed as uncertain parameters. The mean and standard deviation of drag coefficient are simultaneously minimized to obtain non-dominated robust optimal designs. The developed robust design optimization approach via the variable fidelity Kriging model is shown to be useful for efficient search of robust airfoil designs.
