Three kinds of training algorithms for efficient global optimization(EGO) method are investigated.A kind of training algorithm based on low-discrepancy sequences is proposed to reduce randomness of EGO method.Performance of EGO method depends on a good training algorithm.Since training problems in EGO are non-convex and non-smooth,meta-heuristic algorithms,random algorithm and low-discrepancy sequences are chosen to address five benchmark optimization problems and two aerodynamic shape optimization problems.In these problems,differential evolution algorithm was found the best in meta-heuristic algorithms.Training algorithm based on low-discrepancy sequences can effectively reduce randomness of EGO method and Faure sequence has the best performance.

Navier-Stokes equations are solved numerically to simulate vortex motion in separated flows using detached-eddy simulation (DES) method in Saplart-Allmaras one equation model. A finite volume scheme is employed. Spatial discretization is performed with Jameson central scheme. Time integration is implemented by a dual time-stepping approach. Vortex structures of flow over a circular cylinder or a stall airfoil simulated agree well with physical analyses and experimental data.