Efficient global optimization of vortex generators on a super critical infinite-wing using Kriging-based surrogate models

Multi-objective optimization of vortex generators (VGs) on a transonic infinite-wing is performed using computational fluid dynamics (CFD) and the multi-objective genetic algorithm (MOGA) coupled with surrogate models. VG arrangements are defined by five design variables: height, length, incidence angle, chord location, and spacing. The objective functions are to maximize lift-drag ratio at low angle of attack, to maximize lift coefficient at high angle of attack, and to shift chordwise separation location to downstream at high angle of attack. In order to evaluate these objective functions of each individual in MOGA, the ordinary Kriging surrogate model and the radial basis function (RBF)/Kriging-hybrid surrogate model are employed because CFD analysis of the wing with VGs requires a large computational time. Non-dominated solutions are classified into five clusters which have different aerodynamic characteristics. Comparing five clusters, it is revealed that the balance among three objective functions is controlled mainly by VG height, spacing, and their ratio. The solutions in each cluster have specific values of these three parameters, which identify the aerodynamic characteristics. Additionally, appropriate values of design variables for generating the vortex most efficiently are investigated.