Surrogate-based multi-objective optimization and data mining of vortex generators on a transonic infinite-wing

Multi-objective optimization and data mining of vortex generators (VGs) on a transonic infinite-wing was performed using computational fluid dynamics (CFD), surrogate models, and a multi-objective genetic algorithm (MOGA). VGs arrangements were defined by five design variables: height, length, incidence angle, spacing, and chord location. The objective functions which should be maximized were three: lift-drag ratio at low angle of attack, lift coefficient at high angle of attack, and chordwise separation location at high angle of attack. In order to evaluate these objective functions of each individual in MOGA, the response surface methodology with Kriging model and the modified version of it was employed because CFD analysis of the wing with VG requires a large computational time. Two types of data mining method: analysis of variance (ANOVA) and self-organizing map (SOM), were applied to the result of the optimization. It was revealed by ANOVA that the ratio of spacing to height and the incidence angle had significant influences to maximizing each objective function. By using SOM, VG designs were split into four types which have different aerodynamic characteristics respectively. The appropriate values of parameters were identified by SOM.