Design Rule Extraction Using Multi-Fidelity Surrogate Model for Unmanned Combat Aerial Vehicles

Abstract

Due to the instability of the pitching moment induced by the strong vortical flow at moderate to high angles of attack in the unmanned combat aerial vehicle (UCAV) configuration, previous design studies focused primarily on low-speed flight conditions where the longitudinal stability is a serious issue. Furthermore, because of the limitation of computational cost, univariate analyses based on low-fidelity flow solvers are performed to investigate the relationships between design variables and quantities of interest. In this study, since the high-speed condition may occupy a high proportion in the UCAV mission, it was additionally considered through multi-objective optimization. A high-fidelity flow solver is used for both low-speed and high-speed conditions to accurately capture the vortical flow structure, and corresponding computational costs are relieved by multi-fidelity surrogate modeling. Based on this surrogate model, design rules are extracted, and the effects of design variables are investigated through univariate and multivariate data mining techniques. At last, universal design rules that can be referenced regardless of the parameterization method are visualized in an airfoil shape using proper orthogonal decomposition. The time-efficient design rule extraction framework and the approach to analyzing extracted design rules presented in this study can be extended to any engineering field.