Design exploration and optimization of aerodynamics and radar cross section for a fighter aircraft

Abstract

The design for the conflicting requirements of low drag and low Radar Cross Section (RCS) causes difficulty in achieving an aerodynamically superior stealth aircraft. To cater the issue, a multidisciplinary design exploration and optimization framework is proposed. A shared parameterized aircraft geometry is used for high fidelity aero-stealth analysis using Computational Fluid Dynamics (CFD) and Shooting and Bouncing Rays (SBR) techniques. A surrogate model using a machine learning approach involving Gaussian Process (GP) modelling is generated for efficient and rapid design space exploration. A gradient-free metaheuristic optimization scheme using multi-objective Genetic Algorithm (GA) is employed to minimize drag and RCS. The results show that the proposed framework provides a reliable environment for the multidisciplinary design exploration of an aerial vehicle.