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.
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