Optimization of Aerodynamic Design of a Winged UAV Through Genetic Algorithms and Large Eddy Simulation

• Published in: International journal of aerospace engineering 2024-11, Vol.2024 (1)
• Main Authors: Ebrahimi, Mohammad Javad, Pasandidehfard, Mahmoud, Esmaeili, Ali, Moghimi Esfandabadi, Mohammad Hossein
• Format: Article
• Language: English
• Subjects: Aircraft; Artificial intelligence; Aviation; Design of experiments; Design optimization; Efficiency; Energy consumption; Finite volume method; Flow velocity; Genetic algorithms; Large eddy simulation; Neural networks; Optimization; Production increases; Reynolds number; Simulation; Software; Taguchi methods; Turbulence models; Unmanned aerial vehicles; Variables
• ISSN: 1687-5966; 1687-5974
• DOI: 10.1155/ijae/2698950

This paper focuses on the optimization of a winged UAV airframe utilizing a preliminary configuration and the combination of the Taguchi experimental design method and the evolutionary genetic optimization algorithm, along with computational fluid dynamics. Simulations were carried out based on the Reynolds‐averaged Navier–Stokes (RANS) equations and SST k ‐ ω turbulence model and the large eddy simulation (LES) for a speed of 50 m/s and a Reynolds number of 1.6 million. Optimization was conducted for six variables in a single stage, with 25 tests performed. The tested case is a winged UAV with a Lambda‐shaped airframe design named SACCON, featuring wings with a tilt angle of 53°. The objective function in this study is the ratio of lift to drag specified. A genetic algorithm is used in conjunction with an initial configuration to create a new aircraft. Ultimately, the newly designed airframe demonstrates a 14% improvement in aerodynamic efficiency compared to the default state, and the lift‐to‐drag ratio increases by 55%, successfully concluding the optimization process.