Computational Fluid Dynamics (CFD) Modeling for Bio-Inspired Aerodynamic Optimization in Autonomous Drones

This study explores the aerodynamic benefits of bio-inspired design modifications for autonomous drones using advanced Computational Fluid Dynamics (CFD) simulations. Four bio-inspired configurations—leading-edge serrations, winglets, riblet surfaces, and curved wings—were assessed and compared against a baseline drone model to evaluate their impact on aerodynamic performance. The results indicated that all bio-inspired designs significantly enhanced lift, reduced drag, and improved overall aerodynamic efficiency. The leading-edge serration configuration achieved the highest performance gains, with a 33.6% increase in maximum lift coefficient (CL) and a 29.5% improvement in lift-to-drag ratio (CL/CD), primarily due to delayed flow separation and reduced turbulence. Winglets minimized wingtip vortices, leading to an 18.3% reduction in drag coefficient (CD) and improved lift efficiency. Riblet surfaces moderately decreased drag by streamlining boundary layer flow, while the curved wing design enhanced stability and manoeuvrability at high angles of incidence. These findings demonstrate the potential of bio-inspired designs to optimize drone performance, extending their operational range and adaptability across varying flight conditions. The study provides valuable insights for development of next-generation UAVs, offering a pathway to improved energy efficiency, flight stability, and versatility in diverse operational environments.