Multi-parameter optimization of bio-inspired corrugated airfoils for micro air vehicles

This study investigates the effect of downward deflection of a bio-inspired corrugated airfoil on steady-state and dynamic properties for micro air vehicles (MAVs). An efficient data-driven optimization framework proposes to optimize the airfoil. Based on the surrogate model optimization workflow, this framework deeply integrates computational fluid dynamics simulations, neural network surrogate models, and the particle swarm optimization algorithm to systematically optimize the downward deflection angles of the airfoil's multiple troughs, successfully obtaining an optimal geometric configuration. By numerical simulations based on the static and dynamic motion trajectories of the MAV, the optimized airfoil shows superior performance than baseline airfoil. Static aerodynamic analysis indicates that the optimized airfoil achieves an 8%–135% improvement in the lift-to-drag ratio across a 0°–36° angle-of-attack range, and delays the critical stall angle from 12° to 17°. Flow mechanism analysis reveals that the performance leap stems from a more favorable pressure distribution and a more stable boundary layer, which effectively suppresses large-scale flow separation. Dynamic flapping simulation shows that optimized airfoil achieved a 6.5% increase in average lift and a 9.2% increase in average thrust than baseline airfoil over a complete cycle. These demonstrates the importance of downward deflection to the aerodynamic performance improvement of corrugated airfoils and engineering value of the proposed optimization framework in addressing multi-parameter airfoil design for MAVs.