Experimentally Informed Model for Predicting Performance of Autorotating Samara Rotor

The design of micro air vehicles (MAVs) has increasingly drawn inspiration from natural fliers in pursuit of greater flight performance and efficiency. Among these, winged seeds known as samaras have gained significant interest for generating exceptionally high lift. These natural rotors achieve this by sustaining a stable leading-edge vortex (LEV) atop of the wing during autorotation, making them promising models for small-scale autorotating rotors and MAVs. Despite continued interest, a validated and computationally efficient model that can reliably predict their autorotational performance is still lacking. In response, this paper presents an experimentally informed low-order numerical model applicable to both natural samaras and artificial microrotors. The main aerodynamic framework of the model is based on Blade Element Momentum Theory, but the adopted load formulation extends to account for LEV effects, which were experimentally informed by wind-tunnel tests. Extensive measurements of autorotating maple seeds were conducted, and the results were integrated into the numerical model. The model demonstrates good agreement across a wide range of flow velocities and pitch settings. The developed model provides an experimentally validated low-order framework for predicting autorotational performance at low Reynolds numbers and offers practical utility for the aerodynamic design of biologically inspired rotors and samara-inspired MAVs.