Identification of induced inflow factor of tiltrotor based on augmented dynamic inflow model

Abstract Aerodynamic modeling of tiltrotor aircraft is inherently limited by incomplete aerofoil data for blades constructed from multiple aerofoil sections. This data deficiency leads fundamental errors into the blade element theory (BET) inputs, resulting in inaccurate induced velocity and unsteady load predictions that compromise flight control design. To overcome this limitation, a corrective induced inflow factor k is introduced into an augmented Pitt–Peters dynamic inflow model. For fixed tilt angles, k is efficiently identified via an optimisation framework. The fmincon algorithm attains the optimal k with a computational speed 5–6 times faster than the genetic algorithm, while maintaining thrust coefficient errors within 1.26%. For continuous tilting, a direction-aware, time-varying quadratic model for k ( t ) is proposed, which uses the Fréchet distance metric to accurately reconstruct the aerodynamic hysteresis loop and eliminate spurious load predictions during rotation reversal. Validated through wind tunnel tests, the framework reduces the maximum thrust prediction error in dynamic tilting from 34.10 to 10.70%. The corrected aerodynamic model supports real-time simulation and provides a reliable foundation for advanced tiltrotor control system design.