A triboelectric airflow field sensor enables cross-scale multi-parameter sensing in meter-scale flapping-wing aircrafts.

Accurate in-situ sensing of unsteady aerodynamic parameters is essential yet challenging for closed-loop control in meter-scale flapping-wing aircraft. This task requires simultaneous monitoring of three coupled parameters: local wind speed (LWS), angle of attack (AoA), and flapping frequency (FF). Here, we report a conformal triboelectric airflow field sensor that converts airflow-induced aeroelastic vibrations into electrical signals. Its low-damping apertured cantilever design provides a broad sensing range and rich vibration features, enabling accurate aerodynamic parameter decoding with a deep-learning model. To address cross-scale discrepancies, we use transfer learning to adapt representations learned from low-cost small-scale wind tunnel data to data-limited full-scale conditions, achieving mean absolute errors of 0.04 m·s-1 for LWS, 0.07° for AoA, and 0.06 Hz for FF. Outdoor flight tests demonstrate real-time trend reconstruction and maneuver-correlated monitoring of the decoded parameters. This study provides a scalable biomimetic airflow sensing solution for future closed-loop control of meter-scale flapping-wing aircraft.