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

Authors

Wenyu Zhao, Jiaze Li, Waner Lin, Yingtian Xu, Yunlong Hu, Jiahao Fan, Haoyu Wang, Yu Chen, Erzhen Pan, Zhenglong Sun, Ziya Wang

Published in

Science advances. Volume 12. Issue 34. Pages eaee7066. Aug 21, 2026. Epub Aug 21, 2026.

Abstract

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.

PMID:
42627883
Bibliographic data and abstract were imported from PubMed on 22 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.