Stochastic global identification of a bio inspired self sensing composite UAV wing via wind tunnel experiments
In this work, the system design, integration, and wind tunnel experimental evaluation are presented for a bioinspired self sensing intelligent composite unmanned aerial vehicle (UAV) wing. A total of 148 micro sensors, including piezoelectric, strain, and temperature sensors, in the form of stretchable sensor networks are embedded in the layup of a composite wing in order to enable its self sensing capabilities. Novel stochastic system identification techniques based on time series models and statistical parameter estimation are employed in order to accurately interpret the sensing data and extract real time information on the coupled air flow structural dynamics. Special emphasis is given to the wind tunnel experimental assessment under various flight conditions defined by multiple airspeeds and angles of attack. A novel modeling approach based on the recently introduced Vector dependent Functionally Pooled (VFP) model structure is employed for the stochastic identification of the "global" coupled airflow structural dynamics of the wing and their correlation with dynamic utter and stall. The obtained results demonstrate the successful system level integration and effectiveness of the stochastic identification approach, thus opening new perspectives for the state sensing and awareness capabilities of the next generation of "fly by fee" UAVs.
SPIE Smart Structures and Materials Nondestructive Evaluation and Health Monitoring