Self-Sensing Self-Diagnostic Structures for Next Generation Intelligent Aerospace Systems

The objective of this work is to outline a novel unified design, sensing, fabrication, and data-driven modeling and analysis approach for future self-sensing self-diagnostic intelligent aerospace structures with state sensing and awareness capabilities. The experimental assessment of is resented for an intelligent composite UAV wing with embedded distributed micro-sensor networks. The sensor networks consist of piezoelectric, strain, and temperature sensors in order to enable the data-driven modeling and interpretation and structural health monitoring of the wing under varying flight states and uncertainty. Piezoelectric sensors are employed in two modes: (i) passive mode to sense the ambient vibration of the wing in order to model and interpret the structural dynamic response and relate it to critical aerodynamic/ aeroelastic phenomena such as stall and flutter; (ii) active mode, as both actuators and sensors to implement an active sensing acousto-ultrasound pitch-catch SHM approach. A novel modeling approach based on the recently introduced Vector-dependent Functionally Pooled (VFP) model structure is employed for the stochastic data-driven global modeling of the wing dynamics based on a series of wind tunnel experiments. In addition, the strain distribution is established under the considered flight states and critical areas of the flight envelope are identified. The obtained results demonstrate the successful integration of the micro-fabricated stretchable sensor networks with the composite materials of the wing, as well as the effectiveness of the stochastic "global" modeling and active sensing SHM approaches, proving their integration potential for the next generation of self-sensing self-diagnostic aerospace structures.