Intelligent Multi-scale Sensors for Damage Identification and Mitigation in Woven Composites for Aerospace Structural Applications

Abstract

Abstract : Combining global and local measurements at multiple physical scales was demonstrated to provide a physically based framework to estimate the location, severity and type of damage in composite airframe structures. Unique spectral signatures of fiber Bragg grating sensors were correlated to individual failure modes of representative composite laminates. A combined experimental/numerical formulation for the optimization of sensor placement for an embedded sensor network was then developed based on a cost function of component lifetime. The cost incorporated both the increase in lifetime through the identification of damage modes through the sensor responses and the decrease in lifetime through the host-sensor interactions. The optimization methodology was based on a combined theoretical/experimental approach incorporating both the experimentally driven characterization of the role of embedded sensors on the component lifetime and computational modeling of damage mechanisms and sensor-host interactions within the composite material. The optimization procedure, as a function of embedded sensor density, revealed regions where the component lifetime was increased and decreased. A specialized finite element formulation was then derived and implemented to predict how the electrical-mechanical-thermal behavior of carbon nanotube (CNT) reinforced polymer composites is affected by electron tunneling distances, volume fraction, and physically realistic tube aspect ratios.