The Digital Twin concept represents an innovative method to monitor and predict the performance of an aircraft’s various subsystems. By creating ultra-realistic multi-physical computational models associated with each unique aircraft and combining them with known flight histories, operators could benefit from a real-time understanding of the vehicle’s current capabilities. One important facet of the Digital Twin program is the detection and monitoring of structural damage. Recently, a method to detect fatigue cracks using the transformation response of shape memory alloy (SMA) particles embedded in the aircraft structure has been proposed. By detecting changes in the mechanical and/or electromagnetic responses of embedded particles, operators could detect the onset of fatigue cracks in the vicinity of these particles. In this work, the development of a finite element model of an aircraft wing containing embedded SMA particles in key regions will be discussed. In particular, this model will feature a technique known as substructure analysis, which retains degrees of freedom at specified points key to scale transitions, greatly reducing computational cost. By using this technique to model an aircraft wing subjected to loading experienced during flight, we can simulate the response of these localized particles while also reducing computation time. This new model serves to demonstrate key aspects of this detection technique. Future work, including the determination of the material properties associated with these particles as well as exploring the positioning of these particles for optimal crack detection, is also discussed.

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Developing novel techniques for monitoring structural integrity has become an important area of research in the aerospace community. One new technique exploits the stress-induced phase transformation behavior in shape memory alloy particles embedded in a structure. By monitoring changes in the mechanical and/or electromagnetic behavior of such particles, the formation or propagation of fatigue cracks in the vicinity of these particles can be detected. This work demonstrates sensory particle response to local structural damage using finite element modeling for the first time. Using an optimization method to minimize the difference between experimentally measured strain and simulated results, a good approximation of sensory particle properties can be determined and the strong sensory response of the transforming particle demonstrated. To illustrate an application of this method, a multi-scale finite element model of sensory particles embedded in the root rib of an aircraft wing is then considered. In particular, this unique model utilizes substructure modeling to maintain computational efficiency while relating globally applied loads to local structural response, allowing for the consideration of predicted particle response to crack propagation during wing loading. The effect of particle position relative to the crack tip on particle sensory response is assessed. Finally, this work demonstrates how sensory particles can be used to approximate the location of structural damage by interpolating a stress field based on the responses of multiple sensory particles in the vicinity of a propagating crack.

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