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Computationally Efficient Analysis of SMA Sensory Particles Embedded in Complex Aerostructures Using a Substructure Approach

[+] Author Affiliations
Brent Bielefeldt, Darren Hartl

Texas A&M University, College Station, TX

Jacob Hochhalter

NASA Langley Research Center, Hampton, VA

Paper No. SMASIS2015-8975, pp. V001T02A007; 10 pages
  • ASME 2015 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Volume 1: Development and Characterization of Multifunctional Materials; Mechanics and Behavior of Active Materials; Modeling, Simulation and Control of Adaptive Systems
  • Colorado Springs, Colorado, USA, September 21–23, 2015
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-5729-8
  • Copyright © 2015 by ASME


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.

Copyright © 2015 by ASME



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