18 April 2016 Analysis of shape memory alloy sensory particles for damage detection via substructure and continuum damage modeling
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Abstract
The ability to monitor and predict the structural health of an aircraft is of growing importance to the aerospace industry. Currently, structural inspections and maintenance are based upon experiences with similar aircraft operating in similar conditions. While effective, these methods are time-intensive and unnecessary if the aircraft is not in danger of structural failure. It is imagined that future aircraft will utilize non-destructive evaluation methods, allowing for the near real-time monitoring of structural health. A particularly interesting method involves utilizing the unique transformation response of shape memory alloy (SMA) particles embedded in an aircraft structure. By detecting changes in the mechanical and/or electromagnetic responses of embedded particles, operators could detect the formation or propagation of fatigue cracks in the vicinity of these particles. This work focuses on a finite element model of SMA particles embedded in an aircraft wing using a substructure modeling approach in which degrees of freedom are retained only at specified points of connection to other parts or the application of boundary conditions, greatly reducing computational cost. Previous work evaluated isolated particle response to a static crack to numerically demonstrate and validate this damage detection method. This paper presents the implementation of a damage model to account for crack propagation and examine for the first time the effect of particle configuration and/or relative placement with respect to the ability to detect damage.
Conference Presentation
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Brent R. Bielefeldt, Brent R. Bielefeldt, A. Amine Benzerga, A. Amine Benzerga, Darren J. Hartl, Darren J. Hartl, } "Analysis of shape memory alloy sensory particles for damage detection via substructure and continuum damage modeling", Proc. SPIE 9800, Behavior and Mechanics of Multifunctional Materials and Composites 2016, 98000B (18 April 2016); doi: 10.1117/12.2219260; https://doi.org/10.1117/12.2219260
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