Abstract
In this work, the effect of pseudoelastic response of shape memory alloys (SMAs) on passive vibration isolation has been investigated. This study has been conducted by developing, modeling, and experimentally validating a SMA-based vibration isolation device. This device consists of layers of preconstrained SMA tubes undergoing pseudoelastic transformations under transverse dynamic loading. These SMA tubes are referred to as SMA spring elements in this study. To accurately model the nonlinear hysteretic response of SMA tubes present in this device, at first a Preisach model (an empirical model based on system identification) has been adapted to represent the structural response of a single SMA tube. The modified Preisach model has then been utilized to model the SMA-based vibration isolation device. Since this device also represents a nonlinear hysteretic dynamical system, a physically based simplified SMA model suitable for performing extensive parametric studies on such dynamical systems has also been developed. Both the simplified SMA model and the Preisach model have been used to perform experimental correlations with the results obtained from actual testing of the device. Based on the studies conducted, it has been shown that SMA based vibration isolation devices can overcome performance trade-offs inherent in typical softening spring-damper vibration isolation systems. This work is presented as a two-part paper. Part I of this study presents the modification of the Preisach model for representing SMA pseudoelastic tube response together with the implemented identification methodology. Part I also presents the development of a physically based simplified SMA model followed by model comparisons with the actual tube response. Part II of this work covers extensive parametric study of a pseudoelastic SMA spring-mass system using both models developed in Part I. Part II also presents numerical simulations of a dynamic system based on the prototype device, results of actual testing of the device and correlations of the experimental cases with the model predictions.
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More From: Journal of Intelligent Material Systems and Structures
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