Internal loops in superelastic shape memory alloy wires under torsion - Experiments and simulations/predictions

INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES

卷:51

Article

Rao, Ashwin¹  Ruimi, Annie²  Srinivasa, Arun R.^1,2 

[1] Texas A&M Univ, Dept Mech Engn, College Stn, TX 77843 USA

[2] Texas A&M Univ Qatar, Dept Mech Engn, Doha, Qatar

关键词: Shape memory alloy (SMA);    Superelastic effect;    Internal loops;    Torsion;    Design;    Return point memory (RPM);    Hysteresis;    Preisach;    Thermomechanical;   

DOI  :  10.1016/j.ijsolstr.2014.09.002

来源: Elsevier

【 摘 要 】

Understanding torsional responses of shape memory alloy (SMA) specimens under partial or fully transformed cases with internal loops is of particular importance as the entire response might not be always utilized and only a portion of the entire response (internal loop) might be of significance to designers. In this work, we present experimental results of large complex loading and unloading torsional cycles which were conducted on superelastic SMA wires, under isothermal conditions with the purpose of elucidating the torsional internal loop response during loading and unloading. Such data hereto has not been available in open literature. Utilizing this data, we model the torsional response of superelastic SMA wires subjected to various loading and unloading situations that can result in different extents of transformation. A thermodynamically consistent Preisach model (Rao and Srinivasa, 2013) captures such complex internal loops with a high degree of precision by modeling driving force for phase transformation vs. volume fraction of martensite relationships. This approach is different from capturing purely phenomenological stress strain or stress temperature Preisach models. The thermodynamic approach utilized here has broader predictive capability. The model predictions indicate good agreement with the internal loop structures even though only the outer loop information was used for model calibration. The addition of a single inner loop information for model calibration greatly improves the predictions. (C) 2014 Elsevier Ltd. All rights reserved.

【 授权许可】

Free