scholarly journals Data-driven Techniques to Estimate Parameters in the Homogenized Energy Model for Shape Memory Alloys

2011 ◽  
Author(s):  
John H. Crews ◽  
Ralph C. Smith ◽  
Kyle M. Pender ◽  
Jennifer C. Hannen ◽  
Gregory D. Buckner
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Energy Model ◽  
Data Driven ◽  
Homogenized Energy Model

scholarly journals Data-driven techniques to estimate parameters in the homogenized energy model for shape memory alloys

2012 ◽  
Vol 23 (17) ◽  
pp. 1897-1920 ◽  
Author(s):  
John H Crews ◽  
Ralph C Smith ◽  
Kyle M Pender ◽  
Jennifer C Hannen ◽  
Gregory D Buckner
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Energy Model ◽  
Data Driven ◽  
Homogenized Energy Model

Density Function Optimization for the Homogenized Energy Model of Shape Memory Alloys

2011 ◽  
Author(s):  
John H. Crews ◽  
Ralph C. Smith
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Probability Density ◽  
Energy Model ◽  
Probability Density Functions ◽  
Density Functions ◽  
Normal Probability ◽  
Homogenized Energy Model ◽  
Log Normal ◽  
Normal Probability Density

In this paper, we present two methods for optimizing the density functions in the homogenized energy model (HEM) of shape memory alloys (SMA). The density functions incorporate the polycrystalline behavior of SMA by accounting for material inhomogeneities and localized interaction effects. One method represents the underlying densities for the relative stress and interaction stress as log-normal and normal probability density functions, respectively. The optimal parameters in the underlying densities are found using a genetic algorithm. A second method represents the densities as a linear parameterization of log-normal and normal probability density functions. The optimization algorithm determines the optimal weights of the underlying densities. For both cases, the macroscopic model is integrated over the localized constitutive behavior using these densities. In addition, the estimation of model parameters using experimental data is described. Both optimized models accurately and efficiently quantify the SMA’s hysteretic dependence on stress and temperature, making the model suitable for use in real-time control algorithms.

scholarly journals Using Shape Memory Alloys: A Dynamic Data Driven Approach

2013 ◽  
Vol 18 ◽  
pp. 1844-1850 ◽  
Author(s):  
Craig C. Douglas ◽  
Victor Calo ◽  
Derrick Cerwinsky ◽  
Li Deng ◽  
Yalchin Efendiev
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Data Driven ◽  
Dynamic Data ◽  
Data Driven Approach

scholarly journals A free energy model for thin-film shape memory alloys

2003 ◽  
Author(s):  
Jordan E. Massad ◽  
Ralph C. Smith ◽  
Gregory P. Carman
Keyword(s):  
Thin Film ◽  
Free Energy ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Energy Model ◽  
Free Energy Model
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