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一维伊夫申-彭斯形状记忆合金本构模型的灵敏度与不确定性分析

Analysis of One-Dimensional Ivshin-Pence Shape Memory Alloy Constitutive Model for Sensitivity and Uncertainty.

作者信息

Islam A B M Rezaul, Karadoğan Ernur

机构信息

Robotics & Haptics Lab, School of Engineering & Technology, Central Michigan University, Mount Pleasant, MI 48859, USA.

出版信息

Materials (Basel). 2020 Mar 24;13(6):1482. doi: 10.3390/ma13061482.

Abstract

Shape memory alloys (SMAs) are classified as smart materials due to their capacity to display shape memory effect and pseudoelasticity with changing temperature and loading conditions. The thermomechanical behavior of SMAs has been simulated by several constitutive models that adopted microscopic thermodynamic or macroscopic phenomenological approaches. The Ivshin-Pence model is one of the most popular SMA macroscopic phenomenological constitutive models. The construction of the model requires involvement of parameters that possess inherent uncertainty. Under varying operating temperatures and loading conditions, the uncertainty in these parameters propagates and, therefore, affects the predictive power of the model. The propagation of uncertainty while using this model in real-life applications can result in performance discrepancies or failure at extreme conditions. In this study, we employed a probabilistic approach to perform the sensitivity and uncertainty analysis of the Ivshin-Pence model. Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods were used to perform the sensitivity analysis for simulated isothermal loading/unloading at various operating temperatures. It is evident that the model's prediction of the SMA stress-strain curves varies due to the change in operating temperature and loading condition. The average and stress-dependent sensitivity indices present the most influential parameters at several temperatures.

摘要

形状记忆合金(SMA)因其能够随温度和加载条件变化呈现形状记忆效应和伪弹性而被归类为智能材料。SMA的热机械行为已通过多种本构模型进行模拟,这些模型采用微观热力学或宏观唯象学方法。伊夫申 - 彭斯模型是最流行的SMA宏观唯象学本构模型之一。该模型的构建需要涉及具有内在不确定性的参数。在不同的工作温度和加载条件下,这些参数的不确定性会传播,从而影响模型的预测能力。在实际应用中使用该模型时,不确定性的传播可能导致性能差异或在极端条件下失效。在本研究中,我们采用概率方法对伊夫申 - 彭斯模型进行敏感性和不确定性分析。索博尔方法和扩展傅里叶振幅敏感性测试(eFAST)方法用于对不同工作温度下的模拟等温加载/卸载进行敏感性分析。很明显,由于工作温度和加载条件的变化,该模型对SMA应力 - 应变曲线的预测会有所不同。平均敏感性指数和与应力相关的敏感性指数显示了在几个温度下最具影响力的参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49f7/7143482/1f17ebf8f2b9/materials-13-01482-g001.jpg

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