基于灵敏度的虚拟场识别超弹性本构参数

A. Tayeb, J. Cam, E. Robin, F. Canévet, M. Grédiac, E. Toussaint, X. Balandraud
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引用次数: 2

摘要

在本研究中,采用虚拟场方法(VFM)从非均质试验中识别超弹性模型的本构参数。数字图像相关(DIC)用于估计识别过程所需的位移场和应变场。考虑了两种不同的超弹性模型:Mooney模型和Ogden模型。将VFM应用于Mooney模型,由于应力相对于这些参数的线性,导致一个涉及超弹性参数的线性系统。在Ogden模型的情况下,应力是超弹性参数的非线性函数,一个合适的程序应使用来确定虚拟场,导致最佳识别。这使得辨识过程变得复杂,并影响辨识的鲁棒性。这就是为什么Marek等人(2017)最近提出的基于各向异性塑性的基于灵敏度的虚拟场方法已经成功地应用于超弹性情况的原因。得到的结果清楚地突出了这种反辨识方法在非线性系统中的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensitivity based virtual fields for identifying hyperelastic constitutive parameters
: In this study, the Virtual Fields Method (VFM) is applied to identify constitutive parameters of hyperelastic models from a heterogeneous test. Digital image correlation (DIC) was used to estimate both displacement and strain fi elds required by the identi fi cation procedure. Two different hyperelastic models were considered: the Mooney model and the Ogden model. Applying the VFM to the Mooney model leads to a linear system which involves the hyperelastic parameters due to the linearity of the stress with respect to these parameters. In the case of the Ogden model, the stress is a nonlinear function of the hyperelastic param- eters and a suitable procedure shall be used to determine virtual fi elds leading to the best identi fi cation. This complicates the identi fi cation procedure and affects its robustness. This is the reason why the sensitivity based virtual fi eld approach recently proposed in case of anisotropic plasticity by Marek et al. (2017) has been successfully implemented to be applied in case of hyperelasticity. Results obtained clearly highlight the bene fi ts of such an inverse identi fi cation approach in case of non-linear systems.
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