Inertia-Based Identification of Elastic Anisotropic Properties for Materials Undergoing Dynamic Loadings Using the Virtual Fields Method and Heterogeneous Impact Tests

Jiawei Fu, Kaiyu Zhu, X. Nie, Yuyuan Tang, Zefei Yang, L. Qi
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引用次数: 3

Abstract

Abstract The characterization of material anisotropic properties at high strain rates remains a challenging work due to the limitations of the conventional dynamic testing methods. In this study, a novel inverse identification strategy is proposed based on the dynamic virtual fields method (VFM) to enable a simultaneous identification of the anisotropic constitutive parameters from a single high speed impact test of a non-uniform specimen. This is implemented by taking advantage of the heterogeneous full-field strain and inertial acceleration data, in which the restrictions of one-dimensional stress wave propagation and homogeneous deformation states for the conventional methods are released. Specifically, the dynamic VFM-based identification algorithm is developed first. Then, several virtual impact tests with different specimen configurations are designed to provide distinct stress/strain distributions. Next, the simulated full-field strain and acceleration data are utilized to extract the input target orthotropic stiffness components. The results show that the identification accuracy of the anisotropic parameters is highly dependent on the heterogeneity of the stress/strain distributions. Also, the identification results are unstable for different time steps but can be significantly improved using a minimization algorithm on multiple time steps. Finally, the influences of noise, impact speed and loading mode are analyzed in the sensitivity study.
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基于惯性的动态载荷下材料弹性各向异性的虚拟场识别和非均质冲击试验
由于传统动态测试方法的局限性,表征材料在高应变速率下的各向异性特性仍然是一项具有挑战性的工作。本文提出了一种基于动态虚拟场法(VFM)的反识别策略,实现了非均匀试件高速冲击试验中各向异性本构参数的同时识别。该方法利用非均质全场应变和惯性加速度数据,消除了传统方法对一维应力波传播和均匀变形状态的限制。具体而言,首先开发了基于vfm的动态识别算法。然后,设计了几种不同试样配置的虚拟冲击试验,以提供不同的应力/应变分布。其次,利用模拟的全场应变和加速度数据提取输入目标正交各向异性刚度分量。结果表明,各向异性参数的识别精度高度依赖于应力/应变分布的非均质性。此外,在不同的时间步长下,识别结果是不稳定的,但在多时间步长上使用最小化算法可以显著改善识别结果。最后,在灵敏度研究中分析了噪声、冲击速度和加载方式的影响。
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