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多轴载荷下随机纤维材料的强度

Strength of stochastic fibrous materials under multiaxial loading.

作者信息

Deogekar S, Picu R C

机构信息

Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

出版信息

Soft Matter. 2021 Jan 21;17(3):704-714. doi: 10.1039/d0sm01713b. Epub 2020 Nov 20.

Abstract

Many biological and engineering materials are made from fibers organized in the form of a stochastic crosslinked network, and the mechanics of the network controls the behavior of the material. In this work we investigate the strength of stochastic networks without pre-existing damage which fail due to crosslink rupture. Athermal networks ranging from approximately affine to strongly non-affine are subjected to multiaxial loading and the strength is evaluated using numerical models. It is observed that once the stress is normalized by the strength measured in uniaxial tension, the failure surface becomes approximately independent of network parameters. This extends the relation between strength and network parameters previously established in (S. Deogekar, M. R. Islam, R. C. Picu, Parameters controlling the strength of stochastic fibrous materials, Int. J. Solids Struct., 2019, 168, 194-202) to the multiaxial case. The failure surface depends on both first two invariants of the stress. Strongly non-affine networks behave somewhat different from the affine networks under loadings close to the hydrostatic and pure shear loading modes, while the difference disappears in the first quadrant of the principal stress space. The results are compared with experimental data from the literature.

摘要

许多生物和工程材料是由以随机交联网络形式组织的纤维制成的,网络的力学性能控制着材料的行为。在这项工作中,我们研究了没有预先存在损伤的随机网络的强度,这些网络由于交联断裂而失效。对从近似仿射到强非仿射的无热网络进行多轴加载,并使用数值模型评估强度。可以观察到,一旦应力通过单轴拉伸测量的强度进行归一化,失效面就变得近似独立于网络参数。这将先前在(S. Deogekar、M. R. Islam、R. C. Picu,《控制随机纤维材料强度的参数》,《国际固体结构杂志》,2019年,第168卷,第194 - 202页)中建立的强度与网络参数之间的关系扩展到了多轴情况。失效面取决于应力的前两个不变量。在接近静水压力和纯剪切加载模式的载荷下,强非仿射网络的行为与仿射网络有些不同,而在主应力空间的第一象限中这种差异消失。将结果与文献中的实验数据进行了比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a563/7856081/358c91ac2491/nihms-1650077-f0013.jpg

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