Compression of Fibrous Assemblies: Revisiting the Stress–Density Relation

Compression of Fibrous Assemblies: Revisiting the Stress–Density Relation
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纤维组件的压缩:重新审视应力与密度关系

DOI:
10.1115/1.4056180
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发表时间:
2023
期刊:
Journal of Applied Mechanics
影响因子:
--
通讯作者:
Negi, Vineet
Negi, Vineet
中科院分区:
--
文献类型:
--
作者:
Picu, Catalin R.;Negi, Vineet

文献摘要

相似文献

许多工程材料都是由纤维制成的,并且纤维组件通常在制造过程中被压实。压缩导致纤维之间形成接触,从而导致硬化。单轴应力 S 与纤维体积分数 φ 之间的关系呈幂律形式。基于微观力学考虑的这种关系的推导将结构演化作为输入,该结构演化由网络的平均段长度 lc 与电流密度 ρ 的依赖性表示(ρ 定义为每单位网络体积的纤维总长度)。在这项工作中,我们重新审视这个问题,同时考虑到平均段长度应仅由传输负载的光纤接触来定义。我们使用卷曲纤维组件压缩的数值模拟来表明,当使用这个定义时,在足够大的应变下。纯粹的几何考虑需要这样做,并且我们观察到这适用于压实的早期阶段。在预应力网络中,密度-平均段长度缩放的格式为所有应变。这对应力和纤维体积分数之间的关系有影响。对于 ρ 与 lc 缩放,其中 φ0 是初始或参考纤维体积分数;然而,当 n = 2 时,n = 3。这些预测与文献中的实验数据进行了比较。
Many engineering materials are made from fibers, and fibrous assemblies are often compacted during the fabrication process. Compression leads to the formation of contacts between fibers, and this causes stiffening. The relation between the uniaxial stress, S, and the volume fraction of fibers, φ, is of power law form. The derivation of this relation based on micromechanics considerations takes as input the structural evolution represented by the dependence of the mean segment length of the network, lc, on the current density, ρ (ρ is defined as the total length of fiber per unit volume of the network). In this work, we revisit this problem while considering that the mean segment length should be defined exclusively by fiber contacts that transmit load. We use numerical simulations of the compression of crimped fiber assemblies to show that, when using this definition,at large enough strains. Purely geometric considerations require that, and we observe that this applies in the early stages of compaction. In pre-stressed networks, the density–mean segment length scaling is of the format all strains. This has implications for the relation between stress and the fiber volume fraction. For both ρ versus lcscalings,, where φ0is the initial or reference fiber volume fraction; however, n = 3 whenand n = 2 for. These predictions are compared with experimental data from the literature.