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A strain-rate dependent 3-D micromechanical model for finite element simulations of plain weave composite structures

机译:应变速率相关的3-D微机械模型,用于平纹编织复合结构的有限元模拟

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摘要

A new 3-D constitutive model is developed for finite element simulations of plain weave composites (PWC) structures and implemented in the explicit finite element software DYNA3D. The aim of this work is to develop a material model for PWC which incorporates their significant deformation characteristics like strain rate sensitivity, progressive failure phenomena, and nonlinearity in shear and yet is computationally efficient. The constitutive relations are obtained using micromechanical analysis of a simple representative unit cell developed earlier in [1] to predict the mechanical properties of PWC. The matrix is modeled as a viscoplastic strain-rate dependent material, thereby incorporating strain-rate sensitivity in the composite model. Nonlinearity in shear is modeled using an explicit strain dependent relation for shear moduli of the constituents. A set of strain based failure criteria and corresponding stiffness degradations is used to model the progressive failure behavior. The constituents are checked for various modes of failure at each time step and if found to have failed, their moduli are reduced based on the mode. Only fiber breakage is assumed as ultimate failure of the composite. Initially, the yarns and the matrix are assumed to be transversely isotropic and isotropic materials respectively. They become orthotropic with the advent of initial failure. Experimental results available in the literature are used to verify the model's predictions.
机译:针对平织复合材料(PWC)结构的有限元模拟,开发了一种新的3-D本构模型,并在显式有限元软件DYNA3D中实现了该模型。这项工作的目的是开发一种用于PWC的材料模型,该模型包含了其显着的变形特性,例如应变率敏感性,渐进式破坏现象和剪切非线性,但计算效率很高。本构关系是通过对[1]中先前开发的用于预测PWC力学性能的简单代表性晶胞进行微机械分析而获得的。将该基质建模为依赖粘塑性应变率的材料,从而将应变率敏感性纳入复合模型中。使用显式应变相关关系来对成分的剪切模量进行建模,以模拟剪切中的非线性。一组基于应变的破坏准则和相应的刚度退化用于对渐进破坏行为进行建模。在每个时间步检查组成部分的各种失败模式,如果发现失败,则根据该模式降低其模量。仅纤维断裂被认为是复合材料的最终破坏。最初,假定纱线和基体分别是横向各向同性和各向同性的材料。随着初始故障的出现,它们变成正交各向异性的。文献中提供的实验结果用于验证模型的预测。

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