橡胶粘结颗粒材料粘弹性性能的试验研究与离散元模拟

发布时间：2018-04-29 21:05

本文选题：橡胶粘接颗粒材料 + 离散元　；参考：《固体力学学报》2017年05期

【摘要】：制备了颗粒规则四方排列和六方排列的橡胶粘接颗粒材料试样,实验测试了所制备试样在单向拉伸载荷下的应力松弛曲线和不同应变率时的应力应变曲线.基于所测试的应力松弛曲线,采用曲线拟合方法得到了所测试材料的宏观Burger’s粘弹性本构模型参数.采用离散元模型中单元间连结模型代表颗粒间橡胶粘接剂的作用,并基于试样的宏观Burger’s模型参数与离散元模型中细观Burger’s连结模型参数间的关系,建立了橡胶粘接颗粒材料的无厚度胶结离散元分析模型.最后采用所建立的离散元模型计算了所测试试样的松弛和拉伸力学性能.离散元预测结果与实验结果的对比表明,采用无厚度胶结离散元模型能较好的计算颗粒规则排列的橡胶粘接颗粒材料松弛和拉伸力学性能,但基于应力松弛实验拟合而来参数不能准确反应橡胶粘接剂在高应变率条件下其力学性能的应变率相关性.
[Abstract]:Rubber bonded granular materials with regular tetragonal and hexagonal particles were prepared. The stress-relaxation curves and stress-strain curves of the prepared samples under uniaxial tensile loading and at different strain rates were experimentally tested. Based on the measured stress relaxation curve, the parameters of the macroscopic Burger's viscoelastic constitutive model of the tested materials are obtained by curve fitting method. In the discrete element model, the interunit connection model is used to represent the effect of the rubber adhesive between particles, and based on the relationship between the macroscopic Burger's model parameters of the sample and the mesoscopic Burger's connection model parameter in the discrete element model. A discrete element analysis model of rubber bonded granular materials without thickness is established. Finally, the relaxation and tensile mechanical properties of the tested samples are calculated by using the established discrete element model. The comparison between the prediction results of discrete element and the experimental results shows that the relaxation and tensile mechanical properties of rubber bonded particles with regular grain arrangement can be calculated by using the discrete element model without thickness. However, based on the stress relaxation experiment fitting, the parameters can not accurately reflect the strain rate dependence of the mechanical properties of rubber adhesives under the condition of high strain rate.
【作者单位】：武汉理工大学新材料力学理论与应用湖北省重点实验室;武汉理工大学理学院工程结构与力学系;武汉理工大学材料复合新技术国家重点实验室;
【基金】：国家自然科学基金青年基金项目(11202154)资助
【分类号】：O341

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