有限元法
结构工程
振动
复合数
材料科学
屈曲
复合材料
工程类
声学
物理
作者
Dan Luo,Zhong Yifeng,Senbiao Xi,Zheng Shi
标识
DOI:10.1016/j.tws.2021.108503
摘要
In this article, to remove the periodicity requirement in the thickness direction of plain-woven composite plates with finite thickness, a novel equivalent plate model (2D-EPM) based on the variational asymptotic method was established to study the static and dynamic behavior of plain-woven composite plates under different boundary and load conditions. The effective plate properties were obtained by the constitutive modeling of the unit cell, and inputted into the 2D-EPM to perform static and dynamic analysis. The accuracy and computational efficiency of the equivalent model were verified by comparing with the static and dynamic results of a three-dimensional finite element model (3D-FEM). The influence of the geometric parameters (yarn width, height, and spacing) and yarn laying angle on the equivalent stiffness and vibration characteristics of the plate were also examined. The results of the proposed model more closely with the experimental data than those of the 3D-FEM. This was possibly because the periodic boundary conditions in the thickness direction overestimated the stiffness of the plate. In addition, the local field distributions within the unit cell were well captured. The non-interlaced parts of the yarns were most likely to fail or become damaged. Overall, this research work has shown the ability of the 2D-EPM to predict the static displacement, global buckling and free vibration of plain-woven composite plates with finite thickness. • A 2D-EPM of PWCP was established and verified by comparing with the 3D-FEM. • The finite thickness effect of plain-woven composite plate can be accurately simulated. • The effects of the yarn laying angle and geometric parameters on the equivalent stiffness are analyzed. • The pointwise anisotropy within the unit cell of the PWCP is well captured. • The DOFs of the 2D-EPM are greatly reduced, resulting in high calculation efficiency.
科研通智能强力驱动
Strongly Powered by AbleSci AI