• 文献标题:   Thermomechanical in-plane dynamic instability of asymmetric restrained functionally graded graphene reinforced composite arches via machine learning-based models
  • 文献类型:   Article
  • 作  者:   YANG ZC, ZHAO SY, YANG J, LIU AR, FU JY
  • 作者关键词:   defective graphene, functionally graded arch, asymmetric elastic constraint, dynamic instability, thermomechanical action, genetic programming assisted, micromechanical model
  • 出版物名称:   COMPOSITE STRUCTURES
  • ISSN:   0263-8223 EI 1879-1085
  • 通讯作者地址:  
  • 被引频次:   7
  • DOI:   10.1016/j.compstruct.2023.116709 EA JAN 2023
  • 出版年:   2023

▎ 摘  要

This paper studies the thermomechanical in-plane dynamic instability of asymmetric restrained functionally graded graphene reinforced composite (FG-GRC) arches, where graphene sheets with atom vacancy defects are distributed along the arch thickness according to a power law distribution. The temperature-dependent mechanical properties of the graphene reinforced composites are determined by a genetic programming (GP) assisted micromechanical model. The governing equations for the thermomechanical in-plane dynamic instability are derived by Hamilton's principle and solved by differential quadrature method (DQM) in conjunction with Bolotin method. Comprehensive numerical studies are performed to examine the effects of vacancy defect and graded distribution of graphene, temperature variation, load position, as well as boundary conditions on the free vibration, elastic buckling, and dynamic instability behaviors of the FG-GRC arch. Numerical results show that the structural performance of the FG-GRC arch is weakened by graphene defect and temperature rise and is significantly influenced by both graphene distribution and boundary conditions.