Small-scale and memory-dependent effects on thermoelastic damping analysis of composite microplate resonators reinforced with graphene nanoplatelets

Abstract

The extraordinary properties of graphene nanoplatelets (GPLs) have attracted great attention in the thermomechanical behaviors. Additionally, thermoelastic damping (TED), as a dominant intrinsic dissipation mechanism, is a major challenge in optimizing high-performance micro/nano-resonators. However, the classical TED models fail to explain the thermomechanical behavior considering the influences of the small-scale effect and the memory-dependent effect. To fill these gaps, the present study aims to investigate TED analysis of functionally graded (FG) microplate resonators reinforced with GPLs in the frame of the modified strain gradient theory and the fractional dual-phase-lag heat conduction model. Four patterns of GPLs distribution namely the UD, FG-O, FG-X, and FG-A pattern distributions are taken into account, and the effective material properties of the plate-type nanocomposite are evaluated based on the Halpin–Tsai model. The energy equation and the motion equation in the Kirchhoff microplate model are solved, and then, the closed-form analytical expression of TED is obtained by complex frequency technique. A detailed parametric study has been conducted to discuss the influence of the material length-scale parameter, the fractional-order parameter, and the total weight fraction of GPLs on the TED. The results demonstrated that the energy dissipation of FG microplate resonators reinforced with GPLs is determined by the small-scale effect, the memory-dependent effect, and the total weight fraction of GPLs. This article provides a theoretical approach to predict TED in the design of FG microplate resonators reinforced with GPLs with high performance.