Dynamic buckling of functionally graded graphene nanoplatelets reinforced composite shallow arches under a step central point load. (20th January 2020) - Yang, Zhicheng; Liu, Airong; Yang, Jie; Fu, Jiyang; Yang, Bin
Abstract: This paper presents dynamic buckling analysis for a functionally graded graphene nanoplatelets reinforced composite (FG-GPLRC) arch subjected to a step central point load at its center. The arch is composed of multiple composite layers reinforced with graphene nanoplatelets (GPLs) which are evenly distributed in each layer while the GPL weight fraction changes from layer to layer along the thickness direction. The effective materials properties are predicted by Halpin-Tsai micromechanics model for each GPLRC layer. Analytical solutions for symmetric limit point dynamic buckling load and anti-symmetric bifurcation dynamic buckling load for fixed and pinned FG-GPLRC arches are obtained by using energy-based methods. The critical geometric parameters governing the dynamic buckling mode switching behavior are also identified and discussed. It is found that the dynamic stability of the arch can be considerably improved by adding a small amount of GPLs as reinforcing nanofillers, and both symmetric limit point dynamic buckling and anti-symmetric bifurcation dynamic buckling can happen to pinned FG-GPLRC arch while the fixed FG-GPLRC arch can buckle in a symmetric mode only. The influences of GPLs distribution, concentration, dimension of GPL as well as the arch geometrical parameters on the dynamic buckling behavior of FG-GPLRC arches are comprehensively investigated through parametric studies. Highlights: Dynamic stability behavior of FG-GPLRC arches under a step point load is investigated. Analytical solutions for symmetric and anti-symmetric dynamic buckling load of FG-GPLRC arches are derived. Critical geometric parameters governing the dynamic buckling mode switching behavior are discussed. Significant reinforcing effect of graded GPLs is demonstrated. GPL distribution with best dynamic buckling performance is identified.