Influence of multi-walled carbon nanotubes reinforced honeycomb core on vibration and damping responses of carbon fiber composite sandwich shell structures

In the present study, vibration and damping characteristics of the multi-walled carbon nanotubes (MWCNT) reinforced honeycomb embedded sandwich composite shell structures have been investigated numerically using finite element (FE) method. The efficacy of the FE method by deriving the governing equations using higher order theory is verified by comparing the natural frequencies assessed using ABAQUS 3D FE model. The influence of MWCNT reinforcement, support condition, and radius of curvature on the dynamic performance of honeycomb sandwich shell with carbon fiber reinforced polymer (CFRP) composite face sheets are explored. In addition, the optimal ply orientations of the various configurations of CFRP sandwich shells with MWCNT/GFRP honeycomb are identified using the developed FE model coupled with genetic algorithm (GA) to enrich the natural frequencies and loss factors. Further, it can be observed that the reinforcement of MWCNTs in honeycomb core, geometry of shell structure, and optimal ply orientations significantly influences the natural frequencies and loss factors of the sandwich composite shell structures.

Automated summary

In this study, the vibration and damping characteristics of multi-walled carbon nanotubes (MWCNT) reinforced honeycomb embedded sandwich composite shell structures were investigated numerically using finite element (FE) method. The efficacy of the FE method was verified by comparing the natural frequencies assessed using ABAQUS 3D FE model. The study explored the influence of MWCNT reinforcement, support condition, and radius of curvature on the dynamic performance of honeycomb sandwich shell with carbon fiber reinforced polymer (CFRP) composite face sheets. Additionally, the optimal ply orientations of various configurations of CFRP sandwich shells with MWCNT/GFRP honeycomb were identified using the developed FE model coupled with genetic algorithm (GA) to enhance the natural frequencies and loss factors.