Functionally graded nonlocal thermoelastic nanobeam with memory-dependent derivatives

Abstract

The purpose of this study is to investigate vibrations in 2D functionally graded nanobeams (FGN) with memory-dependent derivatives. A sinusoidal variation of temperature is assumed. The dimensionless expressions for axial displacement, thermal moment, lateral deflection, strain and temperature distribution are found in the transformed domain using Laplace Transforms, and the expressions in the physical domain are derived by numerical inversion techniques. The nanobeam is simply supported at the both ends and have constant temperatures. The FGN is a non-homogenous composite structure with constant structural variations along with the layer thickness, changing from ceramic at the bottom to metal at the top. Adding non-local MDD to thermoelastic models opens up new possibilities for the study of thermal deformations in solid mechanics. The effect of different kernel functions and periodic frequency of thermal vibration is illustrated graphically for lateral deflection, axial displacement, strain, temperature, and thermal moment.Article highlightsA novel model of vibrations in a functionally graded nanobeams is presented.The medium is subjected to sinusoidal variation of temperature.Dynamic response of memory dependent derivative theory of thermoelasticity and non-local parameter is investigated.The effects of kernel functions and periodic frequency of thermal vibration on all physical fields are investigated and shown graphically.