Geomechanics and Engineering

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Resonance frequency and stability of composite micro/nanoshell via deep neural network trained by adaptive momentum-based approach

  • Yan, Yunrui (College of Material science and Nanoengineering, Rice University)
  • Received : 2021.08.17
  • Accepted : 2022.01.21
  • Published : 2022.03.10
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Abstract

In the present study, the effects of thermal loading on the buckling and resonance frequency of graphene platelets (GPL) reinforced nano-composites are examined. Functionally graded (FG) material properties are considered in thickness direction for the thermal responses of the composite. The equivalent material properties are obtained using Halphin-Tsai nano-mechanical model for composite layers. Moreover, the effects of nano-scale sizes are taken into account, employing functionally modified couple stress (FMCS) parameter. In this regard, for the first time, it is demonstrated that at certain values of GPL weight fraction, thermal buckling occurs. In obtaining results of vibrational behavior, both analytical solution and deep neural network (DNN) methods are used. The DNN method needs low computational costs to predict the resonance behavior. A comprehensive parametric study is conducted to indicate the effects of several geometrical, material, and loading conditions on the vibrational and buckling behavior of cylindrical shell structures made of GPL-nanocomposites. It is shown that the effect of temperature change on the occurrence of buckling is vital while it has a negligible impact on the resonance frequency of the structure. Moreover, the size-dependency of the results is demonstrated, and it cannot be neglected in nano-scales.

Keywords

References

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