Nonlinear electro-thermo-mechanical dynamic behavior of complexly curved GPL-reinforced panels with auxetic core

Mechanics of Advanced Materials and Structures, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 17

Published: Nov. 17, 2024

This research aims to study the nonlinear electro-thermo-mechanical dynamic responses of cylindrical, sine, and parabolic graphene platelets reinforced panels with auxetic cores piezoelectric layers. By applying higher-order shear deformation theory, viscoelastic foundation model, approximate technique for stress function, Rayleigh dissipation energy method, governing formulations are established. The natural frequency is determined explicitly, Runge-Kutta method used acquire time amplitude numerically. noticeable effects piezoelectrical layers, core, GPL parameters on mechanical thermal buckling vibration recognized from numerical investigations.

Language: Английский

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A theoretical approach for thermomechanical shock analysis of doubly curved panel with respect to geometrical and physical parameters using machine-leaning-based algorithm

Mechanics of Advanced Materials and Structures, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 20

Published: Dec. 3, 2023

In this work, for the first time, bending information of doubly curved panel under thermo-mechanical shock loading (MSL) with respect to geometrical and physical parameters via both numerically, machine-leaning-based algorithms is presented. The graphene nanoplatelets composites are used reinforce current composite along latitudinal direction. It should be note that reinforced in direction GPLs due thermo-MSL material properties structure obtained aid role mixture Halpin–Tsai micromechanics model. As well as this, GPL distribution's thermal conductivity each pattern considered high accuracy work's Three-dimension (3D) elasticity theory, linear thermo-elasticity principle, constitutive heat conduction equation, relaxation time equation present mathematical modeling work. After obtaining governing equations, equations solved numerical solution using Chebyshev–Gauss–Lobatto function, spatial Laplace methods. Deep neural networks (DNNs) a machine learning method train test results from modeling. predicting results, compared outcomes show low computational cost DNNs algorithm, can predicted instead other Finally, some suggestions improving behavior external loading.

Language: Английский

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Introducing innovative deep neural networks to predict natural frequency of the nanocomposites reinforced concrete structures under external loading

Mechanics of Advanced Materials and Structures, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 13

Published: June 6, 2024

The dynamic response of concrete structures reinforced with nanocomposites to supersonic airflow represents a critical aspect in aerospace and defense applications, necessitating accurate predictive models for enhanced structural integrity performance. In this study, we introduce innovative deep neural networks (DNNs) as novel approach predict the behavior such under conditions. Traditional modeling techniques often face challenges capturing intricate interactions between material properties, geometry, dynamics, particularly presence nanocomposite reinforcements. DNNs offer promising solution by leveraging their ability learn complex patterns nonlinear relationships from extensive datasets. This paper presents comprehensive framework developing deploying DNN-based prediction, encompassing network architecture design, training strategies, data preprocessing tailored unique characteristics nanocomposite-reinforced structures. Through series case studies comparative analyses, demonstrate effectiveness accuracy airflow, including phenomena vibration, flutter, aerodynamic instability. Furthermore, discuss potential advantages associated adoption model interpretability, computational efficiency, requirements. Finally, outline future research directions opportunities further advancing application addressing engineering beyond.

Language: Английский

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