Size-dependent linear and nonlinear vibration of functionally graded CNT reinforced imperfect microplates submerged in fluid medium

Hoseinzadeh, Mohammad; Pilafkan, Reza; Maleki, Vahid Arab

doi:10.1016/j.oceaneng.2022.113257

Cited by 21 publications

(2 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These parameters are determined via trial and error. In this research, various configurations of layers and nodes, such as [10,10], [32,32], [32,32,32,32], [2,4,8,16,32,64], and [64, 32,16,8,4,2]were considered for constructing the NN, and the network was trained with every configuration. The best results corresponded to the [32,32,32,32,32,32] configuration; hence, this layout was used.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…They solved the nonlinear governing equations of microbeams by employing the Galerkin and step-by-step linearization methods. Hoseinzadeh et al [8] applied first-order shear deformation theory in conjunction with the Galerkin and multiple time scales methods to analyze linear and nonlinear vibrations of FG microplates reinforced with carbon nanotubes immersed in a fluid medium. Van Vinh and Tounsi [9] investigated the free vibration of FG sandwich nanoplates under the effect of spatial variation of the nonlocal parameter.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Size-dependent bending and buckling of two-dimensional functionally graded microplates, an artificial neural network approach

Taghizadeh,

Mahdavian,

Askari

2023

Phys. Scr.

View full text Add to dashboard Cite

The main goal of the present study is to focus on the application of artificial neural network (ANN) in predicting the bending and buckling behaviors of size-dependent small-scale micro-plates. To this end, the recently introduced thick microplates made of two-dimensional functionally graded materials (2D-FGM) with simply supported boundary conditions are considered. Adopting the modified couple stress and third-order shear deformation theories together with the Ritz method, the bending and buckling ANN models, including nine and ten input variables, are trained by two databases containing 8842 and 9980 random data for each of these two analyses, respectively. The selected network has six hidden layers, each of them contains 32 nodes. Employing the present ANN model, whose determination coefficient is 98.6%, the variation of microplate deflection and its buckling load versus the input variables are investigated. It is observed that despite the long run-time and the complexities involved in the solution procedures associated with the governing equilibrium and eigenvalue equations, the ANN models enjoy fast and accurate predictions. The rest of the present work is devoted to optimizing the geometric and material variables of a 2D-FGM microplate with respect to the buckling load via the genetic algorithm (GA) method whose fitness function is evaluated by the trained ANN. The results reveal that the combination of the ANN and GA can be treated as a promising tool for optimizing the geometric and material parameters of a 2D-FGM microplate regarding its buckling load.

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Size-dependent bending and buckling of two-dimensional functionally graded microplates, an artificial neural network approach

Taghizadeh,

Mahdavian,

Askari

2023

Phys. Scr.

View full text Add to dashboard Cite

show abstract

Thermomechanical bending of functionally graded carbon nanotubes reinforced composite plate by meshless method

Li,

Lin,

Guan

et al. 2024

Polymer Composites

View full text Add to dashboard Cite

Functionally graded (FG) carbon nanotubes (CNTs) reinforced composite possessing continuously varied material properties have received extensive concern in the area of aviation, automotive, military, and spaceflight. The nonlinear bending of FG CNT composite is performed by developing a novel meshless model on the base of Smoothed Particle Hydrodynamics (SPH) method. The equivalent temperature relative material properties of composite is established in the light of the extended rule of mixture model. Different gradient dispersions of CNTs are taken into account and modeled. The governing equations are explored using the Mindlin theory and discretized though a symmetric SPH method with high prediction ability. The developed model is verified by analyzed several examples and compared to the solution obtained in literature. Influences of CNT dispersion, content, plate aspect ratio, boundary conditions and lamination angle on the bending response are elaborated. The present study provides an alternative potential method to solve the mechanical performances of FG CNT composites based on the meshless SPH formulation.Highlights Thermo‐mechanics of FG‐CNTRC is firstly solved by SPH method. The model is verified by solving the benchmarks in literature. Nonlinear bending behaviors of FG‐CNTRC are demonstrated. The minimum deflection take place in FG‐X type composite. For laminate plate, the smallest deformation occurs in 0° CNTRC.

show abstract

Vibration analysis of the radio frequency microelectromechanical system microbeam reinforced with copper nanoparticles

Kaleybar,

Zehforoosh,

Zavvari

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

Size-dependent linear and nonlinear vibration of functionally graded CNT reinforced imperfect microplates submerged in fluid medium

Cited by 21 publications

References 40 publications

Size-dependent bending and buckling of two-dimensional functionally graded microplates, an artificial neural network approach

Size-dependent bending and buckling of two-dimensional functionally graded microplates, an artificial neural network approach

Thermomechanical bending of functionally graded carbon nanotubes reinforced composite plate by meshless method

Vibration analysis of the radio frequency microelectromechanical system microbeam reinforced with copper nanoparticles

Contact Info

Product

Resources

About