Dynamics of graphene origami-enabled auxetic metamaterial beams via various shear deformation theories

International Journal of Engineering Science, Journal Year: 2024, Volume and Issue: 203, P. 104123 - 104123

Published: July 27, 2024

Although auxetic metamaterials exhibit unique and unusual mechanical properties, such as a negative Poisson's ratio, their mechanics remains poorly understood. In this study, we model graded beam fabricated from graphene origami-enabled investigate its dynamics the perspective of different shear deformation theories. The metamaterial is composed multiple layers metamaterials, where content origami varies through layered thickness; both property other properties are varied in manner, which effectively be approximated via micromechanical models. Euler-Bernoulli, third-order, higher-order deformable refined theories adopted to continuous system. Following this, governing motion equations derived using Hamiltonian principle then numerically solved weighted residual method. obtained results provide comprehensive understanding how distribution pattern, folding degree, utilisation influence dynamic behaviour beam.

Language: Английский

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A machine learning algorithm-enhanced mathematical simulation based on Carrera unified formulation to estimate fundamental frequency of the FG origami-enabled metamaterial composite system surrounded by auxetic concrete foundation

Mechanics of Advanced Materials and Structures, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 24

Published: Jan. 23, 2025

Language: Английский

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Non-linear mechanics of geometrically imperfect graphene origami-enabled auxetic metamaterial third-order beam structures

International Journal of Non-Linear Mechanics, Journal Year: 2025, Volume and Issue: unknown, P. 105047 - 105047

Published: Feb. 1, 2025

Language: Английский

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Recent advances in graphene origami-enabled auxetic metamaterial structures

Engineering Structures, Journal Year: 2025, Volume and Issue: 333, P. 120203 - 120203

Published: March 31, 2025

Language: Английский

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Transient bending analysis of the origami enriched metamaterial system: Application of deep neural networks in the mathematical framework for metamaterial problems

Mechanics of Advanced Materials and Structures, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 15

Published: May 21, 2024

This study investigates the transient bending behavior of origami-enriched metamaterial systems using advanced computational methods. The structural dynamics these are analyzed through a mathematical framework tailored for problems. Specifically, deep neural networks (DNNs) employed to model and predict responses metamaterials. application DNNs facilitates efficient accurate characterization complex deformation mechanisms inherent in structures. In framework, thick plate theory differential quadrature method, governing equations problem presented composite obtained solved, respectively. Through extensive numerical simulations validation against data, effectiveness, reliability proposed approach demonstrated. results show that by increasing blast index loading parameter, normal stress each direction first increases from lower middle surface then decreases topper one. findings offer valuable insights into systems, contributing development innovative design strategies applications.

Language: Английский

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Artificial neural network-enhanced mathematical simulation based on Carrera unified formulation for dynamic analysis and structural integrity assessment of advanced nanocomposites reinforced tunnel structures

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

Published: Oct. 24, 2024

Language: Английский

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Robust design of multimodal shunt circuits for subsonic flutter control and energy harvesting in main part of intelligent coal mining component: A mathematical approach using Carrera unified formulation

Mechanics of Advanced Materials and Structures, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 21

Published: May 22, 2025

Language: Английский

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Application of machine learning algorithm and Carrera unified formulation in thermal buckling analysis of a functionally graded graphene origami enabled auxetic metamaterial sandwich plate with an auxetic concrete foundation

Mechanics of Advanced Materials and Structures, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 16

Published: Nov. 9, 2024

This study presents a comprehensive thermal buckling analysis of sandwich plates composed functionally graded graphene origami-enabled auxetic metamaterial (FG-GOEAM) face sheets on an concrete foundation, using Carrera's unified formulation (CUF) as the theoretical framework. FG-GOEAM materials are emerging advanced composites, combining exceptional mechanical resilience, tunable behavior, and high stability, making them suitable for extreme environments. By employing CUF, powerful adaptable modeling approach, this work accurately captures complex interactions within structure under loads, incorporating both material gradation properties. To further enhance precision efficiency analysis, deep neural network (DNN) is developed machine learning algorithm to predict critical temperature differences, based dataset generated through mathematics simulation. The DNN model demonstrates excellent predictive capability, validated by close alignment between its estimates CUF results, thus reducing computational costs while maintaining accuracy. Parametric studies conducted assess effects gradation, aspect ratios, foundation properties performance. results highlight superior stability structures potential DNNs serve reliable, computationally efficient tools structural analysis. provides novel, integrated framework high-fidelity prediction in paving way broader applications engineering fields requiring lightweight, thermally stable structures.

Language: Английский

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Application of the Carrera unified formulation and machine learning for vibration analysis of composite structures as the main part of construction robotics

Mechanics of Advanced Materials and Structures, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 23

Published: Jan. 23, 2025

Language: Английский

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Application of innovative SVM-PSO-GA algorithm to study vibrations of improved perovskite solar cells

Mechanics of Advanced Materials and Structures, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: July 31, 2024

This study investigates the vibrations of graphene oxide powders (GOPs) reinforced perovskite solar cells surrounded by an elastic foundation using both mathematical modeling and innovative machine learning algorithms. The incorporation GOPs into matrix enhances mechanical properties stability cells, which are crucial for their durability efficiency. analysis is conducted through application Hamilton's principle, providing a robust theoretical framework deriving governing equations motion. An analytical method employed to solve these equations, allowing accurate prediction vibrational behavior cells. effects various parameters, including stiffness concentration GOPs, systematically examined. presents Support Vector Machine (SVM)-Particle Swarm Optimization (PSO)-Genetic Algorithm (GA) analyze datasets. SVM-PSO-GA algorithm enhance predictive accuracy. integrated approach leverages strengths each model predict results highlight algorithm's effectiveness in capturing complex interactions optimizing design valuable insights improving performance practical applications.

Language: Английский

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