The effect of an auxetic core layer and symmetric FGM face layers on the 3D wave propagation response of sandwich nanoplates

IF 2.5 3区 工程技术 Q2 MECHANICS Archive of Applied Mechanics Pub Date : 2025-02-19 DOI:10.1007/s00419-025-02772-0
Mustafa Eroğlu, İsmail Esen, Mehmet Akif Koç
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Abstract

This study explores the thermomechanical 3D wave propagation behavior of a sandwich nanosensor plate with an auxetic core, leveraging nonlocal strain gradient elasticity and sinusoidal higher-order shear deformation theories. The plate comprises functionally graded ceramic (Si3N4) and metal (Ti6Al4V) face layers, with an auxetic Ti6Al4V core having a negative Poisson's ratio. Governing equations are derived using Hamilton's principle, leading to the Navier solution for 3D wave propagation. The results indicate that increasing the β₁ parameter enhances phase velocities and wave frequencies, while smaller β₃ values significantly impact stiffness and frequency. These findings provide a framework for optimizing the design of nanosensors, ensuring improved performance and reliability in high-temperature applications across various industries.

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外加核心层和对称FGM面层对夹层纳米板三维波传播响应的影响
本研究利用非局部应变梯度弹性和正弦高阶剪切变形理论,探讨了具有辅助核心的三明治纳米传感器板的热力学三维波传播行为。该板由功能梯度陶瓷(Si3N4)和金属(Ti6Al4V)表面层组成,具有负泊松比的辅助Ti6Al4V核心。利用汉密尔顿原理推导出控制方程,得到三维波传播的纳维尔解。结果表明,增加β₁参数会提高相速度和波频率,而较小的β₃值会显著影响刚度和频率。这些发现为优化纳米传感器的设计提供了一个框架,确保在各种行业的高温应用中提高性能和可靠性。
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来源期刊
CiteScore
4.40
自引率
10.70%
发文量
234
审稿时长
4-8 weeks
期刊介绍: Archive of Applied Mechanics serves as a platform to communicate original research of scholarly value in all branches of theoretical and applied mechanics, i.e., in solid and fluid mechanics, dynamics and vibrations. It focuses on continuum mechanics in general, structural mechanics, biomechanics, micro- and nano-mechanics as well as hydrodynamics. In particular, the following topics are emphasised: thermodynamics of materials, material modeling, multi-physics, mechanical properties of materials, homogenisation, phase transitions, fracture and damage mechanics, vibration, wave propagation experimental mechanics as well as machine learning techniques in the context of applied mechanics.
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