Electromechanical responses of piezoelectric nanoplates with flexoelectricity

Yang, Wenjun; Xu, Liang; Shen, Shengping

doi:10.1007/s00707-015-1373-8

Cited by 103 publications

(31 citation statements)

References 31 publications

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“…In Reference [ 74 ], the flexoelectricity was found to increase the deflection while decreasing the resonant frequency of the plate. However, for the case presented in Reference [ 98 ], opposite trends of the flexoelectricity on the plate deflection and resonant frequency were observed. Recently, Liang et al [ 99 ] studied the buckling and vibration of a piezoelectric nanofilm and found that the critical buckling load and the natural frequency of the film with the consideration of the flexoelectricity are higher than those obtained by the classical piezoelectricity plate theory.…”

Section: Size-dependent Properties Of Pnsmentioning

confidence: 89%

“…The vibration analysis of a simply supported piezoelectric nanobeam revealed that the flexoelectricity tends to reduce its resonant frequency [ 73 ]. The size-dependent bending and vibration characteristics of a clamped and a simply supported nanoplate were studied by Zhang et al [ 74 ] and Yang et al [ 98 ], respectively, based on the Kirchhoff plate model. In Reference [ 74 ], the flexoelectricity was found to increase the deflection while decreasing the resonant frequency of the plate.…”

Section: Size-dependent Properties Of Pnsmentioning

confidence: 99%

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Modified Continuum Mechanics Modeling on Size-Dependent Properties of Piezoelectric Nanomaterials: A Review

Yan

Jiang

2017

Nanomaterials

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Piezoelectric nanomaterials (PNs) are attractive for applications including sensing, actuating, energy harvesting, among others in nano-electro-mechanical-systems (NEMS) because of their excellent electromechanical coupling, mechanical and physical properties. However, the properties of PNs do not coincide with their bulk counterparts and depend on the particular size. A large amount of efforts have been devoted to studying the size-dependent properties of PNs by using experimental characterization, atomistic simulation and continuum mechanics modeling with the consideration of the scale features of the nanomaterials. This paper reviews the recent progresses and achievements in the research on the continuum mechanics modeling of the size-dependent mechanical and physical properties of PNs. We start from the fundamentals of the modified continuum mechanics models for PNs, including the theories of surface piezoelectricity, flexoelectricity and non-local piezoelectricity, with the introduction of the modified piezoelectric beam and plate models particularly for nanostructured piezoelectric materials with certain configurations. Then, we give a review on the investigation of the size-dependent properties of PNs by using the modified continuum mechanics models, such as the electromechanical coupling, bending, vibration, buckling, wave propagation and dynamic characteristics. Finally, analytical modeling and analysis of nanoscale actuators and energy harvesters based on piezoelectric nanostructures are presented.

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Section: Size-dependent Properties Of Pnsmentioning

confidence: 89%

Section: Size-dependent Properties Of Pnsmentioning

confidence: 99%

Modified Continuum Mechanics Modeling on Size-Dependent Properties of Piezoelectric Nanomaterials: A Review

Yan

Jiang

2017

Nanomaterials

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“…Table 4. Material properties of the visco-piezo-flexoelectric nanobeam [36,39,71]. In choosing a value for the nonlocal parameter, we used 0.5 nm < e 0 a < 0.8 nm [72] and 0 < e 0 a ≤ 2 nm [73,74], and on the other side, the SGLP could be chosen arbitrarily.…”

Section: Frequency Analysismentioning

confidence: 99%

On the Dynamics of a Visco–Piezo–Flexoelectric Nanobeam

Malikan

Eremeyev

2020

Symmetry

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The fundamental motivation of this research is to investigate the effect of flexoelectricity on a piezoelectric nanobeam for the first time involving internal viscoelasticity. To date, the effect of flexoelectricity on the mechanical behavior of nanobeams has been investigated extensively under various physical and environmental conditions. However, this effect as an internal property of materials has not been studied when the nanobeams include an internal damping feature. To this end, a closed-circuit condition is considered taking converse piezo–flexoelectric behavior. The kinematic displacement of the classical beam using Lagrangian strains, also applying Hamilton’s principle, creates the needed frequency equation. The natural frequencies are measured in nanoscale by the available nonlocal strain gradient elasticity model. The linear Kelvin–Voigt viscoelastic model here defines the inner viscoelastic coupling. An analytical solution technique determines the values of the numerical frequencies. The best findings show that the viscoelastic coupling can directly affect the flexoelectricity property of the material.

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“…Laboratory tests are limited in measuring, explaining and quantifying the key flexoelectric phenomena at specific conditions [Catalan, Lubk, Vlooswijk et al (2011); Baskaran, He, Chen et al (2011); Bhaskar, Banerjee, Abdollahi et al (2016)]. Besides, further theoretical basis studies have been presented to investigate the flexoelectric phenomena in various structures under different configurations [Sharma, Maranganti and Sharma (2007); Mao and Purohit (2015); Yang, Liang and Shen (2015)]. However, relatively few numerical works have been documented to simulate flexoelectricity.…”

Section: Introductionmentioning

confidence: 99%

Computational Machine Learning Representation for the Flexoelectricity Effect in Truncated Pyramid Structures

Hamdia¹,

Ghasemi²,

Zhuang³

et al. 2019

Computers, Materials &Amp; Continua

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In this study, machine learning representation is introduced to evaluate the flexoelectricity effect in truncated pyramid nanostructure under compression. A Non-Uniform Rational B-spline (NURBS) based IGA formulation is employed to model the flexoelectricity. We investigate 2D system with an isotropic linear elastic material under plane strain conditions discretized by 45×30 grid of B-spline elements. Six input parameters are selected to construct a deep neural network (DNN) model. They are the Young's modulus, two dielectric permittivity constants, the longitudinal and transversal flexoelectric coefficients and the order of the shape function. The outputs of interest are the strain in the stress direction and the electric potential due flexoelectricity. The dataset are generated from the forward analysis of the flexoelectric model. 80% of the dataset is used for training purpose while the remaining is used for validation by checking the mean squared error. In addition to the input and output layers, the developed DNN model is composed of four hidden layers. The results showed high predictions capabilities of the proposed method with much lower computational time in comparison to the numerical model.

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Electromechanical responses of piezoelectric nanoplates with flexoelectricity

Cited by 103 publications

References 31 publications

Modified Continuum Mechanics Modeling on Size-Dependent Properties of Piezoelectric Nanomaterials: A Review

Modified Continuum Mechanics Modeling on Size-Dependent Properties of Piezoelectric Nanomaterials: A Review

On the Dynamics of a Visco–Piezo–Flexoelectric Nanobeam

Computational Machine Learning Representation for the Flexoelectricity Effect in Truncated Pyramid Structures

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