Nonlinear magnetoelectric coupling in magnetostrictive-piezoelectric composites

Lin, Chii-Jeng; Lin, Ying-Zhao

doi:10.1016/j.compstruct.2021.114558

Cited by 20 publications

(4 citation statements)

References 41 publications

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“…The simplified unit-cell model has been recently employed by Lin and Lin 26 for the prediction of the nonlinear behavior of two-phase magnetostrictive-piezoelectric composites, and by Lin and Liu 27 for the simulation of three-phase polymer matrix smart composites. Lin and Lin 28 , Zhan and Lin 29 and Shen and Lin 30 applied the unit-cell model for the study of two-phase magnetostrictive composites.…”

Section: Micromechanics Formulations For Three-phase Magnetoelectric ...mentioning

confidence: 99%

Effective nonlinear responses of three-phase magnetoelectric composites

Lin

Liu

2022

Sci Rep

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A computational method, dubbed simplified unit-cell micromechanics model, is generalized and applied to establish the effective nonlinear responses of three-phase magnetoelectric composites that are composed of two distinct magnetostrictive and piezoelectric phases embedded in elastic polymer matrices. The nature of nonlinear constitutive behavior of each constituent is expected to significantly influence the overall responses of the composites. To obtain the effective nonlinear responses, a mathematical linearization is first introduced to perform the constitutive linearization for the nonlinear materials, and the resulting constitutive equations are then unified and nested into the micromechanics model followed by iterations in order to minimize errors from the linearization process. For the purpose of comparison, we also reformulate the well-established Mori–Tanaka micromechanics model insofar as its mathematical structure is aligned with that of the simplified unit-cell model. Numerical results are first validated against limited experimental measurements available in literature. Parametric studies are then conducted in order to reveal the effect of phase constitutive laws, volume fractions, and geometries on the overall nonlinear responses of there-phase magnetoelectric composites. The contributions of this work complement those of earlier studies that prevalently devoted to two-phase magnetoelectric composites and linear magneto-electro-elastic coupled responses only.

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Section: Micromechanics Formulations For Three-phase Magnetoelectric ...mentioning

confidence: 99%

Effective nonlinear responses of three-phase magnetoelectric composites

Lin

Liu

2022

Sci Rep

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“…Recently, researchers have devoted much effort to modeling magnetoelectric MEMS devices using different approaches, such as analytical methods [26][27][28] and numerical simulations [12,13,29]. These endeavors aim to enhance device performance and comprehend nonlinearity behavior [30][31][32][33]. The field-dependent analytical solutions for laminated magnetoelectric composites [26] proposed tunable frequency-multiplying behavior to optimize the piezomagnetic effect.…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, under DC bias conditions, a comparison of the strain, ME coefficient, and voltage was conducted to optimize sensor performance through resonance-enhanced ME coupling. In terms of nonlinearity, micromechanical formulations have been established for the analysis of ME coupling for different composite structures including 1-3, 0-3, and 2-2 connectivities [32]. The study considered the nonlinearity effect when exposed to significant magnetic/electric fields.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Magnetoelectric Microresonator Using Numerical Method and Simulated Annealing Algorithm

Sadeghi,

Bazrafkan,

Rutner

et al. 2023

Micromachines

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A comprehensive understanding of the linear/nonlinear dynamic behavior of wireless microresonators is essential for micro-electromechanical systems (MEMS) design optimization. This study investigates the dynamic behaviour of a magnetoelectric (ME) microresonator, using a finite element method (FEM) and machine learning algorithm. First, the linear/nonlinear behaviour of a fabricated thin-film ME microactuator is assessed in both the time domain and frequency spectrum. Next, a data driven system identification (DDSI) procedure and simulated annealing (SA) method are implemented to reconstruct differential equations from measured datasets. The Duffing equation is employed to replicate the dynamic behavior of the ME microactuator. The Duffing coefficients such as mass, stiffness, damping, force amplitude, and excitation frequency are considered as input parameters. Meanwhile, the microactuator displacement is taken as the output parameter, which is measured experimentally via a laser Doppler vibrometer (LDV) device. To determine the optimal range and step size for input parameters, the sensitivity analysis is conducted using Latin hypercube sampling (LHS). The peak index matching (PIM) and correlation coefficient (CC) are considered assessment criteria for the objective function. The data-driven developed models are subsequently employed to reconstruct/predict mode shapes and the vibration amplitude over the time domain. The effect of driving signal nonlinearity and total harmonic distortion (THD) is explored experimentally under resonance and sub-resonance conditions. The vibration measurements reveal that as excitation levels increase, hysteresis variations become more noticeable, which may result in a higher prediction error in the Duffing array model. The verification test indicates that the first bending mode reconstructs reasonably with a prediction accuracy of about 92 percent. This proof-of-concept study demonstrates that the simulated annealing approach is a promising tool for modeling the dynamic behavior of MEMS systems, making it a strong candidate for real-world applications.

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“…Frequency-selective and high-accuracy sensors in these categories of devices are so important for some specific applications which can be accessible in magnetic sensors based on magnetoelectric (ME) structures [14,15]. To get a large ME coefficient, nowadays, scientists use composites consisting of piezoelectric and magnetostrictive components in diverse configurations and three different types of 0-3-type particulate, 2-2-type laminates, and 1-3-type fiber composites [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Ultrasensitive Magneto electric Sensor based on Galfenol/ AlN structure

Haghparast

Tehranchi

Hamidi

2022

Preprint

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Ultrasensitive magnetic field sensors based on magnetoelectric (ME) structures have many applications in bio-magnetic sensors and magnetoencephalography (MEG) scanners. Enhance the sensitivity, limit of detection and main frequency of these sensors need simulation process by accurate methods such as finite element method (FEM). To get good sensitivity and efficient benefit, we use a cantilever-type composite structures including galfenol alloy as a magnetostrictive layer and AlN as a piezoelectric layer. Galfenol is an alloy of iron and gallium and its ${\text{Fe}}_{\text{0.83}}{\text{Ga}}_{\text{0.17}}$ structure has a high magnetostriction coefficient and can be used as a thin film. According to the cantilever structure, the maximum bending of the structure is 20 $\text{μm}$. The simulated sensor has a limit of detections of 1 $\raisebox{1ex}{$\text{pT}$}\!\left/ \!\raisebox{-1ex}{$\sqrt{\text{Hz}}$}\right.$ and can measure an AC magnetic field of 1 pT. This sensor has the best performance in the bias DC magnetic field of 2.3 mT and resonance frequency of 2521.8 Hz and has a magnetoelectric coefficient of 4865 ($\raisebox{1ex}{$\text{V}$}\!\left/ \!\raisebox{-1ex}{$\text{cm.Oe}$}\right.$).

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Nonlinear magnetoelectric coupling in magnetostrictive-piezoelectric composites

Cited by 20 publications

References 41 publications

Effective nonlinear responses of three-phase magnetoelectric composites

Effective nonlinear responses of three-phase magnetoelectric composites

Modeling of Magnetoelectric Microresonator Using Numerical Method and Simulated Annealing Algorithm

Ultrasensitive Magneto electric Sensor based on Galfenol/ AlN structure

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