Constitutive models for predicting field-dependent viscoelastic behavior of magnetorheological elastomer using machine learning

2020 ◽  
Vol 29 (8) ◽  
pp. 087001
Author(s):  
Kasma Diana Saharuddin ◽  
Mohd Hatta Mohammed Ariff ◽  
Irfan Bahiuddin ◽  
Saiful Amri Mazlan ◽  
Siti Aishah Abdul Aziz ◽  
...  
Author(s):  
Yancheng Li ◽  
Jianchun Li

This paper presents a recent research breakthrough on the development of a novel adaptive seismic isolation system as the quest for seismic protection for civil structures, utilizing the field-dependent property of the magnetorheological elastomer (MRE). A highly-adjustable MRE base isolator was developed as the key element to form smart seismic isolation system. The novel isolator contains unique laminated structure of steel and MRE layers, which enable its large-scale civil engineering applications, and a solenoid to provide sufficient and uniform magnetic field for energizing the field-dependent property of MR elastomers. With the controllable shear modulus/damping of the MR elastomer, the developed adaptive base isolator possesses a controllable lateral stiffness while maintaining adequate vertical loading capacity. Experimental results show that the prototypical MRE base isolator provides amazing increase of lateral stiffness up to 1630%. Such range of increase of the controllable stiffness of the base isolator makes it highly practical for developing new adaptive base isolation system utilizing either semi-active or smart passive controls. To facilitate the structural control development using the adaptive MRE base isolator, an analytical model was developed to stimulate its behaviors. Comparison between the analytical model and experimental data proves the effectiveness of such model in reproducing the behavior of MRE base isolator, including the observed strain stiffening effect.


2016 ◽  
Vol 84 (2) ◽  
Author(s):  
Charles S. Wojnar ◽  
Dennis M. Kochmann

Microstructural mechanisms such as domain switching in ferroelectric ceramics dissipate energy, the nature, and extent of which are of significant interest for two reasons. First, dissipative internal processes lead to hysteretic behavior at the macroscale (e.g., the hysteresis of polarization versus electric field in ferroelectrics). Second, mechanisms of internal friction determine the viscoelastic behavior of the material under small-amplitude vibrations. Although experimental techniques and constitutive models exist for both phenomena, there is a strong disconnect and, in particular, no advantageous strategy to link both for improved physics-based kinetic models for multifunctional rheological materials. Here, we present a theoretical approach that relates inelastic constitutive models to frequency-dependent viscoelastic parameters by linearizing the kinetic relations for the internal variables. This enables us to gain qualitative and quantitative experimental validation of the kinetics of internal processes for both quasistatic microstructure evolution and high-frequency damping. We first present the simple example of the generalized Maxwell model and then proceed to the case of ferroelectric ceramics for which we predict the viscoelastic response during domain switching and compare to experimental data. This strategy identifies the relations between microstructural kinetics and viscoelastic properties. The approach is general in that it can be applied to other rheological materials with microstructure evolution.


2022 ◽  
Vol 391 ◽  
pp. 114492
Author(s):  
Ari Frankel ◽  
Craig M. Hamel ◽  
Dan Bolintineanu ◽  
Kevin Long ◽  
Sharlotte Kramer

2021 ◽  
Vol 13 (01) ◽  
pp. 2150001 ◽  
Author(s):  
Shoujing Zheng ◽  
Zishun Liu

We propose a machine learning embedded method of parameters determination in the constitutional models of hydrogels. It is found that the developed logistic regression-like algorithm for hydrogel swelling allows us to determine the fitting parameters based on known swelling ratio and chemical potential. We also put forward the neural networks-like algorithm, which, by its own property, can converge faster as the layer deepens. We then develop neural networks-like algorithm for hydrogel under uniaxial load for experimental application purpose. Finally, we propose several machine learning embedded potential applications for hydrogels, which would provide directions for machine learning-based hydrogel research.


2016 ◽  
Vol 70 (2) ◽  
pp. 332-340 ◽  
Author(s):  
Suhas Bhandarkar ◽  
Jacob Betcher ◽  
Ryan Smith ◽  
Bruce Lairson ◽  
Travis Ayers

2021 ◽  
Vol 299 ◽  
pp. 124264
Author(s):  
Alireza Sadat Hosseini ◽  
Pouria Hajikarimi ◽  
Mostafa Gandomi ◽  
Fereidoon Moghadas Nejad ◽  
Amir H. Gandomi

Sign in / Sign up

Export Citation Format

Share Document