A macroscopic model for magnetorheological elastomers based on microscopic simulations

Kalina, Karl A.; Metsch, Philipp; Brummund, Jörg; Kästner, Markus

doi:10.1016/j.ijsolstr.2020.02.028

Cited by 38 publications

(41 citation statements)

References 53 publications

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“…While the material behavior of the MAE’s constituents is often well-known, macroscopic effects of inhomogeneous magnetic and mechanical fields make an experimental characterization of their compound behavior almost impossible [ 42 , 64 ]. To this end, a proper validation of the micro-continuum model can only be performed with well-defined, simplified MAE samples under controlled loading scenarios.…”

Section: Micro-modeling Strategiesmentioning

confidence: 99%

“…To facilitate a full characterization of MAEs on the microscopic and macroscopic scales, a multiscale framework is required. In the following, the procedure according to [ 42 , 68 ], where a macro-continuum model is calibrated from micro-continuum simulations for the simplified two-dimensional case, is briefly presented. The framework represents a decoupled multiscale scheme which—in contrast to, e.g., FE

-approaches [ 39 , 45 ]—can only account for microstructural evolution within the limits of the model calibration.…”

Section: Micro-modeling Strategiesmentioning

confidence: 99%

“…For the fitting of the sets of macroscopic material parameters

, the systematic procedure described in [ 42 , 68 ] is applied: a total number of nine calibration and five validation load cases is performed with RVEs comprising 400 randomly distributed particles at different particle-volume fractions. To prevent an unsystematic RVE behavior, the minimum particle distance during the RVE geometry generation using a random sequential adsorption algorithm is set to 5% of the particle diameter.…”

Section: Micro-modeling Strategiesmentioning

confidence: 99%

“…To prevent an unsystematic RVE behavior, the minimum particle distance during the RVE geometry generation using a random sequential adsorption algorithm is set to 5% of the particle diameter. For the microscale simulations, the constitutive models according to Equations ( 11a ) and ( 12a ) were used—for the values of the individual parameters and the quality of the model calibration, the authors refer to [ 42 , 68 ].…”

Section: Micro-modeling Strategiesmentioning

confidence: 99%

“…To this end, the approach allows for identifying mechanisms leading to magnetically induced deformations and enhanced mechanical moduli on the microstructural level but requires an appropriate computational homogenization procedure to predict the effective material behavior [ 36 , 37 , 38 , 39 ]. In macro-continuum approaches, the MAE is modeled as a homogeneous continuum in which microstructural information are captured via suitable coupling terms; see Figure 1 c. Here, a phenomenological material model is typically fitted to data obtained from experiments [ 40 , 41 ] or more resolved, microscopic analyses [ 42 , 43 , 44 ]. Due to its phenomenological nature, this strategy cannot provide any understanding of microscopic mechanisms which drive the materials’ effective response, but—as it allows a consideration of realistic MAE samples under complex loading conditions—yields relevant information on macroscopic shape effects [ 45 ].…”

Section: Introductionmentioning

confidence: 99%

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Magneto-Mechanical Coupling in Magneto-Active Elastomers

et al. 2021

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In the present work, the magneto-mechanical coupling in magneto-active elastomers is investigated from two different modeling perspectives: a micro-continuum and a particle–interaction approach. Since both strategies differ significantly in their basic assumptions and the resolution of the problem under investigation, they are introduced in a concise manner and their capabilities are illustrated by means of representative examples. To motivate the application of these strategies within a hybrid multiscale framework for magneto-active elastomers, their interchangeability is then examined in a systematic comparison of the model predictions with regard to the magneto-deformation of chain-like helical structures in an elastomer surrounding. The presented results show a remarkable agreement of both modeling approaches and help to provide an improved understanding of the interactions in magneto-active elastomers with chain-like microstructures.

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Section: Micro-modeling Strategiesmentioning

confidence: 99%

-approaches [ 39 , 45 ]—can only account for microstructural evolution within the limits of the model calibration.…”

Section: Micro-modeling Strategiesmentioning

confidence: 99%

“…For the fitting of the sets of macroscopic material parameters

Section: Micro-modeling Strategiesmentioning

confidence: 99%

Section: Micro-modeling Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Magneto-Mechanical Coupling in Magneto-Active Elastomers

et al. 2021

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Prediction for magnetostriction magnetorheological foam using machine learning method

Rohim

Nazmi

Bahiuddin

et al. 2022

J of Applied Polymer Sci

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Magnetorheological (MR) foam is a magnetic polymer composite (MPC) that can be used for soft sensors and actuators in soft robotics. Modeling mechanical properties and magnetostriction behavior of MR foam is critical to developing into MR foam devices. This study uses extreme learning machines (ELM) and artificial neural networks (ANN) to predict magnetostriction behavior.These models describe the nonlinear relationship between different carbonyl iron particle compositions, magnetic field, strain, and normal force. The model's hyperparameters (learning algorithms and activation functions) are varied. For ANN, RMSProp, and ADAM learning algorithms were used with

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Curing spurious magneto‐mechanical coupling in soft non‐magnetic materials

Rambausek

Schöberl

2023

Numerical Meth Engineering

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The present work is concerned with the issue of spurious coupling effects that are pervasive in fully coupled magneto-mechanical finite element simulations involving very soft non-magnetic or air-like media. We first address the characterization of the spurious magneto-mechanical effects and their intuitive interpretation based on energy considerations. Then, as main contribution, we propose two new cures for the issue under consideration that completely prune the undesired spurious magneto-mechanical coupling in non-magnetic media.The proposed methods are compared with established methods in the context of magnetic bodies embedded in (i) air or vacuum and (ii) very soft elastic non-magnetic media. The comparison shows that the proposed approaches are accurate and effective. They, furthermore, allow for a consistent linearization of the coupled boundary value problems, which is crucial for the simulation of compliant structures. For reproducibility and accessibility of the proposed methods, we provide our implementations with Netgen/NGSolve as well as all codes necessary for the reproduction of our results as supplementary material.

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A macroscopic model for magnetorheological elastomers based on microscopic simulations

Cited by 38 publications

References 53 publications

Magneto-Mechanical Coupling in Magneto-Active Elastomers

Magneto-Mechanical Coupling in Magneto-Active Elastomers

Prediction for magnetostriction magnetorheological foam using machine learning method

Curing spurious magneto‐mechanical coupling in soft non‐magnetic materials

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