0
Research Papers

Characteristic Volume Element for Randomly Particulate Magnetoactive Composites

[+] Author and Article Information
Alireza Bayat

Composite and Intelligent Materials Laboratory,
Department of Mechanical Engineering,
University of Nevada, Reno,
Reno, NV 89557
e-mail: abayat@unr.edu

Faramarz Gordaninejad

Composite and Intelligent Materials Laboratory,
Department of Mechanical Engineering,
University of Nevada, Reno,
Reno, NV 89557
e-mail: faramarz@unr.edu

1Corresponding author.

Contributed by the Materials Division of ASME for publication in the JOURNAL OF ENGINEERING MATERIALS AND TECHNOLOGY. Manuscript received November 18, 2016; final manuscript received June 1, 2017; published online August 9, 2017. Assoc. Editor: Ghatu Subhash.

J. Eng. Mater. Technol 140(1), 011003 (Aug 09, 2017) (10 pages) Paper No: MATS-16-1336; doi: 10.1115/1.4037023 History: Received November 18, 2016; Revised June 01, 2017

A scale-dependent numerical approach is developed through combining the finite element (FE)-based averaging process with the Monte Carlo method to determine the desired size of a characteristic volume element (CVE) for a random magnetoactive composite (MAC) under applied magnetic field and large deformations. Spatially random distribution of identically magnetic inclusions inside a soft homogeneous matrix is considered to find the appropriate size of the characteristic volume element. Monte Carlo method is used to generate ensembles of a randomly distributed magnetoactive composite to be applied in the homogenization study. The ensemble is utilized as a statistical volume element (SVE) in a scale-dependent numerical algorithm to search the desired characteristic volume element size. Results of this study can be used to investigate effective behavior and multiscale modeling of randomly particulate magnetoactive composites.

FIGURES IN THIS ARTICLE
<>
Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

(a) The homogenized body and corresponding boundary decomposition in Lagrangian configuration, (b) corresponding SVE, attached to X¯, selected for statistical analysis including randomly distributed circular permeable particle inside a soft matrix, and (c) the effective medium modeled using homogenization approach

Grahic Jump Location
Fig. 2

Schematic of the statistical algorithm used in the study: (a) selection of random meso-scale ensembles of the heterogeneous MAC for a typical test window size and (b) increasing the size of the test window and number of particles toward the convergence window. All ensembles are generated for Af=0.35,d=10,  and dc=1.

Grahic Jump Location
Fig. 3

Computational algorithm used in the statistical approach

Grahic Jump Location
Fig. 4

Results for normalized modulus component, A, versus number of simulations for (a) LD-BC and (b) PF-BC

Grahic Jump Location
Fig. 5

Results for normalized modulus component, B, versus number of simulations for (a) LD-BC and (b) PF-BC

Grahic Jump Location
Fig. 6

Coefficient of variation of (a) 〈A〉 and (b) 〈B〉 versus scale factor, w

Grahic Jump Location
Fig. 7

Average values of (a) 〈A〉 and (b) 〈B〉 versus scale factor, w

Grahic Jump Location
Fig. 8

FE simulation results for T12 component of microscopic stress tensor for SVEs with (a) w=11, with LD-BC, (b) w=11, with PF-BC, (c) w=13, with LD-BC, and (d) w=13, with PF-BC. All plots are normalized by their average value of T12 component.

Grahic Jump Location
Fig. 9

FE simulation results for B2 component of microscopic magnetic induction vector for SVEs with (a) w=11, with LD-BC, (b) w=11, with PF-BC, (c) w=13, with LD-BC, and (d) w=13, with PF-BC. All plots are normalized by their average value of B2 component.

Grahic Jump Location
Fig. 10

FE simulation results for (a) T12 component of microscopic nominal stress tensor, for converged CVEs, w=27, with LD-BC, (b) B2 component of magnetic induction vector for the converged CVE, w=27 with LD-BC, (c) T12 component of microscopic nominal stress tensor, for converged CVEs with w=25, and PD-BC, and (d) B2 component of magnetic induction vector for the converged CVE, w=25 with PD-BC

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In