Adaptive stochastic morphology simulation and mesh generation of high-quality 3D particulate composite microstructures with complex surface texture

Huang, J. and Deng, F. and Liu, L. and Ye, J. (2022) Adaptive stochastic morphology simulation and mesh generation of high-quality 3D particulate composite microstructures with complex surface texture. Computer Methods in Applied Mechanics and Engineering, 393. ISSN 0045-7825

[img]
Text (CMAME-D-21-01821_R1)
CMAME_D_21_01821_R1.pdf - Accepted Version
Restricted to Repository staff only until 22 March 2023.
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.

Download (17MB)

Abstract

Particulate composite materials have a broad range of potential applications in engineering and other disciplines. Accurate modeling of their microstructures and fast generation of the finite element meshes play a vital role in investigating many micromechanical phenomena and improving understanding of the underlying failure mechanisms. Due to the exceedingly intricate multiscale internal structures that they possess, the modeling and meshing of their microstructures still remain difficult in general. In this work, we present a computational framework and methodology for the representation, simulation, and mesh generation of 3D stochastic microstructures of particulate composites. Towards this goal, we propose a multi-level multiscale scheme that allows for capturing the multiscale structures of particulate composite materials at both the coarse and fine scales. A briging scale approach based on heat kernel smoothing is also presented to seamlessly link the coarse and fine scales. In addition to the microstructural modeling of particulate composite materials, we also develop an adaptive curvature-based surface and volume mesh generation algorithm for particulate composite microstructures with complex surface texture. Following the implementation of the morphology and mesh generation algorithm, a series of numerical examples are presented to demonstrate the capability and potential of the proposed method.

Item Type:
Journal Article
Journal or Publication Title:
Computer Methods in Applied Mechanics and Engineering
Additional Information:
This is the author’s version of a work that was accepted for publication in Computer Methods in Applied Mechanics and Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Methods in Applied Mechanics and Engineering, 393, 2022 DOI: 10.1016/j.cma.2022.114811
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1706
Subjects:
ID Code:
168254
Deposited By:
Deposited On:
05 Apr 2022 14:25
Refereed?:
Yes
Published?:
Published
Last Modified:
04 May 2022 02:52