Multi-Scale Genome Modeling for Predicting Fracture Strength of Silicon Carbide Ceramics
Abstract
The variational asymptotic method for unit cell homogenization (VAMUCH) has emerged as a general purpose micromechanics approach capable of predicting the effective properties of heterogeneous materials and recovering the local fields. In this study, a novel micromechanics approach has been developed enabling VAMUCH to homogenize heterogeneous microstructure and predict its crack formation through a multi-scale materials genome model. A variational form for homogenization is formulated in combination with a cohesive zone model. The weak form of the problem is derived using an asymptotic method, discretized using finite element formulations, and implemented into VAMUCH. The advantages of the present approach are demonstrated through homogenizing silicon carbide ceramics and predicting its fracture strength. Both the elastic properties and fracture strength can be predicted in a computationally efficient manner using this approach compared with the multi-scale finite element model.
Recommended Citation
X. Dong and Y. C. Shin, "Multi-Scale Genome Modeling for Predicting Fracture Strength of Silicon Carbide Ceramics," Computational Materials Science, vol. 141, pp. 10 - 18, Elsevier, Jan 2018.
The definitive version is available at https://doi.org/10.1016/j.commatsci.2017.09.012
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
Ceramic materials; Composite micromechanics; Cracks; Forecasting; Fracture; Fracture toughness; Genes; Homogenization method; Micromechanics; Silicon carbide; Computationally efficient; Finite element formulations; Heterogeneous microstructure; Nonlinear homogenization; Silicon carbide ceramic; Unit-cell homogenizations; VAMUCH; Variational asymptotic methods; Finite element method; Fracture strength; Materials genome
International Standard Serial Number (ISSN)
0927-0256
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Elsevier, All rights reserved.
Publication Date
01 Jan 2018