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Finding defects in disorder: Strain-dependent structural fingerprint of plasticity in granular materials.
- Source :
-
Applied Physics Letters . 12/13/2021, Vol. 119 Issue 24, p1-6. 6p. - Publication Year :
- 2021
-
Abstract
- When subjected to loads, granular materials tend to yield and exhibit some localized particle reorganizations. Due to the complex disordered structure of granular materials, it is challenging to identify the key preexisting defects in the static, unloaded structure that eventually promotes dynamical particle rearrangements once a load is applied. Here, based on discrete element simulations of an archetypal frictional granular material model, we introduce a machine learning framework that pinpoints such structural defects with unprecedented accuracy. We show that the optimal structural fingerprint of plastic flow defects depends on strain, wherein the plastic flow is governed by short-range defects at low strain but become dominated by medium-range defects at high strain. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MATERIAL plasticity
*FORENSIC fingerprinting
*MACHINE learning
*GRANULAR materials
Subjects
Details
- Language :
- English
- ISSN :
- 00036951
- Volume :
- 119
- Issue :
- 24
- Database :
- Academic Search Index
- Journal :
- Applied Physics Letters
- Publication Type :
- Academic Journal
- Accession number :
- 154194399
- Full Text :
- https://doi.org/10.1063/5.0068508