Modeling elasticity of cubic crystals using a novel nonlocal lattice particle method

Hailong Chen, Changyu Meng, Yongming Liu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

A novel nonlocal lattice particle method for modeling elastic deformation of cubic crystals was proposed and verified in this paper. Different from all other numerical models, the lattice particle method decomposes the grain domain into regularly packed discrete material particles according to the internal crystal lattice. Two most common Bravais cubic lattices, i.e., the body-centered cubic lattice and the face-center cubic lattice, were studied in this work. Model parameters were derived in terms of the three elastic material constants based on energy equivalency and theory of hyper-elasticity. Different from coordinates transformation used in the classical continuum mechanics theory, rotation of the discretization lattice is employed to equivalently represent the material anisotropy while capturing the underlying microstructure in the proposed model. The validity and prediction accuracy of the proposed model were established by comparing the predicted directional Young’s modulus and the resolved shear stress of different slip systems against analytical solutions.

Original languageEnglish
Pages (from-to)1131-1146
Number of pages16
JournalComputational Mechanics
Volume69
Issue number5
DOIs
StatePublished - May 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords

  • Crystal elasticity
  • Cubic crystals
  • Lattice particle method
  • Lattice rotation
  • Resolved shear stress

ASJC Scopus subject areas

  • Computational Mechanics
  • Ocean Engineering
  • Mechanical Engineering
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Modeling elasticity of cubic crystals using a novel nonlocal lattice particle method'. Together they form a unique fingerprint.

Cite this