A Computational Scheme for the Interaction between an Edge Dislocation and an Arbitrarily Shaped Inhomogeneity via the Numerical Equivalent Inclusion Method


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

The interactions between dislocations and inhomogeneity may play an important role in strengthening and hardening of materials. The problem can be solved analytically only for limited cases of simple geometry. By employing the recently developed numerical equivalent inclusion method, this work presents an effective computational scheme for studying the stress field due to an edge dislocation in the vicinity of an arbitrarily shaped inhomogeneity. The inhomogeneity is treated as an equivalent inclusion that is numerically discretized by rectangular elements. The mismatch between the matrix and the inhomogeneity materials are formulated through Dundurs’ parameters for numerical stability and robustness. The proposed method can efficiently and accurately evaluate the elastic field of the equivalent inclusion with the assistance of a fast Fourier transform based algorithm, constituting an essential refinement of the existing approach in the dislocation-inhomogeneity literature. Several benchmark examples are examined to demonstrate the flexibility, efficiency and accuracy of the present method.

About the authors

P. Li

State Key Laboratory of Mechanical Transmissions

Email: jinxq@cqu.edu.cn
China, Chongqing, 400030

X. Zhang

State Key Laboratory of Mechanical Transmissions

Email: jinxq@cqu.edu.cn
China, Chongqing, 400030

D. Lyu

State Key Laboratory of Mechanical Transmissions

Email: jinxq@cqu.edu.cn
China, Chongqing, 400030

X. Jin

State Key Laboratory of Mechanical Transmissions

Author for correspondence.
Email: jinxq@cqu.edu.cn
China, Chongqing, 400030

L. M. Keer

Department of Mechanical Engineering

Email: jinxq@cqu.edu.cn
United States, Evanston, Illinois, 60208

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2019 Pleiades Publishing, Ltd.