Please use this identifier to cite or link to this item:
Title: Adaptive reproducing kernel particle method using gradient indicator for elasto-plastic deformation
Authors: Liu, HS
Fu, MW 
Keywords: Adaptive analysis
Reproducing kernel particle method
High gradient
Elasto-plastic deformation
Issue Date: 2013
Publisher: Elsevier
Source: Engineering analysis with boundary elements, 2013, v. 37, no. 2, p. 280-292 How to cite?
Journal: Engineering analysis with boundary elements 
Abstract: An adaptive meshless method based on the multi-scale Reproducing Kernel Particle Method (RKPM) for analysis of nonlinear elasto-plastic deformation is proposed in this research. In the proposed method, the equivalent strain, stress, and the second invariant of the Cauchy–Green deformation tensor are decomposed into two scale components, viz., high- and low-scale components by deriving them from the multi-scale decomposed displacement. Through combining the high-scale components of strain and the stress update algorithm, the equivalent stress is decomposed into two scale components. An adaptive algorithm is proposed to locate the high gradient region and enrich the nodes in the region to improve the computational accuracy of RKPM. Using the algorithm, the high-scale components of strain and stress and the second invariant of the Cauchy–Green deformation tensor are normalized and used as criteria to implement the adaptive analysis. To verify the validity of the proposed adaptive meshless method in nonlinear elasto-plastic deformation, four case studies are calculated by the multi-scale RKPM. The patch test results show that the used multi-scale RKPM is reliable in analysis of the regular and irregular nodal distribution. The results of other three cases show that the proposed adaptive algorithm can not only locate the high gradient region well, but also improve the computational accuracy in analysis of the nonlinear elasto-plastic deformation.
ISSN: 0955-7997
DOI: 10.1016/j.enganabound.2012.09.008
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record


Citations as of Sep 16, 2017


Last Week
Last month
Citations as of Sep 23, 2017

Page view(s)

Last Week
Last month
Checked on Sep 17, 2017

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.