A node enrichment adaptive refinement in Discrete Least Squares Meshless method for solution of elasticity problems

M. H. Afshar, J. Amani, M. Naisipour

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)

Abstract

In this paper, an adaptive refinement procedure is proposed to be used with Discrete Least Squares Meshless (DLSM) method for accurate solution of planar elasticity problems. DLSM method is a newly introduced meshless method based on the least squares concept. The method is based on the minimization of a least squares functional defined as the weighted summation of the squared residual of the governing differential equation and its boundary conditions at nodal points used to discretize the domain and its boundaries. A Moving Least Square (MLS) method is used to construct the shape function making the approach a fully least squares based approach. An error estimate and adaptive refinement strategy is proposed in this paper to increase the efficiency of the DLSM method. For this, a residual based error estimator is introduced and used to discover the region of higher errors. The proposed error estimator has the advantages of being available at the end of each analysis contributing to the efficiency of the proposed method. An enrichment method is then used by adding more nodes to the area of higher errors as indicated by the error estimator. A Voronoi diagram is used to locate the position of the nodes to be added to the current nodal configuration. Efficiency and effectiveness of the proposed procedure is examined by adaptively solving two benchmark problems. The results show the ability of the proposed strategy for accurate simulation of elasticity problems.

Original languageEnglish
Pages (from-to)385-393
Number of pages9
JournalEngineering Analysis with Boundary Elements (Print)
Volume36
Issue number3
DOIs
Publication statusPublished - Mar 2012
Externally publishedYes

Keywords

  • Adaptive refinement
  • Discrete Least Squares Meshless method
  • Elasticity problems
  • Error estimate
  • Node enrichment
  • Voronoi diagram

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