A quasi-linear reproducing kernel particle method

Edouard Yreux, Jiun Shyan Chen

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

Reproducing kernel particle method (RKPM) has been applied to many large deformation problems. RKPM relies on polynomial reproducing conditions to yield desired accuracy and convergence properties but requires appropriate kernel support coverage of neighboring nodes to ensure kernel stability. This kernel stability condition is difficult to achieve for problems with large particle motion such as the fragment-impact processes that commonly exist in extreme events. A new reproducing kernel formulation with ‘quasi-linear’ reproducing conditions is introduced. In this approach, the first-order polynomial reproducing conditions are approximately enforced to yield a nonsingular moment matrix. With proper error control of the first-order completeness, nearly second-order convergence rate in L2 norm can be achieved while maintaining kernel stability. The effectiveness of this quasi-linear RKPM formulation is demonstrated by modeling several extremely large deformation and fragment-impact problems.

Original languageEnglish
Pages (from-to)1045-1064
Number of pages20
JournalInternational Journal for Numerical Methods in Engineering
Volume109
Issue number7
DOIs
Publication statusPublished - 2017 Feb 17

All Science Journal Classification (ASJC) codes

  • Numerical Analysis
  • General Engineering
  • Applied Mathematics

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