Dual-Support Smoothed Particle Hydrodynamics for Elastic Mechanics

Zili Dai, Huilong Ren, Xiaoying Zhuang, Timon Rabczuk

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

In the standard smoothed particle hydrodynamics (SPH) method, the interaction between two particles might be not pairwise when the support domain varies, which can result in a reduction of accuracy. To deal with this problem, a modified SPH approach is presented in this paper. First of all, a Lagrangian kernel is introduced to eliminate spurious distortions of the domain of material stability, and the gradient is corrected by a linear transformation so that linear completeness is satisfied. Then, concepts of support and dual-support are defined to deal with the unbalanced interactions between the particles with different support domains. Several benchmark problems in one, two and three dimensions are tested to verify the accuracy of the modified SPH model and highlight its advantages over the standard SPH method through comparisons.

Original languageEnglish
JournalInternational Journal of Computational Methods
DOIs
Publication statusAccepted/In press - 2016
Externally publishedYes

Fingerprint

Mechanics
Hydrodynamics
Linear transformations
Hydrodynamic Model
Linear transformation
Interaction
One Dimension
Three-dimension
Pairwise
Completeness
Two Dimensions
Eliminate
Vary
Benchmark
kernel
Verify
Gradient
Standards

Keywords

  • dual-support
  • elastic mechanics
  • SPH
  • unbalanced interaction
  • wave reflection

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mathematics

Cite this

Dual-Support Smoothed Particle Hydrodynamics for Elastic Mechanics. / Dai, Zili; Ren, Huilong; Zhuang, Xiaoying; Rabczuk, Timon.

In: International Journal of Computational Methods, 2016.

Research output: Contribution to journalArticle

Dai, Zili ; Ren, Huilong ; Zhuang, Xiaoying ; Rabczuk, Timon. / Dual-Support Smoothed Particle Hydrodynamics for Elastic Mechanics. In: International Journal of Computational Methods. 2016.
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