A semi-Lagrangian reproducing kernel particle method with particle-based shock algorithm for explosive welding simulation

Jonghyuk Baek, Jiun Shyan Chen, Guohua Zhou, Kevin P. Arnett, Michael C. Hillman, Gilbert Hegemier, Scott Hardesty

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The explosive welding process is an extreme-deformation problem that involves shock waves, large plastic deformation, and fragmentation around the collision point, which are extremely challenging features to model for the traditional mesh-based methods. In this work, a particle-based Godunov shock algorithm under a semi-Lagrangian reproducing kernel particle method (SL-RKPM) is introduced into the volumetric strain energy to accurately embed the key shock physics in the absence of a mesh or grid, which is shown to also ensure the conservation of linear momentum. For kernel stability, a deformation-dependent anisotropic kernel support update algorithm is proposed, which is shown to capture excessive plastic flow and material separation. A quasi-conforming nodal integration is adopted to avoid the need of updating conforming cells which is tedious in extreme deformations. It is shown that the proposed formulation effectively captures shocks, jet formation, and smooth-to-wavy interface morphology transition with good agreement with experimental results.

Original languageEnglish (US)
Pages (from-to)1601-1627
Number of pages27
JournalComputational Mechanics
Volume67
Issue number6
DOIs
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Ocean Engineering
  • Mechanical Engineering
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'A semi-Lagrangian reproducing kernel particle method with particle-based shock algorithm for explosive welding simulation'. Together they form a unique fingerprint.

Cite this