CG-enriched concurrent multi-scale modeling of dynamic surface interactions between discrete particles and solid continua

Hongyang Cheng*, Stefan Luding, Thomas Weinhart

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
9 Downloads (Pure)


The interaction between granular materials and deformable structures is relevant to many industries, such as mining, construction, and powder processing. Surface coupling between the discrete (DEM) and the finite element method (FEM) is commonly used to numerically describe particle-continuum interactions. Using a recently developed “surface-coupling” method, enriched by coarse graining, a micro-macro transition technique to extract continuum fields from discrete particle data, we study the time evolution of linear momenta and energies in various particle-continuum systems and their dependencies on the coarse-graining (CG) width, the support of the smoothing kernel. Via three numerical examples including (1) a dense granular flow impacting on a flexible obstacle, (2) a viscoelastic cube bouncing and resting on a frictional granular bed, and (3) a monolayer of particles flowing on a cantilever, we show that CG-enriched surface coupling not only leads to more accurate predictions but also reduces excess energies numerically generated by the coupling method, and CG is more effective as the particle-structure interaction becomes dynamic. By varying the CG width, we observe stronger attenuation, decreasing the magnitudes of high-frequency oscillations, facilitating stress relaxation in dissipative coupled systems, and that the identification of the optimal CG width is indeed problem-dependent. [Figure not available: see fulltext.]

Original languageEnglish
Article number722218
JournalActa Mechanica Sinica/Lixue Xuebao
Issue number1
Publication statusPublished - Jan 2023


  • Coarse-graining
  • Concurrent multi-scale modeling
  • Granular flows
  • Particle-structure interaction
  • Surface coupling
  • UT-Hybrid-D
  • 2023 OA procedure

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