A novel partitioning method for block-structured adaptive meshes

Lin Fu, Sergej Litvinov, Xiangyu Y. Hu, Nikolaus A. Adams

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

We propose a novel partitioning method for block-structured adaptive meshes utilizing the meshless Lagrangian particle concept. With the observation that an optimum partitioning has high analogy to the relaxation of a multi-phase fluid to steady state, physically motivated model equations are developed to characterize the background mesh topology and are solved by multi-phase smoothed-particle hydrodynamics. In contrast to well established partitioning approaches, all optimization objectives are implicitly incorporated and achieved during the particle relaxation to stationary state. Distinct partitioning sub-domains are represented by colored particles and separated by a sharp interface with a surface tension model. In order to obtain the particle relaxation, special viscous and skin friction models, coupled with a tailored time integration algorithm are proposed. Numerical experiments show that the present method has several important properties: generation of approximately equal-sized partitions without dependence on the mesh-element type, optimized interface communication between distinct partitioning sub-domains, continuous domain decomposition which is physically localized and implicitly incremental. Therefore it is particularly suitable for load-balancing of high-performance CFD simulations.

Original languageEnglish
Pages (from-to)447-473
Number of pages27
JournalJournal of Computational Physics
Volume341
DOIs
StatePublished - 15 Jul 2017

Keywords

  • Adaptive mesh refinement
  • Dynamic ghost particle method
  • Grid partitioning
  • Lagrangian particle method
  • Multi-resolution cell-linked list
  • Smoothed-particle hydrodynamics

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