Enhancing efficiency in particle aggregation simulations: Coarse-grained particle modeling in the DEM-PBM coupled framework

Tarun De, Ashok Das, Mehakpreet Singh, Jitendra Kumar

Research output: Contribution to journalArticlepeer-review

Abstract

The computational cost of the discrete element method (DEM)-population balance model (PBM) coupled framework is predominantly attributed to DEM simulations. To overcome this challenge, coarse-grained (CG) particles have been introduced in the DEM-PBM coupled framework. In this study, we proposed a new CG-enabled DEM-PBM coupled framework that builds upon the previous work of Das et al. (Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 478 (2261) (2022) 20220076). By incorporating the CG technique, the particle number density is reduced, resulting in fewer collisions compared to the resolved system. To address this issue, a scaling law has been developed to derive the collision frequency of the resolved system from the CG system. The verification of the new scaling law has been demonstrated through various simulation studies. Furthermore, the entire DEM-PBM coupled framework has been modified using the proposed methodology. The efficiency of the CG–DEM–PBM coupled simulation method has been successfully demonstrated through simulations of rotating drum and continuous mixing technology (CMT). Compared to the resolved simulation approach, the newly proposed CG-enabled DEM-PBM coupled framework maintains accuracy in terms of particle size distribution and other essential findings while significantly reducing simulation time.

Original languageEnglish
Article number116436
Pages (from-to)-
JournalComputer Methods in Applied Mechanics and Engineering
Volume417
DOIs
Publication statusPublished - 1 Dec 2023

Keywords

  • Aggregation
  • Coarse graining
  • Collision frequency
  • Computational efficiency
  • Discrete element method
  • Population balance

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