Coupling superquadric Discrete Element Method-Computational Fluid Dynamics (DEM-CFD) with immersed boundary method for particle-resolved direct numerical simulations of non-spherical particle suspension and fluidisation

A novel particle-resolved direct numerical simulations (PR-DNS) method for non-spherical particles is developed and validated in the open-source MFiX (Multi-phase Flow with Interphase eXchanges) code for simulating the suspension of non-spherical particles and fluidisation. The model is implemented by coupling superquadric Discrete Element Method-Computational Fluid Dynamics (DEM-CFD) with the immersed boundary method. The model was first validated by applying it to analyse fluid dynamic coefficients ( $C_{\!D} , C_{\!L} , C_{\!T}$ ) of superellipsoids and cylinders at different Reynolds numbers, and the PR-DNS results closely matched those of previous methods, demonstrating the reliability of the current PR-DNS approach. Then, the model was applied to the simulation of the fluidisation of spheres and cylinders. The PR-DNS results were compared with both particle-unresolved superquadric DEM-CFD simulation and experimental data. The pressure drop, height distribution and orientation distribution of particles were analysed. The results show that the PR-DNS method provides a reliable method for reproducing fluidisation experimental results of non-spherical particles. In addition, the comparison of the drag correction coefficients predicted by existing models with that obtained from PR-DNS results indicates the need for a new drag model for particle-unresolved simulation of non-spherical particles.

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Publication Details

Journal
Journal of Fluid Mechanics
Published
2025-09-18
DOI
https://doi.org/10.1017/jfm.2025.10517
Citations
1
Primary Topic
Lattice Boltzmann Simulation Studies
Type
article
Field-Weighted Citation Impact
1.33
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article

Coupling superquadric Discrete Element Method-Computational Fluid Dynamics (DEM-CFD) with immersed boundary method for particle-resolved direct numerical simulations of non-spherical particle suspension and fluidisation

Xi Gao, Jianjian Dai, Jia Yu, Bing Wang
1 citations
Journal of Fluid Mechanics
Lattice Boltzmann Simulation Studies
1.33
article

Coupling superquadric Discrete Element Method-Computational Fluid Dynamics (DEM-CFD) with immersed boundary method for particle-resolved direct numerical simulations of non-spherical particle suspension and fluidisation

Xi Gao, Jianjian Dai, Jia Yu, Bing Wang
article en
1 citations

Abstract

A novel particle-resolved direct numerical simulations (PR-DNS) method for non-spherical particles is developed and validated in the open-source MFiX (Multi-phase Flow with Interphase eXchanges) code for simulating the suspension of non-spherical particles and fluidisation. The model is implemented by coupling superquadric Discrete Element Method-Computational Fluid Dynamics (DEM-CFD) with the immersed boundary method. The model was first validated by applying it to analyse fluid dynamic coefficients ( $C_{\!D} , C_{\!L} , C_{\!T}$ ) of superellipsoids and cylinders at different Reynolds numbers, and the PR-DNS results closely matched those of previous methods, demonstrating the reliability of the current PR-DNS approach. Then, the model was applied to the simulation of the fluidisation of spheres and cylinders. The PR-DNS results were compared with both particle-unresolved superquadric DEM-CFD simulation and experimental data. The pressure drop, height distribution and orientation distribution of particles were analysed. The results show that the PR-DNS method provides a reliable method for reproducing fluidisation experimental results of non-spherical particles. In addition, the comparison of the drag correction coefficients predicted by existing models with that obtained from PR-DNS results indicates the need for a new drag model for particle-unresolved simulation of non-spherical particles.

Journal of Fluid MechanicsVol. 1019
Technion – Israel Institute of Technology (IL)
Openalex Percentile: Top 18%
Lattice Boltzmann Simulation Studies
1.33
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