DEM study and machine learning model of particle percolation under vibration

S. M. Arifuzzaman, Kejun Dong, Haiping Zhu, Qinghua Zeng

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

This paper aims to understand model the effect of vibration on particle percolation. The percolation of small particles in a vibrated bed of big particles is studied by DEM. It is found the percolation velocity (Vp) decreases with increasing vibration amplitude (A) and frequency (f) when the size ratio of small to large particles (d/D) is smaller than the spontaneous percolation threshold of 0.154. Vibration can enable percolation when the size ratio is larger than 0.154, while Vp increases with increasing A and f first and then decreases. Vp can be correlated to the vibration velocity amplitude under a given size ratio. Previous radial dispersion model can still be applied while the dispersion coefficient is affected by vibration conditions and size ratio. Furthermore, a machine learning model is trained to predict Vp as a function of A, f and d/D, and is then used to obtain the percolation threshold size ratio as a function of vibration conditions.
Original languageEnglish
Article number103551
Number of pages12
JournalAdvanced Powder Technology
Volume33
Issue number5
DOIs
Publication statusPublished - 2022

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