DEM study and machine learning model of particle percolation under vibration

• Published in: Advanced powder technology : the international journal of the Society of Powder Technology, Japan Japan, 2022-05, Vol.33 (5), p.103551, Article 103551
• Main Authors: Arifuzzaman, S.M., Dong, Kejun, Zhu, Haiping, Zeng, Qinghua
• Format: Article
• Language: English
• Subjects: DEM; Granular materials; Machine learning; Percolation; Vibrated bed
• ISSN: 0921-8831; 1568-5527
• DOI: 10.1016/j.apt.2022.103551

[Display omitted] •Particle percolation in vibrated bed simulated by DEM.•Effects of vibration conditions and size ratio on percolation studied.•Percolation velocity correlated with vibration velocity amplitude.•Machine learning of DEM data to model percolation velocity.•Percolation threshold under different vibration conditions obtained by ML. 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.