A fast adhesive discrete element method for random packings of fine particles. (16th January 2019)

• Record Type: Journal Article
• Authors: Chen, Sheng; Liu, Wenwei; Li, Shuiqing
• Is Part Of: Chemical engineering science. Volume 193(2019)
• Page Start: 336
• Page End: 345
• Publication Date: 2019-01-16
• Subjects: Discrete element method -- Reduced stiffness -- Microspheres -- Cohesive particles -- Rolling resistance -- Packing structure; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2018.09.026
• Languages: English
• ISSNs: 0009-2509

Abstract

Highlights: To accelerate DEM, scaling laws are proposed to reduce particle Young's modulus and the surface energy. An inversion method is presented to quickly set the parameters in fast adhesive DEM. The fast DEM retains both micro- and macroscopic properties of adhesive packings. Abstract: Introducing a reduced particle stiffness in discrete element method (DEM) allows for bigger time steps and therefore fewer total iterations in a simulation. Although this approach works well for dry non-adhesive particles, it has been shown that for fine particles with adhesion, system behaviors are drastically sensitive to the particle stiffness. Besides, a simple and applicable principle to set the parameters in adhesive DEM is also lacking. To solve these two problems, we first propose a fast DEM based on scaling laws to reduce particle Young's modulus, surface energy and to modify rolling and sliding resistances simultaneously in the framework of Johnson-Kendall-Roberts (JKR)-based contact theory. A novel inversion method is then presented to help users to quickly determine the damping coefficient, particle stiffness and surface energy to reproduce a prescribed experimental result. After validating this inversion method, we apply the fast adhesive DEM to packing problems of microparticles. Measures of packing fraction, averaged coordination number and distributions of local packing fraction and contact number of each particle are in good agreement with results simulated using original value of particle properties. The new method should be helpful to accelerate DEM simulations for systems associated with aggregates or agglomerates.