Segregation of granular particles by mass, radius, and density in a horizontal rotating drum

Abstract

The impact of particle properties on segregation and mixing of bidisperse granular beds in a rotating horizontal drum have been studied by discrete element method (DEM) simulations. Bidispersities in radius, density, and mass have pronounced influences on the stationary mixing pattern, although they hardly affect the granules' flow regime. At 50% fill level, all beds mix well for a Froude number of ∼0.56, corresponding to a flow regime intermediate to cascading and cataracting, while segregation occurs both at lower (rolling and cascading regime) and higher (cataracting/centrifuging regime) Froude numbers. These observations are explained qualitatively by noticing that the angular drum velocity dictates the flow regime, which in turn determines the effectiveness and direction of four competing (de)mixing mechanisms: random collisions, buoyancy, percolation, and inertia. A further dozen particle properties have been varied, including the friction coefficients and elastic modulus, but these proved inconsequential to the steady‐state degree of mixing. © 2013 American Institute of Chemical Engineers AIChE J, 60: 50–59, 2014

Effects of density ratio, rotation speed, and fill level on density-induced granular streak segregation in a rotating drum

“…Jain et al [1,27] studied the behavior of granular mixing and segregation by combining the size and density effects of a binary mixture in a thin rotating drum. Arntz et al [9] reported that three parameters predominantly determined the segregation behavior; namely, differences in the radius, density, and mass, which they found to be related to percolation, buoyancy, and inertia mechanisms, respectively. Granular materials may also segregate spontaneously in a regular streak segregation pattern.…”

“…Two regions are observed in the granular motions in a rotating drum with partially filled containers: a fixed bed region and a flowing layer region at a free surface where the mixing and segregation of granular matter occur. Core segregation typically occurs in a binary mixture with different sizes or densities in a rotating drum because of percolation and buoyancy effects [1,3,[9][10][11][12]27,28]. Jain et al [1,27] studied the behavior of granular mixing and segregation by combining the size and density effects of a binary mixture in a thin rotating drum.…”

“…Rotating drums are widely used to investigate the flow mechanics and segregation mechanism of granular flows because the flow field in such drums is relatively simple [1,3,[6][7][8][9][10][12][13][14][15][16][17][18][19][20]. Granular flow in a rotating drum has different regimes depending on the Froude number.…”

Modeling of fracture surface of the quasi solid-body zone of motion of the granular fill in a rotating chamber

“…Article [11] presents results of numerical modeling of a three-dimensional non-established flow of active layer of granular particles under the rolling over mode, which was based on the theory of Euler biphase flow and kinetic theory of granular flow. An intermediate, between the cascade and cataract modes, granular flow at the values of filling degree of a chamber at 0.5 and the Froude number of 0.56, employing the method of discrete elements, was examined in [12]. Results of testing and verifying the source data, with the application of the method of discrete elements, for modeling the motion of granular fill in a rotating chamber, are presented in [13].…”

Controlling of Segregation in Rotating Drums by Independent End Wall Rotations

“…Studies of particles segregation in rotating drums have long history since 1939 (Oyama, 1939). Researchers used particles of different sizes, masses, densities, shapes and elasticities to experimentally investigate segregation in dry rotating drums (Kuo et al, 2006a;Arntz et al, 2014) or in drums with different interstitial fluids (Chou et al, 2010). Numerically studies of particle flows/segregation in rotating drums are also conducted by a number of groups using DEM and CFD (Marigo et al, 2012;Huang et al, 2013a;Alizadeh et al, 2014;Marigo and Stitt, 2015).…”