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Influence of Rough and Smooth Walls on Macroscale Granular Segregation Patterns

Size bidisperse granular materials in a spherical tumbler segregate into two different patterns of three bands with either small particles at the equator and large particles at the poles or vice versa, depending upon the fill level in the tumbler. Here we use discrete element method (DEM) simulations with supporting qualitative experiments to explore the effect of the tumbler wall roughness on the segregation pattern, modeling the tumbler walls as either a closely packed monolayer of fixed particles resulting in a rough wall, or as a geometrically smooth wall. Even though the tumbler wall is in contact with the flowing layer only at its periphery, the impact of wall roughness is profound. Smooth walls tend toward a small-large-small (SLS) band pattern at the pole-equator-pole at all but the highest fill fractions; rough walls tend toward a large-small-large (LSL) band pattern at all but the lowest fill fractions. This comes about because smooth walls induce poleward axial drift of small particles and an equator-directed drift for large particles, resulting in an SLS band pattern. On the other hand, rough walls result in both sizes of particles moving poleward at the surface of the flow, but due to radial segregation, small particles percolate lower in the flowing layer where there is a return drift toward the equator while large particles remain at the surface near the pole, resulting in an LSL band pattern. The tendency toward either of the two band patterns depends on the fill level in the tumbler and the roughness of the tumbler's bounding wall.


I. INTRODUCTION
Discrete element method (DEM) simulations are used extensively to study the flow and segregation of granular materials in many situations as a predictive tool and access to data that are otherwise difficult to obtain experimentally. One of the key aspects of any simulation approach is the implementation of boundary conditions at walls. Two types of wall boundary conditions can be implemented in DEM simulations: 1) geometrically smooth surfaces [1][2][3][4][5], which are assumed to have infinite mass and a specified radius of curvature (infinite for planar walls); and 2) a geometrically rough surface made up of a closely packed monolayer of fixed particles conforming to the geometry of the wall surface (for example, see [6][7][8][9][10]).
However, a recent study of monodisperse flow in a spherical tumbler suggests that the results using the latter approach, often called a rough wall, differ from those using a smooth wall, not only locally at the particle scale but also globally across the entire flowing layer [11].
In this paper, we explore the impact of rough and smooth walls on the axial segregation of bidisperse particles in a partially-filled spherical tumbler rotating with angular velocity ω about a horizontal axis. We consider the situation where the free surface is essentially flat and continuously flowing. In this regime, the surface of the flowing layer maintains a dynamic angle of repose β with respect to horizontal that depends on the frictional properties, the diameter d of the particles, and the rotational speed of the tumbler [12][13][14][15]. In experiments with spherical tumblers approximately half filled with a 50%-50% size bidisperse mixture of particles and with smooth walls, large particles accumulate near the poles of the tumbler with a band of small particles at the equator [16,17]. This pattern is inverted for lower fill fractions so that small particles accumulate near the poles with a band of large particles at the equator [17]. We note that multiple bands of small and large particles occur for bidisperse mixtures in long rotating cylindrical tumblers, which are used in applications for materials ranging from foodstuffs to mining to cement, typically after O (10) to O(100) rotations and having a wavelength of about one tumbler diameter [18] under a wide range of conditions [18][19][20][21][22][23][24][25]. In the cylindrical tumbler case, however, large particles segregate near the flat end walls of the tumbler [18,23,26], as a consequence of radial segregation combined with the nonuniform axial distribution of velocity in the flowing layer due to friction at the endwall [27]. This mechanism cannot occur in the spherical tumblers studied here.
Although DEM simulations of spherical tumblers with smooth walls readily reproduce the segregation experiments in an acrylic spherical tumbler [17], the mechanism for the inversion of the segregation bands from large-small-large (LSL) for higher fill fractions to small-large-small (SLS) at lower fill fractions remains unresolved. In an initial effort to understand the segregation mechanism, we attempted further DEM simulations at different fill fractions in spherical tumblers. However, to simplify the implementation of the simulations, we performed the simulations with a rough wall boundary conditions. The results are dramatically different from the simulations with smooth walls, as shown in Fig. 1. For otherwise identical systems (same particle sizes, rotational speed, tumbler diameter, and fill fraction), the surface segregation pattern changes from SLS for smooth walls ( Fig. 1(a)) to LSL for rough walls made of 2 mm diameter particles ( Fig. 1(c)). Using an intermediate wall particle size of 1.5 mm results in no significant surface band formation at all ( Fig. 1(b)).
The unexpectedly strong influence of wall roughness on band formation led us to first study monodisperse flows in a spherical tumbler [11]. For monodisperse flows, the wall roughness strongly affects the particles trajectories, even far from walls. Particle trajectories at the free surface curve further toward the poles for smooth walls than for rough walls.
However, the particle trajectories curve back toward the tumbler equator more in the smooth case as well, resulting in a smaller net poleward drift at the surface for smooth walls than for rough walls.
In this paper, we examine through both DEM simulations and qualitative experiments the impact of wall boundary roughness on band formation in bidisperse flows.

II. QUALITATIVE EXPERIMENTS
Since the different results for smooth and rough walls were initially obtained via DEM simulations, it was crucial to confirm that the predicted segregation patterns did in fact occur experimentally. Qualitative experiments were performed using clear acrylic spheres consisting of two mating hemispheres of diameter D = 14 cm rotated by an electric motor at 14.7 rpm about a horizontal axis. The tumbler was filled to 30% by volume with equal volumes of d = 2 mm and d = 4 mm diameter spherical glass particles. For the rough wall case, the small particles were bonded to the wall of a tumbler using epoxy, thereby reducing the tumbler inner diameter to 13.6 cm. The tumbler was rotated for approximately 100 rotations and stopped so that all the particles were in one hemisphere. Then the upper hemisphere of the spherical tumbler was removed to obtain an image of the surface segregation pattern. The SLS segregation pattern occurs for the smooth tumbler wall ( Fig. 2(a)), while the LSL segregation pattern occurs for the rough wall having 2 mm particles bonded to it ( Fig. 2(b)), confirming the validity of the DEM results and the surprising effect of the wall roughness on the segregation pattern.

III. DEM SIMULATIONS
For the DEM simulations, a standard linear-spring and viscous damper force model [28][29][30][31] was used to calculate the normal force between two contacting particles: F ij n = [k n δ − 2γ n m eff (V ij ·r ij )]r ij , where δ and V ij are the particle overlap and the relative velocity (V i −V j ) of contacting particles i and j respectively;r ij is the unit vector in the direction between particles i and j; m eff = m i m j /(m i + m j ) is the reduced mass of the two particles; k n = m eff [(π/∆t) 2 + γ 2 n ] is the normal stiffness and γ n = ln e/∆t is the normal damping, where ∆t is the collision time and e is the restitution coefficient [28,30]. A standard tangential force model [29,31] with elasticity was implemented: F t ij = − min(|µF n ij |, |k s ζ|)sgn(V s ij ), where V s ij is the relative tangential velocity of two particles [4], k s is the tangential stiffness, µ the Coulomb friction coefficient and ζ(t) = t t 0 V s ij (t )dt is the net tangential displacement after contact is first established at time t = t 0 . The velocity-Verlet algorithm [30,32] was used to update the position, orientation, and linear and angular velocity of each particle. Tumbler walls were modeled as both smooth surfaces (smooth walls) and as a monolayer of bonded particles (rough walls). Both wall conditions had infinite mass for calculation of the collision force between the tumbling particles and the wall.
The spherical tumbler of radius R = 7 cm was filled to volume fraction f with equal volumes of small and large particles of diameter 2 and 4 mm; gravitational acceleration was g = 9.81 m s −2 ; particle properties correspond to cellulose acetate: density ρ = 1308 kg m −3 , restitution coefficient e = 0.87 [29,33,34]. The two species were initially randomly distributed in the tumbler with a total of about 5 × 10 4 particles in a typical simulation. To avoid a close-packed structure, the particles had a uniform size distribution ranging from 0.95d to 1.05d. Unless otherwise indicated, the friction coefficients between particles and between particles and walls was set to µ = 0.7. The collision time was ∆t =10 −4 s, consistent with previous simulations [2,5,35] and sufficient for modeling hard spheres [30,36,37]. These parameters correspond to a stiffness coefficient k n = 7.32 × 10 4 (N m −1 ) [29] and a damping coefficient γ n = 0.206 kg s −1 . The integration time step was ∆t/50 = 2 × 10 −6 s to meet the requirement of numerical stability [30].

A. Segregation patterns
The steady-state concentration profiles corresponding to the segregation patterns in Fig. 1 are shown in Fig. 3. A relative concentration of 1.0 corresponds to pure particles of one size.
The profiles were obtained by determining the size of the particles intersecting a series of planes perpendicular to the axis of rotation (with x = 0 at the equator of the tumbler) and extracting the volume concentration for each species in that plane. This approach allows a much higher axial resolution for the concentration measurements than can be achieved with simple binning. For the SLS pattern in the smooth wall tumbler ( Fig. 3(a)), the three bands are nearly pure (one particle size or the other for most of the width of each band).
On the other hand, for the LSL pattern in the 2 mm rough wall tumbler ( Fig. 3(c)), the bands are less pure and the transition between bands is not as sharp. The 1.5 mm rough wall ( Fig. 3

B. Axial segregation index
The segregation patterns develop over time. A convenient means to quantify the degree of segregation and its time evolution is an axial segregation index, defined as: where x i is the position along the rotation axis with the equator at x = 0, v i is the volume of the particle species i, R is the radius of the sphere, and the summations are over the large and small particles. The axial segregation index is positive for LSL and negative for SLS.
The limit for perfect segregation is always less than one, but depends on the fill fraction. For a 50% full tumbler, perfect segregation would result in a segregation index of approximately ±0.4 based on the tumbler's spherical shape. with experimental results for smooth walls [17]. Further note that LSL segregation (I > 0) is achieved more slowly than SLS segregation (I < 0) for a smooth wall, also consistent with experimental results [17]. However, this is to be expected because the time that particles spend in the flowing layer depends directly on the fill fraction. For low fill fractions, particles pass through the flowing layer more frequently than at high fill fractions for the same elapsed time [35]. Since only particles in the flowing layer have the opportunity to rearrange themselves (unlike particles below the flowing layer, which are locked into place in the bed of particles in solid body rotation), one could reasonably expect the segregation pattern to appear more quickly for low fill fractions than for high fill fractions. Similar results occur for rough walls.
The transition between LSL and SLS occurs at different fill fractions depending on the wall roughness (Fig. 6). The fill fraction for transition decreases from 43% for the smooth wall case to 12% for the 4 mm rough wall case. At high fill fractions, the segregation gets close to nearly perfect LSL segregation, regardless of the wall roughness. Increasing the wall roughness favors LSL segregation. At low fill fractions, the degree of SLS segregation depends on the roughness of the wall, with the smooth wall having the greatest segregation.
However the magnitude of the segregation index for SLS segregation is not as large as with LSL segregation, except for smooth walls. Note that fill fractions lower than those shown in  It is helpful to consider the axial segregation index in Fig. 6 in the context of the segregation patterns in Fig. 4. Moving upward along a vertical line in Fig. 6 at a fill fraction of 30% starts with strong SLS segregation corresponding to Fig. 4(a) for a smooth wall. Increasing the wall roughness to 1 mm particles corresponds to the development of a core of small particles at the equator, shown in Fig. 4(b), reducing axial segregation index, though the poles retain nearly pure small particles. For a 1.5 mm rough wall the segregation index is approximately zero, corresponding to a core of small particles surrounded by large particles extending nearly to the poles, shown in Fig. 4(c). A rougher wall of 2 mm particles corresponds to strong LSL segregation in which large particles dominate near the poles, and small particles reach the flowing layer surface at the equator, shown in Fig. 4(d). This sequence is quite different from what occurs for a 50% fill level. There is almost no difference in the axial segregation index at a 50% fill level in Fig. 6 for different wall roughness values. The LSL segregation patterns shown in Fig. 7 at a 50% fill volume for smooth and 2 mm rough walls bear out the similarity in the axial segregation index.

C. Particle trajectories
Previous results for the effect of wall roughness and fill level on particle trajectories in monodisperse flows provide some insights. The trajectories in the flowing layer for both smooth and rough walls are curved [11,35]. This curvature is negligible at the equator (at x = 0 cm, which is a line of symmetry) and increases moving toward the pole. For particles near the surface of the flowing layer, the trajectory curvature for smooth walls is greater than that for rough walls. However, the net poleward drift at the surface with each pass through the flowing layer for rough walls is larger than for smooth walls. Since surface particles drift poleward, particles deeper in the flowing layer drift toward the equator to conserve mass. As the fill level is reduced, the curvature of the particle trajectories increases for all roughnesses, and the poleward drift decreases only for smooth walls but does not change significantly for rough walls.
These monodisperse results can be used to explain the bidisperse segregation patterns.
Two effects compete to select the segregation pattern. First, particles are subject to a depth dependent poleward drift. Since drift is larger at the surface [11], large particles that have segregated to the surface axially drift further poleward than small particles below the surface.
Large particles remain at the surface due to radial segregation, so they accumulate near the poles. Small particles below the surface in the radially segregated core are transported by the global convection cells: from equator to pole nearer the surface and from pole to equator deeper in the flowing layer [35]. The second effect, which counteracts the drift, is the high curvature of the particle trajectories at the surface of the flowing layer, which is typical of smooth walls [11]. It results in both small and large particles being carried further poleward in the upstream portion of the flowing layer. Small particles tend to fall out of the flowing layer sooner than larger particles due to percolation, thus depositing in the fixed bed when they are closer to the poleward extrema of the trajectory than large particles, which tend to stay near the surface to curve back more toward the equator to deposit at an axial position near where they started. It is likely that for smooth walls, this trajectory curvature effect dominates, leading to the SLS pattern; for rough walls, where the trajectory curvature is smaller, the drift is very efficient and dominates [11] leading to the LSL pattern.
To confirm this mechanism, we plot pairs of trajectories for the two species of particles (2 and 4 mm) starting from the same initial positions in the flowing zone near the surface (specifically, when the center of a particle starts within a sphere of radius 1 mm that is 3 mm below the free surface), for tumblers with smooth or with 2 mm rough walls in Figs. 8 and   9. These trajectories are obtained by averaging thousands of individual particle trajectories starting from the same initial coordinate during the first few seconds (between t = 2 s and t = 6 s) of the flow, before radial segregation is achieved. Like the monodisperse case [11], the trajectories in a tumbler with a smooth wall have a larger curvature than those for the rough case (Fig. 8). However, for the bidisperse case, large particles remain at surface while small particles sink deeper into the flowing layer ( Fig. 9), regardless of whether the walls are smooth or rough.
The trajectory curvature results in the large particles tending toward the equator and small particles toward the poles. This is most evident comparing the trajectories starting at x = 0.032 m, in the smooth wall tumbler (Fig. 8(a)). This process dominates for smooth walls, leading to the SLS segregation pattern. The process is quite rapid, and thus is always dominant in the first moments of the flow. This is evident in Fig. 5 where the segregation indices in all cases are negative during the few first seconds, even in the cases of asymptotic LSL segregated systems. It is also evident that fully developed segregation is reached more quickly for SLS than for LSL (also observed experimentally [28]).
On the other hand, for rough walls, the curvature of the trajectories is less, but the net drift of both particles is toward the pole. Due to the radial segregation, large particles stay at the surface, whereas small particles are deeper in the flowing layer where there is a return flow toward the equator. The drift effect tends toward an LSL segregation pattern and dominates when the trajectory curvature is small.
Analogous results occur when varying the fill level. At low fill levels, particle trajectories are more curved [11], which favors SLS segregation. At high fill levels, trajectories are nearly straight [11], so the axial drift, as the particles segregate to different depths, dominates leading to the LSL segregation pattern.
At steady state for either SLS or LSL patterns, the particle trajectories appear to stabilize the pattern. This is shown in Fig. 10 where we compare the trajectories of large and small particles by plotting pairs of trajectories for the two particle species at the boundary between the large and small particles. In the case of smooth walls, the trajectories for the small and large particles diverge substantially even after only one pass through the flowing layer, resulting in displacement toward the pole for small particles, and displacement toward the equator for large particles, reinforcing the SLS pattern. This is not the case for the LSL segregation pattern for rough walls where displacements are very small, and both toward the pole (see inset of Fig. 10(b)). Both species have nearly the same axial position after one pass through the flowing layer. The axial segregation occurs indirectly as a result of the radial segregation in the flowing layer, which keeps large particles near the surface. Consequently, the return flow toward the equator deep in the flowing layer consists of only small particles. Thus small particles reach the surface only near the equator where the core current emerges. The consequence is a very sharp boundary at the surface between small and large particles for the SLS segregation pattern and a more diffuse boundary for the LSL case (see Fig. 10). These differences are also evident in the experimental results in Fig. 2 and [17].

D. Wall friction and roughness
A question that naturally arises is if wall Coulomb friction could play a similar role to wall roughness in the evolution of segregation patterns based on the implicit assumption that a rough wall should have a similar effect to a high coefficient of friction for a smooth wall. To consider this, we use a higher coefficient of friction for wall-particle interactions than for particle-particle interactions in simulations for smooth walls. As shown in Fig. 11, the segregation evolution for a wall coefficient of friction of 1.5 is nearly identical to that for a wall coefficient of friction of 0.7, which is the particle-particle coefficient of friction. Of course, increasing the coefficient of friction too much results in a non-physical situation in which slip will not occur at all. Hence, it is difficult to explain the differences between smooth and rough walls based on a argument that Coulomb friction equivalent to wall roughness, at least within the constraints of the simulations.
Next, we consider the effect of variation of the roughness of the wall. The evolution of the axial segregation index for the different wall roughnesses and a fill level of 30% is shown in Fig. 12 is little influence on the axial segregation index. This is likely because the smaller flowing particles fill the gaps between the larger wall particles, which alters particle trajectories [11].
However, when the wall particles are smaller than the flowing particles, small changes can have significant impact. This is even more evident when considering the asymptotic value of the segregation index, shown in Fig. 13. The greatest impact on the segregation index for a mixture of 2 and 4 mm particles occurs when the wall particle size is between 1 and 2 mm.
It is also interesting to note that the perfectly smooth wall, modeled as spherical smooth surface with infinite mass, behaves similarly to a rough wall of 0.25 mm particles, suggesting that modeling a wall as fixed particles can be effective so long as the wall particles are much smaller than the flowing particles.

E. Size ratio
To further consider the relative size effects of the tumbler and particles, we performed a limited number of simulations with 1 and 2 mm particles in a 14 cm tumbler with both smooth and 1 mm rough walls. Because many more particles are simulated in this situation than with 2 and 4 mm particles, only low fill levels (30%) and shorter runs were feasible. The results in Fig. 14(a) demonstrate that relative particle and tumbler sizes make a difference for both smooth and rough walls. The decrease in particle size induces an increase in the axial segregation index. For example, in the smooth wall case, the 1 and 2 mm particles form an LSL pattern, while 2 and 4 mm particles form an SLS pattern, consistent with experimental results [17]. This is because particle trajectory curvature increases with particles size, but net axial drift remains almost independent of particles size [11], and large curvature trajectories favor SLS segregation. With trajectory curvature being smaller for small particles, a 1+2 mm particle system will adopt the LSL segregation pattern more easily than a 2+4 mm system, as is observed experimentally [28] and shown in Fig. 14(a). Consider now a 7 cm tumbler (30% full) in which the entire system (particle sizes, wall roughness, and tumbler diameter) is scaled to half the size of the previous simulations, compared to a 30% full 2+4 mm system at the same rotation speed. The differences between the large and small systems are relatively small as shown in Fig. 14(b). Results for the same Froude number, F r = ω 2 R/g, for the 7 cm tumbler at 21 rpm and the 14 cm tumbler 15 rpm are also shown in Fig. 14(b). For now, we simply note that conserving the rotation speed while reducing the system size results in similar axial segregation index curves while conserving the Froude Number results in different degrees of axial segregation for different system sizes. Changing the drum size suggests that the key mechanism is probably the differences in the trajectories for large and small particles. Many questions remain including that of why the curvature and drift are so dependent upon the size of the system and rotation speed. Nevertheless, the non-locality of granular flow (wall roughness modifies the trajectories and band formation far from the wall) is evident, as is the case in many other situations for granular pattern formation.