Mechanism of anomalous sinking of an intruder in a granular packing close to incipient fluidization
- Journal Article
Rights / licenseIn Copyright - Non-Commercial Use Permitted
Objects released into a granular packing close to incipient fluidization may float or sink depending on their density. Contrary to intuition, Oshitani et al. [Phys. Rev. Lett. 116, 068001 (2016)10.1103/PhysRevLett.116.068001] reported that under certain conditions, a lighter sphere can sink further and slower than a heavier one. While this phenomenon has been attributed to a local fluidization around the sinking sphere, its physical mechanisms have not yet been understood. Here, we studied this intriguing phenomenon using both magnetic resonance imaging and discrete particle simulation. Our findings suggest that local fluidization around the sinking sphere and the formation and detachment of gas bubbles play a critical role in driving this anomaly. An analysis of forces acting on the intruder revealed that the upward-directed fluid force acting on a sphere is almost fully counterbalanced by the sum of the net contact forces and the gravitational force acting downward, when the sphere density is close to the bulk density of the granular packing (ρsphere/ρbulk≈1). At the time when bubbles detach from the sphere, the gas pressure gradient experienced by the sphere is slightly attenuated and the sphere is pushed downward by the particle cap located on top of the sphere. Because the deviations from the force equilibrium are small, the sphere sinks slowly. Even after the sphere has reached its final stable depth, local fluidization in combination with bubble formation remains in the proximity of the sphere. Show more
Journal / seriesPhysical Review Fluids
Pages / Article No.
PublisherAmerican Physical Society
Organisational unit03865 - Müller, Christoph R. / Müller, Christoph R.
03628 - Prüssmann, Klaas P. / Prüssmann, Klaas P.
182692 - Understanding multi-phase particulate systems: from (reactive) gas-fluidized beds to dense suspensions via advanced magnetic resonance imaging (MRI) and Lagrangian modeling (SNF)
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