Abstract
Segregation in dense granuar flows occurs due to particles having different properties, with particle size and density playing a dominant role among others such as shape and surface roughness. A good understanding of segregation of realistic materials is required to avoid costly unnecessarily long or re-mixing operations in industrial plants and to predict the evolution of natural hazards like avalanches and pyroclastic flows.
Segregation in sheared granular flows is normally described in terms of kinetic sieving, where the larger particles act as a sieve for smaller particles, and squeeze expulsion, where larger particles are squeezed out of their layer in the opposite direction of the smaller particles. The aim of this research is to better understand the micro-mechanical origins of segregation by numerical simulations and to develop models that can qualitatively predict segregation. The considered system is a monodisperse flow with a single large intruder, effectively removing kinetic sieving, but keeping squeeze expulsion.
The segregation mechanism is visualised by analysing how the intruder size, density and friction affects the granular flow. This is done by converting the discrete particle simulation data into smooth conservative continuum (density, velocity, stress) fields with a technique called coarse graining. These fields show that a large intruder does not fit inside a layer of bulk particles leading to an anisotropic stress field. This observation has inspired new scalings for the lift force on an intruder, proportional to the shear rate and viscosity gradient of the bulk flow. Simulations for many different flows have been performed to confirm this hypothesis.
The fundamental mechanisms discovered in this thesis have improved the understanding of individual particles in granular flows, which can be used to develop more accurate continuum models for segregation. The developed micro-based force model can be used as starting point to develop more sophisticated models that could aid in the engineering of granular materials by balancing size with density and other realistic particle properties with the goal of reducing segregation.
Segregation in sheared granular flows is normally described in terms of kinetic sieving, where the larger particles act as a sieve for smaller particles, and squeeze expulsion, where larger particles are squeezed out of their layer in the opposite direction of the smaller particles. The aim of this research is to better understand the micro-mechanical origins of segregation by numerical simulations and to develop models that can qualitatively predict segregation. The considered system is a monodisperse flow with a single large intruder, effectively removing kinetic sieving, but keeping squeeze expulsion.
The segregation mechanism is visualised by analysing how the intruder size, density and friction affects the granular flow. This is done by converting the discrete particle simulation data into smooth conservative continuum (density, velocity, stress) fields with a technique called coarse graining. These fields show that a large intruder does not fit inside a layer of bulk particles leading to an anisotropic stress field. This observation has inspired new scalings for the lift force on an intruder, proportional to the shear rate and viscosity gradient of the bulk flow. Simulations for many different flows have been performed to confirm this hypothesis.
The fundamental mechanisms discovered in this thesis have improved the understanding of individual particles in granular flows, which can be used to develop more accurate continuum models for segregation. The developed micro-based force model can be used as starting point to develop more sophisticated models that could aid in the engineering of granular materials by balancing size with density and other realistic particle properties with the goal of reducing segregation.
| Original language | English |
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| Qualification | Doctor of Philosophy |
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| Award date | 25 Apr 2019 |
| Place of Publication | Enschede |
| Publisher | |
| Print ISBNs | 978-90-365-4762-8 |
| DOIs | |
| Publication status | Published - 25 Apr 2019 |