Statics and kinematics of granular materials

Journal of Non-Newtonian Fluid Mechanics

Granular materials composed of particles with differing grain sizes, densities, shapes, or surface properties may experience unexpected segregation during flow This review focuses on kinetic sieving and squeeze expulsion, whose combined effect produces the dominant gravity-driven segregation mechanism in dense sheared flows Shallow granular avalanches that form at the surface of more complex industrial flows such as heaps, silos, and rotating drums provide ideal conditions for particles to separate, with large particles rising to the surface and small particles percolating down to the base When this is combined with erosion and deposition, amazing patterns can form in the underlying substrate Gravity-driven segregation and velocity shear induce differential lateral transport, which may be thought of as a secondary segregation mechanism This allows larger particles to accumulate at flow fronts, and if they are more frictional than the fine grains, they can feedback on the bulk flow, causing flow finge

Particle Segregation in Dense Granular Flows

Initial fabrics and their relations to mechanical properties of granular material

There is controversy as to whether a one-dimensional (1D) electron gas can spin polarize in the absence of a magnetic field. Together with a simple model, we present conductance measurements on ultra-low-disorder quantum wires supportive of a spin polarization at B=0. A spin energy gap is indicated by the presence of a feature in the range (0.5-0.7)x2e(2)/h in conductance data. Importantly, it appears that the spin gap is not constant but a function of the electron density. Data obtained using a bias spectroscopy technique are consistent with the spin gap widening further as the Fermi level is increased.

Density dependent spin polarisation in ultra low-disorder quantum wires

Granular materials segregate by size when sheared, which increases the destructive power in avalanches and causes demixing in industrial flows. Here we present a concise theory to describe this phenomenon for systems that for the first time include particles of arbitrary size. The evolution of the grainsize distribution during flow is described based on mass and momentum conservation. The theory is derived in a fivedimensional space, which besides position and time, includes a grainsize coordinate. By coupling the theory with a simple constitutive law we predict the kinematics of the flow, which depends on the grainsize dynamics. We show that the underpinning mechanism controlling segregation is a stress variation with grainsize. The theory, solved by a finite difference scheme, is found to predict the dynamics of segregation consistent with results obtained from discrete element simulations of polydisperse granular flow down inclined planes. Moreover, when applied to bimixtures, the general polydisperse theory reveals the role of grainsize contrast.

Grainsize dynamics of polydisperse granular segregation down inclined planes

When granular materials flow, the constituent particles segregate by size and align by shape. The impacts of these changes in fabric on the flow itself are not well understood, and thus novel non-invasive means are needed to observe the interior of the material. Here, we propose a new experimental technique using dynamic X-ray radiography to make such measurements possible. The technique is based on Fourier transformation to extract spatiotemporal fields of internal particle size and shape orientation distributions during flow, in addition to complementary measurements of velocity fields through image correlation. We show X-ray radiography captures the bulk flow properties, in contrast to optical methods which typically measure flow within boundary layers, as these are adjacent to any walls. Our results reveal the rich dynamic alignment of particles with respect to streamlines in the bulk during silo discharge, the understanding of which is critical to preventing destructive instabilities and undesirable clogging. The ideas developed in this paper are directly applicable to many other open questions in granular and soft matter systems, such as the evolution of size and shape distributions in foams and biological materials.

Dynamic X-ray radiography reveals particle size and shape orientation fields during granular flow.

Virtual and augmented (VAR) technology is in the early stages of being adopted as a teaching platform in higher education. The technology can facilitate immersive learning in environments that are not usually physically accessible to students via 3D models and interactive 360° videos. To date, adoption rates of VAR technology for teaching have not been well described across a higher education institution. Further, there is an absence of information on the optimal VAR laboratory designs and cost per student. In this study, a purpose designed virtual reality laboratory was formed in 2017 at The University of Sydney, housing 26 Oculus Rift headset units. An evaluation was conducted on the design, costs, rates of teaching adoption and student experiences over five teaching periods (2.5 years). Over this period, 4833 students were taught in the laboratory across 7952 student visits. The laboratory was used most by the Faculty of Engineering (53%), followed by the Faculty of Arts & Social Science (23.8%) and Faculty of Science (23.2%). For engineering, the units of study using the laboratory represented only 1.4% of annual faculty subjects offered. This confirms that adoption was in the initial stage of innovation diffusion. The laboratory saw a 250% increase in student numbers over the period of evaluation and 71.5% of students surveyed (n = 295) reported enhanced learning outcomes. The cost per visit was only AU$ 19.50. These findings give confidence to higher education institutions that the right VAR technology infrastructure is a sound educational investment for the future.

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Adoption of virtual reality technology in higher education: An evaluation of five teaching semesters in a purpose-designed laboratory.

The interplay between grain crushing and segregation controls the dynamics of dense granular flows that underpin many natural hazards. We address this issue for the first time by developing a simple lattice model with three interacting rules—for grain crushing, mixing, and segregation. In earthquake faults, particles are trapped, they crush and mix, but do not segregate. In this case the model produces power law distributions, consistent with previous models. When segregation by kinetic sieving is added to the model, we predict depth-dependent lognormal distributions as previously observed, but not explained, in pyroclastic flows, debris flows, rock avalanches, and dry snow avalanches.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2014GL062470

A mixture of crushing and segregation: The complexity of grainsize in natural granular flows

Segregation is a complex and poorly understood phenomenon that is prevalent in many industrial and natural granular flows. When grains flow down a slope [1–5], are spun in a rotating drum [6–8] or shaken in a box [9], we observe those grains organising into intriguing patterns. Kinetic sieving is the dominant mode of segregation in granular avalanches, where separation of particles occurs according to size. Using a cellular automaton we have modelled kinetic sieving as the swapping of particles in a one-dimensional system. From the cellular automaton we have deduced a continuum model to describe the segregation.

Benjy Marks

Papers

Grainsize dynamics of polydisperse granular segregation down inclined planes

Dynamic X-ray radiography reveals particle size and shape orientation fields during granular flow.

Adoption of virtual reality technology in higher education: An evaluation of five teaching semesters in a purpose-designed laboratory.

A mixture of crushing and segregation: The complexity of grainsize in natural granular flows

A cellular automaton for segregation during granular avalanches