A multilayer shallow model for dry granular ows with the (I)-rheology: application to granular collapse on erodible beds

In this work we present a multilayer shallow model to approximate the Navier–Stokes equations with the μ(I)μ(I)-rheology through an asymptotic analysis. The main advantages of this approximation are (i) the low cost associated with the numerical treatment of the free surface of the modelled flows, (ii) the exact conservation of mass and (iii) the ability to compute two-dimensional profiles of the velocities in the directions along and normal to the slope. The derivation of the model follows Fernández-Nieto et al. (J. Comput. Phys., vol. 60, 2014, pp. 408–437) and introduces a dimensional analysis based on the shallow flow hypothesis. The proposed first-order multilayer model fully satisfies a dissipative energy equation. A comparison with steady uniform Bagnold flow – with and without the sidewall friction effect – and laboratory experiments with a non-constant normal profile of the downslope velocity demonstrates the accuracy of the numerical model. Finally, by comparing the numerical results with experimental data on granular collapses, we show that the proposed multilayer model with the μ(I)μ(I)-rheology qualitatively reproduces the effect of the erodible bed on granular flow dynamics and deposits, such as the increase of runout distance with increasing thickness of the erodible bed. We show that the use of a constant friction coefficient in the multilayer model leads to the opposite behaviour. This multilayer model captures the strong change in shape of the velocity profile (from S-shaped to Bagnold-like) observed during the different phases of the highly transient flow, including the presence of static and flowing zones within the granular column

Cellular-Automata model for dense-snow avalanches

This paper introduces a three-dimensional model for simulating dense-snow avalanches, based on the numerical method of cellular automata. This method allows one to study the complex behavior of the avalanche by dividing it into small elements, whose interaction is described by simple laws, obtaining a reduction of the computational power needed to perform a three-dimensional simulation. Similar models by several authors have been used to model rock avalanches, mud and lava flows, and debris avalanches. A peculiar aspect of avalanche dynamics, i.e., the mechanisms of erosion of the snowpack and deposition of material from the avalanche is taken into account in the model. The capability of the proposed approach has been illustrated by modeling three documented avalanches that occurred in Susa Valley (Western Italian Alps). Despite the qualitative observations used for calibration, the proposed method is able to reproduce the correct three-dimensional avalanche path, using a digital terrain model, and the order of magnitude of the avalanche deposit volume

Substrate effects from force chain dynamics in dense granular flows

Granular materials are composed of solid, discrete particles and exhibit mechanical behavior that differs from those of fluids and solids. The rheology of granular flows is principal to a suite of natural hazards. Laboratory experiments and numerical models have adequately reproduced several features observed in terrestrial gravity driven geophysical flows; however, quantitative comparison to field observations exposes a failure to explain the high mobility and duration of many of these flows. The ability of a granular material to resist deformation is a function of the force chain network inherent to the material. This investigation addresses the evolutionary character of force chains in unconfined, two-dimensional, gravity driven granular flows. Our particular emphasis concerns the effects of stress localization on the substrate by dynamic force chain evolution and the implications for bed erosion in dense granular flows. Experimental systems employing photoelastic techniques provide an avenue for quantitative force analysis via image processing and provide dataset that can be used validate discrete element modeling approaches. We show that force chains cause extreme bed force localization throughout dynamic granular systems in spatial and temporal space; and that these localized forces can propagate extensively into the substrate, even ahead of the flow front.M.S.Committee Chair: Dufek, Josef; Committee Member: Frankel, Kurt; Committee Member: Newman, And