A Coupled CFD-DEM Simulation of Immersed Granular Collapse

<p>Natural disasters normally involve the flow of polydispersed granular materials with interstitial fluid which may change the flow dramatically. Here we focus on a typical small-scale case of fluid–particle mixture flows, i.e., the immersed granular collapse using computational fluid dynamics coupled with discrete element method (CFD-DEM). The simulation parameters are calibrated with laboratory experiments and the immersed granular collapse process is reproduced in terms of different aspect ratios. We present a deeper investigation of the collapse based on simulation results. The granular front evolves in three stages, i.e., acceleration, steady propagation, and deceleration. We found that the constant propagation stage is maintained by the transition of particles’ motion from vertical to horizontal and the drag of the fluid. The constant propagation velocity is proportional to the free-fall velocity with a Stokes-number-dependent coefficient and the normalized final runout is linearly correlated with the densimetric Froude number. These conclusions may find its significance in geophysical applications.</p>

Combined Migrations and Time-Depth Conversions in GPR Prospecting: Application to Reinforced Concrete

In this paper, we propose the combination of different migration results achieved on the same data in order to account for different values of the propagation velocities of the electromagnetic waves within the considered Ground Penetrating Radar (GPR) profile. These different values can be the result of a variable lithological composition or (more probably for short Bscans) the results of different moisture levels, or both. Here, we separately consider the two cases of horizontal or vertical variability of the propagation velocity with a transition zone between two zones with constant propagation velocity. Moreover, we also propose a time-depth conversion accounting for these different values of the propagation velocity along the considered GPR Bscan. The method is applied to real data gathered in the field with regard to a concrete coverage containing liner layers.

Analyzing JavaScript Programs Using Octagon Domain

Static analyzers for JavaScript use constant propagation and interval domains to dis- cover numerical properties of program variables. These domains are non-relational and incapable of tracking relationships between variables, leading to imprecise analysis. This paper presents a static analyzer for the full language of JavaScript that employs the octagon domain to capture numerical properties of the program. Our work is built on top of TAJS (type analyzer for JavaScript) which employs a constant propagation domain for numerical properties. We reengineered TAJS’s abstract domain for abstractions of primitive values and its abstract domain for object abstractions and related transfer functions, resulting in an analyzer that is much more precise. Our experiments show an improvement in analysis precision of JavaScript programs with an acceptable increase in cost.