Tracer diffusion in granular shear flows

Tracer diffusion in a granular gas in simple shear flow is analyzed. The analysis is made from a perturbation solution of the Boltzmann kinetic equation through first order in the gradient of the mole fraction of tracer particles. The reference state (zeroth-order approximation) corresponds to a Sonine solution of the Boltzmann equation, which holds for arbitrary values of the restitution coefficients. Due to the anisotropy induced in the system by the shear flow, the mass flux defines a diffusion tensor $D_{ij}$ instead of a scalar diffusion coefficient. The elements of this tensor are given in terms of the restitution coefficients and mass and size ratios. The dependence of the diffusion tensor on the parameters of the problem is illustrated in the three-dimensional case. The results show that the influence of dissipation on the elements $D_{ij}$ is in general quite important, even for moderate values of the restitution coefficients. In the case of self-diffusion (mechanically equivalent particles), the trends observed in recent molecular dynamics simulations are similar to those obtained here from the Boltzmann kinetic theory.Comment: 5 figure

Granular temperature in a gas fluidized bed

In this paper, we present an innovative approach coupling active contours with an ontological representation of knowledge, in order to understand scenes acquired by a moving camera and containing multiple non-rigid objects evolving over space and time. The developed active contours enable both segmentation and tracking of multiple targets in each captured scene over a video sequence with unknown camera calibration. Hence, this active contour technique provides information on the objects of interest as well as on parts of them (e.g. shape and position), and contains simultaneously low-level characteristics such as intensity or color features. The ontology we propose consists of concepts whose hierarchical levels map the granularity of the studied scene and of a set of inter- and intra-object spatial and temporal relations defined for this framework, object and sub-object characteristics e.g. shape, and visual concepts like color. The system obtained by coupling this ontology with active contours can study dynamic scenes at different levels of granularity, both numerically and semantically characterize each scene and its components i.e. objects of interest, and reason about spatiotemporal relations between them or parts of them. This resulting knowledge-based vision system was demonstrated on real-world video sequences containing multiple mobile highly-deformable objects

Granular temperature in a gas fluidized bed

© Springer-Verlag 2007Mark J. Biggs, Don Glass, L. Xie, Vladimir Zivkovic, Alex Buts and M. A. Curt Kounder