Yuri Nahmad-Molinari

Clustering-Induced Attraction in Granular Mixtures of Rods and Spheres

Depletion-induced aggregation of rods enhanced by clustering is observed to produce a novel model of attractive pairs of rods separated by a line of spheres in a quasi-2D, vertically-shaken, granular gas of rods and spheres. We show that the stability of these peculiar granular aggregates increases as a function of shaking intensity. Velocity distributions of spheres inside and outside of a pair of rods trapping a line of spheres show a clear suppression of the momentum acquired by the trapped spheres. The condensed phase formed between the rods is caused by a clustering instability of the trapped spheres, enhanced by a vertical guidance produced by the confining rods. The liberated area corresponding to direct excluded-volume pairs and indirect depletion-aggregated pairs is measured as a function of time. The stability of rod pairs mediated by spheres reveals an attraction comparable in strength to the one purely induced by depletion forces.

Machine Vision for Size Distribution Determination of Spherically Shaped Particles in Dense-Granular Beds, Oriented to Pelletizing Process Automation

A machine vision has been devised for determination of the size distribution of spherically shaped pellets in granular beds under static and dynamic conditions. This machine vision involved establishing the optimal distance between the illumination source and the top layer of the granular bed, as well as the development of the data image acquisition software and control systems, I/O interface hardware, and electromechanical system. The size distribution of pellets given by the whole system gives an error lower than 5% for pellets. The size distribution histogram or the Sauter diameter of the pellets given by the vision system is used as the input signal for controlling an electromechanical scanning device, which can be reliably used for automation tasks. The system software was developed for open access on a commercially widely used platform. As the machine vision is robust, it can be used in industrial environments and it has the potential to contribute in improving and optimizing pelletizing industrial processes. This machine vision is reliable, flexible, user friendly, inexpensive, and easy to implement.

Effective potentials in a bidimensional vibrated granular gas.

We present a numerical study of the spatial correlations of a quasi-two-dimensional granular fluid kept in a nonstatic steady state via vertical shaking. The simulations explore a wide range of vertical accelerations, restitution coefficients, and packing fractions, always staying below the crystallization limit. From the simulations we obtain the relevant pair distribution functions (PDFs), and effective potentials for the interparticle interaction are extracted from these PDFs via the Ornstein-Zernike equation with the Percus-Yevick closure. The correlations in the granular structures originating from these effective potentials are checked against the originating PDF using standard Monte Carlo simulations, and we find in general an excellent agreement. The resulting effective potentials show an increase of the spatial correlation at contact with the decreasing values of the restitution coefficient, and a tendency of the potentials to display deeper wells for more dissipative dynamics. A general exception to this trend appears for a range of values of the forcing, which depends on the restitution coefficient, but not on the density, where resonant bouncing increases correlations, resulting in deeper potential wells. The nature of these resonances is explored and shown to be the result of synchronization in the parabolic flights of the particles.