78 research outputs found
Evolution of force networks in dense granular matter close to jamming
When dense granular systems are exposed to external forcing, they evolve on
the time scale that is typically related to the externally imposed one (shear
or compression rate, for example). This evolution could be characterized by
observing temporal evolution of contact networks. However, it is not
immediately clear whether the force networks, defined on contact networks by
considering force interactions between the particles, evolve on a similar time
scale. To analyze the evolution of these networks, we carry out discrete
element simulations of a system of soft frictional disks exposed to compression
that leads to jamming. By using the tools of computational topology, we show
that close to jamming transition, the force networks evolve on the time scale
which is much faster than the externally imposed one. The presentation will
discuss the factors that determine this fast time scale.Comment: to appear in Powders and Grains, 201
Quantitative Measure of Memory Loss in Complex Spatio-Temporal Systems
To make progress in understanding the issue of memory loss and history
dependence in evolving complex systems, we consider the mixing rate that
specifies how fast the future states become independent of the initial
condition. We propose a simple measure for assessing the mixing rate that can
be directly applied to experimental data observed in any metric space . For
a compact phase space , we prove the following statement. If the
underlying dynamical system has a unique physical measure and its dynamics is
strongly mixing with respect to this measure, then our method provides an upper
bound of the mixing rate. We employ our method to analyze memory loss for the
system of slowly sheared granular particles with a small inertial number .
The shear is induced by the moving walls as well as by the linear motion of the
support surface that ensures approximately linear shear throughout the sample.
We show that even if is kept fixed, the rate of memory loss (considered at
the time scale given by the inverse shear rate) depends erratically on the
shear rate. Our study suggests a presence of bifurcations at which the rate of
memory loss increases with the shear rate while it decreases away from these
points. We also find that the memory loss is not a smooth process. Its rate is
closely related to frequency of the sudden transitions of the force network.
The loss of memory, quantified by observing evolution of force networks, is
found to be correlated with the loss of correlation of shear stress measured on
the system scale. Thus, we have established a direct link between the evolution
of force networks and macroscopic properties of the considered system
- …