James G. Puckett

Equilibrating Temperaturelike Variables in Jammed Granular Subsystems

Although jammed granular systems are athermal, several thermodynamiclike descriptions have been proposed which make quantitative predictions about the distribution of volume and stress within a system and provide a corresponding temperaturelike variable. We perform experiments with an apparatus designed to generate a large number of independent, jammed, two-dimensional configurations. Each configuration consists of a single layer of photoelastic disks supported by a gentle layer of air. New configurations are generated by cyclically dilating, mixing, and then recompacting the system through a series of boundary displacements. Within each configuration, a bath of particles surrounds a smaller subsystem of particles with a different interparticle friction coefficient than the bath. The use of photoelastic particles permits us to find all particle positions as well as the vector forces at each interparticle contact. By comparing the temperaturelike quantities in both systems, we find compactivity (conjugate to the volume) does not equilibrate between the systems, while the angoricity (conjugate to the stress) does. Both independent components of the angoricity are linearly dependent on the hydrostatic pressure, in agreement with predictions of the stress ensemble.

Searching for effective forces in laboratory insect swarms

Collective animal behaviour is often modeled by systems of agents that interact via effective social forces, including short-range repulsion and long-range attraction. We search for evidence of such effective forces by studying laboratory swarms of the flying midge Chironomus riparius. Using multi-camera stereoimaging and particle-tracking techniques, we record three-dimensional trajectories for all the individuals in the swarm. Acceleration measurements show a clear short-range repulsion, which we confirm by considering the spatial statistics of the midges, but no conclusive long-range interactions. Measurements of the mean free path of the insects also suggest that individuals are on average very weakly coupled, but that they are also tightly bound to the swarm itself. Our results therefore suggest that some attractive interaction maintains cohesion of the swarms, but that this interaction is not as simple as an attraction to nearest neighbours.

Local origins of volume fraction fluctuations in dense granular materials

Fluctuations of the local volume fraction within granular materials have previously been observed to decrease as the system approaches jamming. We experimentally examine the role of boundary conditions and interparticle friction μ on this relationship for a dense granular material of bidisperse particles driven under either constant volume or constant pressure. Using a radical Voronoï tessellation, we find the variance of the local volume fraction Φ monotonically decreases as the system becomes more dense, independent of boundary condition and μ. We examine the universality and origins of this trend using experiments and the recent granocentric model [M. Clusel, E. I. Corwin, A. O. N. Siemens, and J. Brujić, Nature (London) 460, 611 (2009); E. I. Corwin, M. Clusel, A. O. N. Siemens, and J. Brujić, Soft Matter 6, 2949 (2010)], modified to draw particle locations from an arbitrary distribution P(s) of neighbor distances s. The mean and variance of the observed P(s) are described by a single length scale controlled by ̅Φ. Through the granocentric model, we observe that diverse functional forms of P(s) all produce the trend of decreasing fluctuations, but only the experimentally observed P(s) provides quantitative agreement with the measured Φ fluctuations. Thus, we find that both P(s) and P(Φ) encode similar information about the ensemble of observed packings and are connected to each other by the local granocentric model.

Evolution of network architecture in a granular material under compression.

As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. Force chains are a prime example of such structures, and are thought to constrain bulk properties such as mechanical stability and acoustic transmission. However, capturing and characterizing the evolving nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. A growing body of work has shown that graph theoretic approaches may provide a useful foundation for tackling these problems. Here, we extend the current approaches by utilizing multilayer networks as a framework for directly quantifying the progression of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and interparticle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the changes in this structure throughout the compression process. We separately consider the network of normal and tangential forces, and find that they display a different progression throughout compression. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be achieved by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than a purely local measure of interparticle forces alone. The results discussed throughout this study suggest that these network science techniques may provide a direct way to compare and classify data from systems under different external conditions or with different physical makeup.

Determining asymptotically large population sizes in insect swarms

Social animals commonly form aggregates that exhibit emergent collective behaviour, with group dynamics that are distinct from the behaviour of individuals. Simple models can qualitatively reproduce such behaviour, but only with large numbers of individuals. But how rapidly do the collective properties of animal aggregations in nature emerge with group size? Here, we study swarms of Chironomus riparius midges and measure how their statistical properties change as a function of the number of participating individuals. Once the swarms contain order 10 individuals, we find that all statistics saturate and the swarms enter an asymptotic regime. The influence of environmental cues on the swarm morphology decays on a similar scale. Our results provide a strong constraint on how rapidly swarm models must produce collective states. But our findings support the feasibility of using swarms as a design template for multi-agent systems, because self-organized states are possible even with few agents.

Photoelastic force measurements in granular materials

Photoelastic techniques are used to make both qualitative and quantitative measurements of the forces within idealized granular materials. The method is based on placing a birefringent granular material between a pair of polarizing filters, so that each region of the material rotates the polarization of light according to the amount of local stress. In this review paper, we summarize the past work using the technique, describe the optics underlying the technique, and illustrate how it can be used to quantitatively determine the vector contact forces between particles in a 2D granular system. We provide a description of software resources available to perform this task, as well as key techniques and resources for building an experimental apparatus.

Intrinsic fluctuations and driven response of insect swarms.

Animals of all sizes form groups, as acting together can convey advantages over acting alone; thus, collective animal behavior has been identified as a promising template for designing engineered systems. However, models and observations have focused predominantly on characterizing the overall group morphology, and often focus on highly ordered groups such as bird flocks. We instead study a disorganized aggregation (an insect mating swarm), and compare its natural fluctuations with the group-level response to an external stimulus. We quantify the swarms frequency-dependent linear response and its spectrum of intrinsic fluctuations, and show that the ratio of these two quantities has a simple scaling with frequency. Our results provide a new way of comparing models of collective behavior with experimental data.

Trajectory entanglement in dense granular materials

The particle-scale dynamics of granular materials have commonly been characterized by the self-diffusion coefficient D. However, this measure discards the collective and topological information known to be an important characteristic of particle trajectories in dense systems. Direct measurement of the entanglement of particle space–time trajectories can be obtained via the topological braid entropy Sbraid, which has previously been used to quantify mixing efficiency in fluid systems. Here, we investigate the utility of Sbraid in characterizing the dynamics of a dense, driven granular material at packing densities near the static jamming point J. From particle trajectories measured within a two-dimensional granular material, we typically observe that Sbraid is well defined and extensive. However, for systems where , we find that Sbraid (like D) is not well defined, signifying that these systems are not ergodic on the experimental timescale. Both Sbraid and D decrease with either increasing packing density or confining pressure, independent of the applied boundary condition. The related braiding factor provides a means to identify multi-particle phenomena such as collective rearrangements. We discuss possible uses for this measure in characterizing granular systems.

Statistical Theory of Correlations in Random Packings of Hard Particles

A random packing of hard particles represents a fundamental model for granular matter. Despite its importance, analytical modeling of random packings remains difficult due to the existence of strong correlations which preclude the development of a simple theory. Here, we take inspiration from liquid theories for the n-particle angular correlation function to develop a formalism of random packings of hard particles from the bottom up. A progressive expansion into a shell of particles converges in the large layer limit under a Kirkwood-like approximation of higher-order correlations. We apply the formalism to hard disks and predict the density of two-dimensional random close packing (RCP), ϕ(rcp) = 0.85 ± 0.01, and random loose packing (RLP), ϕ(rlp) = 0.67 ± 0.01. Our theory also predicts a phase diagram and angular correlation functions that are in good agreement with experimental and numerical data.

Generating ensembles and measuring mixing in a model granular system

A major open question in the field of granular materials is the identification of relevant state variables which can predict macroscopic behavior. We experimentally investigate the mixing properties of an idealized granular liquid in the vicinity of its jamming transition, through the generation of ensembles of configurations under various boundary conditions. Our apparatus consists of a two‐dimensional aggregate of particles which rearrange under agitation from the outer boundaries. As expected, the system acts like a slow liquid at low pressure or low packing fraction, and jams at higher pressure or high packing fraction. We characterize mixing in the system by computing the topological entropy of the braids formed by the trajectories of the grains. This entropy is shown to be well‐defined and very sensitive to the approach to jamming, reflecting the dynamical arrest of the assembly.