Structural aging of a cohesive and amorphous granular solid under cyclic loading

arXiv (Cornell University) · 2026-03-09

We investigate how cyclic loading evolves the structure and deformation behaviors of a granular raft composed of particles floating at an air-oil interface. The raft has a disordered particle packing structure, and is cohesive due to capillary interactions between particles. Under uniaxial cyclic loading with a small strain amplitude, the raft's packing structure experiences an aging process characterized by logarithmically increasing packing fraction and decreasing structural heterogeneity. The observed structural change is due to particle dynamics that are organized around morphologically evolving voids in the raft. The raft is then subjected to quasi-static tension or compression tests until failure. In comparison with non-aged rafts, the rafts that experienced cyclic loading show a higher strength, higher stiffness, and lower ductility, along with qualitatively different features, such as a stress overshoot in the loading curve.

Structural aging of a cohesive and amorphous granular solid under cyclic loading

Open MIND · 2026-03-09

We investigate how cyclic loading evolves the structure and deformation behaviors of a granular raft composed of particles floating at an air-oil interface. The raft has a disordered particle packing structure, and is cohesive due to capillary interactions between particles. Under uniaxial cyclic loading with a small strain amplitude, the raft's packing structure experiences an aging process characterized by logarithmically increasing packing fraction and decreasing structural heterogeneity. The observed structural change is due to particle dynamics that are organized around morphologically evolving voids in the raft. The raft is then subjected to quasi-static tension or compression tests until failure. In comparison with non-aged rafts, the rafts that experienced cyclic loading show a higher strength, higher stiffness, and lower ductility, along with qualitatively different features, such as a stress overshoot in the loading curve.

Structural aging of a cohesive and amorphous granular solid under cyclic loading

University of Michigan Library · 2026-01-01

The mechanical response of an amorphous solid to applied loads is important across various industries, such as with many creams, spreads, glasses, and even soil. It is well known that the preparation history of these solids is necessary information to develop a picture of their loading curves, although existing work towards this direction typically uses conditions that do not reflect those seen in practice. As such, we create an experimental granular raft with macroscopic particles that interact with many-body effects and do not assume volume preserving conditions. The particles that were tracked are polydisperse Styrofoam spheres floating at an air-oil interface, and held together via capillary attractions. They are constrained between two floating boundaries, which are also used to deform the collective granular raft. Inducing small amplitude oscillatory deformation allows us to mechanically age the raft, and monitor the resulting changes to the structure and deformation properties. We observe that the evolving structure is strongly tied to the collapsing of voids, and that the aged rafts are stronger, stiffer, and more brittle. The data regarding the positions of the particles across all experiments are stored here as .mat files (requires Matlab).

Collective behavior and memory states in flow networks with tunable bistability

Nature Communications · 2026-04-02

Multistability-induced hysteresis has been widely studied in mechanical systems, but such behavior has proven more difficult to reproduce experimentally in flow networks. Natural flow networks like animal and plant vasculature can exhibit complex nonlinear behavior to facilitate fluid transport, so multistable flows may inform their functionality. To probe such phenomena in an analogous model system, we utilize an electronic network of hysteretic bistable resistors designed to have tunable negative differential resistivity. We demonstrate our system’s capability to generate complex global memory states in the form of voltage patterns, which is mediated by the tunable nonlinearity of each element’s current-voltage characteristic. We investigate avalanching behavior arising from effective interactions, and demonstrate how to encode explicit interactions of arbitrary form by taking advantage of the tunable circuitry design. Interacting multistable elements produce hysteretic behavior that extends beyond the Preisach model. Here, authors present a bistable electrical resistor which is widely tunable and can encode arbitrary interactions, opening pathways for probing hysteresis in complex networks.

Stochastic dynamics of granular hopper flows: A configurational mode controls the stability of clogs

Physical review. E · 2025-02-24 · 3 citations

Granular flows in small-outlet hoppers exhibit several characteristic but poorly understood behaviors: temporary clogs (pauses) where flow stops before later spontaneously restarting, permanent clogs that last indefinitely, and non-Gaussian, nonmonotonic flow-rate statistics. These aspects have been studied independently, but a model of hopper flow that explains all three has not been formulated. Here, we introduce a phenomenological model that provides a unifying dynamical mechanism for all three behaviors: coupling between the flow rate and a hidden mode that controls the stability of clogs. In the theory, flow rate evolves according to Langevin dynamics with multiplicative noise and an absorbing state at zero flow, conditional on the hidden mode. The model fully reproduces the statistics of pause and clog events of a large (>40000 flows) experimental dataset, including nonexponentially distributed clogging times and non-Gaussian flow rate distribution, and explains the stretched-exponential growth of the average clogging time with outlet size. Further, we identify the physical nature of the hidden mode in microscopic configurational features, including size and smoothness of the static arch structure formed during pauses and clogs. Our work provides a unifying framework for several poorly understood clogging phenomena, and suggests numerous new paths toward further understanding of this complex system.

Collective Behavior and Memory States in Flow Networks with Tunable Bistability

Research Square · 2025-05-15 · 1 citations

Disorder enhances the fracture toughness of 2D mechanical metamaterials

PNAS Nexus · 2025-01-28 · 20 citations

Abstract Mechanical metamaterials with engineered failure properties typically rely on periodic unit cell geometries or bespoke microstructures to achieve their unique properties. We demonstrate that intelligent use of disorder in metamaterials leads to distributed damage during failure, resulting in enhanced fracture toughness with minimal losses of strength. Toughness depends on the level of disorder, not a specific geometry, and the confined lattices studied exhibit a maximum toughness enhancement at an optimal level of disorder. A mechanics model that relates disorder to toughness without knowledge of the crack path is presented. The model is verified through finite element simulations and experiments utilizing photoelasticity to visualize damage during failure. At the optimal level of disorder, the toughness is more than 2.6× of an ordered lattice of equivalent density.

Cornerstones are the key stones: using interpretable machine learning to probe the clogging process in 2D granular hoppers

Soft Matter · 2025-01-01 · 4 citations

The sudden arrest of flow by formation of a stable arch over an outlet is a unique and characteristic feature of granular materials. Previous work suggests that grains near the outlet randomly sample configurational flow microstates until a clog-causing flow microstate is reached. However, factors that lead to clogging remain elusive. Here we experimentally observe over 50 000 clogging events for a tridisperse mixture of quasi-2D circular grains, and utilize a variety of machine learning (ML) methods to search for predictive signatures of clogging microstates. This approach fares just modestly better than chance. Nevertheless, our analysis using linear Support Vector Machines (SVMs) highlights the position of potential arch cornerstones as a key factor in clogging likelihood. We verify this experimentally by varying the position of a fixed (cornerstone) grain, which we show non-monotonically alters the average time and mass of each flow by dictating the size of feasible flow-ending arches. Positioning this grain correctly can even increase the ejected mass by 70%. Our findings suggest a bottom-up arch formation process, and demonstrate that interpretable ML algorithms like SVMs, paired with experiments, can uncover meaningful physics even when their predictive power is below the standards of conventional ML practice.

Editorial: Statistical and Nonlinear Physics Crosses a Threshold

Physical review. E · 2025-08-13

Collective Behavior and Memory States in Flow Networks with Tunable Bistability

ArXiv.org · 2025-02-08

Multistability-induced hysteresis has been widely studied in mechanical systems, but such behavior has proven more difficult to reproduce experimentally in flow networks. Natural flow networks like animal and plant vasculature can exhibit complex nonlinear behavior to facilitate fluid transport, so multistable flows may inform their functionality. To probe such phenomena in an analogous model system, we utilize an electronic network of hysteretic bistable resistors designed to have tunable negative differential resistivity. We demonstrate our system's capability to generate complex global memory states in the form of voltage patterns, which is mediated by the tunable nonlinearity of each element's current-voltage characteristic. We investigate avalanching behavior arising from effective interactions, and demonstrate how to encode explicit interactions of arbitrary form by taking advantage of the tunable circuitry design.