Granular Media Research Articles (Page 1)

A modified soil dynamics shear model for planet wheel based on the friction coefficient of granular medium

We investigate wave scattering in a one-dimensional harmonic chain containing a single defect whose effective mass depends dynamically on the local properties of oscillations, specifically frequency, displacement amplitude, and velocity amplitude. Motivated by systems exhibiting inertial nonlinearities, such as resonant metamaterials, granular media, and relativistic plasmas, we analyze how these forms of mass variability affect spectral transmission. For frequency-dependent masses, transparent modes emerge when the defect inertia matches that of the surrounding lattice at a specific frequency, allowing near-flat-band transmission. In amplitude-dependent scenarios, nonlinearity induces reentrant and multistable transmission behavior, with fully transparent modes appearing for critical nonlinear strengths. The case of velocity-dependent mass exhibits features of both prior cases, including isolated high-transmission branches and multistability near the Brillouin zone edge. Our results provide new insight into wave-defect interactions in nonlinear lattices and suggest mechanisms for tunable transmission and nonlinear filtering in structured media.

In recent years, hopping robots have gained significant attention as a way to develop a planetary rover, which is compact and highly traversable. However, hopping on granular media such as regolith has not yet been sufficiently studied due to the difficulty of modeling the dynamic interaction between robots and sand. The purpose of this study is to develop a design method of a hopping robot leg, and a dynamic interaction model in the process. In contrast to the present sand model based on quasi-static measurement, we fitted a model with dynamic measurement and applied it to multiobjective optimization of leg design. In the optimization process, we proposed a novel evaluation index that simultaneously assesses obstacle avoidance and mobility performance, and optimizes along with the index of compactness. Verification experiments were conducted using hopping robots with an optimized design. The model showed better performance and obtained leg mechanism parameters suitable for the robot design. This indicates that our research can be applied to other robots involving dynamic interaction with granular media, such as high-speed legged robots and planetary lander.

The present Letter investigates a weakly pulsed granular system of polystyrene spheres under longtime microgravity conditions on the International Space Station. The spheres are measured using diffusing wave spectroscopy (DWS) and are described by mean square displacements (MSDs). Our aim is to use this technique to provide the first experimental evidence for glassy dynamics in dense granular media in microgravity and subsequently compare these results with ground-based measurements to see how the nature of these dynamics changes without the influence of gravity. Our results show that as we densify the sample in microgravity, glassy dynamics appear at a volume fraction 1.6% lower than on ground. We also show how the influence of gravity can affect how dense a granular system one can prepare by comparing the final jamming point of our sample on the ISS compared to our ground setup. We show that jamming occurs at a volume fraction 0.5% lower in space compared to on ground, showing that we can create denser states when a granular system is in the presence of a stronger gravitational field.

Tactile-based object retrieval from granular media

Controlling structural vibrations remains a major engineering challenge, particularly for applications requiring efficient energy dissipation. While traditional solutions often rely on viscoelastic multilayers, this study introduces an innovative architected beam exploiting a granular medium in shear, where interparticle friction serves as a dissipative mechanism. To adapt this concept to flexural wave control, a composite beam with a granular core was designed. This device confines a granular medium between two beams, with in-phase flexural movements inducing shear in the granular core, thus activating energy dissipation. A nonlinear homogenized model of a three-layer beam was developed, incorporating a previously established granular behavior law. The vibration attenuation performance was compared to that of conventional viscoelastic multilayer systems. Results demonstrate that the granular architecture offers significant energy dissipation through particle shear, outperforming traditional methods in certain frequency ranges. The study also proposes pathways for experimental implementation, with potential applications in fields requiring high-performance vibration control, such as aerospace or civil engineering. This work opens new perspectives in metamaterial design by combining granular mechanics and structural dynamics for customized vibration attenuation.

Sea turtle hatchlings display maneuvering capabilities across diverse aquatic and coastal terrains. While turning behavior is crucial in aquatic environments, it is equally vital for terrestrial locomotion by hatchlings that must quickly navigate obstacle-rich terrain on their way to the sea. This study introduces a robotic prototype that emulates the turning strategies of juvenile sea turtles to optimize turning rate and energy consumption across diverse terrestrial surfaces. The research investigates the rotational displacement capabilities of a bioinspired robot across five distinct gait configurations: one involving all flippers in a unique pattern, and four employing reduced flipper combinations, including front, diagonal, back, and single flippers. We investigated the robot's turning capabilities on diverse granular and compliant media, including four specified rock sizes, a consistent foam platform, and dry sand. Comparative analyses were conducted using rigid and soft flipper designs. Key locomotion features, including roll, pitch, yaw, and lift height, were quantified for each configuration. The results reveal significant differences in rotational behavior across terrains and gait styles, highlighting the interplay between flipper design, gait strategy, and environmental adaptability. This research advances the understanding of bioinspired robotics for applications in complex and variable environments.

Hygroscopic equilibrium time dependence of stability characteristic angles in granular media

Seismic wave experiments in granular media with applications to asteroids

SPH/FEA method for confined and intruded granular media

Multi-harmonic vibration finishing dynamics: System response and granular media behavior

Analytical investigation of anisotropy and slipping contacts in densely-packed granular media

Abstract Entanglement between objects offers a powerful route to programmable collective behavior in granular media. Here, the concept of entangled granular metamaterials—clusters of geometrically complex grains is introduced, whose interlocking interactions drive emergent and tunable properties. The geometric features that promote entanglement are identified, distinguished from simple contact interactions, and the strength of entanglement is quantified through disentanglement dynamics. By varying the number and shape of grains, the degree of interlocking within the cluster is modulated, and its utility is demonstrated in robotic handling tasks. Using a ferromagnetic grain assembly manipulated by an electromagnet, the metamaterial is deployed onto a second, non‐ferromagnetic cluster of complex targets. The grains penetrate and entangle with the targets, enabling robust collective picking and even selective retrieval of individual objects with intricate geometry. Finally, strategies for controlled separation of the grains from the targets are outlined, establishing a foundation for programmable entanglement in matter manipulation.

This study experimentally investigates the drag resistance and lift force acting on a wedge moving horizontally in granular media under low-speed conditions. The results show that the relationship between drag resistance and velocity varies across different wedge shapes, whereas the lift force generally decreases with increasing velocity regardless of the wedge geometry. Notably, during the initial stage of motion, the wedge experiences a reverse lift force, which acts in the opposite direction to that observed under steady-state conditions, and the impulse generated by this reverse lift decays exponentially as velocity increases. In addition, the experimental results indicate that the density of the tested wedge has no significant effect on either drag resistance or lift force. These findings highlight velocity, shape, and penetration depth as the primary factors influencing the forces acting on a wedge in granular media. This study provides important experimental insights for the design and control of robots capable of efficient locomotion in sandy or soft terrains.

We experimentally study the heterogeneity of strain in a granular medium subjected to oscillatory shear in a rotating drum. Two complementary methods are used. The first method relies on optical imaging and grain tracking, allowing us to compute some components of the strain tensor and their variance. The second method, diffuse acoustic wave spectroscopy (DAWS), provides the quadratic strain within the bulk. Our results show that strain is spatially heterogeneous, with fluctuations about ten times larger than the mean, primarily dominated by variability at the grain scale. We then analyze in detail the strain fluctuations occurring during the forward and backward branches of the shear stress cycles, along with the intracycle plastic strain resulting from each cycle. Both methods reveal that each shear cycle consists of two consecutive diffusive-like branches, and that the resulting plastic strain fluctuations scale with the mean plastic shear strain. We propose that plastic strain fluctuations result from irreversible strain heterogeneity that increases with applied shear-reflected in forward-backward strain anticorrelations-but is constrained by load-controlled induced memory.

Artificial intelligence for computational granular media

Quasi-static loading of granular media as a linear complementarity problem

As collections of grains flow, free-surface deformations often develop. These typically suggest the presence of secondary flows, smaller in magnitude than the primary motion but driving complex three-dimensional internal structures. While one can infer such behaviour from boundaries or simulations, we have not previously been able to directly observe secondary flows experimentally. In this paper we present an experimental confirmation of secondary kinematics within granular media using dynamic x-ray radiography, without needing to stop motion for tomography. Specifically, we create a bulldozing mechanism of conveyor-driven grains. This generates a non-uniform, indented free-surface, hinting that secondary mechanisms are at play alongside the primary regime. Discrete element method simulations are shown to be consistent with this secondary-flow explanation. We then probe further experimentally using two perpendicular x-ray source/detector pairs to measure the velocity inside the bulk. This indeed unveils a complex three-dimensional flow pattern that deviates from the primary vertical planes and must include vortices and convection rolls. This advancement is pertinent for industrial and natural scenarios where grains impact obstacles, and has broader relevance for studying the rheology associated with secondary flows in other amorphous materials such as emulsions, pastes and colloids.

Abstract Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. At a recent Lorentz Center Workshop on “Machine Learning for Discrete Granular Media”, researchers explored how machine learning approaches can aid the development of constitutive laws and efficient data-driven surrogates for granular materials while also addressing uncertainty quantification. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, the workshop brought the ML community up to date with GM challenges. This position paper emerged from the workshop discussions. In this position paper, we define granular materials and identify seven key challenges that characterise their distinctive behaviour across various scales and regimes–ranging from gas-like to fluid-like and solid-like. Addressing these challenges is essential for developing robust and efficient models for the digital twinning of granular systems in various industrial applications. To showcase the potential of ML to the GM community, we present classical and emerging machine/deep learning techniques that have been, or could be, applied to granular materials. We reviewed sequence-based learning models for path-dependent constitutive behaviour, followed by encoder-decoder type models for representing high-dimensional data in reduced spaces. We then explore graph neural networks and recent advances in neural operator learning. The latter captures the emerging field evolution of interacting particles via efficient latent space representation. Lastly, we discuss model-order reduction and probabilistic learning techniques for high-dimensional parameterised systems, both of which are crucial for quantifying and incorporating uncertainties arising from physics-based and data-driven models. We present a typical workflow aimed at unifying data structures and modelling pipelines and guiding readers through the selection, training, and deployment of ML surrogates for granular material simulations. Finally, we illustrate the workflow’s practical use with two representative examples, focusing on granular materials in solid-like and fluid-like regimes.

Mechanical properties of spiral wheels interacting with granular media in grain silo robots using DEM-MBD