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Register a new userAutomatic Design of Mechanical Metamaterial Actuators
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Mechanical metamaterials actuators achieve pre-determined input--output operations exploiting architectural features encoded within a single 3D printed element, thus removing the need of assembling different structural components. Despite the rapid progress in the field, there is still a need for efficient strategies to optimize metamaterial design for a variety of functions. We present a computational method for the automatic design of mechanical metamaterial actuators that combines a reinforced Monte Carlo method with discrete element simulations. 3D printing of selected mechanical metamaterial actuators shows that the machine-generated structures can reach high efficiency, exceeding human-designed structures. We also show that it is possible to design efficient actuators by training a deep neural network, eliminating the need for lengthy mechanical simulations. The elementary actuators devised here can be combined to produce metamaterial machines of arbitrary complexity for countless engineering applications.
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Rigidity regulates the integrity and function of many physical and biological systems. This is the first of two papers on the origin of rigidity, wherein we propose that energetic rigidity, in which all non-trivial deformations raise the energy of a structure, is a more useful notion of rigidity in practice than two more commonly used rigidity tests: Maxwell-Calladine constraint counting (first-order rigidity) and second-order rigidity. We find that constraint counting robustly predicts energetic rigidity only when the system has no states of self stress. When the system has states of self stress, we show that second-order rigidity can imply energetic rigidity in systems that are not considered rigid based on constraint counting, and is even more reliable than shear modulus. We also show that there may be systems for which neither first nor second-order rigidity imply energetic rigidity. The formalism of energetic rigidity unifies our understanding of mechanical stability and also suggests new avenues for material design.
The evolution of porous structure and mechanical properties of binary glasses under tensile loading were examined using molecular dynamics simulations. We consider vitreous systems obtained in the process of phase separation after a rapid isochoric quench of a glass-forming liquid to a temperature below the glass transition. The porous structure in undeformed samples varies from a connected porous network to a random distribution of isolated pores upon increasing average glass density. We find that at small strain, the elastic modulus follows a power-law dependence on the average glass density and the pore size distribution remains nearly the same as in quiescent samples. Upon further loading, the pores become significantly deformed and coalesce into larger voids that leads to formation of system-spanning empty regions associated with breaking of the material.
We explore the compaction dynamics of a granular pile after a hard quench from a liquid into the glassy regime. First, we establish that the otherwise athermal granular pile during tapping exhibits annealing behavior comparable to glassy polymer or colloidal systems. Like those other systems, the pile undergoes a glass transition and freezes into different non-equilibrium glassy states at low agitation for different annealing speeds, starting from the same initial equilibrium state at high agitation. Then, we quench the system instantaneously from the highly-agitated state to below the glass transition regime to study the ensuing aging dynamics. In this classical aging protocol, the density increases (i.e., the potential energy of the pile decreases) logarithmically over several decades in time. Instead of system-wide, thermodynamic measures, here we identify the intermittent, irreversible events (quakes) that actually drive the glassy relaxation process. We find that the event rate decelerates hyperbolically, which explains the observed increase in density when the integrated contribution to the downward displacements is evaluated. We argue that such a hyperbolically decelerating event rate is consistent with a log-Poisson process, also found as a universal feature of aging in many thermal glasses.
In this article we explore how structural parameters of composites filled with one-dimensional, electrically conducting elements (such as sticks, needles, chains, or rods) affect the percolation properties of the system. To this end, we perform Monte Carlo simulations of asymmetric two-dimensional stick systems with anisotropic alignments. We compute the percolation probability functions in the direction of preferential orientation of the percolating objects and in the orthogonal direction, as functions of the experimental structural parameters. Among these, we considered the average length of the sticks, the standard deviation of the length distribution, and the standard deviation of the angular distribution. We developed a computer algorithm capable of reproducing and verifying known theoretical results for isotropic networks and which allows us to go beyond and study anisotropic systems of experimental interest. Our research shows that the total electrical anisotropy, considered as a direct consequence of the percolation anisotropy, depends mainly on the standard deviation of the angular distribution and on the average length of the sticks. A conclusion of practical interest is that we find that there is a wide and well-defined range of values for the mentioned parameters for which it is possible to obtain reliable anisotropic percolation under relatively accessible experimental conditions when considering composites formed by dispersions of sticks, oriented in elastomeric matrices.
Many materials that are out of equilibrium can learn one or more inputs that are repeatedly applied. Yet, a common framework for understanding such memories is lacking. Here we construct minimal representations of cyclic memory behaviors as directed graphs, and we construct simple physically-motivated models that produce the same graph structures. We show how a model of worn grass between park benches can produce multiple transient memories---a behavior previously observed in dilute suspensions of particles and charge-density-wave conductors---and the Mullins effect. Isolating these behaviors in our simple model allows us to assess the necessary ingredients for these kinds of memory, and to quantify memory capacity. We contrast these behaviors with a simple Preisach model that produces return-point memory. Our analysis provides a unified method for comparing and diagnosing cyclic memory behaviors across different materials.
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