The Edwards volume ensemble in cyclically sheared granular experiments.

We experimentally investigate the Edwards volume ensemble in cyclically sheared bidisperse disks of two friction coefficients (µ ≈ 0.3 and µ → ∞) subjected to a range of shear amplitudes γm. Despite the local and global anisotropy, hysteresis, and the potential long-range correlation of the free volume, the Edwards volume ensemble surprisingly provides an excellent statistical description of disk packings in cyclically sheared systems. Our finding can be better understood from the comprehensive analysis of the geometric and statistical properties of Voronoi cells of individual particles. First, the average degrees of anisotropy of Voronoi cells are weak at both the microscopic and macroscopic scales within a range of shear amplitudes γm of up to γm = 12% regardless of the inter-particle friction coefficients µ even though the azimuthal distributions of the Voronoi cell depend on µ. Second, there is only negligible hysteresis of global compactivity and volume fluctuations. Finally, the spatial correlations of the free volume and the orientation are weakly anisotropic and short ranged for practical purposes. Both results are independent of µ. Interestingly, our free-volume statistical results are consistent with the simple physical picture that the free volume is directly proportional to the compactivity.

Turbulent-like velocity fluctuations in two-dimensional granular materials subject to cyclic shear.

We perform a systematic experimental study to investigate the velocity fluctuations in the two-dimensional granular matter of low and high friction coefficients subjected to cyclic shear of a range of shear amplitudes, whose velocity fields are strikingly turbulent-like with vortices of different scales. The scaling behaviors of both the transverse velocity power spectra ET(k) ∝ k-αT and, more severely, the longitudinal velocity power spectra EL(k) ∝ k-αL are affected by the prominent peak centered around k ≈ 2π of the inter-particle distance due to the static structure factor of the hard-particle nature in contrast to the real turbulence. To reduce the strong peak effect to the actual values of αν (the subscript 'ν' refers to either T or L), we subsequently analyze the second-order velocity structure functions of S(2)ν(r) in real space, which show the power-law scalings of S(2)ν(r) ∝ rßν for both modes. From the values of ßν, we deduce the corresponding αν from the scaling relations of αν = ßν + 2. The deduced values of αν increase continuously with the shear amplitude γm, showing no signature of yielding transition, and are slightly larger than αν = 2.0 at the limit of γm → 0, which corresponds to the elastic limit of the system, for all γm. The inter-particle friction coefficients show no significant effect on the turbulent-like velocity fluctuations. Our findings suggest that the turbulent-like collective particle motions are governed by both the elasticity and plasticity in cyclically sheared granular materials.

An Overview on Fatigue of High-Entropy Alloys.

Due to their distinct physical, chemical, and mechanical features, high-entropy alloys have significantly broadened the possibilities of designing metal materials, and are anticipated to hold a crucial position in key engineering domains such as aviation and aerospace. The fatigue performance of high-entropy alloys is a crucial aspect in assessing their applicability as a structural material with immense potential. This paper provides an overview of fatigue experiments conducted on high-entropy alloys in the past two decades, focusing on crack initiation behavior, crack propagation modes, and fatigue life prediction models.

SP-GNN: Learning structure and position information from graphs.

Graph neural network (GNN) is a powerful model for learning from graph data. However, existing GNNs may have limited expressive power, especially in terms of capturing adequate structural and positional information of input graphs. Structure properties and node position information are unique to graph-structured data, but few GNNs are capable of capturing them. This paper proposes Structure- and Position-aware Graph Neural Networks (SP-GNN), a new class of GNNs offering generic and expressive power of graph data. SP-GNN enhances the expressive power of GNN architectures by incorporating a near-isometric proximity-aware position encoder and a scalable structure encoder. Further, given a GNN learning task, SP-GNN can be used to analyze positional and structural awareness of GNN tasks using the corresponding embeddings computed by the encoders. The awareness scores can guide fusion strategies of the extracted positional and structural information with raw features for better performance of GNNs on downstream tasks. We conduct extensive experiments using SP-GNN on various graph datasets and observe significant improvement in classification over existing GNN models.

High-energy velocity tails in uniformly heated granular materials.

We experimentally investigate the velocity distributions of quasi-two-dimensional granular materials uniformly heated by an electromagnetic vibrator, where the translational velocity and the rotation of a single particle are Gaussian and independent. We observe the non-Gaussian distributions of particle velocity, with the density-independent high-energy tails characterized by an exponent of ß=1.50±0.03 for volume fractions of 0.111≤ϕ≤0.832, covering a wide range of structures and dynamics. Surprisingly, our results are not only in excellent agreement with the prediction of the kinetic theories of granular gas but also hold for an extremely high-volume fraction of ϕ=0.832 where the granular material forms a crystalline solid and the kinetic theory of granular gas fails fantastically. Our experiment suggests that the density-independent high-energy velocity tails of ß=1.50 are a characteristic of uniformly heated granular matter.