Particle Coordinates Research Articles

The prediction of dynamical quantities in granular avalanches based on graph neural networks.

The study of granular avalanches in rotating drums is not only essential to understanding various complex behaviors of interest in granular media from a scientific perspective; it also has valuable applications in regard to industrial processes and geological catastrophes. Despite decades of research studies on avalanches, a proper understanding of their dynamic properties still remains a great challenge to scientists due to a lack of state-of-the-art techniques. In this study, we accurately predict the avalanche dynamic features of three-dimensional granular materials in rotating drums, by using graph neural networks on the basis of their initial static microstructures alone. We find that our method is robust to changes in various model parameters, such as the interaction potential, size polydispersity, and noise in particle coordinates. In addition, with the grain-scale velocities obtained either from our network or from numerical simulations, we find an approximately equal and strong correlation between the global velocity and global velocity fluctuation in our 3D granular avalanche systems, which further demonstrates the predictive power of our trained graph neural networks to uncover the fundamental physics of granular avalanches. We expect our method to provide more insight into the avalanche dynamics of granular materials and other amorphous systems in the future.

Microscopic study of the impact of particle morphology on the compressibility of calcareous sands

As a kind of unique marine sediment, Calcareous sand exhibits high compressibility because its irregular particle morphology leads to the formation of large interparticle voids. In this study, using calcareous sand samples from the South China Sea, we employed X-ray CT scanning technology to reveal the microstructural evolution of calcareous sand during compression. This allowed us to establish correlations between particle morphology, relative density, vertical stress, and microstructural parameters. The research findings are as follows: (1) Samples with a higher proportion of cube shape particles have larger initial void ratios, and experience a more significant decrease in void ratio under stress. Dense samples exhibit greater deformation resistance and lower particle breakage rates, resulting in a slower reduction in void ratio under stress. (2) The number of pores in the samples follows a V-shaped trend with an increase in the proportion of cube shape particles, reaching its minimum when the proportions of cube shape and platy shape particles are balanced. The number of pores increases with an increase in vertical stress and relative density. (3) The equivalent pore diameter and pore volume of the samples exhibit an inverted V-shaped trend with an increase in cube shape particle content, reaching their maximum when the proportions of cube shape and platy shape particles are balanced. (4) The more cube shape particles in the sample, the more irregular the pore shapes become. Under vertical stress, the influence of particle morphology on pore shape gradually diminishes until it disappears. (5) The particle coordination number in the samples predominantly falls within the range of 3.79–5.68. The coordination number increases with increasing relative density and vertical stress, with vertical stress exerting a significantly greater influence on the coordination number than relative density. Under vertical stress, samples with more cube shape particles exhibit smaller coordination numbers and higher particle breakage rates.

Simulation of Hydrate Particles Aggregation and Deposition in Gas-Dominated Flow

Summary Owing to low-temperature and high-pressure production environments, hydrate generation, accumulation, and deposition are prone to occur in deepwater oil and gas production wells and transportation pipelines, leading to pipeline blockage and threatening the safety of oil and gas production. To explore the aggregation mechanism and deposition law of hydrate particles in the main gas diversion pipeline, this study considered the adhesion effect of hydrate particles and established a hydrate particle aggregation and deposition model based on theory and experiments. The coupled computational fluid dynamics-discrete element method (CFD-DEM) is used in the simulation calculation. The simulation results were compared with the relevant experimental results, and maximum and average errors of 9.48% and 4.56% were observed, respectively. It was found that the main factor affecting the aggregation of hydrates is the adhesion between particles. As the subcooling temperature increased, the aggregation and adhesion of the hydrate particles increased to varying degrees. The tangential adhesion force between the hydrate aggregate particles was significantly greater than the normal adhesion force, and the adhesion force between the particles gradually increased from the surface to the interior of the aggregates. The coordination number of the hydrate particles can quantitatively characterize the degree of aggregation and is affected by many factors, such as adhesion. By studying the particle coordination number, the evolution of hydrate accumulation and deposition under different conditions can be summarized. Based on the simulation results, the mathematical relationship between different dimensionless numbers and hydrate deposition ratio (HDR) was calculated, and an expression that can predict the HDR was obtained, with an average relative error of 10.155%. This study provides a theoretical basis for predicting the aggregation and deposition of hydrate particles in gas-dominated systems and a reference for the development of hydrate prevention and control plans.

A DEM-based general modeling method and experimental verification for wheat plants in the mature period

Simulation of the wheat harvesting process using the discrete element method is of significant importance in optimizing the components of wheat harvesting machinery. Establishing a discrete element model that can accurately reflect the biomechanical properties of wheat plants is a crucial foundation for simulating harvesting operations. To address the current issues in the discrete element model (DEM) of wheat plants, a general modeling method for wheat plants was developed in this study. Firstly, the shape and size of 10 representative varieties of wheat plants in the mature period from different regions in mainland China to establish the common correlations between the shape and size of wheat plants. On this basis, a geometrical model of wheat plants was built using the particle arrangement method, and the coordinates of the component particles were solved for. Then, the contact mechanical parameters were measured and calibrated through various tests, including the slope test, single-pendulum test, drop test and stacking angle test. Moreover, the parallel bonding mechanical parameters of wheat plants were measured and analyzed by tensile, compression and shear tests. On this basis, the mechanical model of wheat plants was established based on the Hertz-Mindlin contact model and the parallel bonding model (PBM). The geometrical model together with the mechanical model enabled the construction of a DEM-based model of wheat plants with flexible characteristics. Based on the above work, the models that were proposed in this paper for three varieties of wheat were validated by comparing the experimental data and simulation results in terms of the plant stacking test, vibration screening test and impact threshing test. The results demonstrated that the simulation results for all three varieties were similar to the experimental results, which showed the feasibility and effectiveness of the general modeling method proposed in this paper for wheat plants in the mature period. The results of this study lay a foundation for the analysis of the wheat harvest process by the discrete element method.

Research on a new-designed Venturi ejector and dazomet particle motion characteristics for dilute-phase pneumatic conveying systems based on CFD-DEM

To disperse dazomet, a commonly used soil fumigant for soil disinfection, over large soil surfaces, dilute-phase pneumatic conveying can be utilized. Currently, most research has focused on pneumatic conveying systems for large particles, such as coal conveying or pneumatic seeding However, there has been almost no research on the dilute-phase pneumatic conveying of dazomet particles. Therefore, a coupled simulation model based on computational fluid dynamics and discrete element method (CFD-DEM) was established to study the effect of the structural parameters of the Venturi ejector on the motion of particles and airflow fields variation characteristics for the pneumatic conveying system of dazomet particles. Based on the Box–Behnken experimental design method, a 3-factor and 3-level response surface simulation experiment was carried out by selecting the contraction angle, throat diameter, and diffusion angle of the Venturi ejector as factors. The suction-flow ratio, average velocity of particles, average coordinates of particles in the vertical direction, and corresponding standard deviation at the outlet of the pipeline were chosen as response indexes. Multiple linear regression models were established between each factor and response index. The simulation results showed that the impact of factors on the 4 response indexes from strong to weak were throat diameter, contraction angle, and diffusion angle. With decreasing throat diameter, the suction flow ratio and average velocity of the particles increased. The average coordinates of the particles in the vertical direction decreased, while the standard deviation increased, indicating that the pneumatic conveying performance of the system increased. The optimal structural parameters for the contraction angle, throat diameter, and diffusion angle were 34.5°, 15 mm, and 12.0°, respectively. The experimental results demonstrated that the application precisions of the optimised Venturi ejector were 99.23%, 98.46%, and 98.56% at application rates of 15, 20, and 25 g/s, respectively. The results provide a theoretical reference for the structural optimisation of the Venturi ejector and operational performance evaluation of the pneumatic conveying system for dazomet particles.

Physics-informed Bayesian inference of external potentials in classical density-functional theory.

The swift progression and expansion of machine learning (ML) have not gone unnoticed within the realm of statistical mechanics. In particular, ML techniques have attracted attention by the classical density-functional theory (DFT) community, as they enable automatic discovery of free-energy functionals to determine the equilibrium-density profile of a many-particle system. Within classical DFT, the external potential accounts for the interaction of the many-particle system with an external field, thus, affecting the density distribution. In this context, we introduce a statistical-learning framework to infer the external potential exerted on a classical many-particle system. We combine a Bayesian inference approach with the classical DFT apparatus to reconstruct the external potential, yielding a probabilistic description of the external-potential functional form with inherent uncertainty quantification. Our framework is exemplified with a grand-canonical one-dimensional classical particle ensemble with excluded volume interactions in a confined geometry. The required training dataset is generated using a Monte Carlo (MC) simulation where the external potential is applied to the grand-canonical ensemble. The resulting particle coordinates from the MC simulation are fed into the learning framework to uncover the external potential. This eventually allows us to characterize the equilibrium density profile of the system by using the tools of DFT. Our approach benchmarks the inferred density against the exact one calculated through the DFT formulation with the true external potential. The proposed Bayesian procedure accurately infers the external potential and the density profile. We also highlight the external-potential uncertainty quantification conditioned on the amount of available simulated data. The seemingly simple case study introduced in this work might serve as a prototype for studying a wide variety of applications, including adsorption, wetting, and capillarity, to name a few.

A resolved CFD–DEM investigation into the onset of suffusion: effect of confining pressure and stress anisotropy

AbstractThe susceptibility of a granular soil to suffusion is strongly dependent on its grain size distribution (GSD) and the mechanical and hydraulic conditions it is subjected to. This study investigates the onset of suffusion considering the effect of confining pressure and stress anisotropy using a fully resolved computational fluid dynamics and discrete element method (CFD–DEM). Three benchmarks, including the sedimentations of single and two adjacent spheres and the classic one‐dimensional (1D) consolidation are performed to demonstrate the capability of this method for high‐fidelity particle‐fluid simulations. A modified hydraulic criterion for the onset of suffusion considering stress anisotropy is presented. The microstructural changes of soil specimens before and during global suffusion are inspected, with emphasis on the evolutions of particle kinetic energy and displacements, force chain networks, and stress anisotropy. We found that the critical hydraulic gradient is negatively correlated with the confining pressure and the degree of stress anisotropy. Fine particles in the soil matrix are locally detached at small hydraulic gradients before the apparent global suffusion, as manifested by the variation of particle kinetic energy and coordination numbers. The roles of different contact types on force transmission and stress anisotropy in eroded specimens are also examined.

Evolution of particle morphology of quartz sand during one-dimensional compression

Particle breakage alters particle size, shape, surface roughness, and co-ordination number, which in turn changes macro-mechanical behaviour including compressibility and shear strength. This study conducted a series of one-dimensional (1D) compression tests on quartz sands with different initial void ratios, ending the tests at various stress levels up to 13.5 MPa. The size and shape of particles were quantified using a dynamic particle shape analyser, while the surface roughness of thirty randomly selected particles for each sample was measured by an optical interferometer and quantified using flattened root-mean-square roughness (RMSf). The results demonstrate that a unique normal compression line is defined due to extensive particle breakage, with denser sample exhibiting a higher yield stress. Prior to yielding (vertical stress < 6 MPa), the changes in the three shape descriptors are limited, as the primary particle damage modes are abrasion and grinding. Conversely, at high stress level, a greater number of particles undergo splitting or explosive damage, leading to the creation of more irregularly shaped particles. The occurrence of plastic deformation at particle asperities under one-dimensional compression is identified, resulting in a decrease in surface roughness. This study highlights the significant role of the initial void ratio in shaping the evolution of surface roughness of particles under 1D compression.

Orbital structure evolution in self-consistent N-body simulations

ABSTRACT The bar structure in disc galaxies models is formed by different families of orbits; however, it is not clear how these families of orbits support the bar throughout its secular evolution. Here, we analyse the orbital structure on three stellar disc N-body models embedded in a live dark matter halo. During the evolution of the models, discs naturally form a bar that buckles out of the galactic plane at different ages of the galaxy evolution generating boxy, X, peanut, and/or elongated shapes. To understand how the orbit families hold the bar structure, we evaluate the orbital evolution using the frequency analysis on phase space coordinates for all disc particles at different time intervals. We analyse the density maps morphology of the 2:1 family as the bar potential evolves. We showed that the families of orbits providing bar support exhibit variations during different stages of its evolutionary process, specifically prior to and subsequent to the buckling phase, likewise in the secular evolution of the bar. The disc-dominated model develops an internal boxy structure after the first Gyr. Afterwards, the outer part of the disc evolves into a peanut-shape, which lasts till the end of the simulation. The intermediary model develops the boxy structure only after 2 Gyr of evolution. The peanut shape appears 2 Gyr later and evolves slowly. The halo-dominated model develops the boxy structure much later, around 3 Gyr, and the peanut morphology is just incipient at the end of the simulation.

Phase analysis for focus plane detection in digital inline holography: application to three-dimensional locations of drops and threads in a beads-on-a-string structure.

Three-dimensional (3D) phase maps are used in a digital inline holography system to measure the coordinates of tiny particles. The phase signature, previously applied to spherical particles by Yang etal. [Opt. Lett.31, 1399 (2006)OPLEDP0146-959210.1364/OL.31.001399], is extended here to the case of an infinite cylindrical filament to determine the optimal focus plane. The practical implementation of this method is described. Simulations show that this technique provides a very efficient tool to examine the 3D coordinates of micrometric objects. A practical application is given with the investigation of the dynamics of beads-on-a-string structures under an air flow current. These structures, obtained by stretching a viscoelastic fluid element, are of particular interest in this study, since they are characterized by several quasi-spherical beads (i.e., drops) linked by a quasi-cylindrical filament (i.e., thread).

A novel method for measurement of the angle of repose of granular seeds in discrete element methods

Discrete element numerical simulations can help researchers find potential problems in the design phase, shortening the development cycle and reducing costs. In the field of agricultural engineering, more and more researchers are using discrete element methods (DEM) to assist in designing and optimising equipment parameters. Model parameters calibration is a prerequisite for discrete element numerical calculations, and the angle of repose (AoR) is commonly used to calibrate the parameters. However, the measurement of AoR in DEM was not seriously considered in industrial or academic fields. In practice, AoR is measured manually, using 2D digital image processing or using a 3D scan. However, reliable and consistent measurements of AoR in DEM are rarely mentioned. This study suggests an accurate and consistent way to measure AoR in DEM using a novel method to read particle coordinate information directly from the data file; then, the AoR is calculated by linearly fitting the centre coordinates of the outermost particles. Influences of input variables on AoR acquisition are discussed through several examples using customised templates with known angles. Then a comparative study of the accuracy of the measurement of AoR in DEM and the reliability of the parameter calibration results by the manual measurement, 2D digital image processing, and algorithm proposed in this paper was conducted. In case studies with four seed materials, this method prevented the subjective selection of AoR, improved the identification accuracy, and increased the precision and accuracy of DEM calibration. In addition, the time consumption for obtaining AoR using the novel method for measurement is much less than that of 2D.

Numerical Simulation of Mechanical Properties of Soil Considering the Effect of Internal Erosion

The loss of fine particles from the skeleton formed by coarse particles due to seepage action significantly affects the grading, void ratio, and mechanical properties of soil. This results in several issues of engineering hazards. In order to analyze the effect of internal erosion on the mechanical properties of gap-graded soils from macro and micro perspectives, triaxial consolidation and drainage shear tests were simulated in this paper using the particle flow discrete element software PFC3D. A linear contact model was employed to simulate internal erosion by randomly removing fine particles. The results showed that the void ratio of the specimens increased with the erosion degree. The variation in void ratios of the specimens with the erosion degree before loading was greater than those after loading. The peak deviatoric stresses of the specimens decreased with the increase of the erosion degrees. The larger the erosion degree, the more the maximum volumetric strain and the resistance capacity to deformation was also reduced. The average particle coordination number (Z) of the specimens generally tended to decrease as the erosion degree increased. When the average effective stress was not large, the critical state line gradually increased with the erosion degree, while the void ratio was also found to correlate with the erosion degree under the critical state of the specimens with zero average effective stress.

A large expert-curated cryo-EM image dataset for machine learning protein particle picking

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.

Geometrical optics of first-passage functionals of random acceleration.

Random acceleration is a fundamental stochastic process encountered in many applications. In the one-dimensional version of the process a particle is randomly accelerated according to the Langevin equationx[over ̈](t)=sqrt[2D]ξ(t), where x(t) is the particle's coordinate, ξ(t) is Gaussian white noise with zero mean, and D is the particle velocity diffusion constant. Here, we evaluate the A→0 tail of the distribution P_{n}(A|L) of the functional I[x(t)]=∫_{0}^{T}x^{n}(t)dt=A, where T is the first-passage time of the particle from a specified point x=L to the origin, and n≥0. We employ the optimal fluctuation method akin to geometrical optics. Its crucial element is determination of the optimal path-the most probable realization of the random acceleration process x(t), conditioned on specified A,n, and L. The optimal path dominates the A→0 tail of P_{n}(A|L). We show that this tail has a universal essential singularity, P_{n}(A→0|L)∼exp(-α_{n}L^{3n+2}/DA^{3}), where α_{n} is an n-dependent number which we calculate analytically for n=0, 1, and 2 and numerically for other n. For n=0 our result agrees with the asymptotic of the previously found first-passage time distribution.

X-ray 3D imaging–based microunderstanding of granular mixtures: Stiffness enhancement by adding small fractions of soft particles

This research focuses on performing ultrasound propagation measurements and micro-X-ray computed tomography (µXRCT) imaging on prestressed granular packings prepared with biphasic mixtures of monodisperse glass and rubber particles at different compositions/fractions. Ultrasound experiments employing piezoelectric transducers, mounted in an oedometric cell (complementing earlier triaxial cell experiments), are used to excite and detect longitudinal ultrasound waves through randomly prepared mixtures of monodisperse stiff/soft particles. While the fraction of the soft particles is increasing linearly from zero, the effective macroscopic stiffness of the granular packings transits nonlinearly and nonmonotonically toward the soft limit, remarkably via an interesting stiffer regime for small rubber fractions between 0.1 ≲ ν ≲ 0.2. The contact network of dense packings, as accessed from µXRCT, plays a key role in understanding this phenomenon, considering the structure of the network, the chain length, the grain contacts, and the particle coordination. While the maximum stiffness is due to surprisingly shortened chains, the sudden drop in elastic stiffness of the mixture packings, at ν ≈ 0.4, is associated with chains of particles that include both glass and rubber particles (soft chains); for ν ≲ 0.3, the dominant chains include only glass particles (hard chains). At the drop, ν ≈ 0.4, the coordination number of glass and rubber networks is approximately four and three, respectively, i.e., neither of the networks are jammed, and the chains need to include particles from another species to propagate information.

Microwave hybrid and conventional sintering of Al2O3 and Al2O3/ZrO2 multilayers fabricated by aqueous tape casting

AbstractIn this study, microwave hybrid sintering and conventional sintering of Al2O3‐ and Al2O3/ZrO2‐laminated structures fabricated via aqueous tape casting were investigated. A combination of process temperature control rings and thermocouples was used to measure the sample surface temperatures more accurately. Microwave hybrid sintering caused higher densification and resulted in higher hardness in Al2O3 and Al2O3/ZrO2 than in their conventionally sintered counterparts. The flexural strength of microwave‐hybrid‐sintered Al2O3/ZrO2 was 70.9% higher than that of the conventionally sintered composite, despite a lower sintering temperature. The fracture toughness of the microwave‐hybrid‐sintered Al2O3 increased remarkably by 107.8% despite a decrease in the relative density when only 3 wt.% t‐ZrO2 was added. The fracture toughness of the microwave‐hybrid‐sintered Al2O3/ZrO2 was significantly higher (247.7%) than that of the conventionally sintered composite. A higher particle coordination and voids elimination due to the tape casting and the lamination processes, the microwave effect, the stress‐induced martensitic phase transformation, and the grain refinement phenomenon are regarded as the main reasons for the mentioned outcomes.

Study on macro and micro shear strength of continuously graded and gap-graded sand

The particle gradation of sand has a significant influence on its shear strength, yet the similarities and differences between the effects of continuous and gap grading have yet to be fully explored. In this study, the discrete element method (DEM) was used to simulate biaxial tests on granular samples that were both continuously graded and gap-graded. The macroscopic analysis revealed that the shear strength of continuously graded sands increases initially and then decreases as the uniformity of particle size distribution decreases. On the other hand, the lack of medium particles in gap-graded sands amplifies the difference in particle size between coarse and fine particles, leading to a decrease in shear strength. Microscopically, both continuous and gap gradings affect the internal packing structure of the particle assembly, which consequently affects particle stress distributions, contact forces, coordination numbers, stress-induced anisotropies, and contact force networks, thus having an impact on the macroscopic shear strength. The global uniformity of particle size distribution was unidirectionally affected by continuous grading, while gap grading had a locally bidirectional influence. These findings provide a better understanding of the effects of particle grading on the macroscopic shear strength of sands.

Five methods for determining pattern speeds in galaxies

Context. After matter distribution and kinematics, the bar or spiral pattern speeds are the next fundamental parameters determining the dynamics of a galaxy. Aims. New or refined methods for determining instantaneous scalar and vector pattern speeds from a restricted domain are developed for applications in N-body simulations or in galaxies such as the Milky Way, for which the stellar coordinates become increasingly better known. Methods. The general feature used throughout follows from the fact that the time derivative of a function of the coordinates is linearly proportional to its rotation rate and its particle velocities. Knowing these therefore allows retrieving the instantaneous pattern speed vector by linear optimization. Similarly, if an invariant function depends on the position and velocities, then its instantaneous rotation vectors in space can be retrieved. Knowing the accelerations also allows determining the pattern rotation of velocity space. Results. The first three methods are based on the assumed rotational invariance of functions at each point in space or velocity space: (1) the 6D invariant function method, measuring the pattern speed vectors in space and velocity space, (2) the differential/regional 3D Tremaine-Weinberg method, evaluated over regions with a high signal-to-noise ratio, (3) the 3D Jacobi integral method, yielding the potential pattern speed. Extensions to derive the rotation center position, speed, and acceleration are introduced in the first and third methods. The last two methods are based on the assumed invariance of average functions of the particle coordinates: (4) the 2D and 3D moment of inertia methods by using the derivative of the singular value decomposition, (5) the 2D Fourier method (3D for m = 2 mode), giving the mode rotation speeds. Pattern speed accelerations are also derived in the fourth and fifth methods. Conclusions. Depending on the available data in specific problems, the different methods provide a choice of approaches.

Accuracy of Determination of Longitudinal Coordinates of Particles by Digital Holography

Accuracy of Determination of Longitudinal Coordinates of Particles by Digital Holography

Connecting Diffraction Experiments and Network Analysis Tools for the Study of Hydrogen-Bonded Networks.

A self-consistent scheme is presented that is applicable for revealing details of the microscopic structure of hydrogen-bonded liquids, including the description of the hydrogen-bonded network. The scheme starts with diffraction measurements, followed by molecular dynamics simulations. Computational results are compared with the experimentally accessible information on the structure, which is most frequently the total scattering structure factor. In the case of an at least semiquantitative agreement between experiment and simulation, sets of particle coordinates from the latter may be exploited for revealing nonmeasurable structural details. Calculations of some properties concerning the hydrogen-bonded network are also described, in the order of increasing complexity: starting with the definition of a hydrogen bond, first and second neighborhoods are described via spatial correlations functions. Attention is then turned to cyclic and noncyclic hydrogen-bonded clusters, before cluster size distributions and percolation are discussed. We would like to point out that, as a result of applying the novel protocol, these latter, rather abstract, quantities become consistent with diffraction data: it may thus be argued that the approach reviewed here is the first one that establishes a direct link between measurements and elements of network theories. Applications for liquid water, simple alcohols, and alcohol-water liquid mixtures demonstrate the usefulness of the aforementioned characteristics. The procedure can readily be applied to more complicated hydrogen-bonded networks, like mixtures of polyols (diols, triols, sugars, etc.) and water, and complex aqueous solutions of even larger molecules (even of proteins).