Fluidization and Transport of Vibrated Granular Matter:A Review of Landmark and Recent Contributions

We present a short retrospective review of the existing literature about the dynamics of(dry)granular matter under the effect of vibrations.The main objective is the development of an integrated resource where vital information about past findings and recent discoveries is provided in a single treatment.Special attention is paid to those works where successful synthetic routes to as-yet unknown phenomena were identified.Such landmark results are analyzed,while smoothly blending them with a history of the field and introducing possible categorizations of the prevalent dynamics.Although no classification is perfect,and it is hard to distillate general properties out of specific observations or realizations,two possible ways to interpret the existing results are defined according to the type of forcing or the emerging(ensuing)regime of motion.In particular,first results concerning the case where vibrations and gravity are concurrent(vertical shaking)are examined,then the companion situation with vibrations perpendicular to gravity(horizontal shaking)is described.Universality classes are introduced as follows:(1)Regimes where sand self-organizes leading to highly regular geometrical“pulsating”patterns(thin layer case);(2)Regimes where the material undergoes“fluidization”and develops an internal multicellular convective state(tick layers case);(3)Regimes where the free interface separating the sand from the overlying gas changes inclination or develops a kind a patterned configuration consisting of stable valleys and mountains or travelling waves;(4)Regimes where segregation is produced,i.e.,particles of a given size tend to be separated from the other grains(deep containers).Where possible,an analogy or parallelism is drawn with respect to the companion field of fluid-dynamics for which the assumption of“continuum”can be applied.

Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable

One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.

Jetting-based bioprinting:process,dispense physics,and applications

Jetting-based bioprinting facilitates contactless drop-on-demand deposition of subnanoliter droplets at well-defined positions to control the spatial arrangement of cells,growth factors,drugs,and biomaterials in a highly automated layer-by-layer fabrication approach.Due to its immense versatility,jetting-based bioprinting has been used for various applications,including tissue engineering and regenerative medicine,wound healing,and drug development.A lack of in-depth understanding exists in the processes that occur during jetting-based bioprinting.This review paper will comprehensively discuss the physical considerations for bioinks and printing conditions used in jetting-based bioprinting.We first present an overview of different jetting-based bioprinting techniques such as inkjet bioprinting,laser-induced forward transfer bioprinting,electrohydrodynamic jet bioprinting,acoustic bioprinting and microvalve bioprinting.Next,we provide an in-depth discussion of various considerations for bioink formulation relating to cell deposition,print chamber design,droplet formation and droplet impact.Finally,we highlight recent accomplishments in jetting-based bioprinting.We present the advantages and challenges of each method,discuss considerations relating to cell viability and protein stability,and conclude by providing insights into future directions of jetting-based bioprinting.

An inverse analysis of fluid flow through granular media using differentiable lattice Boltzmann method

This study presents a method for the inverse analysis of fluid flow problems.The focus is put on accurately determining boundary conditions and characterizing the physical properties of granular media,such as permeability,and fluid components,like viscosity.The primary aim is to deduce either constant pressure head or pressure profiles,given the known velocity field at a steady-state flow through a conduit containing obstacles,including walls,spheres,and grains.The lattice Boltzmann method(LBM)combined with automatic differentiation(AD)(AD-LBM)is employed,with the help of the GPU-capable Taichi programming language.A lightweight tape is used to generate gradients for the entire LBM simulation,enabling end-to-end backpropagation.Our AD-LBM approach accurately estimates the boundary conditions for complex flow paths in porous media,leading to observed steady-state velocity fields and deriving macro-scale permeability and fluid viscosity.The method demonstrates significant advantages in terms of prediction accuracy and computational efficiency,making it a powerful tool for solving inverse fluid flow problems in various applications.

Mobility and dynamic erosion process of granular flow:insights from numerical investigation using material point method

In order to understand the dynamics of granular flow on an erodible base soil,in this paper,a series of material point method-based granular column collapse tests were conducted to investigate numerically the mobility and dynamic erosion process of granular flow subjected to the complex settings,i.e.,the aspect ratio,granular mass,friction and dilatancy resistance,gravity and presence of water.A set of power scaling laws were proposed to describe the final deposit characteristics of granular flow by the relations of the normalized run-out distance and the normalized final height of granular flow against the aspect ratio,being greatly affected by the complex geological settings,e.g.,granular mass,the friction and dilatancy resistance of granular soil,and presence of water in granular flow.An index of the coefficient of friction of granular soil was defined as a ratio of the target coefficient of friction over the initial coefficient of friction to quantify the scaling extent of friction change(i.e.,friction strengthening or weakening).There is a characteristic aspect ratio of granular column corresponding to the maximum mobility of granular flow with the minimum index of the apparent coefficient of friction.The index of the repose coefficient of friction of granular flow decreased gradually with the increase in aspect ratio because higher potential energy of granular column at a larger aspect ratio causes a larger kinetic energy of granular soil to weaken the friction of granular soil as a kind of velocity-related friction weakening.An increase in granular mass reduces gradually the indexes of the apparent and repose coefficients of friction of granular soil to enhance the mobility of granular flow.The mobility of granular flow increases gradually with the decrease in friction angle or increase in dilatancy angle of granular soil.However,the increase of gravity accelerates granular flow but showing the same final deposit profile without any dependence on gravity.The mobility of granular flow increases gradually by lowering the indexes of the apparent and repose coefficients of friction of granular flow while changing the surroundings,in turn,the dry soil,submerged soil and saturated soil,implying a gradually increased excessive mobility of granular flow with the friction weakening of granular soil.Presence of water in granular flow may be a potential catalyzer to yield a long run-out granular flow,as revealed in comparison of water-absent and water-present granular flows.In addition,the dynamic erosion and entrainment of based soil induced by granular flow subjected to the complex geological settings,i.e.,the aspect ratio,granular mass,gravity,friction and dilatancy resistance,and presence of water,were comprehensively investigated as well.

Alternative Method of Constructing Granular Neural Networks

Utilizing granular computing to enhance artificial neural network architecture, a newtype of network emerges—thegranular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability toprocess both numerical and granular data, leading to improved interpretability. This paper proposes a novel designmethod for constructing GNNs, drawing inspiration from existing interval-valued neural networks built uponNNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzynumbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizesa uniform distribution of information granularity to granulate connections with unknown parameters, resultingin independent GNN structures. To quantify the granularity output of the network, the product of two commonperformance indices is adopted: The coverage of numerical data and the specificity of information granules.Optimizing this combined performance index helps determine the optimal parameters for the network. Finally,the paper presents the complete model construction and validates its feasibility through experiments on datasetsfrom the UCIMachine Learning Repository. The results demonstrate the proposed algorithm’s effectiveness andpromising performance.

A pressure-sensitive rheological origin of high friction angles of granular matter observed in NASA–MGM project

An abnormally high peak friction angle of Ottawa sand was observed in(National Aeronautics and Space Administration) NASA–(Mechanics of Granular Materials) MGM tests in microgravity conditions on the space shuttle. Previous investigations have been unsuccessful in providing a constitutive insight into this behavior of granular materials under extremely low effective stress conditions. Here, a recently proposed unified constitutive model for transient rheological behavior of sand and other granular materials is adopted for the analytical assessment of high peak friction angles. For the first time, this long-eluded behavior of sand is attributed to a hidden rheological transition mechanism, that is not only rate-sensitive, but also pressure-sensitive. The NASA–MGM microgravity conditions show that shear-tests of sand can be performed under abnormally low confining stress conditions. The pressure-sensitive behavior of granular shearing that is previously ignored is studied based on the μ(I) rheology and its variations. Comparisons between the model and the NASA microgravity tests demonstrate a high degree of agreement. The research is highly valid for pressure-sensitive and rate-dependent problems that occur during earthquakes, landslides, and space exploration.

Effect of granular shape on radial segregation in a two-dimensional drum

Granular segregation is widely observed in nature and industry.Most research has focused on segregation caused by differences in the size and density of spherical grains.However,due to the fact that grains typically have different shapes,the focus is shifting towards shape segregation.In this study,experiments are conducted by mixing cubic and spherical grains.The results indicate that spherical grains gather at the center and cubic grains are distributed around them,and the degree of segregation is low.Through experiments,a structured analysis of local regions is conducted to explain the inability to form stable segregation patterns with obviously different geometric shapes.Further,through simulations,the reasons for the central and peripheral distributions are explained by comparing velocities and the number of collisions of the grains in the flow layer.

Filtration performance and modeling of granular bed for dust removal from coal pyrolytic vapors

Dust removal from pyrolytic vapors at high temperatures is an obstacle to the industrialization of the coal pyrolysis process.In this work,a granular bed with expanded perlites as filtration media was designed and integrated into a 10 t·d^(–1)coal pyrolysis facility.The testing results showed that around 97.56%dust collection efficiency was achieved.As a result,dust content in tar was significantly lowered.The pressure drop of the granular bed maintained in the range of 356 Pa to 489 Pa.The dust size in the effluent after filtration exhibited a bimodal distribution,which was attributed to the heterogeneity of the dust components.The effects of filtration bed on pyrolytic product yields were also discussed.A modified filtration model based on the macroscopic phenomenological theory was proposed to describe the performance of the granular bed.The computation results were well agreed with the experimental data.

Finite Element Simulations on Failure Behaviors of Granular Materials with Microstructures Using a Micromechanics-Based Cosserat Elastoplastic Model

This paper presents a micromechanics-based Cosserat continuum model for microstructured granular materials.By utilizing this model,the macroscopic constitutive parameters of granular materials with different microstructures are expressed as sums of microstructural information.The microstructures under consideration can be classified into three categories:a medium-dense microstructure,a dense microstructure consisting of one-sized particles,and a dense microstructure consisting of two-sized particles.Subsequently,the Cosserat elastoplastic model,along with its finite element formulation,is derived using the extended Drucker-Prager yield criteria.To investigate failure behaviors,numerical simulations of granular materials with different microstructures are conducted using the ABAQUS User Element(UEL)interface.It demonstrates the capacity of the proposed model to simulate the phenomena of strain-softening and strain localization.The study investigates the influence of microscopic parameters,including contact stiffness parameters and characteristic length,on the failure behaviors of granularmaterials withmicrostructures.Additionally,the study examines themesh independence of the presented model and establishes its relationship with the characteristic length.A comparison is made between finite element simulations and discrete element simulations for a medium-dense microstructure,revealing a good agreement in results during the elastic stage.Somemacroscopic parameters describing plasticity are shown to be partially related to microscopic factors such as confining pressure and size of the representative volume element.

Micromechanism and mathematical model of stress sensitivity in tight reservoirs of binary granular medium

Research on reservoir rock stress sensitivity has traditionally focused on unary granular structures,neglecting the binary nature of real reservoirs,especially tight reservoirs.Understanding the stresssensitive behavior and mathematical characterization of binary granular media remains a challenging task.In this study,we conducted online-NMR experiments to investigate the permeability and porosity evolution as well as stress-sensitive control mechanisms in tight sandy conglomerate samples.The results revealed stress sensitivity coefficients between 0.042 and 0.098 and permeability damage rates ranging from 65.6%to 90.9%,with an average pore compression coefficient of 0.0168—0.0208 MPa 1.Pore-scale compression occurred in three stages:filling,compression,and compaction,with matrix pores playing a dominant role in pore compression.The stress sensitivity of binary granular media was found to be influenced by the support structure and particle properties.High stress sensitivity was associated with small fine particle size,high fines content,high uniformity coefficient of particle size,high plastic deformation,and low Young's modulus.Matrix-supported samples exhibited a high irreversible permeability damage rate(average=74.2%)and stress sensitivity coefficients(average=0.089),with pore spaces more slit-like.In contrast,grain-supported samples showed low stress sensitivity coefficients(average=0.021)at high stress stages.Based on the experiments,we developed a mathematical model for stress sensitivity in binary granular media,considering binary granular properties and nested interactions using Hertz contact deformation and Poiseuille theory.By describing the change in activity content of fines under stress,we characterized the non-stationary state of compressive deformation in the binary granular structure and classified the reservoir into three categories.The model was applied for production prediction using actual data from the Mahu reservoir in China,showing that the energy retention rates of support-dominated,fill-dominated,and matrix-controlled reservoirs should be higher than 70.1%,88%,and 90.2%,respectively.

Response of the Maize Variety EV87-28 to a Fertilization Strategy Involving Indorama Granular Urea on Ferralsol in the Central Ivory Coast

In Côte d’Ivoire, the decline in soil fertility strongly impacts the productivity of maize (Zea mays L.) on heavily leached ferralitic soil. In this study, the general objective was therefore to improve the productivity of maize EV87-28 on the Ferralsols in pre-forested areas during different cropping seasons. Eight (8) micro-plots were set up according to a total randomization device with three repetitions. Two factors were studied: nitrogen fertilizer modalities (main factor) and crop season (secondary factor). Growth, flowering and yield parameters were measured and analyzed. The results showed that there was no interaction between the nitrogen fertilizer factor and the cropping season factor. In addition, this study showed the short rainy season had the most positive impact on growth, flowering and yield parameters than the long rainy season. The results also showed that the different nitrogen fertilizer modalities had no statistically different effects on growth, flowering and yield parameters. However, quantitative differences were reported, highlighting one nitrogen fertilizer modality, which is the combination of urea granule + farm manure (75% urea indorama granules and 25% farm manure). The combination of urea granule + farm manure (75% urea indorama granules and 25% farm manure) had the best effect on corn grain yield. So, the combination of urea (75%) and manure (25%), that resulted in yield gain, could be recommended for corn fertilization during the small rainy season.

Modelling rapid non-destructive test using light weight deflectometer on granular soils across different degrees of saturation

This study introduces an advanced finite element model for the light weight deflectometer(LWD),which integrates contact mechanics with fully coupled models.By simulating LWD tests on granular soils at various saturation levels,the model accurately reflects the dependence of the LWD modulus on dry density,water content,and effective stress.This model addresses and overcomes the limitations of previous finite element models for this specific problem.Simultaneously,this research presents the first experimentally validated fully coupled contact impact model.Furthermore,the research provides a comparative assessment of elastoplastic and nonlinear elastic models and contrasts an enriched node-tosegment method(developed in this study)with the more precise mortar technique for contact mechanics.These comparisons reveal unique advantages and challenges for each method.Moreover,the study underscores the importance of careful application of the LWD modulus,emphasising the need for sophisticated tools to interpret soil behaviour accurately.

Coupled DEM-FDM analyses of the effects of falling rock’s shape and impact angle on response of granular cushion and rock shed

Rock shed is an effective protection measure against rockfall.To investigate the influences of falling rock’s shape and impact angle on the impact effect of the cushioned rock shed,a modeling approach for a rock shed with a cushion layer using PFC-FLAC.The granular cushion is modeled as an aggregate of discrete non-cohesion particles,while the concrete plate and the beam are modeled as zones.The falling rock with different sphericities and impact angles is modeled as a rigid assembly.The numerical model is validated by comparing the simulation results with experimental and numerical results from previous literature.This model is applied to analyze the effects of rock shape and impact angle on the dynamic interaction effects between falling rock and cushioned rock shed,including the impact force,transmitted bottom force,penetration depth,and plate deflection.The numerical results show that the variation in the falling rock’s shape has different effects on the falling rock with different impact angles.These findings could support rock shed design by revealing the limitations of the assumptions in the past research,which may result in unsafe rock sheds for some rockfall cases.

A novel encoding mechanism for particle physics

This study proposes a novel particle encoding mechanism that seamlessly incorporates the quantum properties of particles,with a specific emphasis on constituent quarks.The primary objective of this mechanism is to facilitate the digital registration and identification of a wide range of particle information.Its design ensures easy integration with different event generators and digital simulations commonly used in high-energy experiments.Moreover,this innovative framework can be easily expanded to encode complex multi-quark states comprising up to nine valence quarks and accommodating an angular momentum of up to 99/2.This versatility and scalability make it a valuable tool.

Evaluation of the coefficient of lateral stress at rest of granular materials under repetitive loading conditions

Although the internal stress state of soils can be affected by repetitive loading,there are few studies evaluating the lateral stress(or K_(0))of soils under repetitive loading.This study investigates the changes in K_(0) and directional shear wave velocity(V_(s))in samples of two granular materials with different particle shapes during repetitive loading.A modified oedometer cell equipped with bender elements and a diaphragm transducer was developed to measure the variations in the lateral stress and the shear wave velocity,under repetitive loading on the loading and unloading paths.The study produced the following results:(1)Repetitive loading on the loading path resulted in an increase in the K_(0) of test samples as a function of cyclic loading number(i),and(2)Repetitive loading on the unloading path resulted in a decrease in K_(0) according to i.The shear wave velocity ratio(i.e.V_(s)(HH)/V_(s)(VH),where the first and second letters in parentheses corresponds to the directions of wave propagation and particle motion,respectively,and V and H corresponds to the vertical and horizontal directions,respectively)according to i supports the experimental observations of this study.However,when the tested material was in lightly over-consolidated state,there was an increase in K_(0) during repetitive loading,indicating that it was the initial K_(0),rather than the loading path,which is responsible for the change in K_(0).The power model can capture the variation in the K_(0) of samples according to i.Notably,the K_(0)=1 line acts as the boundary between the increase and decrease in K_(0) under repetitive loading.

A thermodynamics-based three-scale constitutive model for partially saturated granular materials

A three-scale constitutive model for unsaturated granular materials based on thermodynamic theory is presented.The three-scale yield locus,derived from the explicit yield criterion for solid matrix,is developed from a series of discrete interparticle contact planes.The three-scale yield locus is sensitive to porosity changes;therefore,it is reinterpreted as a corresponding constitutive model without phenomenological parameters.Furthermore,a water retention curve is proposed based on special pore morphology and experimental observations.The features of the partially saturated granular materials are well captured by the model.Under wetting and isotropic compression,volumetric compaction occurs,and the degree of saturation increases.Moreover,the higher the matric suction,the greater the strength,and the smaller the volumetric compaction.Compared with the phenomenological Barcelona basic model,the proposed three-scale constitutive model has fewer parameters;virtually all parameters have clear physical meanings.

Sliding and damming properties of granular debris with different geometric configurations and grain size distributions

Granular debris plays a significant role in determining damming deposit characteristics. An indepth understanding of how variations in grain size distribution(GSD) and geometric configurations impact the behavior of granular debris during the occurrence of granular debris is essential for precise assessment and effective mitigation of landslide hazards in mountainous terrains. This research aims to investigate the impact of GSD and geometric configurations on sliding and damming properties through laboratory experiments. The geometric configurations were categorized into three categories based on the spatial distribution of maximum volume: located at the front(Type Ⅰ), middle(Type Ⅱ), and rear(Type Ⅲ) of the granular debris. Our experimental findings highlight that the sliding and damming processes primarily depend on the interaction among the geometric configuration, grain size, and GSD in granular debris. Different sliding and damming mechanisms across various geometric configurations induce variability in motion parameters and deposition patterns. For Type Ⅰ configurations, the front debris functions as the critical and primary driving component, with energy dissipation primarily occurring through inter-grain interactions. In contrast, Type Ⅱ configurations feature the middle debris as the dominant driving component, experiencing hindrance from the front debris and propulsion from the rear, leading to complex alterations in sliding motion. Here, energy dissipation arises from a combination of inter-grain and grain-substrate interactions. Lastly, in Type Ⅲ configurations, both the middle and rear debris serve as the main driving components, with the rear sliding debris impeded by the front. In this case, energy dissipation predominantly results from grainsubstrate interaction. Moreover, we have quantitatively demonstrated that the inverse grading in damming deposits, where coarse grain moves upward and fine grain moves downward, is primarily caused by grain sorting due to collisions among the grains and between the grain and the base. The impact of grain on the horizontal channel further aids grain sorting and contributes to inverse grading. The proposed classification of three geometric configurations in our study enhances the understanding of damming properties from the view of mechanism, which provides valuable insights for related study about damming granular debris.

Numerical investigation of the effects of soil-structure and granular material-structure interaction on the seismic response of a flat-bottom reinforced concrete silo

In this work,a numerical study of the effects of soil-structure interaction(SSI)and granular material-structure interaction(GSI)on the nonlinear response and seismic capacity of flat-bottomed storage silos is conducted.A series of incremental dynamic analyses(IDA)are performed on a case of large reinforced concrete silo using 10 seismic recordings.The IDA results are given by two average IDA capacity curves,which are represented,as well as the seismic capacity of the studied structure,with and without a consideration of the SSI while accounting for the effect of GSI.These curves are used to quantify and evaluate the damage of the studied silo by utilizing two damage indices,one based on dissipated energy and the other on displacement and dissipated energy.The cumulative energy dissipation curves obtained by the average IDA capacity curves with and without SSI are presented as a function of the base shear,and these curves allow one to obtain the two critical points and the different limit states of the structure.It is observed that the SSI and GSI significantly influence the seismic response and capacity of the studied structure,particularly at higher levels of PGA.Moreover,the effect of the SSI reduces the damage index of the studied structure by 4%.

Exploring device physics of perovskite solar cell via machine learning with limited samples

Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.