Particle Systems Research Articles

Evolution of force networks during stick-slip motion of an intruder in a granular material: Topological measures extracted from experimental data.

In quasi-two-dimensional experiments with photoelastic particles confined to an annular region, an intruder constrained to move in a circular path halfway between the annular walls experiences stick-slip dynamics. We discuss the response of the granular medium to the driven intruder, focusing on the evolution of the force network during sticking periods. Because the available experimental data do not include precise information about individual contact forces, we use an approach developed in our previous work [Basak et al., J. Eng. Mech. 147, 04021100 (2021)0733-939910.1061/(ASCE)EM.1943-7889.0002003] based on networks constructed from measurements of the integrated strain magnitude on each particle. These networks are analyzed using topological measures based on persistence diagrams, revealing that force networks evolve smoothly but in a nontrivial manner throughout each sticking period, even though the intruder and granular particles are stationary. Characteristic features of persistence diagrams show identifiable slip precursors. In particular, the number of generators describing the structure and complexity of force networks increases consistently before slips. Key features of the dynamics are similar for granular materials composed of disks or pentagons, but some details are consistently different. In particular, we find significantly larger fluctuations of the measures computed based on persistence diagrams and, therefore, of the underlying networks, for systems of pentagonal particles.

Solving time-independent Schrödinger equation variationally using random numbers

Finding wavefunctions for even the simplest of interacting particle systems consisting of two particles is extremely difficult. It is therefore highly desirable that an accurate and easily implementable method be available to instructors and students of quantum-mechanics for obtaining wavefunctions for these particles. The usual approach taken to do this is to use parametrized functional form for the wavefunction in conjunction with the variational method to find approximate wavefunction and energy for the ground-state of such systems. In this paper, we employ random numbers to obtain ground-state wavefunctions and energies of two interacting particles in different one-dimensional potentials. The idea behind using random numbers is to search freely for functions that lead to lower and lower energy, converging eventually to its lowest value. The method presented is easily applicable numerically using a simple algorithm, and the wavefunctions obtained are highly accurate. Thus, the method presented makes study of two interacting particles accessible to instructors and students alike in a transparent manner.

Dynamics of Finite Inhomogeneous Particle Systems with Exclusion Interaction

We study finite particle systems on the one-dimensional integer lattice, where each particle performs a continuous-time nearest-neighbour random walk, with jump rates intrinsic to each particle, subject to an exclusion interaction which suppresses jumps that would lead to more than one particle occupying any site. We show that the particle jump rates determine explicitly a unique partition of the system into maximal stable sub-systems, and that this partition can be obtained by a linear-time algorithm using only elementary arithmetic. The internal configuration of each stable sub-system possesses an explicit product-geometric limiting distribution, and the location of each stable sub-system obeys a strong law of large numbers with an explicit speed; the characteristic parameters of each stable sub-system are simple functions of the rate parameters for the corresponding particles. For the case where the entire system is stable, we provide a central limit theorem describing the fluctuations around the law of large numbers. Our approach draws on ramifications, in the exclusion context, of classical work of Goodman and Massey on partially-stable Jackson queueing networks.

Application of Bonded-Block Models to Rock Failure Analysis

Discrete element models are being increasingly applied to model rock failure processes. Bonded-particle models, based on circular or spherical particle systems, have been successfully used for two decades. More recently, bonded-block models, using polygonal or polyhedral elements, have proven to be a powerful alternative. This paper describes the basis of the application of these models in the numerical simulation of failure in rock materials. The critical governing parameters are identified, and their influence is discussed. The model calibration procedure based on the analysis of laboratory tests is discussed. An application example of an underground excavation problem is presented using a simple bonded-block model employing rigid blocks and a bilinear softening contact model. The results show the capability of this approach to reproduce observed failure modes involving block fractures.

On the coercivity condition in the learning of interacting particle systems

In the inference for systems of interacting particles or agents, a coercivity condition ensures the identifiability of the interaction kernels, providing the foundation of learning. We prove the coercivity condition for stochastic systems with an arbitrary number of particles and a class of kernels such that the system of relative positions is ergodic. When the system of relative positions is stationary, we prove the coercivity condition by showing the strictly positive definiteness of an integral kernel arising in the learning. For the non-stationary case, we show that the coercivity condition holds when the time is large based on a perturbation argument.

Application of the combination on data-driven and discrete element model: Particle segregation prediction and classification in rotary drums

With the complexity and variability of particle systems in industrial scenarios and the huge cost of repetitive testing, predicting and classifying particle systems in advance have a certain necessity. Combined with discrete element method (DEM) and data-driven model, this work develops a prediction and classification model for particle flow characteristics based on the long-short term memory (LSTM) method. On the basis of the generated macroscopic and microscopic particle flow characteristic information, the LSTM model was constructed and then described particle flow information based on optimal model parameters determined by hyperparameter optimization. Compared of predicted results with the DEM simulation validated the interpolation and extrapolation ability of the established model. And it has better prediction accuracy and performance comparing with traditional prediction methods. In addition, for the classification of multiple working condition categories, the LSTM model trained from fused dataset obtained the best results with the accuracy of more than 95 % in all cases. With the combination of DEM model and data-driven approach, the rapid prediction of particle flow characteristics and parameter optimization in various industrial applications will be achieved.

Insight into the coupling effect of tortuosity and percolation on the permeability of overlapping ovoidal particle systems

In general, porous media can be represented by the overlapping particle packing systems, where the overlapping-particles themselves are the pore phase that permit fluid flow. A fundamental yet unresolved topic has been determining the percolation threshold and tortuosity of the porous media with anisotropic pores, as well as their quantitative implications on permeability. This work calculates the percolation threshold, tortuosity, and permeability of overlapping ovoidal particle packing systems by theoretical and numerical approaches. To determine the tortuosity, the theoretical percolation-based tortuosity model and numerical direct shortest path searching method are used. And a modified tortuosity formula is developed to characterize how pore shape affect the tortuosity. Then, the permeability of porous media is numerically obtained by the lattice Boltzmann method. According to the obtained tortuosity and permeability, a modified Kozeny-Carman (K-C) equation for porous media is proposed, and the modified K-C equation's dependability is verified.

ANALISIS FUNGSI KORELASI KLASIK DAN KUANTUM UNTUK MODEL CINCIN: SEBUAH REVIEW

A review of the journal entitled 'Classical And Quantum Correlation Functions For A Ring Model' has been carried out which discusses the solutions of classical and quantum correlation functions for the ring model. Classical and quantum correlation functions are derived for systems of non-interacting particles moving in a circle. demonstrated that the decay behavior of the classical expression for the correlation function can be recovered from a strictly periodic quantum mechanical expression by taking the limit ℏ→0, after the appropriate transformation. The aim of this study is to present clearly and in detail how the position correlation function for a system of particles moving in a circle having a certain average energy and written in a form which shows the nature of the transformation. Next, we examine the use of Poisson addition to represent F(z), and how the correlation function becomes identical to the form given by classical statistical mechanics, which exhibits Gaussian decay. The results of this study indicate that the positional correlation function for a system of particles moving in a circle, and having a certain average energy, can be written in a form which shows the nature of the transformation. Then using the Poisson addition formula to represent F(z), the expression is rewritten to enable the limit ℏ→0 to be taken. It was finally found that the correlation function became identical to the form given by classical statistical mechanics, which exhibits a Gaussian decay.

In vivo evaluation of new adjuvant systems based on combination of Salmonella Typhi porins with particulate systems: Liposomes versus polymeric particles

Subunit vaccines that have weak immunogenic activity require adjuvant systems for enhancedcellular and long-acting humoral immune responses. Both lipid-based and polymeric-based particulate adjuvants have been widely investigated to induce the desired immune responses against the subunit vaccines. The adjuvant efficacy of these particulate adjuvants depends upon their physicochemical properties such as particle size, surface charge, shape and their composition. Previously, we showed in vitro effect of adjuvant systems based on combination of chitosan and Salmonella Typhi porins in microparticle or nanoparticle form, which were spherical with positive surface charge. In the present study, we have further developed an adjuvant system based on combination of porins with liposomes (cationic and neutral) and investigated the adjuvant effect of both the liposomal and polymeric systems in BALB/c mice using a model antigen, ovalbumin. Humoral immune responses were determined following priming and booster dose at 15-day intervals. In overall, IgM and IgG levels were induced in the presence of both the liposomal and polymeric adjuvant systems indicating the positive impact of combination with porins. The highest IgM levels were obtained on Day 8, and liposomal adjuvant systems were found to elicit significantly higher IgM levels compared to polymeric systems. IgG levels were increased significantly after booster, particularly more profound with the micro-sized polymeric system when compared to cationic liposomal system with nano-size. Our results demonstrated that the developed particulate systems are promising both as an adjuvant and delivery system, providing enhanced immune responses against subunit antigens, and have the potential for long-term protection.

Large deviations for mean field model in Erdős–Rényi graph

In this paper, we study a particle systems (or interacting diffusions) on an Erdős–Rényi graph with the parameter pN∈(0,1] that behaves like a mean-field system up to large deviations. Our aim is to establish the large deviations for the empirical measure process of particle systems under the condition NpN4→∞ as N→∞, where N is the number of particles. The result is obtained by proving the exponential equivalence between our systems and general interacting systems without random graphs. The multilinear extensions of Grothendieck inequality play a crucial role in our proof.

Complex Networks and Interacting Particle Systems.

Complex networks is a growing discipline aimed at understanding large interacting systems. One of its goals is to establish a relation between the interactions of a system and the networks structure that emerges. Taking a Lennard-Jones particle system as an example, we show that when interactions are governed by a potential, the notion of structure given by the physical arrangement of the interacting particles can be interpreted as a binary approximation to the interaction potential. This approximation simplifies the calculation of the partition function of the system and allows to study the stability of the interaction structure. We compare simulated results with those from the approximated partition function and show how the network and system perspective complement each other. With this, we draw a direct connection between the interactions of a molecular system and the network structure it forms and assess the degree to which it describes the system. We conclude by discussing the advantages and limitations of this method for weighted networks, as well as how this concept might be extended to more general systems.

Comparison of sub-grid drag laws for modeling fluidized beds with the coarse grain DEM–CFD approach

Fluidized particulate systems can be well described by coupling the discrete element method (DEM) with computational fluid dynamics (CFD). However, the simulations are computationally very demanding. The computational demand is drastically reduced by applying the coarse grain (CG) approach, where several particles are summarized into larger grains. Scaling rules are applied to the dominant forces to obtain precise solutions. However, with growing grain size, an adequate representation of the interaction forces and, thus, representation of sub-grid effects such as bubble and cluster formation in the fluidized particulate system becomes challenging. As a result, particle drag can be overestimated, leading to an increase in average particle height. In this work, limitations of the system-to-grain ratio are identified but also a dependency on system width. To address this issue, sub-grid drag models are often applied to increase the accuracy of simulations. Nonetheless, the sub-grid models tend to have an ad hoc fitting, and thorough testing of the system configurations is often missing. Here, five different sub-grid drag models are compared and tested on fluidized bed systems with different Geldart group particles, fluidization velocity, and system-to-grain diameter ratios.

Neural Galerkin schemes with active learning for high-dimensional evolution equations

Deep neural networks have been shown to provide accurate function approximations in high dimensions. However, fitting network parameters requires informative training data that are often challenging to collect in science and engineering applications. This work proposes Neural Galerkin schemes based on deep learning that generate training data with active learning for numerically solving high-dimensional partial differential equations. Neural Galerkin schemes build on the Dirac-Frenkel variational principle to train networks by minimizing the residual sequentially over time, which enables adaptively collecting new training data in a self-informed manner that is guided by the dynamics described by the partial differential equations. This is in contrast to other machine learning methods that aim to fit network parameters globally in time without taking into account training data acquisition. Our finding is that the active form of gathering training data of the proposed Neural Galerkin schemes is key for numerically realizing the expressive power of networks in high dimensions. Numerical experiments demonstrate that Neural Galerkin schemes have the potential to enable simulating phenomena and processes with many variables for which traditional and other deep-learning-based solvers fail, especially when features of the solutions evolve locally such as in high-dimensional wave propagation problems and interacting particle systems described by Fokker-Planck and kinetic equations.

An Improved Vicsek Model of Swarms Based on a New Neighbor Strategy Considering View and Distance

Collective behaviors in nature and human societies have been intensively studied in recent decades. The Vicsek model is one of the typical models that explain self-ordered particle systems well. In the original Vicsek model, the neighbor strategy takes all its neighbors’ mean directions into account when updating particles’ directions, which leads to a longer convergence time and higher computation cost due to the excess number of neighbors. In this paper, we introduce a new neighbor strategy to the Vicsek model. It defines that each particle will only select a certain number of particles with the farthest distance that fall into its vision sector as its neighbors. In addition, we classify the Vicsek model as the static model and the dynamic model according to whether the features of particles in the model are constant or not. Moreover, we design a new rule to apply the new neighbor strategy to dynamic Vicsek models. The simulation results indicate that our new neighbor strategy can significantly decrease the average number of particles’ neighbors but still be able to further enhance the Vicsek model’s convergence performance. The comparative results found that the static and dynamic model applied with the new neighbor strategy outperforms the models that only apply view restriction or remote neighbor strategy in noiseless and noisy conditions.

A Simulation Method for Layered Filling of Grain Piles Based on the Discrete Element Method

The Discrete Element Method (DEM) has been widely employed to investigate the behavior of particle systems at a macroscopic scale. However, effectively simulating the gradual filling of bulk cereal grains within silos using the DEM remains a formidable challenge due to time constraints. Thus, there is a critical need to develop a simplified model capable of substantially reducing the computational time required for simulating cereal grain accumulation. This study introduces a Layered Filling Method (LFM) designed to expedite the computational process for cereal grain piles within silos. By utilizing particle kinetic energy as a specific criterion, this model identifies particles as stable situations when their kinetic energy drops below a designated threshold. Throughout the filling process, lower particles that were judged to satisfy the condition of stability are isolated, forming sub-heaps that are exempt from persistent detection. The whole particle heap is subsequently segregated into multiple sub-piles and a main pile till the process’s culmination, and these divisions are merged back together. In order to validate the model’s feasibility and accuracy, a comparative analysis was performed on the characteristics of the porosity and airflow patterns of grain piles generated using the LFM and the progressive filling method (PFM), respectively. The research results indicate that there is a marginally higher porosity value in the grain pile simulated by the LFM in comparison to the PFM. However, the average relative error remains below 5.00%. Both the LFM and PFM exhibit a similar spiral upward trend in the simulated airflow paths. Notably, the LFM demonstrates a substantial reduction in the time required to construct grain piles.

DEM‐CFD analysis of swelling behaviors of binary particle systems with a microscopic diffusion model

AbstractSwelling, that is, volumetric expansion, of particles is ubiquitous in many industrial systems containing both swellable particles and non‐swellable particles. However, the interactions between these two types of particles and the internal stress distribution within the binary particle systems are not yet fully understood. The dynamic behavior of binary mixtures with both swellable and non‐swellable particles was explored for the first time in this study by employing a microscopic diffusion swelling model that considers the change in microstructure of swelling particles implemented in the discrete element method coupled with computational fluid dynamics (DEM‐CFD). The expansion of particle bed consists of super absorbent polymers (SAPs) particles, that is, the swellable particles, and polyformaldehyde (POM) particles, that is, the non‐swellable particles, in a rectangle container is then analyzed experimentally and numerically. The results show that a tapered pattern of SAP particles and an arch pattern of POM particles are formed during the swelling process. The Lacey index is used to quantify the mixing degree of the binary particle system, which increases with time until the POM particle layer is fully encompassed by SAP particles. The mean stress distribution within the particle system is also examined, revealing that the swollen SAP particles migrate upward through a “low‐stress channel,” pushing the POM particles in the central area outward. Moreover, it is found that, for case with a relative low friction, SAP particles penetrate the POM particle layer along the container walls, while for the cases with relative high friction, SAP particles penetrate the POM particle layers through the central regions of the container.

Chitosan-based pulmonary particulate systems for anticancer and antiviral drug carriers: A promising delivery for COVID-19 vaccines

Chitosan (CHI) has antimicrobial and anticancer characteristics in addition to being biodegradable and biocompatible as a polymer. It has recently attracted a lot of attention as a potential drug carrier framework material for pulmonary therapeutic transport, particularly in the context of treating infectious diseases and cancer. CHI mucoadhesive, permeation-enhancing, and site- and cell-specific properties all play a key role in the therapeutic carrier's efficacy in the pulmonary circulation. Numerous microencapsulation and micro-nanomixing techniques have been developed to provide nanocarriers with the proper aerodynamic profile for effective pulmonary aerosolization and inhalation. Acute respiratory distress syndrome (ARDS) and cancer are the outcomes of a subsequent infection with COVID-19. The effectiveness of anti-CoV and anticancer treatments using CHI and its derivatives varies with molecular weight, substituent type, and degree of substitution. From the viewpoint of drug delivery (DD), in addition to its therapeutic performance, CHI is anticipated to attract more attention throughout the coming decade.

A resolved SPH-DEM coupling method for analysing the interaction of polyhedral granular materials with fluid

Hybrid fluid–particle systems are prevalent in nature and engineering practices, but accurately simulating the dynamic behaviour is challenging due to their inherent strong non-linearity. This study proposes a three-dimensional resolved numerical framework for analysing complex shape polyhedron–fluid interaction. The weakly compressible smoothed particle hydrodynamics (SPH) is employed to describe the complex hydrodynamic behaviour, and the discrete element method (DEM) is applied to solve the motion of irregular polyhedral granular materials. An accurate contact collision algorithm is implemented in the coupling model to address the dynamic response of polyhedral particles. The high-fidelity modelling of the complex morphology of granular materials is realised. Hence, the coupling code is developed based on two high-performance open-source platforms, and an efficient two-way coupling scheme is designed to manage the fluid–particle interaction under a unified time framework. The reliability and the applicability of the SPH-DEM solver are demonstrated through extensive validations based on existing experimental and numerical data involving violent free-surface flow impacting multiple rigid bodies and cube deposition. Furthermore, a more complex case of wave-flow impact on rock piles is conducted, exhibiting great potential to employ this model in real-life engineering problems.

Preface

2023 3rd International Conference on Fluid and Chemical Engineering (ICFCE 2023) has been successfully held on July 20-21, 2023, in Wuhan China. Organized by Hubei Zhongke Institute of Geology and Environment Technology, ICFCE 2023 intends to invite worldwide famous scientists, experts, scholars, and researchers for academic presentations.ICFCE 2023 proceeding is a collection of outstanding submissions in the field of biofluid mechanics, chemically reacting fluids and combustion, computational fluid dynamics (CFD), experimental fluid mechanics, flow through porous media, fluid–solid interactions (FSI), geophysical fluid dynamics, granular/suspension flows, heat and mass transfer, hydrodynamics, physical, theoretical and computational chemistry, chemical engineering fundamentals, chemical reaction engineering, chemical engineering equipment design and process design, thermodynamics, catalysis & reaction engineering, particulate systems, crystallization, etc. Around 70 articles were selected from more than 150 submissions after peer-review, and authors and guests participated in ICFCE 2023 with keynote speeches, oral presentations and posters, promoting the communication among researchers and experts from institutes and universities, including Ningbo University, University of Sharjah, China Association for Science and Technology, Tianjin University, China Agricultural University, South China University of Technology, Nanjing University of Aeronautics and Astronautics, etc.Cordial gratitude is delivered to Technical Program Committee of ICFCE 2023 for the professional work on guarantying the high standard of conference as well as the proceedings. We believe the proceedings will enhance the exchange of ideas and state-of-art knowledge on Fluid and Chemical Engineering.Professor Omid MahianXian Jiaotong University, ChinaAssociate Professor Sébastien DéonUniversity of Bourgogne-Franche-Comté, FranceList of Technical Committee, Chair, Editor, International Committee Members are available in this pdf.

A study on light extinction model and inversion of mixed particle system based on Monte Carlo method

The extinction characteristics of mixed particles, which are composed of various types of particles, appear to be more complex. To statistically analyze photon destinations and investigate the extinction characteristics of mixed particles by tracing all underlying events in detail, a Monte Carlo method-based extinction model has been developed. Meanwhile, an extinction spectrum measurement system was designed. A series of experiments for both single-type particle and mixed-particle systems were performed. Afterwards, global optimization algorithms are introduced to perform simultaneous inversion of sizes of mixed particles and mixing ratio based on the experimental spectrum. The inversion results show that the error for any single parameter is <2%. Performing a simultaneous inversion for particle sizes and mixing ratios leads to an increase in the relative error, but it does not exceed 9%. It indicates that the model could simultaneously invert the mixing ratio and handle particle systems with two different sizes.