Discrete Particle Research Articles

Structured bubbling in vibrated gas-fluidized beds of binary granular particles: experiments and simulations.

Mixing and segregation of granular particles on the basis of size and density from vertical vibration or upward gas flow is critical to a wide range of industrial, agricultural and natural processes. Recently, combined vibration and gas flow under certain conditions has been shown to create periodically repeating structured bubbling patterns within a fluidized bed of spherical, monodisperse particles. Here, we demonstrate with experiments and simulations that structured bubbling can form in binary mixtures of particles with different size and density, but with similar minimum fluidization velocities. Structured bubbling leads to particles mixing regardless of initial particle configuration, while exciting particles with only gas flow produces smaller unstructured bubbles which act to segregate particles. Discrete particle simulations match the experimental results qualitatively and, in some regards quantitatively, while continuum particle simulations do not predict mixing in the case of structured bubbling, highlighting areas for future model improvement.

Characteristics of Coal Dust Deposition in Boiler Tail Gas Pipelines

Coal dust deposition in boiler tail gas pipelines can significantly affect boilers’ thermal and energy efficiency. This study investigates the deposition characteristics of coal dust particles in boiler tail gas tubes in variable cross-section tubes. Numerical simulations were performed using the Reynolds Stress Model and the Discrete Particle Model. User-defined functions coding is used to construct the particle deposition model in the particle deposition model. The study analyses the distribution of turbulent kinetic energy locations in the gradient tube, compares the distribution of particle deposition on its wall, and concludes that the deposition distribution of coal dust particles in the gradient tube is slightly different for different particle sizes. Smaller particles have a higher deposition efficiency in equal cross-section pipes than larger particles. Particle size also has a significant effect on pipe taper and expansion. The results of this study can provide theoretical guidance for optimising the design of boiler tail gas pipelines, improving energy efficiency, and reducing environmental pollution.

Characteristics of Coal Dust Deposition in Boiler Tail Gas Pipelines

Coal dust deposition in boiler tail gas pipelines can significantly affect boilers’ thermal and energy efficiency. This study investigates the deposition characteristics of coal dust particles in boiler tail gas tubes in variable cross-section tubes. Numerical simulations were performed using the Reynolds Stress Model and the Discrete Particle Model. User-defined functions coding is used to construct the particle deposition model in the particle deposition model. The study analyses the distribution of turbulent kinetic energy locations in the gradient tube, compares the distribution of particle deposition on its wall, and concludes that the deposition distribution of coal dust particles in the gradient tube is slightly different for different particle sizes. Smaller particles have a higher deposition efficiency in equal cross-section pipes than larger particles. Particle size also has a significant effect on pipe taper and expansion. The results of this study can provide theoretical guidance for optimising the design of boiler tail gas pipelines, improving energy efficiency, and reducing environmental pollution.

Balanced allocation of teaching information resources based on discrete particle swarm optimisation algorithm

Balanced allocation of teaching information resources based on discrete particle swarm optimisation algorithm

Mesoscale simulations of diffusion and sedimentation in shape-anisotropic nanoparticle suspensions.

We determine the long-time self-diffusion coefficient and sedimentation coefficient for suspensions of nanoparticles with anisotropic shapes (octahedra, cubes, tetrahedra, and spherocylinders) as a function of nanoparticle concentration using mesoscale simulations. We use a discrete particle model for the nanoparticles, and we account for solvent-mediated hydrodynamic interactions between nanoparticles using the multiparticle collision dynamics method. Our simulations are compared to theoretical predictions and experimental data from existing literature, demonstrating good agreement in the majority of cases. Further, we find that the self-diffusion coefficient of the regular polyhedral shapes can be estimated from that of a sphere whose diameter is the average of their inscribed and circumscribed sphere diameters.

Numerical simulation of altering the raw meal inlet position in a novel swirl precalciner

Conducted a numerical simulation to model a novel swirl precalciner, investigating how altering in the position of the raw meal inlet affects the internal gas flow, temperature field, and component concentration field within the precalciner. Applied the Realizable k-? two-equation turbulent model to the continuous phase. For the particle phase (pulverized coal), employed the Discrete Particle Model and the discrete random walk model. Simulated the combustion of pulverized coal and the decomposition of calcium carbonate by using the Species Transport model combined with the Finite-Rate/Eddy-Dissipation model. Modeled the generation of NOx using a NOx model. The results show that, in comparison to the condition with four raw meal inlets, the six raw meal inlets condition has a better coupling of pulverized coal combustion and raw meal decomposition. The decomposition rate of raw meal has seen a slight improvement, and there is a significant improvement in the occurrence of localized high temperatures within the precalciner, resulting in a reduction of the outlet NOx concentration from 1251 ppm to 225 ppm.

A dynamic incentive mechanism for data sharing in manufacturing industry

Data sharing is a critical component in a blockchain traceability platform. Therefore, creating a reasonable incentive mechanism to ensure that all enterprises participate in data sharing is vital for blockchain platforms. Currently, many researchers employ evolutionary game theory to analyze problems related to data sharing. However, evolutionary game theory typically assumes that the population composed of enterprises is mixed uniformly. Enterprises in the manufacturing industry are not uniformly mixed, as they tend to have specific connections with each other due to the size of enterprises and volume of business. Therefore, a networked evolutionary game is introduced to solve this problem. Firstly, an incentive model for enterprises sharing data is established. Then, a scale-free network is employed to simulate the connections between enterprises. To comprehensively consider the individual and group benefits of enterprises in the game, this study designs a strategy update rule for networked evolutionary game based on Discrete Particle Swarm Optimization and Variable Neighborhood Descent algorithm. To tackle the challenge of determining reasonable incentive values in networked evolutionary games, this study proposes a dynamic incentive mechanism based on the Q-Learning algorithm. Finally, the experiments indicate that this method can successfully facilitate the stable involvement of enterprises in data sharing.

Multi-objective flexible job-shop scheduling in hospital using discrete particle swarm optimization algorithm with adaptive inertia weight (DPSO-AIW)

Multi-objective flexible job-shop scheduling in hospital using discrete particle swarm optimization algorithm with adaptive inertia weight (DPSO-AIW)

Improved Algorithm of Partial Transmit Sequence Based on Discrete Particle Swarm Optimization

Orthogonal frequency division multiplexing (OFDM) in 5G has many advantages; however, one of the disadvantages is that the superposition of a large number of subcarriers leads to a high peak-to-average power ratio (PAPR) of the transmit signal. A high PAPR results in high-power amplifier distortion and performance degradation. The partial transmit sequence (PTS) algorithm is commonly used for PAPR reduction. It enumerates all combinations of phase factors, weighs the signal using each phase factor combination, and finds the set of phase factors that minimizes the PAPR value of the OFDM signal. The advantage of the PTS is that it determines the optimal solution through enumeration; however, its major drawback is the higher complexity caused by the use of enumeration. Some studies have introduced the discrete particle swarm optimization (DPSO) algorithm instead of enumeration to determine the optimal solution of the PTS algorithm. As an excellent optimization method, the DPSO algorithm represents each individual as a solution during the optimization. Through iterative updates of the initial population, individuals in the population continuously move closer to the optimal solution. This approach significantly reduces complexity compared with the exhaustive enumeration used in the traditional PTS algorithm. However, the disadvantage of the general DPSO algorithm is that it can result in premature and early convergence, which leads to degradation of the PAPR reduction performance. In this study, we propose an improved method based on the general DPSO-based PTS algorithm, and the improved algorithm MDPSO-PTS adopts dynamic time-varying learning factors, which can find the optimal combination of phase factors more efficiently. The MDPSO-PTS algorithm expands the search space when seeking the optimal combination of phase factors. This avoids the drawback of premature convergence commonly observed in general DPSO-PTS algorithms, preventing early consideration of local optima as global optima. A comparative simulation of the improved MDPSO-PTS algorithm with the general DPSO-PTS algorithm shows that the improved algorithm has stronger PAPR reduction, whereas the complexity remains basically unchanged. A comparative simulation with the traditional PTS algorithm shows a significant reduction in complexity, with only a slight, acceptable loss of reduction performance.

An improved method combined modified drag model and modified erosion model based on LES for solid-liquid multiphase flow and erosion with application in a 90° elbow

In various industrial processes, solid-liquid multiphase flows involving continuous carrier and discrete particle phases have erosion challenges. In this work, a modified drag model that considers the influence of turbulence intensity and a modified erosion model that considers the effect of low-angle impact are developed. An improved method combined modified drag model and modified erosion model based on LES is proposed for numerical simulation of solid-liquid multiphase flow and erosion. The commercial software ICEM for grid generation and ANSYS FLUENT for LES are used in the present work. In validation test of velocity distribution of solid-liquid multiphase flow in a vertical pipe, the mean relative error of the liquid phase pulsation velocity is 10.01 % for modified drag model, whereas the mean relative error is 16.11 % for original drag model. In validation test of the maximum erosion depth from a submerged impact test at a tilt angle of 45°, the relative error is 0.23 % for modified erosion model, while the relative error is 13.39 % for original erosion model. Then, the improved method is employed to compute the characteristics of solid-liquid flow and erosion in a 90° elbow. Particles have a significant influence on liquid flow in the shear layer area where the inner arch separation zone interacts with the main flow, and this influence is most pronounced at the position of r* = 0.3 at yd = 0.4D. At this position, particles result in 15.27 % reduction in liquid velocity and 8.75 % increase in turbulence intensity. The mean relative error of the elbow erosion rate by using the improved method is 16.95 %, whereas it is 31.34 % of the reference work. The erosion is affected by impact velocity, angle, and particle number, and the most significant parameter is the particle number in the severe erosion area.

Reduced-Order Model for Predicting the Performance of Small-Scale Unidirectional Cyclone Aerosol Separators

This paper presents a reduced-order model for the design of miniaturized unidirectional air cyclones serving as gas–liquid separators for the removal of oil aerosols from a gas stream. Conservation equations for mass, momentum and energy form the basis for predicting the pressure loss within the model; a modified residence-time model is employed for the separation behavior of oil droplets on the cyclone wall. CFD analysis results for one of the investigated geometries are also presented, serving to validate the velocity field employed within the simplified model and providing predictions for particle trajectories and overall separation efficiency. The numerical model is based on the incompressible RANS equations using the k-ω-SST turbulence model and a Lagrangian description of the dispersed phase (Discrete Particle Method, DPM) with a random-walk model for turbulent dispersion. Numerical solutions have been obtained using the commercial FV (Finite-Volume) solver ANSYS Fluent version 2022R1. Both models are benchmarked against experimental data obtained for a large number of cyclone geometries. Good agreement between experimental results, numerical results and results from the reduced-order model is found. The derived reduced-order model is suitable for rapid design of uniflow cyclones in the investigated configuration space.

Wide-field surface plasmon resonance microscope based on polyethylene oxide/polyacrylic acid brushes

Wide-field surface plasmon resonance microscope (WF-SPRM) based on polyethylene oxide/polyacrylic acid (PEO/PAA) polyelectrolyte brushes (PEBs) is presented for particle detection application. PEO acts as an H-bond acceptor, while PAA serves as an H-bond donor, forming hydrogen-bonding complexes within the brushes. Morphological, chemical, and crystal structural analyses confirm that the PEO/PAA brushes undergo a transition from a collapsed to a stretched state as the solvent pH is increased from 1 to 10. This pH-dependent change also renders the PEO/PAA brushes more hydrophilic. Additionally, the electrical conductivity and refractive index of the PEO/PAA brushes increase concomitantly with the increase of solvent pH. Furthermore, theoretical and experimental approaches study the sensitivity of WF-SPRM utilizing Au-(PEO/PAA) polyelectrolyte layers. The theoretical sensitivity of WF-SPRM is enhanced from 118.5 deg./RIU for the Au-layer to 178.1 deg./RIU for Au-(PEO/PAA PEBs). Moreover, the signal-to-noise ratio for the Au-(PEO/PAA PEBs) layer is 20 ± 1, indicating improved sensing performance compared to the Au-layer (signal-to-noise ratio of 6 ± 1). A mathematical model to describe the discrete particle detection by WF-SPRM is presented, where results demonstrate a good agreement between the calculated intensity profile and experimental data.

Clogging of noncohesive suspensions through constrictions using an efficient discrete particle solver with unresolved fluid flow.

When objects are forced to flow through constrictions their transport can be frustrated temporarily or permanently due to the formation of arches in the region of the bottleneck. While such systems have been intensively studied in the case of solid particles in a gas phase being forced by gravitational forces, the case of solid particles suspended in a liquid phase, forced by the liquid itself, has received much less attention. In this case, the influence of the liquid flow on the transport efficiency is not well understood yet, leading to several apparently trivial but yet unanswered questions, e.g., would an increase of the liquid flow improve the transport of particles or worsen it? Although some experimental data are already available, they lack enough detail to give a complete answer to such a question. Numerical models would be needed to scrutinize the system deeper. In this paper, we study this system making use of an advanced discrete particle solver (mercurydpm) and an approximated numerical model for the liquid drag and compare the results with experimental data.

Surface plasmon coupling between wide-field SPR microscopy and gold nanoparticles

The coupling behavior of the wide field surface plasmon microscopy (WF-SPRM) with single-, two-, and multiple-gold nanoparticles (AuNPs) with different AuNPs sizes is investigated using theoretical, simulation, and experimental approaches. The signal intensity of a single AuNP increases from 208 a.u. to 583 a.u. as particle size increases from 40 to 80 nm, which evidences the signal-building mechanism of Rayleigh scattering theory. A discrete particle model of SPR is used to understand the interaction between an Au-layer and a single AuNP. The calculated intensity profile of the single AuNP from the discrete particle model is accepted with the experimental data. In addition, the superposition between 2-AuNPs surface plasmon waves is studied using the finite element method as well as experimental data from WF-SPRM. The surface plasmon waves around the two particles generate an interference pattern. Finally, it is demonstrated that plasmonic multiple particles scattering can be represented by an effective media, which is described by Maxwell-Garnet equations.

A Blended Instructional Design for Civil Litigation Combining Multi-Objective Optimization Algorithms in a Rule of Law Context

Abstract The current traditional means of teaching adopt the indoctrination teaching mode. Students passively accept information, learning enthusiasm is not high, and the traditional teaching mode needs to be reconstructed urgently. This paper constructs a blended teaching mode for civil litigation based on the WeChat public platform and provides the implementation process for the blended teaching mode. In order to enable students to obtain civil litigation-related resources more in line with the learning characteristics of the WeChat platform, a multi-objective learning path optimization algorithm is proposed, and the discrete binary particle swarm algorithm is used to optimize and solve the multi-objective learning path. The teaching experiment method is utilized to select the research object and method to study the teaching effect of the blended teaching mode and then obtain the students’ satisfaction with the blended teaching mode. The results show that through the BPSO algorithm, after the number of iterations reaches 150 times, its fitness value is around 10-5, the average running time of the 20 test functions is around 500ms, and the mean value of students’ satisfaction with the blended teaching mode is 9.31 points. The blended teaching mode of civil litigation in the context of the rule of law can help students better establish the thinking of the rule of law and promote the improvement of students’ learning ability, and multi-objective optimization can help students better obtain the optimal learning path.

Four-dimensional flow field near a sphere settling in Newtonian fluid

This paper presents time-resolved, three-dimensional measurements coupling particle trajectories with flow fields around settling spheres in Newtonian fluids. The experiments cover a range of particle Reynolds numbers (Re), spanning from 1.6 to 6. Our calculated drag coefficients, derived from sphere trajectories, closely align with values reported in the literature. Notably, our high spatial resolution reveals oscillations, potentially corresponding to the “streamwise oscillations” phenomenon discussed by Horowitz and Williamson [“The effect of Reynolds number on the dynamics and wakes of freely rising and falling spheres,” J. Fluid Mech. 651, 251 (2010)]. For a single sphere, we extract the three-dimensional flow field using particle tracking velocimetry. Discrete particle tracks are meticulously interpolated onto a regular grid using a fine-scale reconstruction based on the vortex-in-cell method. Leveraging the known sphere position, we introduce a sphere-centered coordinate system, enabling time-averaging of flow properties. Additionally, we interpolate and analyze the pressure field on the sphere's surface, employing proper orthogonal decomposition to unveil distinct pressure fluctuation modes.

Particle dynamics in vertical vibration-driven immersed granular systems: A study with resolved computational fluid dynamics-discrete element method

Vibration-driven immersed granular systems (VIGSs) are ubiquitous in nature and industry. However, particle dynamics in 3D VIGSs is hard to obtain directly from experiments. The resolved Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) is introduced to study a cylindrical VIGS subjected to vertical vibration focusing on particle dynamics. A Voronoi-weighted Gaussian interpolation (VWGI) method is used to convert the discrete particle information into a continuous field. The VWGI method enables the estimation of the continuous field for granular systems, especially for those with large-scale non-uniformity and heterogeneity particle distribution in local cells. The results show that the periodic variation of the system's kinetic energy is caused by the collision between the lower particles and the vibrating wall, and the particle kinetic energy decreases with height rising. A velocity spatial structure of convection, moving from the cylinder center to the sidewall, is observed in both immersed and dry systems away from the bottom. Vibration-driven particles can exhibit a similar flow structure to natural convection. Compared to the dry system, the convection strength and momentum transfer in the VIGS are higher, while the momentum diffusion is lower. The fluid restrains the particle energy acquisition and enhances the energy dissipation of the “heated” particles, while the formation of the fluid convection benefits the particle convection directionality. This resolved CFD-DEM study with the VWGI method provides useful results of the particle dynamics in VIGSs, which could provide guidance for some practical applications in minerals processing involving vibration-driven immersed granular systems.

Rigid clumps in the MercuryDPM particle dynamics code

Discrete particle simulations have become the standard in science and industrial applications exploring the properties of particulate systems. Most of such simulations rely on the concept of interacting spherical particles to describe the properties of particulates, although, the correct representation of the nonspherical particle shape is crucial for a number of applications. In this work we describe the implementation of clumps, i.e. assemblies of rigidly connected spherical particles, which can approximate given nonspherical shapes, within the MercuryDPM particle dynamics code. MercuryDPM contact detection algorithm is particularly efficient for polydisperse particle systems, which is essential for multilevel clumps approximating complex surfaces. We employ the existing open-source CLUMP library to generate clump particles. We detail the pre-processing tools providing necessary initial data, as well as the necessary adjustments of the algorithms of contact detection, collision/migration and numerical time integration. The capabilities of our implementation are illustrated for a variety of examples.

Optimizing the Isolation Forest Algorithm for Identifying Abnormal Behaviors of Students in Education Management Big Data

With the changes in educational models, applying computer algorithms and artificial intelligence technologies to data analysis in universities has become a research hotspot in the field of intelligent education. In response to the increasing amount of student data in universities, this study proposes to use an optimized isolated forest algorithm for recognizing features to detect abnormal student behavior concealed in big data for educational management. Firstly, it uses logistic regression algorithm to update the calculation method of isolated forest weights, and then uses residual statistics to eliminate redundant forests. Finally, it utilizes discrete particle swarm optimization to optimize the isolated forest algorithm. On this basis, improvements have also been made to the traditional gated loop unit network. It merges the two improved algorithm models and builds an anomaly detection model for collecting college student education data. The experiment shows that the optimized isolated forest algorithm has a recognition accuracy of 0.986 and a training time of 1 second. The recognition accuracy of the improved gated loop unit network is 0.965, and the training time is 0.16 seconds. In summary, the constructed model can effectively identify abnormal data of college students, thereby helping educators to detect students' problems in time and helping students to improve their learning status.

Coalescence and mixing dynamics of droplets in acoustic levitation by selective colour imaging and measurement

Acoustic levitation is well-suited to ‘lab-on-a-drop’ contactless chemical analysis of droplets. Rapid mixing is of fundamental importance in lab-on-a-drop platforms and many other applications involving droplet manipulation. Small droplets, however, have low Reynolds numbers; thus, mixing via turbulence is not possible. Inducing surface oscillation is effective in this regard, however, the relationship between internal flow and mixing dynamics of droplets remains unclear. In this study, we conducted a set of simultaneous optical measurements to assess both the flow field and the distribution of fluid components within acoustically levitated droplets. To achieve this, we developed a technique to selectively separate fluorescent particles within each fluid, permitting the measurement of the concentration field based on the data from the discrete particle distribution. This approach revealed a relationship between the mixing process and the internal flow caused by surface oscillation. Thus, the internal flow induced by surface oscillation could enhance droplet mixing. Our findings will be conducive to the application and further development of lab-on-a-drop devices.