Dynamic Behavior Of Particles Research Articles

Transport behavior of particles and evolution of plugging zones in rough fractures: Insights from a novel coupled CFD-DEM model

The meso-mechanism of particle bridging and plugging in rough fractures remains unclear. Therefore, this study explores a novel meso-model for simulating particle transport in rough fractures by combining Computational Fluid Dynamics and Discrete Element Method (CFD-DEM). The dynamic behavior of particles and the corresponding evolution mechanism of plugging zones are investigated. The simulation innovatively uses particle boundaries to correct fluid flow, greatly simplifying the fluid mesh partitioning while ensuring accuracy. The results reveal the connection between particles’ transport and bridging behavior in rough fracture spaces, the distribution characteristics of plugging zones, and fluid flow characteristics. The distribution of bridging favorable zones determines the position and pattern of the front edge of subsequent plugging zones. The stress accumulated by particles has little effect on the strong force chain of the front edge. The force chain strength of the double-particle bridging structure is greater than that of the single-particle bridging structure. Due to weak shear planes, the double-particle bridging structure is more prone to shear failure and instability under external loads. Improving the particle size distribution range can reduce bridging blind zones and achieve better sealing performance. The results provide theoretical help for the design of field operation processes.

Dynamic capture behavior of rotating matrix to paramagnetic particles in centrifugal HGMS process: Based on FEM

Centrifugal high gradient magnetic separation (CHGMS) was developed to produce a high-quality magnetic product from weakly magnetic materials. In this research, the dynamic behavior of particles in the CHGMS process and the magnetic capture of rotating wires to paramagnetic particles were analyzed through Finite Element Method (FEM). It was found that particles move spirally with slurry in the separation area of the separator, due to the stirring of magnetic wires, and the motion traces of paramagnetic particles are more tortuous and complicated than those of non-magnetic particles. The capture capacity of elliptical wire to paramagnetic particles is stronger than that of circular wire, but is weaker than that of square one. The distance from rotating wires to the center of matrix significantly influences the mass weight of paramagnetic particles captured onto the wires, and there is a clear interaction between wire layers during the capture process of rotating matrix.

CFD-DEM simulations of a stirred liquid bath containing particles based on a theoretical scaling framework

BackgroundCoupled computational fluid dynamics-discrete element method (CFD-DEM) simulations are valuable tools for studying particle-fluid coupling problems. However, because of the many particles involved, full-scale simulations are not feasible. MethodsA theoretical scaling framework developed in our previous work was introduced for the first time in the CFD-DEM simulations. A 1/2 scaled-down numerical model was established based on the physical model used to observe the dynamic behavior of dross particles in a hot-dip galvanizing bath. The applicability of the theoretical scaling framework for the CFD-DEM simulations was demonstrated by comparing the results of the physical and scaled-down numerical models. Additionally, the effects of the belt width and velocity on the flow field and dynamic behavior of the particles were investigated. Significant findingsThe results show that the evolution of the particle-accumulation morphology at different belt widths in scaled-down simulations agreed with that in physical model experiments, confirming that the theoretical scaling framework can be used in CFD-DEM simulations and is advantageous in significantly reducing number of particles required for the simulations. As the belt speed decreased, the number of suspended particles also decreased. Furthermore, a reduction in the belt width led to a shift of the aggregation region of the suspended particles away from the vicinity of the symmetrical plane of the bath toward the sidewalls. This shift of the aggregation region was beneficial in preventing the adhesion of dross particles to the surface of the steel strip during the hot-dip galvanizing process.

Simulation of Heterogeneous Particle Deposition using Digital Visual Fortran

Granular particles have unique behaviour because it can behave like solids, liquids, and gaseous subtances. So, it is necessary to discuss the dynamics of these particles. Dynamical particles involve normal force, tangential force, and gravity force. Deposition is dropping particles at a medium, it produces a pile of particles. First step of simulation is assigned collision criteria. Next step is simulation using digital visual Fortran software and the final step is to analyze it. Based on simulation, dynamic behavior of particles is seen through on video created by assembling images of simulation result. The largest particles are fallen spread to underlying medium then followed by smaller particles. It fallen on center of pile until particles below bears a much heavier than the mass of particles. The smaller particles on center of pile pushes at the bottom and it makes cavity. As a result, the pile seems to have two peaks on static condition

NeuroPNM: Model reduction of pore network models using neural networks

Reacting particle systems play an important role in many industrial applications, for example biomass drying or the manufacturing of pharmaceuticals. The numerical modeling and simulation of such systems is therefore of great importance for an efficient, reliable, and environmentally sustainable operation of the processes. The complex thermodynamical, chemical, and flow processes that take place in the particles are a particular challenge in a simulation. Furthermore, typically a large number of particles is involved, rendering an explicit treatment of individual ones impossible in a reactor-level simulation. One approach for overcoming this challenge is to compute effective, physical parameters from single-particle, high-resolution simulations. This can be combined with model reduction methods if the dynamical behaviour of particles must be captured. Pore network models with their unrivaled resolution have thereby been used successfully as high-resolution models, for instance to obtain the macroscopic diffusion coefficient of drying. Both parameter identification and model reduction have recently gained new impetus by the dramatic progress made in machine learning in the last decade. We report results on the use of neural networks for parameter identification and model reduction based on three-dimensional pore network models (PNM). We believe that our results provide a powerful complement to existing methodologies for reactor-level simulations with many thermally-thick particles.

Experimental and numerical investigations of particle suspension suppression mechanisms for a particle-suppression device in a stirred liquid bath

Particle suspension in a turbulent flow can seriously affect the performance of manufactured products in many industrial processes in which the motion of particles cannot be modeled using the numerical method because of the enormous number of particles. Therefore, in this study, a full-scale computational fluid dynamics (CFD) simulation and a 1/5 scaled-down water model experiment were employed to investigate the flow pattern and dynamic behavior of particles in a continuously stirred vessel system. Based on the understanding of the suspension mechanism of settling particles, a particle-suppression device was designed to realize the harmless movement and deposition of particles. The results showed that the flow guidance and division mechanisms of the particle-suppression device led to the inhibition of particle suspensions. In addition, the optimal parameter combination for the device from the water model experiment combined with the orthogonal experimental design, resulted in a 98.3% reduction in the concentration of suspended particles. The suspension of particles was effectively suppressed, which improves product quality and production efficiency. Reliable results can be achieved by combining CFD simulations and water model experiments.

Effects of bubbles on particle dynamic behavior and concentration distribution in High-viscosity liquid under negative pressure

The vacuum method is a widely adopted technique for eliminating bubbles from polymers containing particles. To investigate the influence of bubbles on the behavior of particles and the concentration distribution in high-viscosity liquids under negative pressure, experimental and numerical methods have been employed. The experimental findings demonstrated a positive correlation between the diameter and rising velocity of bubbles and the negative pressure. As the negative pressure increased from − 10 kPa to − 50 kPa, the position of the region where the particles were concentrated in the vertical direction was elevated. Furthermore, when the negative pressure exceeded − 50 kPa, the particle distribution became sparse and layered locally. The Lattice Boltzmann method (LBM) integrated with the discrete phase model (DPM) was utilized to investigate the phenomenon, and the outcomes revealed that rising bubbles have an inhibitory effect on particle sedimentation, and the extent of inhibition was determined by the negative pressure. In addition, vortexes generated by differences in the rising velocity between bubbles resulted in a particle distribution that was sparse and layered locally. This research provides a reference for achieving desired particle distributions using a vacuum defoaming approach and should be further studied to extend its applicability to suspensions containing particles with different viscosities.

Quantum and classical branching flow in space and time

Branching flow, a phenomenon known for steady wave propagation in two-dimensional weakly correlated random potential, is also present in the time-dependent Schr\"odinger equation for a single particle in one dimension, moving in a fluctuating random potential. We explore the two-dimensional parameter space of this model using numerical simulations and identify its classical regions, where just one classical parameter is sufficient for its specification, and its quantum region, where such a simplification is not possible. We also identify the region of the parameter space where known analytical results of a classical white noise model are relevant. Qualitative behavior of quantum and classical particle dynamics is discussed in terms of branching time scale and a new time scale related to a particle's kinetic energy.

The cooperative migration dynamics of particles correlates to the nature of hexatic–isotropic phase transition in 2D systems of corner-rounded hexagons

In the two-dimensional (2D) melting transition of colloidal systems, the hexatic–isotropic (H–I) transition can be either first-order or continuous. However, how particle dynamics differs at the single-particle level during these two different melting transitions remains to be disclosed. In this work, by Brownian dynamics (BD) simulations, we have systematically studied the dynamic behavior of corner-rounded hexagons during the H–I transition, for a range of corner-roundness ζ = 0.40 to 0.99 that covers the crossover from the continuous to first-order nature of H–I transition. The results show that hexagons with ζ ≤ 0.5 display a continuous H–I transition, whereas those with ζ ≥ 0.6 demonstrate a first-order H–I transition. Dynamic analysis shows different evolution pathways of the dominant cluster formed by migrating particles, which results in a droplet-like cluster structure for ζ = 0.40 hexagons and a tree-like cluster structure for ζ = 0.99 hexagons. Further investigations on the hopping activities of particles suggest a cooperative origin of migrating clusters. Our work provides a new aspect to understand the dependence of the nature of H–I transition on the roundness of hexagons through particle dynamic behavior.

Analysis of particle dynamics in a rotating dish

Although the rotating dishes are widely used in industrial processes, there are not many studies regarding the behavior of granular flow in this type of device. It is reported that the particle dynamics directly influences the dish performance in relation to its applications, e.g., in the granulation process. This work provides an experimental study about the effect of the main variables (rotational speed, angle of inclination, and physical properties of particles) on the behavior of particle dynamics in a rotating dish. The conditions, under which transitions between the flows regimes (rolling, cascading, cataracting, and centrifuging) occurred, were identified and the effects of the studied variables were quantified. The increase in the rotational speed and filling degree and the decrease in the angle of inclination caused a decrease in the angle of departure of the particles and anticipated the transition between flow regimes, demonstrating that both responses are correlated. Correlations proposed in the literature for predictions of the critical rotational speed related to the centrifuging regime were analyzed. A new equation was proposed, which reduced the average percentage deviation from 52.83% to 14.27% when compared to experimental data. • The particle dynamic behavior in a dish granulator was investigated. • The influence of relevant variables was quantified. • The transitions between the characteristic flow regimes were identified. • A new equation to predict the critical rotational speed was proposed.

Effect of noncircularity on the dynamic behaviors of particles in a disformal rotating black-hole spacetime

A disformal rotating black-hole solution is a black-hole solution in quadratic degenerate higher-order scalar-tensor theories. It breaks the circular condition of spacetime different from the case of the usual Kerr spacetime. This study investigated the dynamic behaviors of the motion of timelike particles in such disformal black-hole spacetime with an extra deformation parameter. Results showed that the characteristics of the particle's motion depend on the sign of the deformation parameter. For the positive deformation parameter, the motion is regular and orderly. For the negative one, as the deformation parameter changes, the motion of the particles undergoes a series of transitions between the chaotic motion and the regular motion and falls into the horizon or escapes to spatial infinity. This means that the dynamic behavior of timelike particles in the disformal Kerr black-hole spacetime with noncircularity becomes richer than that in the usual Kerr black-hole case.

Artificial neural network-based sensitivity analysis and experimental investigation of liquid–solid fluidization technique for low-grade coal upgradation

Liquid-solid fluidization technique is being applied where low-grade coal or minerals enrichment is mostly density-based. Static and dynamic behavior of particles in a fluid medium has been extensively investigated over the years because of its dynamic applications across various industries. In this work, bed characterization studies and experiments have been conducted to study coal washing ability of the liquid-solid fluidized bed separator. Results have been recorded in terms of ash rejection%, combustible recovery% and separation efficiency%. Minimum fluidization velocity and pressure drop values have been predicted using existing theoretical correlations and compared with the experimental values. A three-layered (4:5:3) feedforward back-propagation (FFBP) neural network model was developed using Levenberg-Marquardt algorithm, LOGSIG and MSE as training, transfer and performance functions respectively. Garson’s algorithm and connection weight approach have been employed for sensitivity analysis to interpret the neural network results physically. Coefficients of correlation, all R (including training, validation & testing datasets) obtained for outputs ash rejection (R = 0.9960), combustible recovery (R = 0.9952) and separation efficiency (R = 0.9944) suggest that predicted values are in agreement with the experimental values and the developed model is a good fit.

Diffusion-controlled, Size-based Separation of Narrowedly Distributed Suspensions in Pressure-driven Flows Through Microfluidic Bumper Arrays

Microfluidic bumper arrays (also referred to as Deterministic Lateral Displacement devices, DLD) have been proposed in the last fifteen years as a simple and effective mean to implement the label-free, size-based separation of a suspension of micrometric-sized particles. The separation resolution in these devices resolution is significantly higher than that associated with more traditional separation techniques such as, e.g., SEC columns. In DLD devices, the suspended particles are dragged by a pressure-driven flow through a periodic array of obstacles, typically hosted in a channel with rectangular cross-section. Experiments have proven that if a focused current entraining a suspension of particles of different size is continuously introduced upstream the obstacle array, size-sorted populations of particles can be collected at different locations of the device outlet. This is because, as a consequence of the fluid drag and of the hydrodynamics-mediated collisions with the obstacles, particles of different size follow on the average differentmigration pathts, which are at an angle with respect to the average direction of the carrier flow. Based on the DLD separation mechanism, prototypes have been constructed and used for sorting and isolating suspensions of clinical and biological interest, ranging from the size of red blood and circulating tumor cells, down the nanometric scale of exosomes. Recently, a novel chromatographic use of DLD devices has been proposed, where the suspension is separated in time and space coordinates by exploiting the dependence on particle mobility on particle size. By investigating particle transport in a idealized setting (associated with point-sized obstacles), it has been argued that the separation-enhancing effect due to mobility should allow, in principle, to effectively separate particles suspensions characterized by a narrow size distribution to very high degrees of resolution. In this contribution, the possibility of reproducing the same phenomenon in a classical pressure-driven flow in the presence of finite-sized obstacles is explored. Specifically, it is shown that (ideal) conditions similar to the type of particle motion predicted for point-size obstacles can be obtained even in the presence of obstacles of finite dimension, provided that the obstacle shape near the collision point is accurately tuned to obtain a diffusion-controlled behavior of particle dynamics.

Direct numerical simulation of freely falling particles by hybrid immersed boundary – Lattice Boltzmann – discrete element method

In this study, a coupling rule based on Lattice Boltzmann Method (LBM), Immersed Boundary Method (IBM), and Discrete Element Method (DEM) was developed to simulate settling of particles in Newtonian liquid. The direct forcing IB-LBM, which was enhanced by the split-forcing technique, was utilized to account for fluid/solid interactions. Dynamic behavior of particles during collisions was evaluated by DEM, in which contact forces were computed based on overlap distances and relative velocity between two particles. The lubrication force was also included in equation of motions that allowed the coupled IB-LB-DEM to accurately account for the fluid film before collision. The hybrid model was verified by examining the freely falling of single and tandem circular particles in a vertical box. The particle-particle collision was included in the model and particular attention was given to the examination of the effects of collision parameters including restitution and friction coefficients on the particle sedimentation behavior. The transverse and longitudinal positions and velocities of two falling particles are compared for a wide range of physical parameters. Finally, free falling of 28 particles for two different friction coefficients were simulated to show capability of the new model for analyzing particulate flows.

The effect and dynamic behavior of particles in high-current vacuum arc interruptions

Microparticles produced by high current vacuum arcs significantly reduce the insulation performance of vacuum interrupters. The objective of this paper is to observe the generation and dynamics of microparticles during the arcing and post-arcing period, and to further reveal the possible effects of particles during interruption of a high current vacuum arc. A plasma diagnosis system based on a laser-shadow technique was used to measure the particles in a demountable vacuum chamber. The results show that contact materials affect particle generation patterns. For a CuCr contact with a higher proportion of chromium, the formation of a melting pool is much more difficult and the flow of liquid metal on the surface can be suppressed, which also impedes the formation of particles. Moreover, reignition after arc extinction was found to be triggered by some incandescent particles. These hot particles have two effects on breakdown events during the post-arcing phase: they trigger a micro-discharge between a particle and the electrode, and new weak points are left on the surface that enhance the local field. More crucially, the lifetime of spontaneous particles spans up to hundreds of milliseconds after current zero, which indicates that long-delayed breakdown is correlated with the existence of particles. In addition, some particles could remain suspended near the electrode or oscillate between the contacts. The dynamic behavior of these particles can significantly lower the breakdown threshold and increase the possibility of breakdown.

Characterization of the interacting gas-particle dynamic system in cold spray nozzles in dimensional and non-dimensional form

In order to study fundamental particle and gas dynamic behavior in the cold spray process, a simplified model was created assuming all dependent process parameters to vary in the streamwise x-coordinate only. This model resulted in a coupled system of ordinary differential equations describing gas and particle interactions in terms of basic conservation principles for mass, momentum and energy within a converging-diverging nozzle. The system of ordinary differential equations along with a prescribed set of initial conditions for all dependent variables at x = 0, forms an initial value problem which was integrated in the x-direction using a 4th order Runge-Kutta method to determine the velocity and temperature of the gas and particle phases. The numerical solutions from the model were then validated by comparison with steady, axisymmetric, two-dimensional computational models incorporating the relevant PDE's using the CFD software. To obtain additional insight into the dynamic and thermal behavior of the coupled system, applicable scales were chosen to cast the simplified dimensional governing equations in non-dimensional form. One of the significant results of this simplified one-dimensional study is the parameterization that falls out naturally from the scaled equations, thus conferring a more general means to characterize (in non-dimensional terms) the relative gas-particle momentum and energy exchange in higher dimensional problems. From the scaled results it was also discovered that there is no advantage in using lighter, more expensive carrier gases such as helium to accelerate larger, heavier particles, and thus considerable cost savings can result through the use of compressed air.

Disaggregation and separation dynamics of magnetic particles in a microfluidic flow under an alternating gradient magnetic field

How to prevent particle aggregation in the magnetic separation process is of great importance for high-purity separation, while it is a challenging issue in practice. In this work, we report a novel method to solve this problem for improving the selectivity of size-based separation by use of a gradient alternating magnetic field. The specially designed magnetic field is capable of dynamically adjusting the magnetic field direction without changing the direction of magnetic gradient force acting on the particles. Using direct numerical simulations, we show that particles within a certain center-to-center distance are inseparable under a gradient static magnetic field since they are easy aggregated and then start moving together. By contrast, it has been demonstrated that alternating repulsive and attractive interaction forces between particles can be generated to avoid the formation of aggregations when the alternating gradient magnetic field with a given alternating frequency is applied, enabling these particles to be continuously separated based on size-dependent properties. The proposed magnetic separation method and simulation results have the significance for fundamental understanding of particle dynamic behavior and improving the separation efficiency.

Discussions on the correspondence of dissipative particle dynamics and Langevin dynamics at small scales

We investigate the behavior of dissipative particle dynamics (DPD) within different scaling regimes by numerical simulations. The paper extends earlier analytical findings of Ripoll, M., Ernst, M. H., and Espa˜nol, P. (Large scale and mesoscopic hydrodynamics for dissipative particle dynamics. Journal of Chemical Physics, 115(15), 7271–7281 (2001)) by evaluation of numerical data for the particle and collective scaling regimes and the four different subregimes. DPD simulations are performed for a range of dynamic overlapping parameters. Based on analyses of the current auto-correlation functions (CACFs), we demonstrate that within the particle regime at scales smaller than its force cut-off radius, DPD follows Langevin dynamics. For the collective regime, we show that the small-scale behavior of DPD differs from Langevin dynamics. For the wavenumber-dependent effective shear viscosity, universal scaling regimes are observed in the microscopic and mesoscopic wavenumber ranges over the considered range of dynamic overlapping parameters.

Numerical Simulation and Experimental Study of Particle Dynamics in a Rotating Drum with Flights

Rotary dryers are widely used in various industries. Although numerous research efforts have focused on characterizing the dynamics of these equipments, the design of rotating dryers is complex, and theoretical studies are necessary to gain an in-depth understanding of the dynamics of particles in these dryers. This paper aims to investigate the particle dynamic behavior in a rotating drum with flights, based on CFD and experimental results. In the numerical study it was used the Eulerian-Eulerian multiphase model along with the kinetic theory of granular flow. The holdups of solids in the flights were compared with experimental data, using a methodology created specifically for this purpose. The simulated results were in good agreement with the experimental data and the present work has shown that the Eulerian approach has been able to predict the fluid dynamics behavior in different operating conditions.

Accelerating Cell Dynamics Simulations of Soft Materials using CUDA-GPU

Soft materials are a highly demanded class of research for predicting the characteristics of phase separation and self-assembly into nanoscale structures. One of the most well-known methods to demonstrate and simulate dynamic behavior of particles, such as particle tracking, and to consider different effects of simulation parameters is cell dynamic simulation. In fact, cell dynamic simulation as a cellular computerization can be used to investigate different aspects of morphological topographies of soft material system. To achieve accurate and efficient computational results for cell dynamic simulation method, two factors are essential and vital: first, computing/calculating time-scale; and second, simulation system size. Consequently, finding available computing algorithms and resources such as sequential algorithm for implementing a complex technique and achieving precise results is critical and expensive. Therefore, it is very important to consider a parallel algorithm and programming model to solve time-consuming and massive computational processing. This paper presents a novel GPU-based parallel acceleration approach to cell dynamics simulations (CDS) for a spherical phase diblock copolymer under a shear flow. A new achievement is based on a commodity NVIDIA Quadro K5000 graphic card and Compute Unified Device Architecture (CUDA) C language.