Search Results (2,040)

This paper presents a roughness surface model for Lagrangian simulations that interacts with both temperature and vorticity fields. The chosen problem is the uniform flow around a rough circular cylinder heated with constant temperature under mixed convection. The methodology used is the Temperature Particles Method (TPM), in which both vorticity and temperature fields are discretized in particles to simulate the real flow in a purely Lagrangian form. The simulation is computationally extensive due to the application of the Biot–Savart law for the two fields and the calculation of buoyancy forces, which is alleviated by the use of parallel programming with OpenMP. The simulation of roughness effects for both fields is obtained using a Large Eddy Simulation (LES) model for vorticity, based on the second-order velocity structure function, which is correlated with the thermal diffusivity through the turbulent Prandtl number. In general, the results indicate that roughness increases the drag coefficient, while an increase in the Richardson number reduces this coefficient. Full article

Particles travelling within and interacting with any fluid media are found in both natural phenomena and industrial processes. Through these interactions, the particles experience a drag force, heavily influenced by their morphology, and significantly affecting their dynamics. This study examines the relationship between particle morphology and the drag force exerted on them, using both empirical models and computational simulations. The findings indicate that for regular and irregular particles of diverse morphologies, a combination of existing empirical models can predict the drag force within a 40% error margin. However, these models may fall short of meeting the accuracy demands in certain applications. To address this, the study provides clear guidelines for selecting the most suitable drag model based on particle morphology and flow regime. Full article

Cyclone separators are widely used for gas–solid separation due to their robustness and low operating cost. This study focuses on the experimental validation of a single cyclone configuration and the development of a MATLAB-based numerical framework. The model employs a Euler–Lagrange approach to capture centrifugal, drag, and gravitational forces acting on spherical polyethylene particles (D = 5 mm). Laboratory-scale measurements of airflow, pressure drop, and separation efficiency showed strong agreement with the numerical model (deviation < 6%), confirming its reliability for the single cyclone case. Beyond this validated framework, exploratory simulations were carried out for series and parallel cyclone configurations to provide predictive insights into possible design trade-offs. Unlike high-fidelity CFD–DEM models, which are computationally intensive and allow detailed turbulence and particle–particle interactions, the present MATLAB model is simplified but transparent and fast to implement. Its originality lies in demonstrating a low-cost, experimentally calibrated tool that can support preliminary design decisions. The multi-cyclone results should be interpreted as predictive trends, as no direct experimental validation was possible within the present setup. These findings offer preliminary guidance for balancing efficiency, energy demand, and throughput in applied mechanics of multiphase flow systems. Full article

The utilization of hydrocyclones dates back more than a century. As the key channel for multiphase flow, the inlet chamber exerts a notable influence on the separation efficiency of hydrocyclones. Conventional feed bodies mainly adopt straight lines as guidelines. During the transition of fluid from linear motion to circumferential motion, significant kinetic energy loss and particle misalignment are exhibited, resulting in relatively low classification accuracy of the hydrocyclone. Therefore, in this study, a hydrocyclone featuring a complex curved inlet chamber structure was designed, and numerical analysis was employed to examine the particle distribution and aggregation characteristics within both the inlet chamber and the hydrocyclone. Supplemented with RSM/VOF/TFM simulations and quartz sand experimental validation, this study compares the separation performance of the complex curved inlet with the conventional linear inlet. The results indicate the following: when particle sizes are small, particles are dispersed throughout the hydrocyclone and inlet chamber, exhibiting a disordered state, which leads to poor classification performance. As particle size increases, particles gradually form layers along the radial direction; larger particles tend to accumulate on the hydrocyclone wall. When the particle concentration is maintained within a specific range, it promotes the migration of fine particles toward the center, thereby reducing the likelihood of fine particles entering the outer vortex and allowing for more precise classification of fine particles. As the particle concentration increases, the cutting ability of the hydrocyclone progressively diminishes; when the concentration exceeds 20%, the maximum underflow recovery rate for particles smaller than 50 µm is only 60%, resulting in significant coarse overflow and a notable decrease in classification precision. Furthermore, as the inlet concentration increases, the dispersion index for 0.5 µm particles ranges from 0.6 to 1.6, for 4 µm particles from 0.6 to 1.4, and for 60 µm particles from 0.6 to 1. The decreasing dispersion index indicates an increasing classification force, which aids in the formation of a coarse particle layer on the wall. The conclusions and data obtained provide a theoretical foundation and empirical support for the design of innovative inlet chamber structures. Full article

The Discrete Element Method (DEM) has become a cornerstone for analysing granular flow and mixing phenomena in static mixers. This review provides a comprehensive synthesis that distinguishes it from previous studies by: (i) covering a broad range of static mixer geometries, including Kenics, SMX, and Sulzer designs; (ii) integrating experimental validation methods, such as particle tracking, high-speed imaging, Particle Image Velocimetry (PIV), and X-ray tomography, to assess DEM predictions; and (iii) systematically analyzing computational strategies, including advanced contact models, hybrid DEM-CFD/FEM frameworks, machine learning surrogates, and GPU-accelerated simulations. Recent advances in contact mechanics—such as improved cohesion, rolling resistance, and nonspherical particle modelling—have enhanced simulation realism, while adaptive time-stepping and coarse-graining improve computational efficiency. DEM studies have revealed several non-obvious relationships between mixer geometry and particle dynamics. Variations in blade pitch, helix angle, and element arrangement significantly affect local velocity fields, mixing uniformity, and energy dissipation. Alternating left–right element orientations promote cross-sectional particle exchange and reduce stagnant regions, whereas higher pitch angles enhance axial transport but can weaken radial mixing. Particle–wall friction and surface roughness strongly govern shear layer formation and segregation intensity, demonstrating the need for geometry-specific optimization. Comparative analyses elucidate how particle–wall interactions and channel structure influence segregation, residence time, and energy dissipation. The review also identifies current limitations, highlights validation and scale-up challenges, and outlines key directions for developing faster, more physically grounded DEM models, providing practical guidance for industrial mixer design and optimization. Full article

Particle histories critically influence product quality in spray drying processes, encompassing statistical data on particle dynamics and behavior inside the chamber, including temperatures, moisture levels, wall impacts, and residence times. This study presents the first systematic parametric assessment of how chamber geometry influences particle histories in spray drying, extending previous work on airflow dynamics. A design of experiments (DOE) methodology combined with cost-efficient CFD simulations was employed to establish quantitative parameter–response relationships. The results reveal two distinct classes of particle responses: (i) residence time, moisture content, and wall temperature, which are primarily governed by chamber aspect ratio and drying air flow rate, and (ii) particle–wall impact behavior, which is dominated by chamber topology. Inlet swirl modulates all particle histories, differentially impacting final product quality and energy efficiency. These findings provide predictive guidelines for chamber design and operation, while the methodology offers a general framework for scale-up analyses and parametric CFD studies of particle-laden multiphase processes. Full article

Debris flow erosion is key to the escalation of potential hazards, which may jeopardize the sustainable development of nearby human habitats. However, studies pertaining to this issue are impeded by the intricate interactions of flow and sediment. Thus, this study introduces an unresolved CFD-DEM coupled simulation primarily relying on a classical numerical simulation and a physical experiment to study the critical erosion process of debris flows on a dry basal sediment under laboratory scale. Results indicate that three layers of substrate bed can be verified during the erosion process, and there is a positive correlation between the erosion depth and the particle size with the Froude number of debris flow, as well as between the erosion length and the critical entrainment transport with the total mass of debris flow. In addition, thresholds for the collisional point load and the impact energy of debris flow head, which are essential for predicting the attenuating rates of erosive depth and the critical entrainment transport, have been proposed based on data regressions derived from power functions. The coupled numerical approach is capable of accurately simulating the erosion behavior of debris flows similarly to physical model experiments, thereby providing both theoretical and practical insights into the dynamics of erosion. Full article

This study investigates the steady-state behavior of a four-way opposed diaphragm pump. Simulations and experimental results confirm that peak stress locations align with observed damage sites. During the return stroke, diaphragm flipping induces tension at the flow-fixed interface edges, creating stress concentrations that contribute to fatigue and failure. Particle image velocimetry (PIV) shows that, under constant flow, increased voltage enhances umbrella valve opening, accelerates movement, broadens flow distribution, and disrupts symmetry. At 90°, valve-edge velocity exhibits sharp, high-amplitude oscillations and a narrow, elongated return region. Vortices near the valve port interfere with fluid motion, causing pressure fluctuations and potential sealing issues or increased opening resistance. Higher flow rates intensify vortex strength and shift their position, generating diaphragm pressure differentials that alter flow direction and velocity, reducing stability and inducing secondary vortices. Compared to a modified diaphragm, the standard type shows more complex vortex structures, greater flow instability, and dynamic response degradation under identical pressure and varying flow. These fragmented vortices further disrupt flow, affecting pump performance. The findings provide design insights for diaphragm pump optimization. Full article

A continuum model for describing pseudo-turbulent flows of a dispersed phase is developed using a statistical approach based on the kinetic equation for the probability density of particle velocity and temperature. The introduction of the probability density function enables a statistical description of the particle ensemble through equations for the first and second moments, replacing the dynamic description of individual particles derived from Langevin-type equations of motion and heat transfer. The lack of detailed dynamic information on individual particle behavior is compensated by a richer statistical characterization of the motion and heat transfer within the particle continuum. A numerical simulation of the unsteady flow of a gas–particle suspension generated by the interaction of a shock wave with a particle cloud is performed using an interpenetrating continua model and equations for the first and second moments of both gas and particles. Numerical methods for solving the two-phase gas dynamics equations—formulated using a two-velocity and two-temperature model—are discussed. Each phase is governed by conservation equations for mass, momentum, and energy, written in a conservative hyperbolic form. These equations are solved using a high-order Godunov-type numerical method, with time discretization performed by a third-order Runge–Kutta scheme. The study analyzes the influence of two-dimensional effects on the formation of shock-wave flow structures and explores the spatial and temporal evolution of particle concentration and other flow parameters. The results enable an estimation of shock wave attenuation by a granular backfill. The extended pressure relaxation region is observed behind the cloud of particles. Full article

Natural gas hydrate (NGH) is a highly promising alternative energy source for the future, which is widely distributed in marine clayey-silty sediments. Permeability is the key factor determining the efficiency of NGH exploitation. However, clay particles can migrate and clog the pores, leading to a decrease in reservoir permeability during the development of NGH. This review summarizes the permeability damage law during the NGH production from clayey-silty sediments, with a focus on the influence of clay particle migration. For the scientific problem of clay particle migration, the governing equation of clay particle migration was first clarified through force balance analysis. Then, the influencing factors and laws of clay particle migration were systematically summarized from two aspects: internal factors such as clay type, content, particle size, reservoir heterogeneity, and external conditions such as salinity, flow rate, temperature, pH, and stress field. The detachment, migration, aggregation and clogging characteristics of clay particles in porous media were observed and outlined based on microscopic visualization technology. Thirdly, the numerical simulation methods of particle migration were summarized, and the permeability damage laws and its influence mechanism were analyzed. Finally, the limitations on clay particle migration and permeability damage in the current research were discussed, and corresponding suggestions were given to promote the efficient development of NGH. Full article

The deep-sea mining pump is a core component in deep-sea mineral resource extraction, whose performance directly determines the transportation efficiency of coarse-grained ore and overall system reliability. However, deep-sea mining pumps suffer from severe abrasion of internal components due to continuous impact by coarse ore particles, leading to short service life and high maintenance costs. These issues adversely impact the economics and continuity of mining operations. Consequently, studying the solid-liquid flow to understand wear mechanisms and develop optimized, wear-resistant designs is crucial for enhancing pump performance. This paper establishes a fully coupled solid-liquid two-phase flow platform by integrating Fluent and EDEM, based on an artificial diffusion-based coarse-particle CFD-DEM (Computational Fluid Dynamics-Discrete Element Method) approach, to systematically investigate the critical technical issue of internal pump wear. The study finds that wear in traditional spiral centrifugal pump blades is primarily concentrated on the leading edge and the middle section. On the leading edge, wear comprises 56.4% cutting wear and 44.7% impact wear; in contrast, cutting wear accounts for 96.8% of the total wear in the middle section. To address the premature failure of traditional impeller blades caused by localized wear concentration, this paper proposes an optimized design for a novel spiral centrifugal impeller with segmented blades. By modifying the impeller structure, the proposed design relocates the primary wear zones to the leading edges of the two blade segments, thereby facilitating the application of anti-wear treatments. Full article

The concrete pouring process is difficult to observe inside formwork. With increasingly complex formwork systems and denser reinforcement layouts, honeycomb defects and surface pores are prone to form at beam–column joint core locations. The modeling of pouring processes that were performed earlier is insufficient and there is relatively little research on simulating concrete void defects at typical joints. Therefore, a refined numerical model based on the three-dimensional particle flow method was established to simulate the flow of fresh concrete within formwork and predict concrete voids after pouring. The feasibility of the particle flow method was verified through numerical simulations of slump flow and J-ring tests. Several groups of joint models were set up based on different influencing factors, and the developed particle flow model was used for pouring simulations to investigate the influence of various factors on concrete void formation. The results show that the void volume and distribution patterns obtained from experiments and simulations are basically consistent. The numerical model can accurately simulate the working performance of self-compacting concrete and predict the size and location distribution of pouring defects. Based on both experimental and numerical studies, the following suggestions are proposed to avoid potential void defects in practical concrete pouring projects: reasonably select the number and diameter of joint longitudinal bars; appropriately increase the spacing of column stirrups; appropriately reduce the maximum coarse aggregate particle size; and choose concrete with better fluidity and filling ability. Full article

In particle image velocimetry (PIV), overexposure is particularly common in regions with high illumination. In particular, strong scattering or background reflection at the liquid–gas interface will make the overexposure phenomenon more obvious, resulting in local pixel saturation, which will significantly reduce the particle image quality, and thus reduce the particle recognition rate and the accuracy of velocity field estimation. This study addresses the overexposure challenges in particle image velocimetry applications, mainly to address the challenge that the velocity field cannot be measured due to the difficulty in effectively detecting particles in the exposed area. In order to address the challenge of overexposure, this paper does not use traditional frame-based high-speed cameras, but instead proposes a particle image velocimetry algorithm based on adaptive integral spike camera data using a neuromorphic vision sensor (NVS). Specifically, by performing target-background segmentation on high-frequency digital spike signals, the method suppresses high illumination background regions and thus effectively mitigates overexposure. Then the spike data are further adaptively integrated based on both regional background illumination characteristics and the spike frequency features of particles with varying velocities, resulting in high signal-to-noise ratio (SNR) reconstructed particle images. Flow field computation is subsequently conducted using the reconstructed particle images, with validation through both simulation and experiment. In simulation, in the overexposed area, the average flow velocity estimation error of frame-based cameras is 8.594 times that of spike-based cameras. In the experiments, the spike camera successfully captured continuous high-density particle trajectories, yielding measurable and continuous velocity fields. Experimental results demonstrate that the proposed particle image velocimetry algorithm based on the adaptive integration of the spike camera effectively addresses overexposure challenges caused by high illumination of the liquid–gas interface in flow field measurements. Full article

This article demonstrates how the non-intrusive techniques PLIF (Planar Laser-Induced Fluorescence) and PIV (Particle Image Velocimetry) are used to study fluid flow and mixing in a water model of a continuous casting tundish. These techniques validate CFD models by providing hydrodynamic data and by testing the models’ ability to predict mixing through simulated concentration field evolution under defined process conditions. Using PIV and PLIF yields more accurate information on turbulent mixing and impurity transport than traditional methods. Access to flow and concentration field evolution enables more precise mathematical model refinement and clarifies the impact of tundish design or operational changes on hydrodynamics and mixing. Relative errors in chemical evolution are approximately 20%, whereas velocity errors vary depending on the measurement plane, being lower for longitudinal planes and higher for transversal planes. This suggests that the turbulence model does not fully capture all low- and high-velocity zones. This approach supports reliable flow and mixing predictions in metallurgy and related fields. Full article

An integrated approach for enhancing microparticle separation efficiency in acoustofluidic lab-on-a-chip systems is presented, combining numerical modeling in COMSOL 6.2 Multiphysics^® with reinforcement learning techniques implemented in Python 3.10.14. The proposed method addresses the limitations of traditional parameter tuning, which is time-consuming and computationally intensive. A simulation framework based on LiveLink™ for COMSOL–Python integration enables the automatic generation, execution, and evaluation of particle separation scenarios. Reinforcement learning algorithms, trained on both successful and failed experiments, are employed to optimize control parameters such as flow velocity and acoustic frequency. Experimental data from over 100 numerical simulations were used to train a neural network, which demonstrated the ability to accurately predict and improve sorting efficiency. The results confirm that incorporating failed outcomes into the reward structure significantly improves learning convergence and model accuracy. This work contributes to the development of intelligent microfluidic systems capable of autonomous adaptation and optimization for biomedical and analytical applications, such as label-free separation of microplastics from biological fluids, selective sorting of soot and ash particles for environmental monitoring, and high-precision manipulation of cells or extracellular vesicles for diagnostic assays. Full article