Search Results (291)

Underwater jet scouring is an efficient, flexible underwater dredging technique, yet its complex physical mechanisms and dynamic evolution hinder dredging effectiveness evaluation. Existing studies mostly use empirical formulas and neglect the sediment properties’ influence on scour holes. This study integrates numerical simulation, theoretical derivation, and sediment characteristics to develop a universal model for efficiently predicting underwater jet scour hole morphology, overcoming existing models’ limitations of over-simplifying complex physics and insufficient experimental data alignment. Using CFD–DEM coupling to simulate scouring, it correlates key physical parameters (average/maximum shear rate, average/maximum shear velocity) with jet characteristics (nozzle diameter, velocity, distance) via theoretical derivation and simplifications, validated using multi-condition simulation data. Comparative analysis shows maximum relative errors of 13% for depth and 7% for width, confirming the engineering applicability in scour hole prediction. Full article

Linear die filling is currently widely employed in industries. However, there is no comprehensive and systematic model to describe the powder die filling process. This paper utilizes dimensional analysis to extract and analyze various factors that affect the flow characteristics of powder based on DEM-CFD simulations. Several dimensionless parameters including the ratio of particle size to die depth ($d_p{h}_D^{-1}$), solid density number (${\rho }_p{\rho }_g^{-1}$), shoe speed number ($v{\rho }_g{L}_D{\mu }^{-1}$), and force number ($G_p{F}_{Drag}^{-1}$) were proposed based on the Pi theorem. The results showed that the filling ratio δ increased with the increase in $d_p{h}_D^{-1}$ and ${\rho }_p{\rho }_g^{-1}$ due to $G_p{F}_{Drag}^{-1}$ rising. But it decreased with the increase in $v{\rho }_g{L}_D{\mu }^{-1}$ due to the shortening of effective filling time. Finally, a semi-empirical modeling of linear die filling was developed, taking the critical value ${(d_p{h}_D^{-1})}_{90}$ as the dependent variable and the solid density number (${\rho }_p{\rho }_g^{-1}$) and shoe speed number ($v{\rho }_g{L}_D{\mu }^{-1}$) as independent variables. Hence, this model provides a new approach to computing the smallest shoe speed and designing the sizes of dies based on measurable material properties under complete die filling. Full article

Internal soil erosion caused by water infiltration around defective buried pipes poses a significant threat to the long-term stability of underground infrastructures such as pipelines and highway culverts. This study employs a coupled computational fluid dynamics–discrete element method (CFD–DEM) framework to simulate the detachment, transport, and redistribution of soil particles under varying infiltration pressures and pipe defect geometries. Using ANSYS Fluent (CFD) and Rocky (DEM), the simulation resolves both the fluid flow field and granular particle dynamics, capturing erosion cavity formation, void evolution, and soil particle transport in three dimensions. The results reveal that increased infiltration pressure and defect size in the buried pipe significantly accelerate the process of erosion and sinkhole formation, leading to potentially unstable subsurface conditions. Visualization of particle migration, sinkhole development, and soil velocity distributions provides insight into the mechanisms driving localized failure. The findings highlight the importance of considering fluid–particle interactions and defect characteristics in the design and maintenance of buried structures, offering a predictive basis for assessing erosion risk and infrastructure vulnerability. Full article

This study investigates the interaction between airflow and low-density bulk particles within vertical screw conveyors and examines its impact on conveying performance. A combined simulation approach integrating the Discrete Element Method and Computational Fluid Dynamics was employed to model both single-phase particle flow and gas–solid two-phase flow. A periodic model was developed based on the structural characteristics of the conveyor. Particle motion dynamics under both single-phase and coupled two-phase conditions were analyzed using EDEM and coupled Fluent-EDEM simulations. The effects of key operational parameters, including screw speed, filling rate, and helix angle, on mass flow rate were systematically evaluated. A comprehensive performance index was established to quantify conveying efficiency, and its validity was confirmed through analysis of variance on the regression model. Finally, the response surface methodology was applied to optimize parameters and determine the optimal combination of screw speed and filling rate to enhance mass flow efficiency. The results indicate that the gas–solid two-phase flow model provides a more accurate representation of real-world conveying dynamics. Future research may extend the model to accommodate more complex material conditions. Full article

Shale gas is a low-carbon unconventional natural gas resource. The development of shale gas helps to optimize the energy structure and reduce carbon emissions. However, the needle throttle valves (NTVs) commonly used in shale gas fields are usually severely eroded by solid particles. Based on the method of CFD-DEM coupling calculation, this paper constructs a gas–solid two-phase flow erosion model of the NTV and studies the influence of different placement methods, valve opening degrees, and other factors on particle movement and valve erosion. This research found that the spool is the area of the valve that is most severely eroded, and when placed horizontally, it has a serious ‘bias wear’ phenomenon. The research results herein can provide references for the design optimization and on-site maintenance of valve performance. Full article

The operational reliability of industrial cooling systems is critically compromised by the crystallization of ammonium chloride (NH₄Cl) in the terminal sections of heat exchangers and at air-cooler inlets. This study systematically investigated the deposition characteristics of NH₄Cl particles in hydrogenation air coolers, along with the factors influencing this process, using a combination of experimental analyses and CFD-DEM coupled simulations. Numerical simulations indicated that gas velocity is the primary factor that governs the NH₄Cl deposition behavior, whereas the NH₄Cl particle size significantly affects the deposition propensity. Under turbulent conditions, larger particles (>300 μm) exhibit a greater deposition tendency due to increased inertial effects. A power-law equation (R² > 0.75) fitted to the experimental data effectively predicts the variations in the deposition rates across tube bundles. This study offers a theoretical foundation and predictive framework for optimizing anti-clogging design and maintenance strategies in industrial air coolers. Full article

Powder bed fusion using a laser beam (PBF-LB) offers a suitable alternative to manufacturing Ti65 with intricate geometries and internal structures in hypersonic aerospace applications. However, issues such as undesirable surface roughness, defect formation, and microstructural inhomogeneity remain critical barriers to its wide application. In this study, a coupled discrete element method–computational fluid dynamics (DEM-CFD) model was utilized to investigate the spreading behavior of Ti65 powder in a multi-layer PBF-LB process. The macro- and microscopic characteristics of the powder beds were systematically analyzed across different layers and regions under various spreading velocities. The results show that the packing density and uniformity of the powder beds in multi-layer PBF-LB of Ti65 powder improves as the number of solidified layers increases. Poor bed quality is observed in the first two layers due to a strong boundary effect, while a stable and denser powder bed emerges from the fourth layer. The presence of a previously solidified region strongly influences its neighboring unsolidified areas, enhancing density in the upstream region and causing looser packing downstream. Additionally, due to the existence of a solidified region, the height of the powder bed progressively decreases along the spreading direction. Full article

Chili pepper (Capsicum annuum L.), a globally important economic crop, faces production challenges characterized by high labor intensity, cost, and inefficiency. Intelligent technologies offer key opportunities for sector transformation. This review begins by outlining the diversity of major chili pepper cultivars, differences in key quality indicators, and the resulting specific harvesting needs. It then reviews recent progress in intelligent perception, recognition, and automation within the chili pepper industry. For perception and recognition, the review covers the evolution from traditional image processing to deep learning-based methods (e.g., YOLO and Mask R-CNN achieving a mAP > 90% in specific studies) for pepper detection, segmentation, and fine-grained cultivar identification, analyzing the performance and optimization in complex environments. In terms of automation, we systematically discuss the principles and feasibility of different mechanized harvesting machines, consider the potential of vision-based keypoint detection for the point localization of picking, and explore motion planning and control for harvesting robots (e.g., robotic systems incorporating diverse end-effectors like soft grippers or cutting mechanisms and motion planning algorithms such as RRT) as well as seed cleaning/separation techniques and simulations (e.g., CFD and DEM) for equipment optimization. The main current research challenges are listed including the environmental adaptability/robustness, efficiency/real-time performance, multi-cultivar adaptability/flexibility, system integration, and cost-effectiveness. Finally, future directions are given (e.g., multimodal sensor fusion, lightweight models, and edge computing applications) in the hope of guiding the intelligent growth of the chili pepper industry. Full article

This study achieves the particle-resolved modeling of biomass pyrolysis via a novel approach of integrating the Discrete Element Method (DEM) with a semi-detailed chemical kinetic mechanism. By coupling CFD-DEM with a 36-step reaction network, the multiscale interactions between particle-scale hydrodynamics and the formation kinetics of 19 tar components under varying temperatures (630–770 °C) are elucidated. Levoglucosan (44.79%) and methanol (18.64%) are identified as primary tar components. Combined with these, furfural (C₅H₄O₂, 7.22%), methanal (CH₂O, 6.75%), and glutaric acid (C₅H₈O₄, 4.20%) account for over 80% of all the tar components. The secondary decomposition pathways are successfully captured, and changes in the reaction rates, as seen in triglycerides (R23: 307.30% rate increase at 770 °C) and tannins (R24: 265.41% acceleration), are quantified. This work provides the ability to predict intermediate products, offering critical insights into reactor optimization. Full article

This study presents a comprehensive numerical investigation of the Laser Powder Bed Fusion (LPBF) process for pure molybdenum, focusing on high-precision modeling and process optimization. The powder spreading behavior is simulated using the Discrete Element Method (DEM), while molten pool dynamics are analyzed through Computational Fluid Dynamics (CFD). Optimization of process parameters is performed using FLOW-3D Release 7 software in conjunction with the HEEDS-SHERPA algorithm. A total of 247 simulations are conducted to assess the effects of four critical parameters: laser power (50–400 W), scanning speed (80–300 mm/s), laser spot diameter (40–100 µm), and powder layer thickness (50–100 µm). The optimal parameter set—350 W laser power, 120 mm/s scanning speed, 50 µm spot diameter, and 50 µm layer thickness—results in an 80% laser absorption rate, a 60% reduction in micro-porosity, and over a 30% enhancement in both molten pool volume and surface area. Utilizing a fine 10 µm mesh resolution enables detailed insights into temperature gradients and phase transition behavior. The findings highlight that optimized parameter selection significantly improves the structural integrity of Mo-based components while minimizing manufacturing defects, thus offering valuable guidance for advancing industrial-scale additive manufacturing of refractory metals. Full article

Simulative and experimental studies were carried out to address multi-dimensional particle fractionation of non-biological particles according to size, shape, and density inside a high-throughput DLD array. Density sensitive separation was achieved for melamine and polystyrene particles at a diameter of 5 µm at a Reynolds number (Re) of 82, corresponding to an overall flow rate of 11.3 mL/min. This process is very sensitive, as no fractionation occurred for Re = 85 (11.7 mL/min). For the first time, the fractionation of elliptical polystyrene particles (5 × 10 µm) at Re > 1 was investigated up to Re = 80 (11 mL/min). A separation of elliptical particles from spherical melamine particles (5 µm) was observed in single experiments at all investigated Reynolds numbers. However, the separation is not reliably repeatable due to partial clogging of ellipsoidal particles along the posts. In addition, higher concentrations of polydisperse silica suspensions were experimentally investigated by using polydisperse silica particles at concentrations up to 0.4% (m/V) up to Re = 80 (20 mL/min). The separation size generally decreased with increasing Reynolds number and increased with increasing concentration. Separation efficiency decreased with increasing concentration, independent of the Reynolds number. In order to investigate the material-dependent separation in a contactless dielectrophoresis system (cDEP), the resolved CFD-DEM software was extended to calculate dielectrophoretic forces on particles. With this, the second stage of a serial-combined DLD-DEP system was simulated, showing good separation at lower flow rates. For these systems, different fabrication methods to minimize the distance between the electrodes and the fluid as well as the requirement to withstand high-throughput applications, were investigated. Full article

In the marine natural gas hydrate solid-fluidization mining process, current separation devices are insufficient in de-bonding the hydrate cementation between sand particles, affecting the hydrate collection efficiency. To address this issue, a composite separator was designed in this study that is used to restrict the axial movement of cemented particles, thereby achieving the goal of enhanced de-bonding efficiency. A combined computational fluid dynamics–discrete element method simulation method was used to verify the de-bonding performance of this composite separator on weakly cemented hydrate particles with different sizes and to study the influence of the spiral flow channel structural parameters and inlet types on the de-bonding performance. Full article

Accurately predicting the permeability of coarse-grained soils is crucial for ensuring geotechnical safety and performance. In this study, 64 coarse-grained soil (CGS) samples were designed using a negative exponential gradation equation (NEGE), and computational fluid dynamics–discrete element method (CFD-DEM) coupled seepage simulations were conducted to generate a permeability coefficient (k) dataset comprising 256 entries under varying porosity and gradation conditions. Three machine learning models—a neural network model (BPNN), a regression model (GPR), and a tree-based model (RF)—were employed to predict k, with hyperparameters optimized via particle swarm optimization (PSO) and four-fold cross-validation applied to improve generalization. Gray relational analysis (GRA) revealed that all input parameters (α, β, d_max, n) significantly influence k (R > 0.6). The interquartile range (IQR) method confirmed data suitability for modeling. Among the models, BPNN achieved the best performance (R² = 0.99, MAE = 1.5, RMSE = 2.9, U₉₅ = 0.4), effectively capturing the complex nonlinear relationship between gradation and permeability. GPR (R² = 0.92) was hindered by kernel selection and noise sensitivity, while RF (R² = 0.97) was limited by its discrete regression nature. Compared to a traditional empirical model (R² = 0.9031), BPNN improved prediction accuracy by 10.13%, demonstrating the advantage of data-driven methods for evaluating CGS permeability. Full article

The aim of this study was to investigate the influence of operational and design parameters on the conveying efficiency and material layer stability of air slides and to optimize the parameters of the XZ200 air slide. A gas–solid coupled simulation of the conveying process was conducted using ANSYS v2023 and Rocky v23R1 software. Three key variables—inclination angle, input air velocity, and permeable layer porosity—were analyzed to evaluate their effects on wheat flour conveying efficiency and layer stability. Orthogonal experiments and matrix analysis were applied to comprehensively assess the numerical simulation results. The findings reveal that the conveying ratio is positively correlated with input air velocity and inclination angle but negatively correlated with permeable layer porosity. Meanwhile, material layer fluctuation and stability increase with inclination angle but decrease with higher porosity. Through orthogonal testing and matrix analysis, the optimal parameter combination was determined as follows: input air velocity of 1.8 m/s, porosity of 37.84%, inclination angle of 6°, conveying ratio of 96.52%, and material layer fluctuation of 4.39 mm. This study provides a reference methodology for gas–solid coupled simulation in air slide design and offers practical guidance for parameter optimization in air slide systems. Full article