Search Results (250)

Blast furnace (BF) ironmaking is a complex multiphase process involving gas–solid flow, heat transfer, chemical reactions, burden movement, and phase transformation under high-temperature conditions. Since many internal states of the blast furnace cannot be directly observed during operation, numerical simulation and mathematical modeling have become important tools for understanding furnace behavior and optimizing operational parameters. This paper reviews recent advances in blast furnace numerical simulation and internal state reconstruction methods. Existing approaches, including packed-bed flow models, cohesive zone reconstruction methods, burden distribution models, and temperature field prediction methods, are summarized and discussed. In addition, the evolution of blast furnace mathematical models from early one-dimensional steady-state formulations to modern three-dimensional multifluid and hybrid simulation approaches is reviewed. Recent developments in computational fluid dynamics (CFD), the discrete element method (DEM), digital twin, and data-driven modeling are also discussed. Compared with traditional simplified models, modern multidimensional and hybrid approaches show improved capability in describing asymmetric furnace inner states, multiphase transport behavior, and operational parameter effects under industrial conditions. However, challenges still remain in achieving computational efficiency, parameter calibration, multiphase coupling, and real-time industrial application. Future studies are expected to focus on the integration of mechanism-based simulation and intelligent data-driven methods to improve prediction accuracy, operational adaptability, and intelligent control capability in blast furnace ironmaking. Full article

Natural weak discontinuities, such as natural fractures, bedding planes, and coal–rock interfaces, are widely developed in deep coal reservoirs. During hydraulic fracture propagation, induced fractures readily interact with these weak planes through crossing, deflection, and combined activation, thereby forming complex fracture geometries and significantly affecting proppant transport and placement. To clarify the transport behavior of proppant under different fracture geometries, four representative tortuous wedge-shaped fractures were constructed to characterize typical fracture propagation patterns in deep coal reservoirs, namely a vertical straight fracture (“|”), a horizontal straight fracture (“—”), a T-shaped fracture, and a cross-shaped fracture (“+”). On this basis, a two-way coupled fluid–particle model was established using the CFD–DEM method to systematically investigate proppant migration, settling, and placement in different fractures, as well as the effects of injection velocity, particle size, and fluid viscosity. The results show that fracture geometry exerts a significant influence on proppant transport patterns and placement performance. Specifically, proppant transport in the “|”-shaped, T-shaped, and “+”-shaped fractures can be divided into three distinct stages: rapid start-up, stratified transport, and front advancement. In contrast, particles in the “—”-shaped fracture are only weakly affected by gravity and remain almost entirely in an orderly front-advancement regime, exhibiting the most stable and continuous placement behavior. Increasing injection velocity and fluid viscosity both improve proppant placement uniformity and markedly promote branch entry in the T-shaped fracture, whereas their improvement in the “+”-shaped fracture is relatively limited. When the fluid viscosity increases from 1 mPa·s to 5 mPa·s, the placement uniformity coefficient (PUC) of the “—”-shaped, “|”-shaped, T-shaped, and “+”-shaped fractures increases by approximately 3.2%, 5.6%, 6.3%, and 7.1%, respectively. These findings provide mechanistic insight into geometry-dependent proppant transport and placement in complex fractures of deep coal seams, and offer theoretical support for hydraulic fracturing design and parameter optimization. Full article

Lost circulation remains a major challenge during well construction, often leading to non-productive time, increased material consumption, and additional treatment costs. In field practice, the selection of lost circulation materials (LCMs) is still largely based on empirical rules or laboratory testing; however, these approaches are not always suitable for rapid decision-making under variable downhole conditions. This study presents a physics-guided surrogate modeling framework for predicting fracture sealing performance and supporting injection strategy selection. The approach combines laboratory observations with coupled computational fluid dynamics and discrete element method (CFD-DEM) simulations to represent both measured behavior and a broader range of mechanically consistent sealing scenarios. The final dataset included 300 cases, comprising 45 physical experiments and 255 CFD-DEM-generated synthetic cases. A hybrid machine learning architecture based on Temporal Convolutional Networks and Artificial Neural Networks was developed to predict sealing pressure under different material and fluid conditions. The model achieved an R² of 0.89 and a mean absolute percentage error of 6.4%, while showing 94% agreement with laboratory-based recommendations for injection strategy. The proposed framework can therefore serve as a rapid engineering support tool for preliminary formulation screening and a more computationally efficient digital workflow for fracture sealing design in drilling operations. Full article

In gas drilling, the local annular contraction caused by a drillpipe tool joint can markedly reduce cuttings’ carrying capacity and increase the risk of localized blockage and sand bridging near the tool-joint region, thereby threatening hole cleaning and drilling safety. To investigate this problem, a three-dimensional CFD–DEM two-way coupling model was established by considering the geometric features of the drillpipe tool joint and gas–solid interaction. The effects of gas mass flow rate, solids feed rate, and particle diameter on local cuttings’ transport states and annular pressure-drop responses near the tool joint were systematically analyzed. The results show that three typical local transport states can develop near the tool-joint region, namely continuous passage, fallback, and clogging accompanied by sand-bridge formation. Fallback cases occur only within a finite interval around the critical gas mass flow rate for cuttings’ transport. Under the geometric and operating conditions considered in this study, localized clogging first appears when the particle diameter reaches approximately 10.5 mm, and the proportion of clogging cases increases rapidly with a further increase in particle diameter. Increasing the solids feed rate intensifies particle retention, accumulation, and collision near the tool joint, promotes earlier clogging, and markedly narrows the operating range of continuous passage; stable clogging is difficult to form when the solids feed rate is below 8 kg/s. Distinct annular pressure-drop histories correspond to different local transport states, with low amplitude fluctuation for continuous passage, repeated pulsation for fallback, and sustained growth in pressure difference magnitude for developing clogging accompanied by sand bridge formation. These results demonstrate a clear correspondence between local transport states near the tool joint and annular pressure-drop responses under the investigated geometry and operating window. They provide a mechanism-level basis for interpreting localized blockage near the drillpipe tool joint, while quantitative field application requires calibration for the specific annular clearance, monitoring interval, gas-injection condition, and cuttings’ loading condition. Full article

Rough-walled fractures in conglomerate reservoirs promote near-wellbore proppant deposition, nonuniform flow, and insufficient distal support, making proppant-schedule screening difficult using small-scale smooth-slot tests alone. This study develops a benchmark-constrained and cost-aware hierarchical screening workflow by integrating a 20 m rough-wall physical experiment, transient Fluent simulations, and archived short-time EDEM sensitivity records. The benchmark experiment used a 20 m × 4.5 m × 10 mm artificial rough-wall fracture and ten operating conditions involving pumping rate, fluid viscosity, proppant size, and sand concentration. In the Fluent model, wall roughness was treated as a regularized roughness representation, and the carrier fluids were modeled using Newtonian constant viscosities measured from laboratory calibration. The experimental effective propped area ranged from 25.5% to 65.1%. Within single-factor comparison subsets, medium viscosity improved support continuity, pumping-rate gains became limited near 0.20 m³/min, particle size affected the balance between distal coverage and bed stability, and 300 kg/m³ sand concentration caused blockage. Image-segmentation-based comparison showed that Fluent captured the main wedge-shaped deposition morphology and screening-level geometric trends. The archived EDEM records indicated that grid-resolution refinement and mixed particle-size representation substantially increased computational cost. A Case 10 mesh-sensitivity check further confirmed that mesh refinement did not alter the first-order deposition morphology. The proposed workflow uses Fluent for whole-domain rapid screening and reserves EDEM/CFD-DEM for targeted short-time sensitivity checks. Full article

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain limited under unstructured field conditions involving soil heterogeneity, crop variability, climatic disturbance, and nonlinear machinery–environment interactions. This review systematically examines the evolution of intelligent optimisation design for agricultural machinery from conventional simulation-based methods to artificial intelligence (AI)- and digital twin (DT)-enabled paradigms. First, mathematical modelling, response surface methodology, discrete element method (DEM), computational fluid dynamics (CFD), multi-body dynamics (MBD), heuristic algorithms, and early AI-assisted surrogate optimisation are reviewed to clarify their contributions and limitations. Second, frontier enabling technologies are analysed, including agriculture-specific large models, generative AI, lightweight edge intelligence, deep reinforcement learning (DRL), embodied AI, federated learning (FL), and privacy-preserving computing. Third, system-level applications integrating DT and AI are discussed, with emphasis on full-lifecycle machinery optimisation, device–edge–cloud collaborative control, multi-agent fleet coordination, predictive maintenance, and Agriculture 5.0-oriented intelligent equipment systems. Key deployment bottlenecks are further identified, including sim-to-real inconsistency, virtual–physical mismatch in DTs, edge-side trade-offs among accuracy, latency, energy consumption, and cost, insufficient validation standards, and economic adoption barriers. Finally, a 2025–2030 roadmap is proposed, highlighting large-model–DT closed loops, control biomimetics, green low-carbon optimisation, and trustworthy human–machine symbiosis for sustainable Agriculture 5.0. Full article

In biomass pellet combustion, the formation of ash layers on particle surfaces severely hinders combustion reactions and heat transfer, while the key parameters governing particle motion behavior and ash pre-separation in rotating cone burners remain insufficiently understood. To address these challenges and to optimize particle mixing and ash separation performance, this study adopts a combined numerical approach. The discrete element method (DEM) coupled with the Hertz–Mindlin (no-slip) contact model is employed to simulate particle motion and mixing dynamics, while a separate cold-state computational fluid dynamics (CFD) model based on the Realizable k-ε turbulence model and the discrete phase model (DPM) with Rosin–Rammler particle size distribution is established to investigate ash separation mechanisms. The Lacey mixing index is used to quantify mixing uniformity, and grid independence verification is performed to ensure numerical reliability. Key findings reveal that the rolling regime (rotational speed: 1.7–11 r/min), a uniform particle size of 25 mm, and a cone inclination angle of 45° collectively optimize particle mixing. Rotational speed is identified as the dominant factor affecting mixing effectiveness. Furthermore, an optimal secondary-to-primary air ratio of approximately 7:3 (within the tested range) balances enhanced centrifugal separation with flow field stability by mitigating backflow and excessive turbulence. This work not only fills the knowledge gap regarding the coupled effects of operational and structural parameters on particle behavior in rotating cone burners but also provides novel, quantitative guidance for the rational design and parameter tuning of such burners to improve combustion efficiency and operational stability. Full article

Fluid–structure interaction (FSI) research is crucial for applications in fields such as naval engineering, geological hazards, and biomechanics. Traditional grid-based methods (such as CFD) often face challenges in simulating large-deformation flow fields and complex boundary conditions, where mesh distortion can compromise simulation accuracy. Building upon the DualSPHysics5.2 framework, this study leverages the strengths of weakly compressible SPH (WCSPH) in modeling free surface flows and large-deformation fluids, as well as the discrete element method (DEM), for accurately describing particle collisions and fragmentation behaviors. We propose an improved MSPH-DEM coupling algorithm that incorporates moving least squares (MLS) correction for kernel function gradient optimization. This algorithm utilizes MLS-based gradient correction to achieve smoother fluid surfaces as well as bidirectional coupling between fluids and particles. Experimental validation demonstrates that in dam break simulations, this method reduces pressure errors. In the dam break impacting a cube experiment, it enhances accuracy, while in the dam break impacting a baffle experiment, the horizontal displacement of marker points closely aligns with the experimental values from Liao et al. This approach effectively improves the accuracy of the simulations of FSI problems, offering a more reliable numerical simulation methodology for engineering applications such as geological hazard prevention. Full article

Aiming at the problems of low separation efficiency and high impurity content in the cleaning process of moist rice threshed materials, this study systematically explored the influence mechanism of moisture on the segregation behavior of rice threshed products by combining physical experiments and CFD-DEM coupling simulation. Physical test results show that moist conditions significantly change the properties of threshed materials: the impurity mass fraction increases from 3.1% to 6.8%, the straw breakage rate rises from 5.97% to 6.99%, and the working load and unbalanced dynamic load of the threshing unit increase noticeably. On this basis, a gas–solid coupling simulation model of the cleaning device embedded with the Johnson–Kendall–Roberts (JKR) cohesive contact model is established. It is found that moist threshed materials exhibit an obvious stratified movement on the sieve surface, in which the bottom straw layer moves slowly while the upper material layer flows rapidly, resulting in a 50% increase in the sieve surface load. This phenomenon directly accounts for the rising impurity content observed in experiments. Through integrated experimental and simulation analysis, this research clarifies the macroscopic laws of moisture effects and reveals three key pathways for moisture to deteriorate cleaning performance: changing physical characteristics of threshed materials, enhancing inter-particle adhesion, and forming stratified movement. The findings provide an innovative research scheme and reliable theoretical support for the design and optimization of high-efficiency cleaning devices for rice combine harvesters. Full article

To elucidate the hydraulic transport characteristics of coarse-particle slurry in deep-sea mining vertical lifting pipelines and the governing effects of key operating parameters, a bidirectionally coupled CFD-DEM model was established, in which seawater was treated as the continuous phase and ore particles were treated as the discrete phase, while particle–fluid momentum exchange and particle–particle/particle–wall collisions were explicitly accounted for. The effects of inlet velocity, feed concentration, particle size, and particle shape on local particle concentration, local particle flow rate, and particle volume fraction distribution were systematically investigated. The results show that increasing the inlet velocity markedly reduces local particle concentration, increases the local particle flow rate, and promotes a faster transition of the solid–liquid two-phase flow toward a uniformly mixed state. Increasing the feed concentration enhances the conveying capacity, but simultaneously increases the risk of particle aggregation. The effect of particle size on local concentration is non-monotonic: the local concentration is relatively high at approximately 20 mm, whereas smaller particles exhibit better flow uniformity. The effect of particle shape is mainly manifested under low-velocity and high-concentration conditions, and gradually weakens with increasing inlet velocity. The present results provide a theoretical basis for parameter optimization of deep-sea mining vertical lifting systems. Full article

Subsoiling can break the plough pan and improve the root growth environment. The effect of the traditional subsoiler is poor, as it relies only on the chisel tine, but pneumatic subsoiling can improve the soil structure more efficiently through the negative pressure generated by the jet hole. This research used computational fluid dynamics and the discrete element method to optimize the biomimetic structure of the jet hole, model the pneumatic subsoiling process at a depth of 330 mm, and observe the movement of soil particles as airflow passes through. The effect of the jet hole at different positions and sizes on the plough pan soil was analyzed, and fluid domains and measurement areas were set up to observe the upward movement, diffusion, stabilization, and settling of soil particles under the action of airflow. The results of the soil bin experiment validated the accuracy of the simulation model through draft force and vertical force, and the average error between the simulation and experimental data was 2.8%. The study revealed that the increase in the rate of soil porosity reached a maximum of 3.65% when the jet hole was positioned above the chisel tine with a radius of 4 mm. The biomimetic jet hole pneumatic subsoiler designed in this study, along with the established CFD-DEM coupled simulation model capable of predicting pneumatic subsoiling performance, can provide references for the design and application of a pneumatic subsoiler. Furthermore, it also provides a theoretical basis for understanding the mechanism of airflow on soil during pneumatic subsoiling operations. Full article

Laser cladding processing efficiency is often limited by low powder utilization. To address this, our study elucidates the mechanism by which powder feeding parameters influence powder stream convergence, aiming to optimize these parameters. A three-dimensional model of a wide-band symmetrical nozzle was developed using a Computational Fluid Dynamics—Discrete Element Method (CFD-DEM) coupling method to simulate the gas–solid flow. Single-factor tests and experimental validation confirmed the model’s reliability. The results identify carrier gas flow as the key parameter controlling the focal length and powder concentration, while the powder feed rate primarily governs the concentration on the focal plane. These findings provide a theoretical foundation for optimizing laser cladding parameters to enhance powder utilization. Full article

Porous medium combustion technology, renowned for high efficiency and low emissions, is widely applied in industrial and heating fields. This study numerically investigates pore-scale heat transfer, flame morphology, reaction rate distribution during standing combustion in a one-layer randomly packed bed, and flow parameter effects on flame behavior. A 3D randomly packed model (tube-to-particle diameter ratio D/d = 10) is developed using the discrete element method (DEM) and coupled with computational fluid dynamics (CFD) to resolve pore-scale transport processes. Results show that exothermic combustion converts internal energy to kinetic energy, significantly accelerating pore-scale flow velocity in the combustion zone. Increasing the equivalence ratio enhances flame stability, elevating solid–fluid temperatures by 200 K and expanding the combustion zone volume by 20%. The pore Reynolds number promotes inertial mixing and heat redistribution, limiting the solid–fluid temperature difference to 10 K. Local flames evolve from dispersed to wrinkled and undulating. These findings elucidate pore-scale combustion dynamics and guide packed-bed reactor design and optimization. Full article

The stability of subsea tunnels is governed by the strong coupling among stress redistribution, damage evolution, and seepage flow (Stress–Damage–Seepage, SDS). The dynamic interplay, especially under high water pressure, often leads to catastrophic failures, yet its mechanisms, particularly the role of support timing, remain insufficiently understood due to limitations in conventional numerical methods. This study aims to unravel the SDS coupling mechanisms during tunnel excavation under high hydraulic head, and to quantitatively investigate how support timing influences the stability of the surrounding rock within this coupled system. A coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) framework was employed. In this approach, excavation-induced damage, crack propagation, and fluid–particle interactions are explicitly resolved at the particle scale, whereas the macroscopic permeability evolution is captured through an imposed empirical exponential relationship. Simulations were conducted under both steady-state and transient seepage conditions with varying stress ratios and water heads. High-head transient seepage intensifies SDS coupling, dynamically redistributing seepage forces to damage zone edges and amplifying damage. Support timing critically mediates this interaction: premature support risks tensile failure at the tunnel periphery, while delayed support allows a vicious cycle of shear failure and increased inflow. Optimal “timely” support, applied after initial deformation, diverts high seepage forces inward, minimizing final damage. The spatiotemporal synchronization of transient seepage forces with damage evolution is pivotal for stability. Support timing acts as a key control variable. The CFD-DEM framework effectively elucidates these micro-mechanisms, providing a scientific basis for the dynamic design of support in high-pressure subsea tunnels. Full article

The foundry industry produces over 66 million tons of mixed casting waste sand, containing toxic and harmful substances such as phenols and aldehydes, every year, which has caused serious soil pollution, water source pollution, and large amounts of CO₂ emissions. Green resource recycling and utilization are urgently needed. The hot method circulating fluidized bed furnace is currently the mainstream technology for the regeneration of casting waste sand. However, traditional equipment has a series of key technical bottlenecks, such as VOC (volatile organic compound) emissions, low yield of fine sand, poor stability of phase change sand, and uneven fluidization, which directly limit the effectiveness, large-scale promotion, and application of waste sand regeneration. This study, based on a self-designed experimental prototype, constructed models with different hood densities and inlet air velocity parameters. A CFD-DEM coupled model, combined with two turbulence models, was used for numerical simulations and experimental validation, and the optimal combination of fluidization parameters was determined. The study confirmed that the k–ω SST model is more suitable for precise simulation of such gas–solid two-phase flows. The research revealed quantitative relationships between key parameters and sand particle fluidization states, addressing the core problem of uneven fluidization in conventional bubbling furnaces and providing important guidance for the optimized design of new thermal cycle bubbling furnaces. It has significant engineering value for promoting the efficient resource utilization of foundry waste sand and the green and sustainable development of the industry. Full article