Search Results (460)

This study investigates the optimization of crushing and screening efficiency in hammer mill systems through aerodynamic analysis. The research focuses on cucumber vine stalks characterized by high moisture content, elevated cellulose concentration, and pronounced mechanical toughness. Using key operating parameters that significantly influence the gas flow field as the starting point, single-phase gas flow field numerical simulations and characteristic simulations were conducted using the computational fluid dynamics (CFD) software Fluent. A two-way coupling method combining Fluent and the discrete element method (DEM) software EDEM was employed to perform gas–solid coupled numerical simulations and operational characteristic simulations of the pulverizer’s grinding and screening process. This revealed the influence patterns of gas flow disturbances on the grinding and screening process and the mechanism for performance enhancement. Finally, field testing was conducted. Based on experimental results, the optimized operating parameters were determined as follows: rotor speed of 2569 r/min, fan opening of 62.55%, and feed rate of 7.64 kg/min. Under these optimized conditions, the crushing productivity of cucumber vine stalks reached 337 kg/h, with an energy consumption of 5.59 kW·h/t. The deviation between the actual and theoretical values for productivity was less than 6%, while the deviation for energy consumption per ton was less than 3%. These findings provide a theoretical foundation and experimental basis for further research into the mechanism of external airflow disturbance in the crushing and screening process, aiming to enhance crushing efficiency and reduce energy consumption. Full article

The impingement of a molten droplet on a solid surface, forming a “splat,” is a fundamental phenomenon observed across numerous industrial surface engineering techniques. For example, thermal spray deposition is widely used to create metal, ceramic, polymer, and composite coatings that are vital for aerospace, biomedical, electronics, and energy applications. Significant progress has been made in understanding droplet impact behavior, largely driven by advancements in high-resolution and high-speed imaging techniques, as well as computational resources. Although droplet impact dynamics, splat morphology, and interfacial bonding mechanisms have been extensively reviewed, a comprehensive overview of the mechanical behaviors of single splats, which are crucial for coating performance, has not been reported. This review bridges that gap by offering an in-depth analysis of bonding strength and residual stress in single splats. The various experimental techniques used to characterize these properties are thoroughly discussed, and a detailed review of the analytical models and numerical simulations developed to predict and understand residual stress evolution is provided. Notably, the complex interplay between bonding strength and residual stress is then discussed, examining how these two critical mechanical attributes are interrelated and mutually influence each other. Subsequently, effective strategies for improving interfacial bonding are explored, and key factors that influence residual stress are identified. Furthermore, the fundamental roles of splat flattening and formation dynamics in determining the final mechanical properties are critically examined, highlighting the challenges in integrating fluid dynamics with mechanical analysis. Thermal spraying serves as the primary context, but other relevant applications are briefly considered. Cold spray splats are excluded because of their distinct bonding and stress generation mechanisms. Finally, promising future research directions are outlined to advance the understanding and control of the mechanical properties in single splats, ultimately supporting the development of more robust and reliable coating technologies. Full article

To address the challenges of variable target scale, complex background, blurred image, and serious occlusion in the yield detection of Yunnan large-leaf tea tree, this study proposes a deep learning network DE-YOLOv13-S that integrates the visual mechanism of primates. DynamicConv was used to optimize the dynamic adjustment process of the effective receptive field and channel the gain of the primate visual system. Efficient Mixed-pooling Channel Attention was introduced to simulate the observation strategy of ‘global gain control and selective integration parallel’ of the primate visual system. Scale-based Dynamic Loss was used to simulate the foveation mechanism of primates, which significantly improved the positioning accuracy and robustness of Yunnan large-leaf tea tree yield detection. The results show that the Box Loss, Cls Loss, and DFL Loss of the DE-YOLOv13-S network decreased by 18.75%, 3.70%, and 2.54% on the training set, and by 18.48%, 14.29%, and 7.46% on the test set, respectively. Compared with YOLOv13, its parameters and gradients are only increased by 2.06 M, while the computational complexity is reduced by 0.2 G FLOPs, precision, recall, and mAP are increased by 3.78%, 2.04% and 3.35%, respectively. The improved DE-YOLOv13-S network not only provides an efficient and stable yield detection solution for the intelligent management level and high-quality development of tea gardens, but also provides a solid technical support for the deep integration of bionic vision and agricultural remote sensing. Full article

Modern machining industries require high precision and efficiency in machine tools, where thermal deformations significantly impact accuracy. This study focuses on optimizing the structural parameters of a vertical turning center to minimize thermal displacements affecting machining precision. The optimization process is divided into parametric and topological methodologies. The parametric approach targets three primary objectives: minimizing mass (q₁), maximizing static stiffness (q₂), and reducing thermal displacement (q₃). Multi-criteria optimization techniques, including Pareto-based and scalarization methods, are applied to balance these conflicting factors. Finite Element Analysis (FEA) models assist in evaluating machine stiffness and displacement, with constraints imposed on structural mass and stiffness to maintain performance. Parametric optimization, using iterative computational algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), refines rib and wall thicknesses of the lathe table to achieve displacement reductions. The optimization process successfully lowers displacement at critical measurement points while maintaining structural integrity. Hybrid PSO (hPSO) outperforms other algorithms in achieving optimal parameter sets with minimal computational effort. Topological optimization, based on the Solid Isotropic Microstructure with Penalization (SIMP) method, further enhances structural efficiency by refining material distribution. The iterative process identifies optimal energy flow paths while ensuring compliance with mechanical constraints. A hybrid approach integrating parametric adjustments with topological refinement leads to superior performance, achieving a 43% reduction in displacement at key measurement points compared to the initial design. The final optimized design reduces mass by 1 ton compared to the original model and 2.5 tons compared to the best rib–wall optimization results. The study’s findings establish a foundation for implementing active deformation compensation systems in machine tools, enhancing machining precision. The integration of parametric and topological optimization presents a robust framework for designing machine tool structures with improved thermal stability and structural efficiency. Full article

Wind and sand disaster prevention is a critical challenge for global environmental sustainability, with mechanical wind fences being key engineering measures. Current fences, including solid and permeable types, often struggle to balance environmental impact, windbreak efficiency, and stability. Solid fences provide effective sand control but have limited windbreak efficiency, while permeable fences improve airflow but require deep burial and are prone to erosion on uneven terrain. This study proposes a novel composite wind fence with a polylactic acid (PLA) sandbag base and a fiber mesh top, combining stability and permeability. We assessed windbreak performance using computational fluid dynamics simulations and verified results through wind tunnel experiments. Results show that the novel composite wind fence enhances windbreak efficiency and stability by optimizing airflow distribution, with the PLA sandbag base suppressing high–speed airflow and mesh fence weakening of leeward side vortices. Under wind speeds of 10 m/s, 18 m/s, and 28 m/s, the effective protection distance of the novel composite wind fence improved by 22.33% to 36.51%, 10.96% to 34.22%, and 0.94% to 28.98%, respectively, compared to single PLA and mesh wind fence. The optimal row spacing for the novel wind fences in three rows is 12 h when the incoming wind speed is 10 m/s, while the recommended spacings are 8 h and 6 h for wind speeds of 18 m/s and 28 m/s, respectively, ensuring continuous and effective protection. These findings present a novel wind fence technology with improved wind resistance, a more stable structure, and prolonged protective effects, offering an effective solution for environmental conservation initiatives aimed at preventing wind and sand disasters while promoting the sustainability of ecosystems. 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

End-stage temporomandibular joint (TMJ) disorders often necessitate total joint replacement, and the selection of biomaterial directly impacts long-term outcomes. Ti6Al4V and CoCrMo are commonly used alloys, yet their biomechanical performance in patient-specific prostheses remains insufficiently compared. This study aimed to evaluate the mechanical response of custom TMJ prostheses fabricated from these alloys using finite element analysis (FEA). A three-dimensional mandibular model was created from computed tomography data, and a patient-specific prosthesis was designed in SolidWorks (Dassault Systèmes, SolidWorks Corp., Waltham, MA, USA) and analyzed in ANSYS Workbench 2022 R1 (Ansys Inc., Canonsburg, PA, USA). Physiological loading was simulated by applying forces at the insertion sites of the temporalis, masseter, and medial pterygoid muscles. In the Ti6Al4V model, maximum von Mises stresses reached 192.18 MPa on the mandibular component and 92.004 MPa on the fossa prosthesis, whereas the CoCrMo model demonstrated higher stresses of 204.31 MPa and 94.182 MPa, respectively. Both alloys exhibited similar stress distributions, but Ti6Al4V generated lower stress magnitudes, indicating more favorable load transfer and a reduced risk of mechanical overload on articulating components. These findings underscore the significance of alloy selection in optimizing TMJ prostheses and demonstrate the value of FEA as a tool for guiding future patient-specific designs. Full article

Thin steel plates with stiffeners are widely employed in several branches of engineering, combining mechanical strength with low weight and serving as both structural and cladding components. However, the influence of the side-wall inclination angle of hat-stiffeners on the stiffness distribution and deflection patterns of steel plates remains insufficiently explored. This study conducts computational modeling to evaluate the deflection of thin steel plates reinforced with hat-stiffeners. The plates were considered simply supported and subjected to a uniformly distributed load. The Constructal Design method and the exhaustive search technique were employed, allowing for geometric evaluation and optimization. A fraction corresponding to 30% of the plate volume was removed and redistributed to generate longitudinal hat-stiffener geometries by varying its side-wall angle and thickness. The smaller base of the hat-stiffeners was imposed as a geometric constraint and therefore kept fixed. The results indicate a nonlinear trend between the side-wall angle, the moment of inertia, and the resulting deflection, leading to a new geometrical pattern that connects the angular inclination to the overall stiffness behavior of the plate. Angles between 105° and 130° provided the best performance, reducing the maximum deflection by 93.72% compared with the reference plate and improving it by around 7.5% relative to previous studies. These findings illustrate how geometric configuration can enhance performance in line with Constructal Design principles. Full article

Background/Objective: The rise in bacterial resistance necessitates novel therapeutic strategies beyond conventional antibiotics. Antimicrobial peptides represent promising candidates but face challenges such as instability, enzymatic degradation, and host toxicity. To overcome these limitations, conjugation and structural modifications are being explored. This study focuses on designing peptide-based inhibitors of the quorum-sensing (QS) regulator LasR in Pseudomonas aeruginosa, a key mediator of biofilm formation and antibiotic resistance. Methods: Rationally designed tripeptides and dipeptides conjugated with coumarin-3-carboxylic acid and dihydro-3-amino-2-(3H)-furanone were evaluated using molecular docking. The most promising ligand–protein complexes were further analyzed using molecular dynamics (MD) simulations conducted with the CHARMM-GUI and AMBER tools to assess the stability of the ligand–protein complex systems, and the binding affinities were evaluated using Molecular Mechanics–Poisson Boltzmann Surface Area (MM-PBSA) calculations. Pharmacokinetic and toxicity profiles were predicted using ADMETLab 3.0. Selected compounds were synthesized via solid-phase peptide synthesis, structurally confirmed by ¹H NMR and ESI-MS, and tested for antibacterial and antibiofilm activity against P. aeruginosa ATCC 27853. Results: Computational analyses identified several promising inhibitors with stronger binding affinities than the native autoinducer OdDHL. Coumarin conjugates C004 and C006 showed superior docking scores, while MM-PBSA indicated P004 and C004 had the most favorable binding energies. MD simulations confirmed stable ligand–protein complexes. ADMET predictions highlighted C004 and C006 as having excellent pharmacokinetic properties. Experimental assays showed moderate antibacterial activity (MIC 512–1024 µg/mL) and strong antibiofilm inhibition, particularly for C004 (83% inhibition at ½ MIC). Conclusions: The study demonstrates that peptide–coumarin conjugates, especially C004, are promising tools for disrupting QS and biofilm formation in P. aeruginosa. Further optimization and in vivo validation are needed to advance these compounds toward therapeutic application. Full article

MXenes, an emerging class of two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides, have demonstrated exceptional potential in tribology: the study of friction, wear, and lubrication. Their remarkable mechanical strength, thermal stability, and tunable surface chemistry make them ideal candidates for solid lubricants, lubricant additives, and protective coatings in mechanical systems. This review comprehensively examines the tribological performance of MXenes under diverse environmental conditions, including high temperatures, vacuum, humid atmospheres, and liquid lubricants. A particular emphasis is placed on the influence of surface terminations (-OH, -O, -F) on friction reduction and wear resistance. Additionally, we discuss strategies for enhancing MXene performance through hybridization with polymers, nanoparticles, and ionic liquids, enabling superior durability in applications ranging from micro/nano-electromechanical systems (MEMS/NEMS) to aerospace and biomedical devices. We also highlight recent advances in experimental characterization techniques and computational modeling, which provide deeper insights into MXene tribomechanics. Despite their promise, key challenges such as oxidation susceptibility, high synthesis costs, and performance variability hinder large-scale commercialization. Emerging solutions, including eco-friendly synthesis methods and optimized composite designs, are explored as pathways to overcome these limitations. Overall, MXenes represent a transformative avenue for developing next-generation tribological materials that combine high efficiency, sustainability, and multifunctionality. Continued research and innovation in this field could unlock groundbreaking advancements across industrial and engineering applications. Full article

Asteroids are remnants of primordial material from the early stages of solar system formation, approximately 4.6 billion years ago, and they preserve invaluable records of the processes underlying planetary evolution. Investigating asteroids provides critical insights into the mechanisms of planetary development and the potential origins of life. To enable efficient sample acquisition under vacuum and microgravity conditions, this study introduces a two-stage gas-driven asteroid sampling strategy. This approach mitigates the challenges posed by low-gravity environments and irregular asteroid topography. A coupled computational fluid dynamics–discrete element method (CFD–DEM) framework was employed to simulate the gas–solid two-phase flow during the sampling process. First, a model of the first-stage gas-driven sampling device was developed to establish the relationship between the inlet angle of the gas nozzle and the sampling efficiency, leading to the optimization of the nozzle’s structural parameters. Subsequently, a model of the integrated two-stage gas-driven sampling and sample-delivery system was constructed, through which the influence of the second-stage nozzle inlet angle on the total collected sample mass was investigated, and its design parameters were further refined. Simulation outcomes were validated against experimental data, confirming the reliability of the CFD–DEM coupling approach for predicting gas–solid two-phase interactions. The results demonstrate the feasibility of collecting asteroid regolith with the proposed two-stage gas-driven sampling and delivery system, thereby providing a practical pathway for extraterrestrial material acquisition. Full article

Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components. While deep learning-based methods have emerged as promising alternatives to traditional numerical simulations, they often suffer from systematic errors caused by geometric mismatches between predicted and ground truth meshes. To overcome this limitation, we propose a novel boundary geometry-constrained neural framework that establishes direct point-wise mappings between spatial coordinates and full-field physical quantities within the deformed domain. The key contributions of this work are as follows: (1) a two-stage strategy that separates geometric prediction from physics-field resolution by constructing direct, point-wise mappings between coordinates and physical quantities, inherently avoiding errors from mesh misalignment; (2) a boundary-condition-aware encoding mechanism that ensures physical consistency under complex loading conditions; and (3) a fully mesh-free approach that operates on point clouds without structured discretization. Experimental results demonstrate that our method achieves a 36–98% improvement in prediction accuracy over deep learning baselines, offering a efficient alternative for high-fidelity simulation of large thermoplastic deformations. Full article

To address the prominent issues in current potato peeling processes (such as high labor intensity, excessive flesh loss, hard-to-remove peel from bud eyes/concaves), a non-contact waterjet method was proposed. Based on the computational fluid dynamics (CFD) method, the Fluent software was used to simulate and analyze the flow field of fan-shaped nozzle models with different slot angles. The simulation results indicated that the 25° scattering angle nozzle had excellent performance: it ensured effective potato surface coverage and minimized jet energy loss, fitting peeling needs. A one-way fluid–structure interaction (FSI) model of the nozzle–potato system was built to study waterjet–potato mechanical interactions. Surface stress distribution under waterjet impact was analyzed, and jet dynamic pressure was mapped to solid stress via FSI interface load transfer. Simulations revealed that with a 25° scattering angle, 200 mm standoff distance, and 5 MPa pressure, the maximum shear stress at potato surface characteristic points was 0.032 MPa—within the 0.025–0.04 MPa target range and matching potato skin–substrate peeling strength threshold. This confirmed the energy–mechanical response coordination, validated by experiments. The research results can provide an effective technical reference for potato peeling processing. Full article

Liquid microlenses and their arrays (LMLAs) have emerged as a transformative platform in adaptive optics, offering superior reconfigurability, compactness, and fast response compared to conventional solid-state lenses. This review summarizes recent progress from an application-oriented perspective, focusing on actuation mechanisms, fabrication strategies, and functional performance. Among actuation mechanisms, electric-field-driven approaches are highlighted, including electrowetting for shape tuning and liquid crystal-based refractive-index tuning techniques. The former excels in tuning range and response speed, whereas the latter enables programmable wavefront control with lower optical aberrations but limited efficiency. Notably, double-emulsion configurations, with fast interfacial actuation and inherent structural stability, demonstrate great potential for highly integrated optical components. Fabrication methodologies—including semiconductor-derived processes, additive manufacturing, and dynamic molding—are evaluated, revealing trade-offs among scalability, structural complexity, and cost. Functionally, advances in focal length tuning, field-of-view expansion, depth-of-field extension, and aberration correction have been achieved, though strong coupling among these parameters still constrains system-level performance. Looking forward, innovations in functional materials, hybrid fabrication, and computational imaging are expected to mitigate these constraints. These developments will accelerate applications in microscopy, endoscopy, AR/VR displays, industrial inspection, and machine vision, while paving the way for intelligent photonic systems that integrate adaptive optics with machine learning for real-time control. Full article

The erosion mechanism of hydrocyclones under air column conditions is still unclear. In this paper, Computational Fluid Dynamics–Discrete Phase Model (CFD-DPM) technology is adopted to perform transient simulations of the three-phase flow (liquid–gas–solid) within a hydrocyclone. The Reynolds Stress Model (RSM) and Volume of Fluid (VOF) model are adopted to simulate the continuous phase flow field within the hydrocyclone, while the DPM coupled with the Oka erosion model is used to predict the particle flow and erosion mechanisms on each wall within the hydrocyclone. The particle sizes considered are 15 μm, 30 μm, 60 μm, 100 μm, 150 μm, and 200 μm, respectively, with a density of 2600 kg/m³. The particle velocity is consistent with the fluid velocity at 5 m/s, the total mass flow rate is 6 g/s, and the volume fraction is less than 10%. The results indicate that the cone section suffers the severest erosion, followed by the overflow pipe, column section, infeed section, and roof section. The erosion in the cone section reaches its maximum value near the underflow port, with an erosion rate approximately 6.8 times that of the upper cone section. The erosion distribution in the overflow pipe is uneven. The erosion of the column section exhibits a spiral banded distribution with a relatively large pitch. The erosion rate in the infeed section is approximately 1.47 times that of the roof section. Full article