Search Results (7,528)

Efficient traffic-state prediction at urban intersections is a critical component of intelligent transportation systems(ITS), as traffic conditions are influenced by dynamic factors such as traffic demand variability, infrastructure constraints, and operational traffic-control policies. This study proposes a deep-learning-based approach for short-term traffic-state classification using real-world traffic data collected during 2022 at the Alésia intersection in Paris. The objective is to classify traffic conditions into five operational states: Unknown, Flowing, Pre-saturated, Saturated, and Blocked. To investigate the impact of temporal modeling on traffic-state recognition, four deep learning architectures were evaluated under identical experimental conditions: Artificial Neural Networks (ANN), Simple Recurrent Neural Networks (RNN),Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU). Considering the highly imbalanced nature of the dataset, model performance was assessed using complementary metrics including Accuracy, Precision, Recall, F1-score, Macro-F1 score, and Balanced Accuracy. Experimental results demonstrate that recurrent architectures substantially outperform the ANN baseline, highlighting the importance of temporal dependencies in traffic-state classification. While the conventional RNN achieves high overall accuracy, its performance on minority traffic states remains limited. Among the evaluated models, the LSTM achieves the highest Balanced Accuracy (70.91%), indicating superior recognition of underrepresented traffic conditions. The GRU attains the highest overall F1-score (0.9256) and Macro-F1 score (0.497), while maintaining competitive classification accuracy (91.01%), providing the most favorable trade-off between global predictive performance and balanced class-wise recognition.The analysis of learning curves, classification reports, and confusion matrices further confirms the effectiveness of gated recurrent architectures for handling highly imbalanced multiclass traffic-state classification problems. These findings provide practical insights for the deployment of intelligent traffic-monitoring systems capable of supporting real-time traffic management and decision-making in urban environments. Full article

Magnetic fluid seals use magnetic field gradients generated by permanent magnets, pole pieces, and rotating shafts to confine ferrofluid in the sealing gap and form multiple liquid sealing rings. Compared with mechanical and labyrinth seals, they exhibit low wear, high cleanliness, low friction loss, and near-zero leakage, making them suitable for high-vacuum equipment, semiconductor devices, clean robotic joints, and rotary feedthrough systems. This review summarizes the development, theoretical basis, experimental methods, structural design, performance characteristics, failure mechanisms, numerical modeling approaches, and engineering applications of magnetic fluid sealing technology. Quantitative comparisons show that ferrofluid seals generally provide a single-stage pressure-bearing capacity of approximately 10–20 kPa with near-zero leakage and good self-replenishment, whereas magnetic powder seals can reach approximately 50–100 kPa per stage but suffer from higher leakage and poor self-recovery. Under high-speed conditions, centrifugal depletion, viscous heating, carrier-liquid volatilization, and interfacial instability become the dominant causes of performance degradation. The reviewed literature indicates that pole-tooth geometry, magnetic-circuit topology, saturation magnetization, thermal transport, and medium compatibility jointly determine sealing reliability. Future research should focus on high-saturation and low-vapor-pressure ferrofluids, optimized pole-tooth and magnetic-circuit structures, magnetic–flow–thermal coupling, integrated cooling, online monitoring, life prediction, and standardized reliability evaluation. Full article

Carbon capture and storage (CCS) technology can play an important role in meeting net-zero ambitions; however, its successful deployment depends on the transport and storage infrastructure for CO₂, as they are the backbone of the carbon management industry. Among the key integrity threats for dense-phase and supercritical CO₂ pipelines, acid precipitation or dropout in CO₂-rich streams containing reactive impurities (SO_x, NO_x, H₂S, H₂O, O_2, etc.) is one of the most serious. These impurities can alter phase behavior, promote formation of highly acidic liquid-phase condensates, and trigger severe localized corrosion and rapid wall-thickness loss. This review focuses on understanding the effects of specific combinations of impurities on CO₂ phase envelopes, acid formation, and corrosion mechanisms in pipelines under realistic flow and operating conditions. It further assesses mitigation and design strategies, including impurity specification and control, deep dehydration, operational envelope management, corrosion-resistant alloys, internal linings and advanced coatings, and emerging modeling tools for predicting corrosive dropout. The knowledge gap in long-term performance under multi-impurity conditions, thermo-hydraulic transients, and coupled corrosion damage is highlighted. Additionally, the importance of future experimental, modeling, and standards development work to enable safe, cost-effective material solutions for CCS technology deployment is proposed. Full article

Efficient aerosol delivery to the maxillary sinuses remains challenging because narrow ostia limit sinus entry. This in vitro study evaluated whether low-frequency, large-amplitude pulsatile flow can deliver humidifier-generated water aerosols to the maxillary sinuses, compared retention with e-vapor under identical conditions, and identified setup modifications required for water aerosol transport. Experiments used three transparent anatomically realistic sinonasal models: two single-passage models with narrow-long (NL) and wide-short (WS) ostial geometries, and one dual-passage dual-maxillary-sinus (RL) model. Water aerosols and e-vapor were delivered using a modified servo-actuated syringe generator under fixed conditions: 50 mL stroke volume, 0.33 Hz frequency, 1 L/min vacuum-induced flow, and 1.5 min delivery. Water aerosols were larger than e-vapor aerosols (D50 = 5.553 µm vs. 3.394 µm) and required setup modification because of greater wall interactions, condensation, coalescence, and transport losses. Pulsatile delivery achieved plume entry into all tested maxillary sinuses. E-vapor showed greater retained mass than water aerosols in NL (1.060 ± 0.152 vs. 0.540 ± 0.089 mg) and WS (0.800 ± 0.071 vs. 0.520 ± 0.110 mg). Water-sensitive Sar-Gel visualization confirmed bilateral water aerosol retention in RL. These findings support pulsatile delivery as a feasible strategy for water aerosol transport to the maxillary sinuses but with a lower efficiency than e-vapor aerosols. Full article

A frequent and sudden two-phase (solid–liquid) geological hazard in mountainous areas, the evolution of debris flows involves the coupling of multiple physical fields, making monitoring and early warning particularly challenging. To accurately reveal the dynamic patterns of debris flow evolution and improve early-warning accuracy, this study focused on the Ni Chang Valley area in Shimian County, Ya’an City, Sichuan Province. Based on the COMSOL Multiphysics coupling simulation platform, a multiphysics bidirectionally strongly coupled numerical model was proposed and constructed, integrating the SPH (smoothed particle hydrodynamics) meshless particle method, FLO-2D shallow-water dynamics, and the MassFlow full-process simulation approach. Using COMSOL as a unified framework, this model employs MassFlow’s deep-integration, continuous medium method to simulate rainfall triggering and material source activation, FLO-2D’s shallow-water equations to describe macroscopic flow-deposition processes, and SPH’s mesh-free particle method to accurately capture large deformations and free-surface flow. The model fully reproduces the entire dynamic chain of debris flow processes, from rainfall triggering and soil mobilization to fluid transport and channel deposition. The reliability and accuracy of the model were verified by comparing it with field measurements from the 20 September 2022 historical debris flow event at Ni Chang Valley. Quantitative analysis indicates that when the viscosity coefficient increases from 0.1 Pa·s to 100 Pa·s, the flow velocity decreases by approximately 47% and the flow depth increases by approximately 62%. When the yield stress increases from 1 Pa to 100 Pa, the deposition area shrinks from 269,900 m² to approximately 109,000 m², a reduction of about 60%. Combining the results of the dynamic analysis, daily maximum temperature, daily precipitation, moisture content, mud-water level, and ground surface displacement were selected as core monitoring indicators. The analytic hierarchy process (AHP) was used to determine the weights of each indicator, and a data- and physics-driven weighted summation model for debris flow monitoring and early warning was constructed to achieve a five-level debris flow monitoring and early-warning system. Historical disaster cases demonstrate that this early-warning model can provide advance predictions of debris flow disasters up to 2 h and 40 min in advance. The warning lead time is sufficient, the grading logic is clear, and the model is capable of accurately capturing precursor information on disasters. Full article

Diesel-powered equipment is widely used in underground coal mines for auxiliary transportation, material handling, and equipment relocation because of its long operating endurance, convenient refueling, and strong adaptability to complex operating conditions. However, carbon monoxide (CO) emissions from such equipment can accumulate locally under restricted ventilation, idling, and frequent start–stop operation, thereby threatening occupational health and mine safety. This review focuses on CO purification technologies for diesel-powered mining equipment. The operating characteristics and influencing factors are analyzed, and different technical routes are compared, including in-cylinder control, wet scrubbing, adsorption, non-thermal plasma (NTP), and catalytic oxidation. Recent advances in noble-metal catalysts, transition-metal and CeO₂-based reducible oxide catalysts, and single-atom catalyst (SAC) design strategies are summarized. Research progress in exhaust aftertreatment systems is also discussed. Overall, CO purification for diesel-powered mining equipment requires coordinated optimization of low-temperature activity, safety-oriented thermal management, flow resistance, and long-term operational stability. Future research should focus on structured catalytic units, durability under coupled exhaust conditions, online monitoring, and field validation to improve the compatibility of CO purification systems with underground mining conditions. Full article

Multi-scale vortex structures constitute the intrinsic skeleton of turbulent flows and govern the energy cascade process in oceanic turbulence. Elucidating their evolutionary dynamics is crucial for understanding turbulent mixing and transport. In this study, we develop an innovative Energy Cascade Multilayer Directed Network (ECMDN) framework grounded in complex network theory to directly characterize nonlinear energy coupling pathways and directional transfers among multi-scale vortices in real marine environments. By integrating multi-parameter fusion node definitions, multi-scale interaction detection, and energy transfer direction identification, the ECMDN reconstructs the nonlinear turbulent system into a topologically interpretable structure. The emergent network properties enable quantitative characterization of intermittency and inhomogeneity in the energy cascade, offering new insights into vortex interactions and cross-scale energy transfer mechanisms. Compared with conventional cascade diagnostics including spectral flux, third-order velocity structure functions, multifractal analysis and shell models that require homogeneity and local equilibrium assumptions and only output global averaged energy flux, the proposed ECMDN multilayer network retains point-wise depth coordinates of each vortex interaction, separates directed forward/inverse energy edges, and quantifies intermittency via topological metrics. Analysis of the single Shenhu thermocline shear segment demonstrates these differentiated analytical capabilities of the proposed framework. Application to shear measurements from the Shenhu Sea reveals the simultaneous occurrence of forward and inverse energy cascades, manifesting a synchronous dual-energy-cascade pattern. This indicates that vortices at a given scale can concurrently transfer energy to larger- or smaller-scale structures and receive energy from larger- or smaller-scale counterparts during the cascade process. Our findings observe a typical synchronous dual-energy-cascade pattern in the strong thermocline of the Shenhu Sea, providing a novel theoretical and methodological framework for investigating the spatiotemporal evolution of stratified ocean turbulent mixing and advancing our understanding of geophysical fluid dynamics. Full article

Microfluidic fuel cells offer a promising route for portable power generation; however, scaling paper-based systems remains challenging because capillary-driven flow can limit fuel distribution and electrochemical performance. This work investigates the experimental performance and computational fluid dynamics (CFD) behavior of air-breathing paper-based microfluidic fuel cell ($\mu$FCs) stacks powered directly by commercial tequila (35 vol.% ethanol). Single cells with electrode areas ranging from 0.5 cm × 0.5 cm to 3 cm × 3 cm were evaluated to determine the optimal design, followed by the construction of 4-cell and 6-cell series-parallel stacks. The smallest electrode (0.5 cm × 0.5 cm) achieved the highest power density (0.142 mW cm⁻²) and open-circuit voltage (0.92 V). Scaling to a 6-cell stack increased the maximum power density to 3.20 mW cm⁻² and the voltage to 1.39 V, outperforming the 4-cell configuration (1.09 mW cm⁻² and 1.07 V). Computational Fluid Dynamics simulations revealed that fuel velocity decreased from 2.8 × 10⁻² m s⁻¹ near the inlet to approximately 1.0 × 10⁻⁶ m s⁻¹ in the final cells because of porous-medium resistance, explaining the observed mass-transport limitations. The results demonstrate that tequila can be directly used as a sustainable fuel source and that optimized stack architectures significantly enhance power generation in paper-based microfluidic fuel cells. Full article

This study presents a computational analysis of degradation caused by cavitation and hydro-abrasive erosion in a low-head axial microturbine ($H=4\mathrm{m}$), incorporating strategic air injection as a passive mitigation technique. Using Computational Fluid Dynamics (CFD) within ANSYS CFX 2025 R2, the study investigates hydrodynamic performance and the spatial distribution of surface wear across the runner blades. The turbine geometry was developed from aerofoil profiles mapped onto cylindrical coordinates, using a structured three-dimensional mesh with localized refinement to ensure grid independence. Physical modeling employed the Shear Stress Transport (SST) turbulence model, with cavitation dynamics governed by the Rayleigh–Plesset equation and sediment transport modeled using a Lagrangian framework incorporating the Finnie erosion model. The numerical framework showed good agreement with reference characteristic curves, confirming its predictive accuracy. The results indicate that vapor cavities form predominantly on the suction side, whereas solid particle erosion highly concentrated on the pressure side of the blades, where the outer $20\%$ of the span accounts for over $91\%$ of the total erosion intensity. Parametric assessments of controlled air injection revealed a highly non-linear mitigation response, identifying IAVF 2 as the optimal air-injection case. This configuration reduced integrated erosion by $0.95\%$ and maximum localized erosion by $6.17\%$. In contrast, excessive air volumes accelerated material removal due to localized flow distortion. The findings indicate that carefully controlled air injection is a viable strategy for extending the operational lifespan of small-scale hydropower assets. Full article

Perforated vortex generators have been widely investigated as a passive heat transfer enhancement technique due to their ability to modify local flow structures through perforation-induced bleed flows. However, their thermo-hydraulic performance is strongly dependent on geometric and flow conditions, and a consistent enhancement effect has not been universally observed. In this study, the mechanism of heat transfer enhancement as well as flow behaviors associated with perforation-induced bleed flows are elucidated through an analysis of the generation and interference behaviors of mixed coherent structures induced by vortex generators punched with multi-holes (PMHVGs). The results showed that the beveled edges of the PMHVGs are responsible for initiating the formation of mixed coherent structures, while local fluid-pressure gradients are identified as the primary driving factor behind their development. Once formed, the perforation-induced bleed flows exert interference on other coherent structures, thereby reducing both their formation intensity and interaction strength. After their generation, the mixed coherent structures contribute to thermal energy transport within the flow through their near-wall ejection and sweep motions. Full article

The magnetic susceptibilities of various electrically insulating ordinary fluids depend on their local states, such as their density and temperature. When such fluids, which can be characterized as either paramagnetic or diamagnetic and occur commonly in nature, are subjected to magnetic field gradients, it induces an effective body force—the Kelvin force. This force, which depends on the susceptibility and the gradient of the square of the magnetic field strength, can become one of the effective mechanisms for modulating the flow and transport, particularly where terrestrial gravity becomes negligible, such as in free space or under microgravity conditions. For the first time, we developed a theoretical model demonstrating that a viscous current can be generated due to the contrasts between the magnetic susceptibilities of the intruding and ambient fluids in the presence of gradients in magnetic fields, analogous to the viscous gravity current in terrestrial situations. We derived similarity solutions for the two-dimensional and axisymmetric currents arising from a balance between the Kelvin buoyancy and viscous forces with a prescribed power law for the magnetic field strength. These determine the shape and various spreading relationships of the viscous current. For a prescribed time variation in the source flux, it is shown that a family of scaling laws exists for the spreading rate and the thickness of the current, which depend on the steepness of the magnetic field gradient. Unlike gravity, since the driving horizontal buoyancy arising from the Kelvin force is externally specified, it potentially offers a mechanism to control the characteristic shape and the rate of motion of the viscous current. Full article

This article discusses the relevant problem of ensuring transport connectivity under the conditions of temporal restrictions of the road network, which arise during repair, communal and emergency operations. It is established that the existing organizational and intellectual methods of traffic management do not eliminate physical decrease in road capacity, while construction of stationary structures with different levels is limited by high costs and long terms of implementation. The above substantiates the need for the development of mobile overpasses as adaptive engineering solutions ensuring continuity of the traffic flows. The purpose of the research is to develop a scientifically substantiated theoretical and experimental methodology for designing a mobile overpass as an integrated system “structure-moving load”, taking into account its dynamic behavior. The paper proposes an integrated approach based on the use of physical similarity theory and dimensionless analysis. A differential equation of dynamic bending of a beam on an elastic foundation is formulated taking into account inertia, damping, base reaction and the effect of a moving mass, and then its nondimensionalization is performed to obtain a similarity criteria system. The scientific novelty of the research consists in developing a system of dimensionless criteria to describe the relationship between the structural, dynamic and operational parameters of a mobile overpass, as well as in the formation of a criterion base for large-scale modeling and transfer of the results to full-scale structures. The proposed methodology describes the mobile overpass as an integrated transport-engineering system accounting for the coupled interaction between the deformable structure, moving traffic load, elastic foundation, and damping effects. Experimental verification was performed on a specially designed stand in the scale 1:4. The results obtained showed the quasi-static nature of the structure performance with moderate damping and rigid base. It is established that the distribution of engineering stresses along the span length has a regular character and retains its shape when the load level changes, which confirms fulfillment of similarity conditions. Regression analysis revealed a close to linear dependence of stresses on the load mass with a high degree of confidence $(R^2\approx 0.995)$. The practical significance of the research consists in creating an engineering method for express design of mobile overpasses, which allows for assessing their stress–strain state, stability and serviceability without expensive full-scale tests. The proposed approach can be used in designing temporary transportation structures under the conditions of urban area, and in operation in areas of road operations and emergency situations. Full article

Topological photonic crystals provide an important platform for robust light transport and light-field manipulation. To meet the demands for developing multifunctional and densely integrated photonic circuits, it is necessary to flexibly control light flow with multi-degrees of freedom while maintaining strong topological protection. In this work, we investigate multifunctional topological photonic crystals based on hybrid topological domain walls, which support valley-polarized chiral edge states (VCES). Based on hybrid domain walls, we design two types of compact topological photonic devices. By exploiting direction-selective coupling between valley edge states (VES) and VCES, we construct nonreciprocal coupled waveguides with a nonreciprocal transmission ratio of 10 dB and output-port isolation ratio of more than 30 dB. Moreover, through different configurations of the direction of external magnetic field, we construct a multi-channel selective routing device that enables the configurable transport of valley-polarized electromagnetic waves among multiple channels. Hybrid topological waveguides provide a foundation for designing novel photonic devices, offering the potential for realizing multifunctional integrated topological photonic networks in both classical and quantum regimes. Full article

Accurate prediction of natural gas thermophysical properties is essential for applications in production and transportation engineering, including reservoir simulation and flow modeling. Although machine learning (ML) techniques have been widely used, most studies focus on the estimation of these properties, with limited integration into practical applications. In this study, we propose a supervised model based on a Backpropagation Neural Network for simultaneous estimation of four interdependent properties: compressibility factor (Z), viscosity (μ), density (ρ) and gas formation volume factor (B_g). The multi-output model was trained on 58,165 data points generated from thermodynamic correlations, using pressure, temperature, composition (mole fractions of N₂, CO₂ and H₂S), and gas specific gravity as inputs. The results yielded RMSE values of 5.56 × 10⁻⁴, 3.24 × 10⁻⁵, 3.01 × 10⁻², and 6.33 × 10⁻⁴ for Z, μ, ρ and B_g, respectively, with R² coefficients close to unity. The model’s applicability was evaluated by integrating the Z-factor into pressure drop calculations in pipelines using the Cullender and Smith method, resulting in a mean percentage error of 3.78%, close to the traditional method (3.83%). The results indicate that the model is an efficient and consistent alternative, highlighting the potential for integrating ML with classical hydraulic models. Full article

As global super-large open-pit mines expand in scale and extraction depth, conventional single-stage planning cannot meet the combined demands of productivity and resource recovery, making the shift to underground block caving inevitable. This study outlines the systemic challenges of block-scale extraction and the rationale for adopting multi-level spatial design decision-making. Four core model categories are briefly proposed: ultimate pit limit optimization, gravity flow simulation for draw strategy, long-term production scheduling for large-scale computation, and probabilistic frameworks addressing geological and market uncertainty. A Bayesian network-based block decision model is then proposed and decoupled into three physical decision tiers. The first tier incorporates energy prices, transport costs, and ore prices to establish an economic boundary rating robust to market volatility. The second tier aggregates mining units with discrete-event perturbations to produce a reliability-oriented production rating. The third tier integrates rock mechanics parameters with in situ monitoring data to derive a physics-informed safety rating. The three ratings are synthesized via Bayesian inference and evaluated within a multi-attribute utility function encompassing net present value, safety index, downside risk, and information risk. A feedback module quantifies the economic benefit of uncertainty reduction, yielding a closed-loop intelligent system spanning macroeconomic boundary definition to operational safety alerting. Finally, the main conclusion of this study is that integrating macro-economic volatility with rock mechanics through a dynamic Bayesian framework is essential for managing the open-pit to underground transition. The results indicate that leveraging the Value of Information for real-time risk diagnosis significantly reduces conservative design losses, providing a quantifiable and robust decision-making paradigm for super-large mining systems. Full article