Search Results (4,526)

Windage power losses (WPLs) can take a noticeable toll on the efficiency of high-speed gear transmissions, especially in helical gears, where complex 3D airflow patterns increase aerodynamic drag. In this work, we measured the WPL of a helical gear using a combination of analytical models, experiments, and CFD simulations. A custom test rig recorded windage losses at four speeds—2000, 3000, 4000, and 5000 rpm—producing values between 1.33 W and 21.67 W. We then compared these results with predictions from commonly used analytical methods (Dawson, Lord, ISO/TR 13593, ANSI/AGMA 6011-I03). These models showed discrepancies of about 25–35%, largely because they were not developed with helical gear geometries in mind. To complement this, CFD simulations carried out in SolidWorks Flow Simulation closely matched the experimental data, with an average deviation of just 4.99%. The combined results highlight the dominant mechanisms contributing to windage losses, assess the accuracy and limitations of each method, and identify the operating regimes where discrepancies are most pronounced. The findings offer a validated framework for predicting windage losses in industrial helical gears and support the development of more efficient gearbox designs. Full article

Permeability anisotropy, which is widely present in natural soil deposits, plays an important role in controlling groundwater flow patterns and ground deformation during deep excavation dewatering. However, isotropic assumptions are still commonly adopted in engineering practice, making it difficult to accurately capture realistic subsurface hydraulic conditions. In this study, a deep foundation pit of a metro station in Jinan, China, is taken as a case study. A three-dimensional excavation–dewatering model incorporating permeability anisotropy is established using PLAXIS 3D to systematically investigate the influence of the permeability ratio (Kx/Kz) ranging from 0.1 to 10 on the seepage field evolution, dewatering influence radius, ground surface settlement, and consolidation time history. The results indicate that increasing permeability anisotropy promotes a fundamental transition of the seepage regime from vertically concentrated recharge to laterally dominated radial flow. Correspondingly, the dewatering influence radius exhibits a pronounced non-monotonic response to Kx/Kz, decreasing significantly with increasing permeability ratio and reaching a minimum at approximately Kx/Kz ≈ 5, followed by a slight rebound. Meanwhile, surface settlement profiles evolve from a localized concentration pattern to a widely distributed form as permeability anisotropy increases, accompanied by a remarkable outward expansion of the settlement influence zone. Both the magnitude and spatial distribution of settlement show high sensitivity to variations in permeability anisotropy. Based on these findings, a three-stage conceptual seepage structure model accounting for permeability anisotropy is proposed, characterized by vertically dominated flow, a transitional competition regime, and horizontally dominated flow. The staged evolution of seepage structures is shown to govern the non-monotonic variation in the dewatering influence radius and the spatial–temporal response of ground settlement. The results indicate a dual-scale influence mechanism of permeability anisotropy on dewatering-induced hydro-mechanical behavior, providing a theoretical basis for refined dewatering design and environmental impact assessment in deep excavation projects. Full article

This paper presents a multimodal sensing approach for fine-grained soccer action recognition using synchronized mm-wave FMCW radar and multiview RGB cameras. A TI IWR1443BOOST FMCW radar and three Sony IMX296 global-shutter cameras were used to record seven soccer-related actions in different movement directions in an outdoor environment. Range–Doppler radar processing is applied to extract global mel features and CFAR-localized block representations of mel and radar spectrogram features to capture both coarse and fine micro-Doppler characteristics. Camera features are derived from bounding box, HOG, optical flow, and pose estimations. Classification is performed using logistic regression as the classical model and various deep models. Performance is evaluated using cross-validation. Radar alone achieved moderate performance (0.897 F1_macro using TCN), successfully identifying coarse motion but showing limited separability for dribbling-based actions. Camera-only models achieve near-perfect accuracy (≥0.997 F1_macro using 1D-CNN), with the confusion matrices being nearly perfectly diagonal already. The best performance is obtained from a cross-modal transformer with multiple cameras (0.998 F1_macro). These results demonstrate that a camera by itself performs strongly for the action recognition task but also that radar–camera fusion can improve robustness and enhance the discrimination of finer soccer player movements for outdoor analytics and player monitoring applications. Full article

The flow field regulation and medium migration characteristics during aggregate slurry grouting in rough fractures are directly related to the grouting repair engineering in various geotechnical projects. The selected three grouting velocities (0.5, 0.55, 0.6 m/s) are within the typical range of 0.3–0.8 m/s for high-pressure jet grouting in geothermal reservoirs. This study uses the Hurst exponent method to construct a 3D rough fracture model and simulates cement slurry flow and aggregate migration based on Fluent–EDEM two-way coupling, analyzing flow field characteristics and their impact on aggregate migration. Results show that differences in flow field pressure and viscosity affect rough fracture flow field distribution and aggregate migration, leading to segmented non-uniform velocity—higher in the ascending section (Up-leg) and Down-leg (Down-leg) and stable in the gentle section (Flat-leg) of the rough fracture—coupled with wall morphology. Particle motion is controlled by the flow field, consistent with the pattern shown in velocity contours, verifying that geometry, pressure and shear characteristics collectively govern fluid and particle movement. Pressure contours show that the pressure distribution in rough fractures is coupled with wall morphology: high pressure occurs at abrupt sections, while pressure is stable in Flat-leg. Viscosity contours indicate that the proportion of high-viscosity regions at abrupt sections is lower than that in Flat-leg. This provides theoretical support for optimizing aggregate slurry migration, improving flow field uniformity, reducing grout waste, and enhancing the construction quality and efficiency of underground engineering Full article

This research develops a new low-cost method for continuous flow monitoring in open channels. Flow is calculated using a standard 1D hydraulic model that integrates surveyed cross-sections and water level measurements at the boundaries of a studied reach, from which the name Boundary Water Level Method (BWLM) is derived. By implementing low-cost ultrasonic sensors for water level measurement, the method gains advantage for application on smaller channels, which are often not included in national hydrological monitoring networks due to limited budgets. New and innovative monitoring methods in hydrology are a necessary alternative to increasing the monitoring budgets, especially for continuous, real-time flow monitoring. Like any novel method, it requires validation under the intended environmental conditions, especially when designed primarily for ungauged channels. Validation was conducted on two test-sites by comparing the BWLM discharge and the discharge from official hydrological stations, with an error of up to 15%. BWLM provides reliable discharges using estimated hydraulic roughness values based on the literature and experience. Sensitivity analysis of the estimated hydraulic roughness coefficient demonstrated a substantial influence on the resulting discharge values. This has to be considered when implementing the method in unstudied basins. Full article

High-temperature proton exchange membrane fuel cells (HT-PEMFCs) coupled with methanol reforming hold promise for distributed energy systems, yet channel hydrodynamics and geometry optimization remain underexplored. This study develops a 3D multiphysics model to investigate coupled behaviors in HT-PEMFCs fueled by methanol reformate. Results reveal bifurcation-induced Dean vortices have dual effects: they cause flow maldistribution (15–18% velocity deviation) and contribute 50% of inlet pressure loss, while generating a lateral pumping effect that enhances local mass transfer. A continuous parametric sweep of channel widths (0.9–1.9 mm) identifies a voltage-dependent performance crossover—narrower channels (1.3 mm) excel at high voltages by improving electronic conduction, whereas wider channels (1.5 mm) perform better at low voltages by mitigating mass transfer limitations. These findings provide quantitative design criteria for optimizing flow field geometry in HT-PEMFC stacks. Full article

This paper investigates co-continuous structures in immiscible polymer blends through three-dimensional (3D) computational calculations based on a multiphase phase-field equation for fluid flow. The mathematical model describes phase separation with the Cahn–Hilliard (CH) equation and fluid motion with the incompressible Navier–Stokes (NS) equations. Both polymers are treated as Newtonian viscous fluids, and the model includes surface tension, viscosity, and volume fraction effects. A semi-implicit finite difference method (FDM) solves the CH equation, and a projection method maintains the incompressibility of the flow field. Multigrid techniques solve the nonlinear systems efficiently. In addition, a connectivity-based detection algorithm determines whether a phase forms a connected structure that reaches all boundaries of the numerical domain. The numerical results show that the morphology changes from a droplet–matrix structure to a co-continuous structure as the volume fraction increases. The interfacial area per unit volume reaches a local maximum near the transition between these two regimes. Full article

A rapid quasi-one-dimensional (quasi-1D) reacting-flow analysis code was developed for the preliminary assessment of rocket-based combined cycle engines over a broad flight envelope. The internal flow was modeled as steady and quasi-1D in a variable-area duct by solving the coupled conservation equations together with species transport, and finite-rate chemical kinetics were included to represent combustion-induced heat release and composition change. To incorporate configuration-dependent mixing effects that affect RBCC heat release evolution and thermal choking tendencies, a streamwise mixing efficiency distribution was extracted from non-reacting 3D CFD and prescribed as an input to the quasi-1D formulation to represent the progressive availability of reactable fuel along the flowpath. A mode-dependent solution strategy was established by separating the computation into scramjet mode and ramjet mode procedures with a switching criterion based on whether a sonic condition occurs within the combustor, allowing thermal choking and mode transition behavior to be addressed within a single framework. The numerical solver was implemented in Python 3.12.2 and integrated using a stiff ordinary differential equation (ODE) scheme to ensure robust convergence in the presence of reaction-induced stiffness. Verification against previously published hydrogen-fueled scramjet results reproduced the overall streamwise trends of key quantities including Mach number, pressure, temperature, and density. The developed code was then applied to an RBCC configuration under operating conditions representative of ERJ and ESJ regimes, and the quasi-1D predictions were compared with cross-section-averaged 3D RANS CFD results, showing consistent mode identification and comparable axial behavior at a level suitable for preliminary analysis with substantially reduced computational cost. Full article

Time headway is a key parameter for describing car-following behavior and microscopic traffic flow characteristics, and it is important for traffic safety analysis, road design, and optimizing intelligent-driving strategies. Existing research offers limited insight into the heterogeneity of time headway under different vehicle types and lane conditions. It is particularly important to investigate how time headway distributions differ across lane–vehicle-type combinations on highways, as these differences can affect safety evaluation and operational performance. This study is based on drone-captured vehicle trajectories from the publicly available HighD dataset. We select 378,751 vehicle–frame trajectory records; these records are used to construct valid follower–leader pairs and derive time headway (THW) samples for distribution fitting. Eight subsets are formed by combining two lane positions (inner vs. outer) and four follower–leader vehicle-type pairs (car–car, car–truck, truck–car, truck–truck). Six candidate distributions (Lognormal, Log-logistic, Burr, Weibull, Gamma, and Logistic) are fitted using maximum likelihood estimation, and their fit is evaluated using Kolmogorov–Smirnov, Anderson–Darling, and Chi-square tests, which are fused via an entropy-weighted composite score for model ranking. Results show pronounced heterogeneity across lane–vehicle-type subsets: Inner-lane samples exhibit smaller and more concentrated time gaps, whereas outer-lane samples show larger mean gaps, stronger dispersion, and heavier upper tails. Overall, Lognormal(3P) is selected as the top-ranked model in 5 of 8 subsets (62.5%), while Burr(4P) (car–truck, outer lane), Gamma(3P) (truck–car, outer lane), and Weibull(3P) (truck–truck, inner lane) are optimal in the remaining subsets. These findings indicate that lane position and vehicle-type pairing materially affect THW distributional characteristics, providing quantitative guidance for lane- and vehicle-aware traffic modeling, safety-oriented assessment, and intelligent-driving strategy design. Full article

Quercetin, a naturally occurring flavonoid, exhibits antiproliferative and pro-apoptotic effects across various cancer models. Topotecan, a topoisomerase I inhibitor, is used in the treatment of retinoblastoma; however, its clinical utility is limited by dose-dependent toxicity. This study aimed to investigate whether quercetin is associated with enhanced topotecan-induced cytotoxicity in retinoblastoma and to explore the underlying mechanisms under both two-dimensional (2D) and three-dimensional (3D) conditions. Cell viability was assessed using the MTT assay, and drug interactions were evaluated using the combination index (CI) based on the Chou–Talalay method. Apoptosis was analyzed by Annexin V-FITC/PI staining and flow cytometry. Reactive oxygen species (ROS) levels and mitochondrial membrane potential were evaluated using fluorometric methods, and N-acetyl-L-cysteine (NAC) was used for functional modulation of oxidative stress. Three-dimensional tumor spheroid models were used to assess treatment effects under conditions that partially recapitulate tumor architecture. Gene expression levels of apoptosis-related markers and PI3K/Akt/mTOR pathway components were analyzed by quantitative real-time polymerase chain reaction (qRT-PCR). The combination of quercetin and topotecan was associated with synergistic cytotoxic effects in Y79 cells (CI < 1), accompanied by increased ROS levels, mitochondrial membrane depolarization, and elevated apoptotic cell death. NAC co-treatment partially attenuated ROS levels and restored cell viability. In 3D spheroid models, combination treatment induced structural disruption, reduced viability, and increased cell death, effects that were partially reversed by NAC. Gene expression analysis revealed upregulation of pro-apoptotic genes and downregulation of survival-related genes, along with increased PTEN expression. Quercetin is associated with enhanced topotecan-induced cytotoxicity in retinoblastoma cells under both 2D and 3D conditions. These effects were associated with ROS-associated cellular stress, mitochondrial dysfunction, and modulation of apoptotic and survival-related pathways. The partial rescue by NAC supports a contributory, but not exclusive, role of oxidative stress. These findings should be interpreted within a preclinical context and suggest that quercetin may represent a potential adjunct strategy warranting further validation in translational and in vivo models. Full article

In this study, Delrin^® (POM) polymer was reinforced with 15 wt.% chopped ramie fiber (RF) to develop a sustainable composite, which was injection-molded and machined using abrasive waterjet machining (AWJM). SEM revealed a skin-core morphology with flow-induced RF alignment and small voids at bundle crossovers, indicating interfacial adhesion. A Taguchi L₉ (3³) design evaluated waterjet pressure (WJP: 100–300 MPa), traverse speed (TS: 100–200 mm/min), and stand-off distance (SoD: 1–3 mm) on kerf width (KW) and surface roughness (SR). Increasing WJP from 100 to 300 MPa lowered mean SR from 6.23 to 4.80 µm (23% reduction) and KW from 1.31 to 1.07 mm (reduction of 18%); enlarging SoD from 1 to 3 mm raised SR from 4.98 to 5.55 µm (an 11% increase) and KW from 1.12 to 1.20 mm (a of 7% increase); and raising TS from 100 to 200 mm/min narrowed KW from 1.24 to 1.11 mm (a 10.5% reduction) with a modest SR decrease from 5.45 to 5.28 µm. ANOVA confirmed WJP as the dominant factor for SR (79.8%), as well as a significant SoD (18.3%). For KW, the influence of WJP (68.8%) was substantial, followed by TS (19.9%) and SoD (11%). Linear models captured the trends well (SR: R² = 88.29%; KW: R² = 93.36%). A desirability-based multi-response optimizer yielded ideal conditions for TS (200 mm/min), WJP (300 MPa), and SoD (1 mm), predicting a KW of 0.94 mm and an SR of 4.1567 µm. Confirmation tests produced a KW (0.970 ± 0.01 mm) and SR (4.27 ± 0.05 µm), which are within 3.19% and 2.73% of the predicted values, validating the DoE regression approach. Full article

This study examines the electrohydrodynamic (EHD) behavior of air bubbles rising in deionized water under a non-uniform electric field, with particular emphasis on the influence of applied voltage (0.5–3.0 kV) and gas flow rates of 30 and 40 mL ${\min }^{-1}$ (corresponding to Reynolds numbers of $R{e}_g=107$–142) on bubble dynamics. High-speed imaging reveals bubbles with equivalent diameters in the range of $d_{eq}\approx 0.8$–3.5 mm, enabling a detailed characterization of their deformation, trajectory, and interfacial response under coupled hydrodynamic and electric stresses. At $R{e}_g=107$, bubbles exhibited stable vertical trajectories with negligible lateral displacement, whereas at $R{e}_g=142$, inertial and wake effects induced deviations. Increasing $B{o}_E$ reduced lateral displacement, restoring alignment with the electric field. Bubble rise velocities increased by ∼20–30% with applied voltage due to polarization-driven EHD forces. A transition from hydrodynamically dominated to EHD-dominated regimes was identified. While polarization forces govern the initial bubble motion under a strong electric field, bubbles progressively transition downstream to a hydrodynamic regime as the electric field weakens, reducing the influence of polarization effects. These findings provide quantitative insight into coupled hydrodynamic–electrohydrodynamic interactions and support the development of predictive models for controlling bubble trajectories, with implications for electrically tunable multiphase and microfluidic systems. Full article

Existing energy consumption models suffer from accuracy degradation and limited robustness in complex urban environments due to insufficient consideration of the route spatiotemporal characteristics of electric buses. To address this limitation, a Time-Partitioned Dual-Layer LSTM (TP-D-LSTM) framework driven by cloud data and spatiotemporal characteristics is proposed. First, a spatiotemporal characteristics analysis is conducted on urban bus routes to reveal the underlying traffic flow dynamics. Based on these insights, a time-partitioning strategy is developed to classify the continuous operating data into independent periods while preserving the kinematic continuity of individual trips. Subsequently, a Dual-Layer LSTM (D-LSTM) is constructed to precisely capture the distinct energy consumption mechanisms within each partitioned scenario. Experiments based on real-world cloud-logged data demonstrate that the proposed TP-D-LSTM framework is superior to existing baseline models. By alleviating the limitations of global mixed modeling, the TP-D-LSTM significantly reduces the Root Mean Square Error (RMSE) to 6.15, achieving an improvement of over 50% compared to the D-LSTM, and exhibits remarkable stability under highly volatile traffic conditions. Full article

The study presents an engineering solution for maintaining traffic flow during road and utility operations, such as trench excavation. The analysis of existing organizational and technical approaches, along with global experience in temporary bridge use, showed that most foreign analogs were developed for military purposes and are not fully suitable for urban conditions in Kazakhstan and CIS countries. As an alternative solution, the development of a mobile overpass adapted for operation in dense urban environments is proposed. The present study continues earlier research focused on optimizing the placement of mobile overpass supports while accounting for the nonlinear deformation behavior of the soil foundation. At the previous stage, a rational distance between the supports and the trench edge was substantiated, and horizontal soil deformations were reduced. In the current study, the primary focus is on the design of the undercarriage, which determines the mobility, stability, and operational feasibility of the structure. A morphological analysis and synthesis method is applied to select a rational configuration of the undercarriage. A 3D model and a 1:4 scale test bench were developed, followed by load tests of 50–200 kg. The maximum deflection of −1.19 mm at 200 kg demonstrated an almost linear deformation pattern. The constructed regression model ($R^2=0.97$) confirmed the accuracy and reliability of the design. The developed mobile overpass is versatile, cost-effective, and practical, improving the resilience of urban transport infrastructure, reducing traffic congestion during roadworks, and creating a foundation for serial production in Kazakhstan and CIS countries. Full article

While pre-trained deep learning models have significantly advanced image dehazing, their restoration performance often fluctuates substantially across varying haze densities, leading to inconsistent performance across diverse atmospheric conditions. To address this limitation, this study introduces a performance analysis approach based on Haze Image Clustering (HIC) to systematically evaluate the specialized strengths of various state-of-the-art models within specific haze-level intervals. Building upon these evaluations, we propose a heterogeneous modular framework equipped with a dynamic switching mechanism that adaptively activates the optimal pre-trained module for each detected haze level. Extensive experiments conducted on the OTS and ODF benchmark datasets demonstrate that while individual models exhibit regional performance drops, the proposed framework consistently maintains superior performance across all haze intensities. Quantitative results indicate that the proposed modular network achieves a significant PSNR improvement of up to 6.946 dB compared to DehazeFlow. Furthermore, regarding the no-reference Dehazing Quality Index (DHQI), our framework attains a top score of 68.448, surpassing the best individual baseline. These findings validate that the proposed strategy effectively enhances both restoration fidelity and visual naturalness without the need for additional training or fine-tuning, offering a robust and computationally efficient solution for real-world image dehazing. Full article