Search Results (2,527)

Single-phase grounding is the dominant fault type in urban power distribution networks. Because the total magnetic flux would not change around the cable under a single-phase grounding fault, ferromagnetic zero-sequence current sensors cannot distinguish the faulted phase of belted cables, which are the main type in 10 kV distribution networks. To fill this gap, a two-step methodology is proposed using an annular TMR magnetic sensor to measure the magnetic field intensity at six points on the cable surface and to distinguish the faulted phase using the magnetic field intensity differences between the TMRs. The first step is calculating the rotation angles between the six magnetic sensors and the three cable cores after installation. A differential evolution algorithm is used to calculate the rotation angles in the sensing model. The second step is to detect the fault phase under a single-phase grounding fault transient, with the magnetic field intensity difference taken as the criterion. The methodology is verified through simulation and experiment. The results show that the relative errors of the rotation angles are all less than 1%. Under a single-phase grounding fault, the faulted phase can be accurately identified. The proposed method can effectively identify the faulted phase of 10 kV three-core cables under single-phase grounding and has significant engineering application value. Full article

Reverse engineering (RE) is essential in the automotive and aerospace industries for reconstructing high-precision components, such as exhaust valves, when design documentation is unavailable. However, different measurement methods introduce varied errors that can affect engine performance and safety. This study presents a comparative analysis of contact and optical measurement systems—specifically the CMM Accura II (ZEISS Group, Oberkochen, Germany), Mahr MarSurf XC 20 (Esslingen am Neckar, Germany), GOM Scan 1 (ZEISS/GOM, Braunschweig/Oberkochen, Germany) and MCA-II with an MMD×100 laser head (Nikon Metrology, Leuven, Belgium)—to assess their accuracy in reconstructing exhaust valve geometry. The research procedure involved measuring global surface deviations and critical functional parameters, including stem diameter, straightness, and seat angle. The results indicate that tactile methods (CMM and Mahr) provide significantly higher accuracy and lower dispersion than optical methods. The Mahr system was the most effective for stem precision, while the CMM was the only system to pass the seat angle tolerance requirement unambiguously. In contrast, the MCA-II laser system failed to meet the required precision–mechanical tolerances. The findings suggest that an optimal industrial strategy should adopt a hybrid methodology: utilizing rapid optical scanning (GOM) for general geometry and high-precision tactile systems (CMM, Mahr) for critical functional features. This approach can reduce total inspection time by 30–40% while ensuring technical safety and preventing catastrophic engine failures. Full article

Factor of Safety (FOS) is a significant index to measure the stability condition of a rock slope in mining or civil engineering. In this paper, we evaluate and compare four different machine learning models, Gaussian Process Regressor (GPR), Support Vector Regressor (SVR), Random Forest (RF), and a hybrid genetic algorithm–multi-layer perceptron (GA-MLP), using two separate real-world datasets. The two separate datasets used in this study are from a previously conducted study on highway excavation with rock cutting in China, and another one in a mining site in Peru, with five geotechnical properties used as inputs, including slope height, slope angle, unit weight, cohesion, and friction angle. The two separate datasets were separated into training, validation, and testing datasets. The testing dataset of the models is unseen data used to assess model performance in an unbiased manner. The result shows that the SVR had the highest prediction accuracy, followed by GPR for the mining dataset, and GPR had the highest performance among all the models for the highway excavation dataset. From the boxplot, we can see that SVR, while having the highest predictive accuracy, has a larger variance in prediction compared to GPR for the mining dataset. Full article

This research presents a comprehensive evaluation of semi-elastic polyurethane adhesives used for bonding wooden flooring, with a particular focus on both domestic (oak) and exotic hardwood species (teak, iroko, wenge, merbau). Given the increasing interest in sustainable construction practices and the growing use of diverse wood species in flooring systems, this study aimed to assess the mechanical, morphological, and surface properties of adhesive joints under both standard laboratory and thermally aged conditions. Mechanical testing was conducted according to PN-EN ISO 17178 standards and included shear and tensile strength measurements on wood–wood and wood–concrete assemblies. Specimens were evaluated in multiple aging conditions, simulating real-world application environments. Shear strength increased post-aging, with the most notable improvement observed in wenge (21.2%). Tensile strength between wooden lamellas and concrete substrates remained stable or slightly decreased (up to 18.8% in wenge), yet all values stayed above the 1 MPa minimum requirement, confirming structural reliability. Surface properties of the wood species were characterized through contact angle measurements and 3D optical roughness analysis. Teak exhibited the highest contact angle (74.9°) and the greatest surface roughness, contributing to mechanical interlocking despite its low surface energy. Oak and iroko showed high wettability and balanced roughness, supporting strong adhesion. Scanning electron microscopy (SEM) revealed stable adhesive penetration across all species and aging conditions, with no signs of delamination or interfacial failure. The study confirms the suitability of polyurethane adhesives for durable, long-lasting bonding in engineered and solid wood flooring systems, even when using extractive-rich or dimensionally sensitive tropical species. The results emphasize the critical role of surface morphology, wood anatomy, and adhesive compatibility in achieving optimal bond performance. These findings contribute to improved material selection and application strategies in flooring technology. Future research should focus on bio-based adhesive alternatives, chemical surface modification techniques, and in-service performance under cyclic loading and humidity variations to support the development of eco-efficient and resilient flooring systems. Full article

In the development of multi-vehicle cooperative hardware-in-the-loop (HIL) simulation platforms based on machine vision, accurate vehicle pose estimation is crucial for achieving efficient cooperative control. However, monocular vision systems inevitably suffer from limited fields of view and insufficient image resolution during target detection, making it difficult to meet the requirements of large-scale, multi-target real-time perception. To address these challenges, this paper proposes an engineering-oriented multi-camera cooperative vision detection method, designed to maximize processing efficiency and real-time performance while maintaining detection accuracy. The proposed approach first projects the imaging results from multiple cameras onto a unified physical plane. By precomputing and caching the image stitching parameters, the method enables fast and parallelized image mosaicking. Experimental results demonstrate that, under typical vehicle speeds and driving angles, the stitched images achieve a 93.41% identification code recognition rate and a 99.08% recognition accuracy. Moreover, with high-resolution image (1440 × 960) inputs, the system can stably output 30 frames per second of stitched image streams, fully satisfying the dual requirements of detection precision and real-time processing for engineering applications. Full article

Gravity currents constrained by bottom walls are prevalent in engineering applications such as industrial discharges and deep-sea mining, and will pose significant environmental risks. In this study, the influence of jet source parameters on the dynamics and diffusion characteristics of particle-driven bottom currents was investigated through physical experiments using Digital Image Processing (DIP). This non-invasive technology is cost-effective and exhibits broad applicability. The results demonstrated that the downstream plume front $d_{\mathrm{L}\mathrm{m}\mathrm{a}\mathrm{x}}$, the maximum lift height $h_{\mathrm{L}\mathrm{m}\mathrm{a}\mathrm{x}}$ and the average lift height $h_{\mathrm{a}\mathrm{v}\mathrm{e}}$ all exhibit a decreasing trend with increasing Richardson number (Ri) after impingement, and show a linear increase with rising Reynolds number (Re). The plume diffusion scale S follows a two-stage evolution: during the inertia-dominated stage, S evolves exponentially over time t as $S=a{e}^{bt}$, while in the equilibrium stage of negative buoyancy and turbulent dissipation, S follows a power-law relationship $S=a{t}^b$ (b < 1). The rate of change of S increases with smaller jet angles α, and the variations with dimensionless bottom clearance H/D remain within 10%. The dimensionless average longitudinal expansion rate ${\displaystyle \frac{\overline{E}}{\sqrt{g\prime /D}}}$ reaches minimum values at α = 75°, peaks at H/D = 10, and exhibits a linear decreasing trend with Ri. As Re increases, ${\displaystyle \frac{\overline{E}}{\sqrt{g\prime /D}}}$ displays a three-stage fluctuating behavior. This study provides valuable experimental data that improve the understanding of gravity current behavior under wall confinement and support the predictive modelling of gravity current. Full article

Instability in dump slopes frequently induces landslides, a process governed by complex factors. To investigate the impact of gradation composition on dump slope stability, four distinct gradations were designed, and large-scale laboratory triaxial tests were conducted to characterize their strength and deformation behaviors under varying confining pressures. Concurrently, numerical models of dump slopes with these four gradations were established using Particle Flow Code (PFC) to simulate rainfall infiltration processes. Through a comparative analysis of particle contact force chains, pore water pressure evolution, particle displacement under varying rainfall durations, and safety factors under natural and rainfall conditions, the mechanisms governing the influence of gradation composition on slope stability were elucidated from both macroscopic and microscopic perspectives. Results indicate the following: (1) Gradation composition significantly affects the strength and deformation characteristics of dump materials. Sample group 3 (with a fine-to-coarse particle ratio of 4:6) exhibited the highest strength among the four test samples, with peak deviatoric stresses of 610 kPa, 1075 kPa, and 1539 kPa under confining pressures of 200 kPa, 400 kPa, and 600 kPa, respectively. Its corresponding shear strength parameters were a cohesion of 38.45 kPa and an internal friction angle of 32.55°. In contrast, sample group 4 (fine-to-coarse ratio of 6:4) showed the lowest strength, with peak deviatoric stresses of 489 kPa, 840 kPa, and 1290 kPa under the same confining pressures, and shear strength parameters of c = 25.35 kPa and φ = 30.02°. (2) Gradation modulates contact forces and failure modes via a “skeleton-filling” mechanism. (3) Gradation plays a critical role in controlling pore water pressure evolution and the seepage characteristics of the dump slope model. Among the four designed gradations and their corresponding numerical models, Model 3 was characterized by the highest contact forces and the lowest pore water pressure. It exhibited the highest stability under both natural and rainfall conditions, with safety factors of 1.70 and 1.22, respectively. Conversely, Model 4 showed weak particle contact forces and high pore pressure, demonstrating the poorest stability. It yielded safety factors of only 1.25 and 1.02 under natural and rainfall-saturated conditions, indicating that it represents the least favorable gradation composition. These findings provide valuable references for the optimization of dumping processes and stability control in similar engineering projects. Full article

It is promising to in situ retrofit the activated sludge process with a membrane bioreactor (MBR) to increase treatment capacity and improve effluent quality in a textile dyeing wastewater treatment plant (WWTP). Membrane selection among commercial products for real engineering applications is critical for this specific wastewater, and little information is available in the literature. This study systematically evaluated the application potential of two flat-sheet microfiltration membranes made of polyvinylidene fluoride (PVDF) and polyether sulfone (PES) in pilot-scale MBRs for in situ retrofitting textile dyeing WWTP. During the four stages with different loads, both membranes achieved nearly the same effluent quality and rejection performance. Both membranes showed little trans-membrane pressure (TMP) increase at an average flux of 15 L/(m²·h) with sub-critical flux characteristics, and showed a sharp TMP increase with super-critical flux characteristics observed at an average flux of 18/22.5 L/(m²·h). After 74 d of filtration, at an average sludge concentration of 12,000 g/L, the PVDF membrane showed less variation in pore size distribution and bubble point pressure, while the PES membrane showed less change in permeability and contact angle. Both membranes met general MBR requirements due to the minimizing pristine effects of both membranes by this specific wastewater matrix. The PVDF membrane showed better anti-fouling capability, especially during high-/over-load stages, and thus was suggested for MBR retrofit, with a sustainable membrane flux below 18 L/(m²·h). Full article

The 2-segment even-split layout in various stator asymmetry layouts effectively mitigates the amplitude of the high-engine-order (HEO) forced response induced by the vane passing frequency (VPF). However, it may increase the level of the low-engine-order (LEO) forced response. A 2-segment non-even-split layout has been proposed in a previous study to reduce the amplitude of LEO aerodynamic excitation arising from the 2-segment even-split layout. This paper presents a full-annular unsteady forced response analysis of a single-stage turbine conducted using an in-house code to compare the aerodynamic excitations on the rotor blades across different 2-segment non-even-split layouts. The analysis reveals that an inappropriate circumferential angle assignment of the 2-segment non-even-split layout is ineffective in simultaneously suppressing the high amplitudes of both HEO and LEO aerodynamic excitations. Determining the optimal layout by calculating various circumferential angle assignments individually incurs significantly high computational costs. To address this issue, a fast and accurate simplified model for stator asymmetry is proposed in this study. The accuracy of the simplified model is validated by comparing its results with the suppression effects of aerodynamic excitation obtained from numerical simulations. The optimal stator asymmetry layout for a single-stage turbine is identified through this simplified model. The results indicate that the selected optimal layout can reduce VPF aerodynamic excitation of the symmetric layout by 45.14% and the 3-engine-order (3EO) aerodynamic excitation introduced by the 2-segment even-split layout by 43.56%, while the negative impact on the aerodynamic performance is significantly smaller than that of the 2-segment even-split layout. This study provides a robust theoretical foundation for enhancing the application of stator asymmetry in engineering, which demonstrates its practical engineering value. Full article

Multi-axle vehicles are commonly used for heavy-duty special operations, which easily leads to high driving torque demands when adopting distributed electric drive configurations. This study achieves the objective of reducing the driving torque of each in-wheel motor while controlling the stability of multi-axle vehicles. Taking a five-axle distributed drive test vehicle as the research object, a hierarchical control strategy integrating active all-wheel steering and direct yaw moment control is proposed. The upper layer is implemented based on model predictive control, with fuzzy control introduced to dynamically adjust control weights; the lower layer accomplishes the allocation of targets calculated by the upper layer through minimizing the objective function of tire load ratio. A linear parameter varying (LPV) tire model is introduced into the vehicle model to improve the calculation accuracy of tire lateral forces, and a neural network method is employed to solve the real-time performance issue of the model predictive control (MPC) controller. The proposed strategy is verified through a combination of simulation and real vehicle tests. High-speed condition simulations demonstrate that the AWS/DYC strategy significantly outperforms the ARS/DYC approach: compared to the active rear-wheel steering strategy, while the sideslip angle is reduced by 90.98%, the peak driving torque is reduced by 30.78%. Notably, tire slip angle analysis reveals that AWS/DYC maintains relatively uniform slip angle distribution across axles with a maximum of 4.7°, entirely within the linear working region, optimally balancing tire performance utilization with lateral stability while preserving safety margin, whereas ARS/DYC causes slip angles to exceed 11.9° at the rear axle, entering saturation. Low-speed real vehicle tests further confirm the engineering applicability of the strategy. The proposed method is of significant importance for the application of distributed drive configurations in the field of special vehicles. Full article

Ultra-high heat input welding offers high efficiency for large-scale offshore engineering, but excessive heat input can degrade low-temperature toughness. This study investigates the microstructural evolution and impact toughness of EH36 ship steel under high heat inputs (300–500 kJ/cm) using Gleeble-3500 thermal simulation, Charpy impact tests, and multi-scale characterization (OM, SEM, EBSD). Results show that impact toughness peaks at 400 kJ/cm, with surface and core energies reaching 343.33 J and 215.18 J, respectively. The optimal toughness is attributed to the formation of acicular ferrite and a high fraction of high-angle grain boundaries (up to 48.7%), which effectively inhibit crack propagation. These findings provide a practical basis for selecting heat input to balance welding efficiency and mechanical performance in marine steel fabrication. Full article

The on-machine measurement (OMM) of aero-engine blades is a critical technology for enabling closed-loop manufacturing. However, when using line laser sensors with a fixed scanning pose to measure free-form surfaces, the variation in surface geometry leads to changing incident angles, which in turn induce non-stationary noise. To address this issue, this paper proposes a multi-sensor fusion method utilizing Adaptive Heteroscedastic Gaussian Process Regression (AHGPR) based on a Spatial-Angle-Balanced Sampling (S-ABS) strategy. The AHGPR explicitly integrates the physical mapping of incident angle errors into its covariance structure, thereby automatically adjusting observation weights according to the local geometric posture. Concurrently, the S-ABS strategy captures the high-error characteristic points with large incident angles while maintaining a globally uniform spatial distribution. The experimental data indicate that this approach addresses the sampling deficiency encountered at the leading and trailing edges and in areas with large incident angles. The proposed approach reduced the impact of optical deviations on measurement accuracy and improved the precision of the process. Full article

Seismic monitoring is a crucial step in ensuring the safety and resilience of building structures. The implementation of effective monitoring systems, particularly across large-scale, complex building clusters, is currently hindered by the limitations of traditional sensor placement methods, which suffer from low efficiency, high subjectivity, and difficulties in replication. This paper proposes an innovative AI-based Automated Layout Method for seismic monitoring devices, leveraging building geometric recognition to provide a scalable, quantifiable, and reproducible engineering solution. The core methodology achieves full automation and quantification by innovatively employing a dual-channel approach (images and vectors) to parse architectural floor plans. It first converts complex geometric features—including corner coordinates, effective angles, and concavity/convexity attributes—into quantifiable deployment scoring and density functions. The method implements a multi-objective balanced control system by introducing advanced engineering metrics such as key floor assurance, central area weighting, spatial dispersion, vertical continuity, and torsional restraint. This approach ensures the final sensor configuration is scientifically rigorous and highly representative of the structure’s critical dynamic responses. Validation on both simple and complex Reinforced Concrete (RC) frame structures consistently demonstrates that the system successfully achieves a rational sensor allocation under budget constraints. The placement strategy is physically informed, concentrating sensors at critical floors (base, top, and mid-level) and strategically utilizing external corner points to maximize the capture of torsional and shear responses. Compared with traditional methods, the proposed approach has distinct advantages in automation, quantification, and adaptability to complex geometries. It generates a reproducible installation manifest (including coordinates, sensor types, and angle classification) that directly meets engineering implementation needs. This work provides a new, efficient technical pathway for establishing a systematic and sustainable seismic risk monitoring platform. Full article

Tetra-missing rib honeycombs (TMRHs), characterized by monoclinic geometry, exhibit high elastic stiffness but suffer from poor deformation stability and reduced axial load-bearing capacity, which limit their applicability in energy-absorbing and load-sensitive engineering structures. To address these inherent drawbacks, this study proposes two symmetry-enhanced tetra-missing rib honeycomb configurations through overall axisymmetric design and subunit-level symmetric optimization. A finite element model was established in Abaqus/Explicit and validated against quasi-static compression experiments, demonstrating good agreement in deformation modes and mechanical responses. Systematic numerical investigations were then conducted to compare the mechanical properties and deformation behaviors of three honeycomb layouts, including the conventional TMRH and the proposed symmetric designs. Furthermore, the effects of impact velocity on mechanical performance were examined to evaluate the dynamic response characteristics of the structures. Finally, the influence of subunit angle parameters on the stiffness, energy absorption, and deformation stability of the tetra-missing rib honeycombs was comprehensively analyzed. The results provide insight into the role of symmetry and geometric parameters in improving the mechanical performance of TMRH-based structures and offer guidance for the design of high-performance auxetic honeycombs. Full article

Pintle injectors offer variable thrust capability and combustion stability advantages for liquid rocket engines. This study presents experimental and numerical investigation of spray characteristics for a liquid–liquid pintle injector using water as simulant. Ten cold flow tests covering total momentum ratio (TMR) from 0.36 to 2.76 captured spray angle variations from 26° to 80°. Computational fluid dynamics (CFD) simulations using Ansys Fluent 2025 R1 with the Volume of Fluid method and dispersed interface modeling showed good agreement with experimental spray angles for TMR > 0.74 (error < 8%), but demonstrated increasing discrepancy at lower TMR values (up to 62% error at TMR = 0.36). This deviation indicates limitations of steady-state RANS models in capturing unsteady, fuel-dominated flow regimes. The experimental dataset provides validation benchmarks for CFD modeling and contributes to injector design optimization for sounding rocket applications. Full article