Search Results (498)

Journal bearings are prone to failure due to lubrication state degradation under extreme operating conditions. To address the unclear transition mechanism and undefined state boundaries under insufficient lubrication, a coupled tribological model of engine journal bearings was established. Through parameter analysis and dynamic failure mechanism study, the effects of radial clearance, temperature, rotational speed, load, and surface roughness on the lubrication state transition were revealed. The results indicate that radial clearance, oil temperature, rotational speed, applied load and surface roughness are all decisive factors for lubrication transition, and every parameter has its unique critical threshold; once exceeding the limit, the oil film integrity is damaged and the lubrication rapidly shifts from mixed lubrication toward boundary lubrication. After crossing critical thresholds, aggravated asperity contact further triggers continuous temperature rise and viscosity reduction, which may induce closed-loop thermal deterioration and eventually accelerate bearing failure. The research findings provide a theoretical basis for robust design and operational safety monitoring of journal bearings. Full article

Synthetic Aperture Radar (SAR) is widely used for marine oil spill surveillance due to its all-weather capabilities and sensitivity to sea surface roughness. However, oil slicks often appear as dark formations that can be confounded with visually similar “look-alikes”, making automated detection and boundary delineation challenging. This study proposes a two-stage deep learning framework for oil spill mapping in Sentinel-1 SAR imagery. First, a ConvNeXt-T classifier screens image patches for likely slick presence, reducing the search space for dense prediction. Second, spill boundaries are extracted with a domain-adapted Segment Anything Model (SAM) configured for prompt-free, single-shot segmentation. The input representation is enhanced by combining preprocessed Sentinel-1 VV backscatter with Gray-Level Co-occurrence Matrix (GLCM) texture measures (homogeneity and variance) to better separate oil from heterogeneous background sea at the segmentation level. Quantitative evaluation against established segmentation baselines demonstrates that our adapted SAM achieves the highest overall accuracy, reaching an F1-score of 0.86. This outperforms traditional models such as UNet and CBDNet (0.83), as well as DeepLabV3, SegNeXt, and OFCNet (all at 0.82). Furthermore, an analysis of the wind speed on the test set shows that wind speed affects detectability but does not by itself determine segmentation quality. The results indicate that combining transformer-based screening with efficient foundation-model adaptation can provide accurate and scalable oil spill mapping for operational SAR monitoring. Full article

The beneficial utilization of small and medium floods requires a clear flood-control safety boundary before floodwater can be moderately stored and regulated as a water resource. For the Three Gorges Reservoir, a large river-type reservoir with long-distance backwater effects and tributary blocking, this boundary cannot be determined solely from the dam-front water level. This study developed a one-dimensional unsteady hydrodynamic model with dynamic roughness calibration to investigate the risk-constrained flood-season operating water level of the Three Gorges Reservoir. Typical flood events and the 20-year return period design flood were used to examine the responses of the maximum dam-front flood-regulation water level, excess flood volume, longitudinal water levels, and exceedance risk at key reservoir-area sections under different initial regulation water levels and release-discharge conditions. The results show that the Changshou reach is the main control section for high-water-level inundation risk under the study scenarios. When the initial regulation water level is at or below 155 m, the dam-front flood-regulation water level, the peak water level at Changshou, and the exceedance duration generally vary only slightly. When the initial regulation water level exceeds 155 m, these risk indicators increase markedly, indicating a reduced flood-control safety margin. Perturbation analysis further shows that the dam-front flood-regulation indicators are relatively insensitive to small roughness and dam-front boundary perturbations, whereas the Changshou water level and exceedance duration are more sensitive to roughness and flood-volume perturbations. Therefore, 155 m should be interpreted as a conservative operational reference boundary under the current design-flood framework, existing operation rules, and the assumption of no forecast-based pre-release, rather than as an absolute safety threshold. Increasing release discharge can reduce high-water-level risk in the reservoir area under preset release limits, but its practical application must remain conditional on downstream flood-control constraints and real-time flood-conveyance capacity. The results provide a hydrodynamic basis for risk-constrained flood-season operation of large river-type reservoirs. Full article

This paper focuses on the characterization of the micro-leakage channel geometry in the flange-gasket rough contact interface of hazardous chemicals transport vehicles. This work represents the first step in a multi-physics simulation framework for optical-fiber-based micro-leakage monitoring. Directly establishing a full-scale contact model from micron-scale rough peaks and valleys to the decimeter-scale flange structure would lead to extremely high computational costs; a nonlinear contact model based on quasi-representative volume element (quasi-RVE) and quasi-periodic boundary condition (quasi-PBC) is proposed in this paper. Quasi-RVE refers to a local region selected from the overall rough surface. Unlike a traditional RVE that requires strict geometric periodicity, the quasi-RVE is only approximately consistent with the overall surface with respect to key morphological parameters and volume parameters. Quasi-PBC only imposes in-plane displacement compatibility constraint on the relative side boundary without imposing periodic constraints in the peak-valley height direction. In this paper, the average interface gap and its distribution are selected as the geometric descriptors of the micro-leakage channel, and the reliability of the contact model is verified by comparing with the existing experimental and numerical results. On this basis, the influences of surface roughness, gasket material and loading conditions on the geometric characteristics of the micro-leakage channel are further analyzed. The results show that the lower stiffness gasket is easier to fit with the rough flange surface under the same load conditions, so as to obtain a larger contact area and a smaller average gap. The quasi-RVE contact model established in this paper can effectively reduce the computational scale of contact analysis of the rough sealing interface, and provide reliable channel geometric information for subsequent micro-leakage fluid simulation and optical fiber signal response simulation. Full article

Landslide detection has largely relied on supervised learning or DEM-based representations, which can limit rapid deployment and generalization across heterogeneous terrain. In this work, we present a zero-shot, fully unsupervised framework that identifies landslide-like geomorphic instability candidates from raw UAV-mounted LiDAR, removing the need for labeled data, pre-event baselines, or rasterized terrain abstractions. Our approach is motivated by the observation that landslides manifest as localized geometric inconsistencies in the terrain surface. We capture this through a multi-scale formulation that combines point-level and cluster-level indicators of instability. At the point level, a PCA-based residual depth metric reduces slope-induced bias and highlights surface discontinuities, while local concavity captures terrain depletion patterns. At the cluster level, geomorphometric descriptors such as curvature concentration, surface roughness, elevation discontinuity, and slope variation are extracted using density-aware 3D clustering and integrated through adaptive feature fusion. The resulting probabilistic instability field enables spatially coherent delineation of landslide scars, including rupture boundaries, displaced material, and emerging failure regions. In addition, the detected patches provide useful priors for post-event susceptibility analysis without requiring temporal observations. Experiments across diverse geomorphic settings show that the proposed method improves detection of subtle terrain disturbances compared to DEM-based pipelines and supervised learning approaches, while remaining robust to noise and terrain variability. Overall, this work demonstrates that geometry-driven, unsupervised inference on raw 3D data can serve as a practical and scalable alternative for near real-time landslide detection using UAV-based systems. Full article

Laser cladding has attracted considerable attention for titanium alloy surface modification owing to its high energy density, rapid cooling rate, and excellent metallurgical bonding capability. To investigate the effect of Nb content on the microstructure and properties of NiTi-based coatings, composite coatings containing 10–40 wt% Nb were fabricated on a titanium alloy substrate via laser cladding. The effects of Nb content on phase constitution, microstructure evolution, mechanical properties, tribological performance, residual stress, and surface topography were systematically characterized using XRD, SEM, EDS, microhardness testing, wear testing, digital image correlation, and atomic force microscopy. The results show that increasing Nb content significantly affected the solidification behavior and phase evolution of the coatings. With increasing Nb addition, the dominant phase gradually evolved from NiTi to a coexistence structure of NbTi₄ and NiTi, while Ti dilution and elemental segregation became increasingly pronounced. The crystallite size increased from 19.63 nm to 25.91 nm, accompanied by intensified dendritic segregation and surface roughening. Among all samples, the coating containing 10 wt% Nb exhibited the best overall performance, characterized by the finest microstructure, the lowest surface roughness, the lowest residual stress, and the best wear resistance. The superior performance of the low-Nb coating was mainly associated with its finer and more homogeneous microstructure, reduced elemental segregation, lower stress concentration, and enhanced grain-boundary strengthening effect. Excessive Nb addition intensified Ti dilution, grain coarsening, residual stress accumulation, and microstructural heterogeneity, thereby degrading the overall coating performance. More importantly, this study reveals that Nb-regulated Ti dilution behavior governs the synergistic evolution of elemental segregation, surface roughening, residual stress accumulation, and tribological degradation during laser cladding. This work provides new insight into the process–structure–property relationship of NiTi-based composite coatings and offers theoretical guidance for the composition optimization and engineering application of high-performance laser-clad coatings on titanium alloys. Full article

This paper presents a reproducible simulation benchmark for rough surface interferometric profilometry. The benchmark compares three complete reconstruction pipelines under matched detected count assumptions: classical four-step phase-shifting interferometry (PSI), direct coincidence proxy reconstruction, and hybrid coarse-to-fine reconstruction in which a coincidence-derived observable supplies the coarse fringe-order prior. Fifty-nine focus variation (FV) topographies exported as Mountains/DigitalSurf .sur files (Digital Surf, Besancon, France) provide a shared FV prior for simulated optical observations. The coincidence channel is a simulation proxy rather than a validated quantum hardware implementation. The main result is architectural role separation. On the measured surface benchmark, the hybrid branch gives the lowest median detrended height RMSE (314.0 nm) and wins on 32 of 59 surfaces. The same ordering is retained in a rate-based coincidence control, with median hybrid RMSE of 290.9 nm under ideal matched-count rates and 376.3 nm under detector non-idealities. Roughness endpoints define the boundary of this result: hybrid gives the lowest matched bandwidth $S_a$ and $S_q$ errors, whereas direct coincidence proxy reconstruction is selectively strongest for $S_z$ and remains process-dependent. Classical two-colour and classical frontier controls show that following the broad long-wavelength envelope is not sufficient evidence for overall architecture-level superiority within this simulation benchmark. The benchmark identifies coincidence-derived information as most useful when used as a coarse prior inside a hybrid estimator, while final fine texture remains anchored by short-wavelength PSI. Full article

Thermal spray technologies are widely used in aerospace, gas turbine, and automotive industries, where nickel-based superalloys are valued for their mechanical strength and resistance to oxidation and corrosion at elevated temperatures. This study investigates the microstructure and tribological performance of Ni–5Al/Inconel 625 composite coatings deposited on AISI 1025 steel using wire arc spraying, aiming to provide a cost-effective alternative to bulk superalloys and more advanced thermal spray techniques. Microstructural characterization was performed using optical microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy, while surface roughness, microhardness, and dry sliding wear behavior were evaluated using ball-on-disk tests against Al₂O₃ counter-bodies. Confocal microscopy and three-dimensional triboscopic imaging were employed to analyze wear-track morphology and friction behavior. X-ray diffraction (XRD) analysis confirmed the presence of a predominantly intermetallic Ni₃Al (γ′) phase with secondary NiAl in the bond coat, indicating significant interdiffusion between the NiAl bond coat and the Inconel 625 top coat. The top coat exhibited a face-centered cubic (FCC) γ Ni-based solid solution. The coatings exhibited a typical lamellar structure with low porosity (2%–3%) and oxide content of 12%–15%, primarily chromium and niobium oxides located at splat boundaries. Abrasion, combined with interlamellar decohesion, was identified as the dominant wear mechanism. Post-deposition polishing reduced surface roughness from 11.9 µm to 2.12 µm, leading to a 2.5-fold reduction in wear volume and a significant decrease in debris pile-up. The corresponding specific wear rates were approximately 9.3 × 10⁻⁵ mm³/Nm and 3 × 10⁻⁵ mm³/Nm for the as-prepared and polished conditions, respectively, which are within the range reported in the literature for similar coatings. These findings demonstrate that wire arc-sprayed Ni–5Al/Inconel 625 coatings, particularly after polishing, offer improved wear resistance while maintaining cost-effectiveness, making them a promising alternative for tribological applications. Full article

To enhance immersion in virtual reality (VR) environments and improve the fidelity of virtual tactile interaction, this study proposes a perceptually grounded haptic-rendering framework for fine surface-texture simulation. The framework is centred on a Perceptual Haptic Spectrum Model (PHSM), which maps virtual surface attributes, including hardness, elasticity, roughness, friction, and microtexture periodicity, to multi-band tactile targets in perceptual frequency space. A Just Noticeable Difference (JND)-inspired parameterisation strategy is used as a design guideline to avoid imperceptible or redundant actuation, while region-specific response functions adapt the output to the fingertip centre, finger pad, and lateral edge. To improve reproducibility, the revised manuscript now specifies the flexible thin-film force/strain-sensor cell, array quantity, 320 Hz per-cell acquisition setting, signal-conditioning pipeline, contact-state classification rules, delay budget, and dual-actuation scheduling logic. The sensing design is based on a commercial flexible piezoresistive force-sensor cell with microsecond-level response time and a 12-bit ADC acquisition chain that provides a sufficient aggregate sampling margin for a 7–21 cell array. Manufacturer-supported sensor performance and prototype-level acceptance criteria are reported for response time, linearity, repeatability, hysteresis, drift, SNR, contact-state detection, latency, and durability. The system remains a proof-of-concept platform rather than a completed large-scale psychophysical validation. Within these boundaries, the results show coherent integration of perceptual modelling, multi-rate sensing, state monitoring, predictive feedforward control, and coordinated haptic actuation for fine VR texture rendering. Full article

Triboelectric nanogenerators (TENGs) offer a promising route for self-powered microscale energy harvesting, yet their design optimisation remains empirically challenging due to the complex interplay of material surface physics, device geometry and operating mode. In this work, we present an integrated framework that combines atomic force microscopy (AFM) characterisation, COMSOL Multiphysics 6.0 finite element simulation and physics-informed conditional variational autoencoder (cVAE) to predict and optimise TENG output performance. Four polymer dielectric materials, HDPE, LDPE, TPU, and PMMA, were characterised via Kelvin Probe Force microscopy (KPFM) for work function, surface potential and surface roughness. Surface charge density was calculated from measured KPFM potential using the parallel plate capacitor model and used as a boundary condition in COMSOL Multiphysics simulations for contact-separation and lateral sliding TENG mode for dielectric film thicknesses of 50 µm and 100 µm. The simulated open circuit voltage (Voc) and short circuit charge (Qsc) across gap distances up to 150 mm formed the training dataset for a cVAE model with eight physicochemical condition features. The trained model demonstrated strong reconstruction accuracy (R² ≥ 0.94) and enables generative prediction across unseen design spaces. Results reveal that the LDPE/TPU pair at 50 µm thickness consistently achieves the highest electric outputs in both modes, and the sliding mode yields 25–30% higher voltages than the contact separation mode across all material pairs. This study provides a transferable data-efficient methodology for accelerating TENG material and geometry optimisation. Full article

Ti-6Al-4V internal flow channels are difficult to finish because direct liquid metal-driven abrasive slurry polishing is constrained by both the limited driving capability of the liquid metal and the continuous formation of a dense surface reaction layer in alkaline electrolytes. In this study, the direct polishing behavior of Ti-6Al-4V flow channels was first investigated, and a distinct polishing capability boundary was identified through the evolution of surface morphology and areal roughness $S_a$. Although $S_a$ decreased with polishing time, the reduction rate gradually diminished and eventually approached a plateau, indicating pronounced attenuation in effective material removal. Mechanistic analysis revealed that this limitation was governed by the dynamic formation and disruption of the oxide/reaction layer, which progressively shields the metallic substrate from direct abrasive action. To address this issue, an electric-field-assisted chemical pretreatment was introduced prior to the liquid metal-driven abrasive polishing stage, forming a two-step route (AB + P). Comparative experiments among direct polishing (P), chemical pretreatment followed by polishing (B + P), and electric-field-assisted chemical pretreatment followed by polishing (AB + P) showed that AB + P produced more homogeneous surface morphologies, lower $S_a$, and a significantly weaker plateauing tendency than direct polishing. In addition, the attainable limiting surface quality was shifted to a lower level, indicating an extension of the polishing capability boundary. The influence of pretreatment electrification time further revealed the existence of an effective time window, beyond which the polishing improvement gradually saturated. These results demonstrate that electric-field-assisted chemical pretreatment is an effective strategy for mitigating polishing attenuation and enhancing the finishing capability of liquid metal-driven abrasive slurry polishing for Ti-6Al-4V internal flow channels. Full article

In this study, nanolubricants based on SAE 5W-30 mineral oil were formulated using oleic acid-functionalized graphene nanoplatelets (GNPs), and their colloidal stability, rheological behaviour, thermal stability, and tribological performance under cold rolling conditions were systematically investigated. The nanolubricants were prepared at GNP concentrations of 0.05, 0.1, 0.2, 0.4, and 0.6 wt%. FT-IR analysis confirmed successful functionalization, evidenced by the characteristic C=O band at approximately 1710 cm⁻¹ and changes in CH₂ stretching vibrations in the 2850–3000 cm⁻¹ range. UV–VIS results indicated initially homogeneous dispersions; however, after three days, relative concentrations decreased to 95%, 90%, and 75% for 0.05, 0.2, and 0.6 wt% GNPs, respectively. Viscosity measurements showed minimal variation at low concentrations, with only a 0.64% increase at 0.2 wt% compared to the base oil. TGA revealed enhanced oxidative stability at low GNP contents, with the oxidation onset temperature increasing from 205.3 °C to 207.2 °C at 0.05 wt%, while a marked decline was observed at higher concentrations (176.8 °C at 0.6 wt%). In cold rolling experiments at a 3% reduction ratio, the rolling force was measured at 1341 N/mm with the neat lubricant, decreasing to 1210 N/mm with a lubricant containing 0.1 wt% GNPs, corresponding to an approximate 10% reduction. Compared with dry conditions, this reduction was approximately 21%. Surface roughness and 3D topography analyses further showed that GNPs-containing lubricants reduced asperities and promoted the formation of a more uniform tribofilm. At low concentrations, the improved lubrication performance of oleic acid-functionalized graphene nanoplatelets is attributed to their homogeneous dispersion in mineral oil, where physically adsorbed oleic acid improves colloidal stability by reducing agglomeration and promotes the formation of a stable tribofilm, facilitating interlayer sliding under boundary lubrication conditions. Overall, the findings demonstrate that oleic acid-functionalized GNPs, when used at optimal concentrations, significantly enhance both lubricant stability and cold rolling performance. Full article

Molecular dynamics (MD) simulations were performed to investigate the dynamic deposition behavior, growth mechanism, and mechanical properties of nickel–tungsten (Ni-W) alloy films on single-crystal Al(001) substrates. The results demonstrate that the incorporation of W atoms lowers the Ehrlich–Schwoebel (ES) barrier for Ni adatoms, facilitating downhill diffusion and effectively suppressing Volmer–Weber (VW) mode, thereby improving surface morphology and reducing film roughness. Additionally, W atoms exhibit a tendency to segregate at grain boundaries, inducing lattice distortion and structural disorder. With increasing W content (≥15 at%), the films undergo a transition from a nanocrystalline to an amorphous structure. Nanoindentation simulations reveal that film hardness increases with W content, with the strengthening mechanism being composition-dependent: dislocation pinning dominates at low W concentrations (≤5 at%), while the formation of an amorphous structure emerges as the primary strengthening mechanism at higher W contents (≥15 at%). This work elucidates the growth regulation and strengthening mechanisms of Ni-W films from an atomic-scale perspective, providing a theoretical foundation and simulation-driven guidance for the design and optimization of high-performance, environmentally benign Ni-W coatings. Full article

The automated production of large-scale labeled datasets from concrete X-ray computed tomography (CT) images is a fundamental prerequisite for training and validating deep learning-based segmentation models. However, existing methods either require extensive manual annotation or rely on domain-specific deep learning models that themselves demand labeled data—a circular dependency. This paper presents a parameter-driven three-class segmentation framework that automatically classifies each pixel in a concrete CT slice into one of three material phases: void (air pores and cracks), coarse aggregate, and mortar matrix, generating annotation masks suitable for large-scale dataset production without manual labeling. The proposed method combines: (1) fixed-threshold void detection calibrated to concrete CT grayscale characteristics; (2) adaptive percentile-based initial segmentation responsive to image-specific statistics; (3) multi-criteria connected component scoring based on area, shape descriptors (circularity, solidity, compactness, extent, aspect ratio), intensity distribution, and boundary gradient; (4) material science-informed size constraints aligned with concrete phase volume fractions; and (5) a material continuity enforcement module that applies topological hole-filling and conditional convex-hull consolidation to eliminate internal contamination within accepted aggregate regions, reducing boundary roughness by 7.6% and recovering misclassified boundary pixels. All parameters are centralized in a configuration file, enabling reproducible batch processing of 224 × 224 pixel CT slices at 0.07–1.12 s per image. Evaluated on 1007 224 × 224 concrete CT patches cropped from 200 representative scan frames, the framework produces three-class segmentation masks with physically consistent void fractions (mean 3.2%), aggregate fractions (mean 32.4%), and mortar fractions (mean 64.4%), all within ranges reported in the concrete CT literature (used as a dataset-scale QC screen, not a validation metric). Primary outputs and the archived image–mask pairs for this work are provided as an 8-bit patch archive. For pixel-wise validation, we report IoU, Dice, and pixel accuracy on an independently labeled subset that can be unambiguously paired with the released predictions: averaged over 57 matched patches, mean pixel accuracy is 88.6%, macro-mean IoU is 74.7%, and macro-mean Dice is 84.9%. The framework provides a fully automated annotation pipeline for dataset production, eliminating manual labeling costs for concrete CT image collections. The generated datasets are suitable for training semantic segmentation networks such as U-Net and its variants. 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