Search Results (13,856)

High-speed railways (HSRs) generate repeatable and spatially extended seismic wavefields, providing useful signals for distributed acoustic sensing (DAS)-based vibration analysis. This study develops an integrated measurement framework for two characteristic frequency eigenquantities in HSR-induced seismic wavefields: train frequency and bridge frequency. Building on established spectral-line, cepstral, and Doppler descriptions of HSR seismic wavefields, we systematize the relevant theoretical expressions, compare frequency-domain correlation and cepstral strategies for train-frequency estimation, and derive a velocity-independent bridge-frequency estimator from paired Doppler-shifted components. DAS data collected along viaduct sections of the Beijing–Guangzhou HSR are used to evaluate the framework across single trains, dense observation traces, and multiple train events. The results show that bridge frequency is more stable than train frequency, with lower measurement variance. The frequency-derived train speeds and carriage lengths fall within typical operating ranges of Chinese HSR trains, and the observed spatial periodicity in frequency measurements is consistent with bridge pier spacing. These findings support accurate frequency measurement and preliminary estimation of train speed, carriage length, and wave velocity from DAS records. Together, they clarify measurable frequency parameters of HSR seismic sources and establish a quantitative source-characterization basis for DAS-based railway vibration analysis and future multi-source monitoring studies. Full article

High-speed electric machines enhance power density and eliminate the need for a gearbox in waste heat recovery microturbine systems. However, existing designs often suffer from high manufacturing costs and complex cooling requirements. This study presents the development, experimental validation, and comparative analysis of three high-speed machine designs. First, a lower-speed induction machine prototype, constructed using standardized components, was tested at an operating speed of 13,000 rpm. This prototype enabled experimental validation of the numerical model used for loss calculations. Experimental results showed total losses of 7.89 kW, closely matching the simulated value of 7.75 kW at an output power of 93.1 kW, i.e., an efficiency of 92.19%. Building on these findings, two smaller machine prototypes were developed: one featuring an induction squirrel-cage rotor and the other employing a surface-mounted permanent magnet rotor topology. Both machines were designed and evaluated using finite element analysis and conjugate heat transfer simulations. Their performance was analyzed under both sinusoidal and pulse-width-modulated voltage supply conditions. At an operating speed of 14,000 rpm, the permanent magnet machine outperformed the induction machine, achieving 63.2 kW of mechanical power and an efficiency of 96.21%, while operating at lower temperatures. In comparison, the induction machine delivered 52.4 kW of mechanical power with an efficiency of 94.64%. The primary novelty and contribution of this work lie in the implementation of a two-pole machine architecture capable of achieving an output power of 100 kW at operating speeds between 20,000 and 25,000 rpm. Compared with similar solutions reported in the literature, the proposed machines feature a simplified bearing arrangement and a more straightforward liquid-cooling system. These characteristics have the potential to reduce manufacturing costs and simplify maintenance during operation. Full article

Periodic abnormal vibration in large pressurized water reactor (PWR) nuclear steam turbine generator units is difficult to diagnose because the parameter-level link among three-pad tilting-pad journal bearings, oil-film thermal asymmetry, and possible Morton-effect susceptibility remains insufficiently quantified. This study investigates an ARABELLE-type unit and establishes a finite-difference bearing rotor model by solving the oil-film thickness equation, Reynolds pressure equation, two-dimensional energy equation, pad moment balance, and linearized stiffness and damping coefficients. The bearing load, radial clearance, rotational speed, and inlet oil temperature are examined as controllable variables, while only the calculated pad temperature rises are directly validated against field measurements from eight support bearings. The calculated temperature rise deviations range from −7% to 8%, with a root-mean-square deviation of approximately 0.32 °C and a mean absolute percentage error of approximately 4.2%. The model results suggest that the load and speed mainly intensify the total oil-film heating, whereas the radial clearance and inlet oil temperature more directly govern the circumferential temperature difference, used here as an indirect indicator of the Morton effect risk. For the investigated Bearing 3, a radial clearance near 0.52 mm and an inlet oil temperature of 48–52 °C are suggested as bearing-specific operating windows for reducing thermal imbalance while maintaining engineering stability. The main contribution is a traceable engineering chain from field abnormal vibration to three-pad bearing thermal asymmetry, cautious Morton-effect risk interpretation, and operational adjustment for large nuclear rotating machinery. Full article

With advances in computer vision and modern surveying technologies, intelligent inspection systems and automatic recognition methods are increasingly used in highway tunnel maintenance. However, existing mobile inspection methods still struggle to balance high-speed operation, fine-crack recognition, and comprehensive assessment of multiple defects. This study proposes an automatic recognition and quantitative assessment method for multiple visible defects in highway tunnels based on a vehicle-mounted multisensor inspection system. The system integrates high-resolution imaging, infrared illumination, 3D laser scanning, mileage positioning, and high-speed data storage, enabling continuous full-section data acquisition at speeds up to 80 km/h. A structural-feature-constrained mileage correction strategy is developed to reduce accumulated localization errors. For crack analysis, a multilevel framework combining two-stage CNN screening, cascaded segmentation, crack trajectory tracking, and subpixel edge extraction is established for crack recognition and 0.1 mm-level width measurement. Water leakage and spalling are extracted through visible–infrared image fusion and adaptive boundary refinement, while cross-sectional deformation is calculated using 3D tunnel axis reconstruction, point-cloud filtering, and cross-section fitting. Field tests and controlled experiments demonstrate that the system can rapidly identify, locate, and quantify multiple tunnel defects, providing a practical reference for intelligent tunnel inspection and maintenance. Full article

Electric ducted fans (EDFs) have emerged as promising propulsion systems due to their compact design, high thrust density, and enhanced operational safety. Accurate prediction of aerodynamic thrust is essential for EDF design and performance evaluation; however, existing numerical studies have not yet provided a systematic comparison of the thrust-prediction capability of different k-ω-based turbulence models in micro-EDF applications. In this study, a dedicated thrust-measurement platform was developed for a 120 mm EDF, and experimental thrust data were obtained under three representative hover operating conditions. Based on these measurements, six turbulence models, including SST, SKω, BSL, GEKO, EARSM, and SST-γ(alg.), were evaluated using three-dimensional CFD simulations. The numerical model was assessed through thrust validation, centerline velocity comparison, power-consistency analysis, grid independence verification, and qualitative flow-field interpretation. A two-factor full-factorial analysis was further conducted to quantify the effects of rotational speed and turbulence model on prediction accuracy and computational cost. The results show that the turbulence model has a stronger influence on the normalized thrust-prediction error than the rotational speed factor over the investigated operating range. The SST-γ(alg.) model achieves the highest thrust-prediction accuracy, with an average relative deviation of 0.47%, but requires the highest computational cost. In comparison, the SST model provides a favorable balance between accuracy and efficiency, with an average relative deviation of 1.79% and an average computation time of 184.33 min, approximately 33% lower than that of the SST-γ(alg.) model. The centerline velocity and power-consistency results further support the comparative model assessment. Overall, this study provides an experimentally validated comparative reference for turbulence model selection in simulations of similar 120 mm EDF under hover conditions. Considering both prediction accuracy and computational efficiency, the SST model can serve as a practical turbulence model choice for engineering parameter optimization of similar micro-EDF configurations.$k-\omega$ Full article

The use of five machine-learning regression models, Gaussian Process Regression (GPR), Support Vector Machine (SVM), XGBoost, CatBoost, and LightGBM, was for predicting the ultimate tensile strength (UTS) of friction stir welded (FSW) AZ31B magnesium alloy joints. A controlled, single-source, experimental dataset comprising 99 observations was created on the same FSW machine under the same laboratory conditions. The dataset covered three feed rates, eleven rotational speeds and three tool tilt angles, and each parameter combination was represented by the mean UTS value from triplicate tensile tests. The input variables were the feed rate, rotational speed and tilt angle, and the prediction target was UTS measured using ASTM E8M-04. To create a more challenging and realistic assessment, we implemented blocked-holdout validation, keeping only the previously unseen rotational speed levels for the test set. Hyperparameters were selected via exhaustive grid search, with 5-fold GroupKFold cross-validation used solely on the training data. Among the models that were tested, GPR demonstrated the best overall blocked-holdout performance, with a R² = 0.985 and RMSE = 1.798 MPa. XGBoost (R² = 0.923) and CatBoost (R² = 0.912) also demonstrated competitive performance. Conversely, LightGBM exhibited the poorest generalization performance (R² = 0.817). The findings suggest that kernel and boosting-based approaches have the capacity to adequately simulate the nonlinear relationship between FSW process parameters and tensile performance, while GPR demonstrated the best generalization under the blocked-holdout evaluation strategy. Full article

Winter underuse of urban parks in severe-cold cities limits year-round outdoor activity, especially for cold-sensitive users. This study developed a comfort–risk–adaptation framework integrating thermal perception, model-estimated cold-stress risk, and behavioral responses. Field microclimate measurements and synchronous questionnaires were conducted in Nanhu Park, Changchun, China, under clear winter conditions, yielding 386 paired human–environment samples. The Universal Thermal Climate Index (UTCI), Required Clothing Insulation (IREQ), wind chill temperature (WCT), and contact cooling indicators were used to quantify thermal exposure and cold-stress risk. Results showed significant spatial differences in wind speed, solar radiation, mean radiant temperature, and UTCI, while air temperature and humidity varied little. The neutral UTCI was 3.14 °C (unweighted) and 3.70 °C (weighted), and the 80% thermal acceptability threshold was −15.24 °C (95% CI: −16.14 to −14.22 °C). Despite acceptable thermal perception, physiological cold-stress risks remained under certain conditions. The findings highlight the need to integrate solar access, wind mitigation, low-conductivity materials, and moderate activity routes to improve winter usability in severe-cold urban parks. Results are condition-specific and reflect observed users under clear to partly cloudy winter daytime conditions rather than universal thresholds. Full article

This study examines the influence of powder characteristics and laser powder bed fusion (LPBF) processing parameters on the microstructure and mechanical performance of ASTM F139 stainless steel fabricated from powders supplied by two manufacturers. Feedstock powders were characterized with respect to chemical composition, particle size distribution, morphology, density, and flowability. Cubic and tensile specimens were produced using different combinations of laser power, scan speed, hatch spacing, and scanning strategy. The fabricated components were evaluated by density and porosity measurements, surface roughness analysis, optical and electron microscopy, hardness testing, and tensile characterization in both horizontal and vertical build orientations. Powder flowability and packing density were found to strongly influence consolidation behaviour, with improved flow characteristics promoting higher densification and reduced porosity. Scanning strategy also affected defect formation, and a 67° interlayer rotation produced lower porosity than the conventional 0°/90° pattern. An optimal processing window was identified at a laser power of 212 W, scan speed of 1600 mm s⁻¹, hatch spacing of 0.07 mm, and layer thickness of 30 μm, yielding components with ~1% porosity, surface roughness below 15 μm, and a density of 7.65 g cm⁻³ (>95% of the theoretical density). Under these conditions, horizontally built specimens exhibited an ultimate tensile strength of 612 ± 43 MPa and a yield strength of 544 ± 37 MPa, exceeding the corresponding values obtained for vertically built specimens. Microstructural characterization revealed a refined cellular austenitic structure associated with epitaxial grain growth during solidification, while fractographic analysis indicated predominantly ductile failure through microvoid coalescence. The results establish clear process–structure–property relationships in LPBF-fabricated ASTM F139 stainless steel and demonstrate that the combined optimization of powder quality, scan strategy, and energy input enables the production of near-full-density components. Full article

A computational comparison between analytical and discrete image-based formulations for contour hologram synthesis is presented. The analytical approach relies on contour parameterization and Fourier-descriptor reconstruction to compute the phase accumulated along a closed contour, while the proposed discrete image-based approach performs contour extraction and phase accumulation directly on sampled spatial grids without explicit parametric modeling. Both methods were implemented under identical computational conditions and evaluated through numerical propagation in the Fraunhofer regime using representative contour geometries of increasing spatial complexity. Reconstruction quality was assessed using the Structural Similarity Index (SSIM), phase topology analysis, and hologram generation time measurements. Results show that both approaches successfully reconstruct the target contours while preserving the prescribed topological charge and phase winding behavior. High structural fidelity was obtained in all cases, with SSIM values above 0.93. The discrete image-based approach consistently produced slightly higher similarity values and achieved substantial reductions in computational cost, with speed-up factors ranging from approximately 120× to more than 1600×, depending on contour complexity. These results demonstrate that direct operation on sampled contour representations provides an efficient alternative to analytical contour parameterization while maintaining reconstruction quality and topological consistency. Full article

Background: Walking is a dynamic activity that relies on inputs from the multisensory system, i.e., somatosensory, vision, and vestibular. These inputs are processed and integrated in the central nervous system to produce motor impulses for efficient walking balance. The Sensory Organization Test (SOT) is established as the gold standard for assessing sensory contributions to standing balance. However, no comparable assessments have been developed for the clinical evaluation of balance during gait. This study evaluated the Gait Sensory Interaction Test (GaitSIT), a novel virtual reality (VR)-based assessment for characterizing sensory-condition-specific changes in walking balance. Methods: The GaitSIT comprises a VR environment with a physical compliant foam walking surface that evaluates gait–balance by systematically manipulating and evaluating the sensory systems. Twenty-nine healthy young adults (mean age 24.9 ± 6.4 years) were instructed to complete 6 m walking trials under six standardized conditions (C): eyes open, eyes closed/dark scene, and rotating visual scenes on a firm surface, then repeated on a foam surface. Wearing an Oculus VR headset, participants were instructed to walk in a straight line at their preferred speed, as naturally as possible, in two test sessions on the same day, followed by a third test session 24 h later. Headset-derived sway measures, including position, velocity, and acceleration data, were recorded, and the continuous trajectory deviation angle (i.e., directional control) and sensory ratios were calculated. Linear mixed-effects models included trial-level walking speed as a covariate. Additionally, participants completed the modified Clinical Test of Sensory Interaction on Balance (mCTSIB) as a clinical standing-balance reference measure; its concurrent-validity findings will be reported separately. Results: Significant condition effects were observed for position, velocity, acceleration, and CTDA after adjustment for trial-level walking speed (all $p<0.001$), indicating that the six sensory conditions elicited distinct gait–balance responses. Significant differences relative to the baseline condition (C1) were observed across conditions C2–C6 for position, C3–C6 for velocity, and C2 and C5 for acceleration. Session effects were not significant for any primary kinematic outcome after speed adjustment. A significant condition × session interaction was observed for position ($p<0.001$), whereas velocity, acceleration, and CTDA demonstrated no significant interactions. Walking speed was significantly associated with position, acceleration, and CTDA, but not velocity. Sensory-ratio analyses revealed larger visual and vestibular ratios relative to somatosensory ratios, with the visual and vestibular ratios generally decreasing across sessions. Conclusions: GaitSIT successfully manipulated sensory conditions during overground walking and produced significant changes in gait-related sway, directional control, and sensory-ratio measures. These findings support the feasibility of GaitSIT as a portable, low-cost, and immersive assessment framework for characterizing sensory-condition-specific gait–balance responses after accounting for walking speed and providing indirect behavioral indices related to sensory reweighting. Full article

The relationship between athletes and equipment in the sport of speed skating is critical. A speed skater’s equipment, namely their skates, is an integral part of a dynamic system that facilitates the translation of human motion to on-ice racing. Although it is common practice to customize the setup of long track speed skates, empirical evidence supporting best practices is relatively undocumented. The exploratory nature of this investigation was intended to address two purposes: (i) profiling skate metrics in a competitive cohort of long track speed skaters and (ii) exploring the association between skate metrics and on-ice race times. Two databases were populated for the purpose of analysis: one for skate metrics and another for on-ice race times. Data were linked to the skates of thirty-one provincial-level long track speed skaters (male n = 19; female n = 12). The skate metrics database was populated by a single equipment technician, trained using measurement protocols consistent with the industry’s standards, and the metrics were grouped into three categories: (i) boot dimensions (n = 4), (ii) blade dimensions (n = 7), and (iii) skate setup (n = 5). The on-ice race time database was populated and collated using a secondary data source and included an aggregate time based on the mean of the three fastest race times per athlete by distance (500 m, 1000 m, and 1500 m) collected from the 2019–2023 seasons. Statistical analyses were conducted within and across the databases to (i) determine the variation in skate metrics and race times across athletes, and (ii) explore the association between skate metrics and race times. Analysis of the skate metrics database revealed coefficients of variance (CVs) for all metrics including the following: boot dimensions (6.95–8.69%), blade dimensions (0.00–14.24%), and skate setup metrics (8.63–17.05%). Of significant interest were large CVs for pivot point position (14.54%) and blade offset (8.63–17.05%), suggesting inconsistency and a potential lack of understanding of the impact of skate setup on performance. No significant correlations were revealed between skate setup metrics and race times. Across the three race distances, regression models were not statistically significant and explained only a small proportion of variance, highlighting the limited understanding between skate setup metrics and race times in practice. Profiling skate metrics and understanding their relationship with race times provides equipment technicians, coaches, and athletes with a baseline to inform decisions when customizing skate setup. Full article

Accurate prediction of nitrogen oxide emissions from marine medium-speed four-stroke diesel engines is crucial for meeting increasingly stringent environmental standards. This paper focuses on the evaluation and selection of the optimal rate constant for the first and most significant reaction of the extended Zeldovich mechanism within a specific simulation framework. A numerical engine model was developed and validated against experimental measurements of combustion pressure, power, and emissions at 81% of the Maximum Continuous Rating (MCR). The research analyzes the influence of thirteen distinct chemical reaction rate constants on the accuracy of nitric oxide concentration predictions under the assumptions and inherent structural limitations of the developed zero-dimensional multi-zone model. The results demonstrate that by carefully selecting these kinetic parameters, the numerical model’s deviation can be reduced to just −0.93%. Crucially, these findings reflect the performance of alternative kinetic formulations within this specific diagnostic modeling framework rather than a fundamental investigation into the universal chemical kinetics of nitrogen oxide formation. By systematically examining the primary Zeldovich reaction through its pre-exponential factor, temperature exponent, and activation energy, this approach provides a reliable calibration framework that significantly improves the fidelity of this specific emission formation simulation without increasing computational complexity. Full article

Dinosaur footprints are among the most abundant trace fossils, but they are not direct records of anatomy, behaviour or faunal composition. They preserve locomotion, substrate interaction and occurrence data only after those signals have been filtered by foot anatomy, movement, sediment properties and preservation. Classical dinosaur ichnology has relied on two-dimensional outlines, linear and angular measurements, qualitative ichnotaxonomy and influential hypotheses about trackmaker identity, speed, social behaviour and evolutionary timing. Here we review how these hypotheses are being reassessed with three-dimensional digitisation, geometric morphometrics, supervised and unsupervised machine learning, and biomechanical simulation. We first consider how different footprint representations, including interpretive outlines, landmarks, silhouettes, depth maps and three-dimensional models, shape the questions that track data can answer. We then assess analytical approaches ranging from multivariate statistics and landmark-based classifiers to convolutional neural networks and β-variational autoencoders. Against this methodological background, we revisit four linked problem domains: ornithopod–theropod discrimination and the Grallator–Anchisauripus–Eubrontes plexus; speed and gait reconstruction; ecological and behavioural interpretations of track abundance, sauropod gauge and trackway arrangement; and macroevolutionary claims about body-size trends, functional morphotypes and avian-like pedal morphologies. Across these cases, newer methods rarely remove ambiguity. They more often show where classical interpretations are robust, where they depend on representation or prior labels, and where competing explanations remain hard to separate. We argue that footprint-based inference is strongest when tracks are treated as preservationally filtered products of anatomy, motion and substrate mechanics, and when they are integrated with skeletal data, experimental analogues and forward models in explicit, uncertainty-aware frameworks. Full article

We develop the geometry of events in a deformable cellular spacetime, extending our earlier cellular-spaces framework from cellular complexes to cellular events complexes. The framework operates within the conformal class of Minkowski space; in four dimensions, this is the vanishing-Weyl-tensor sector, which excludes Schwarzschild, Kerr, and gravitational-wave spacetimes. The framework treats integer counts of cell crossings as the primitive geometric data: spatial separation between events is the shortest count of face-adjacent cells; temporal separation is the cell-crossing count of a reference light pulse. Newton’s universal clock is replaced by an operational one: the temporal count distance is the ratio of cell length to the speed of light through a cell, and because both quantities are invariants of the co-deformation, the temporal count is itself an invariant: temporal separation is operationally measured via light-pulse counts rather than posited as an external coordinate. Under the co-deformation principle, a single positive scalar field $\rho$ (cell density) controls both the rod length and the clock period. We prove six results, all expressed in terms of counts on the cellular events complex, with a smooth conformally flat metric $\tilde{g}=e^{2\phi }\eta$ ($\phi =-\frac{1}{3}ln\rho$) appearing only as the comparison/calibration object for convergence statements. First, the scalar curvature of the smooth comparison metric is the closed-form differential operator $\tilde{R}=2\square \rho /{\rho }^{1/3}-(8/3){(\partial \rho )}^2/{\rho }^{4/3}$. Second, the volume of a small Alexandrov interval admits an explicit asymptotic expansion in the interval height T, with leading correction $Q(m,u)T^2$ involving an anisotropic invariant at the midpoint m. Third, Q is irreducible to scalar and Ricci-directional invariants alone; the explicit decomposition $Q=\frac{1}{45}\tilde{R}+\frac{1}{5}{\tilde{R}}_{uu}+\frac{1}{2}\mathcal{J}$ exhibits a third independent invariant $\mathcal{J}(m,u)={(u·\partial )}^2\phi \left(m\right)$ as new structural content of the Lorentzian diagnostic. Fourth, the discrete-to-continuum convergence of counts on the cellular events complex yields a counts-only curvature estimator with rate $O\left(\sqrt{a}\right)$ at the joint scaling $T\asymp \sqrt{a}$. Fifth, the smooth comparison metric itself is reconstructible from counts on the discrete complex at rate $O\left(a\right)$: the conformally flat Lorentzian geometry is uniquely determined, up to background Minkowski calibration, by the cellular events complex. Sixth, a finite collection of Alexandrov-interval volume measurements at a fixed midpoint suffices to recover the full local curvature data $\{\tilde{R}\left(m\right),{\tilde{R}}_{\mu \nu }\left(m\right),\mathcal{J}(m,u)\}$ at rate $O\left(\sqrt{a}\right)$ (curvature spectroscopy); and the temporal light-tick count $\lambda$ is essential in a precise sense—there exist conformally flat Lorentzian geometries indistinguishable on every spatial slice by the earlier spatial-only diagnostic but distinguished at the origin by the events-space directional invariant. The framework’s scope is the conformal class of Minkowski: flat FLRW in conformal time, leading-order weak-field gravity, and 2D gravity. This paper is a mathematical contribution to discrete-to-continuum geometry on cellular events complexes; it is not a physical theory of gravity. Full article

Forecasting the deformation and movement of cumulus clouds provides an important basis for ultra-short-term solar irradiance nowcasting in photovoltaic (PV) power generation. Existing methods mainly use two-dimensional (2D) ground-based sky images for forecasting, which have limited ability to represent the three-dimensional (3D) spatial structure of cumulus clouds and the influence of wind on cloud motion. In this study, we propose a wind-aware ultra-short-term spatiotemporal forecasting model for 3D cumulus clouds, termed three-dimensional Cloud Long Short-Term Memory with Wind Gate Recurrent Unit (3dCLSTM + WindGRU). The model uses 3dCLSTM to learn the spatial structure and temporal evolution of 3D voxel cumulus cloud sequences, and embeds a WindGRU unit between 3dCLSTM layers to introduce wind speed and wind direction information for wind-driven transient motion modeling. Experiments were conducted on 1-min and 10-min 3D cumulus cloud datasets reconstructed from ground-based sky image datasets collected at sites in California and Colorado, USA. All voxel sequences were resampled to 64 × 64 × 64, with five-step prediction for the 1-min dataset and three-step prediction for the 10-min dataset. The results show that 3dCLSTM achieved a structural similarity index measure (SSIM) of 0.7913 on the 1-min dataset, while 3dCLSTM + WindGRU achieved the best performance on the 10-min dataset, with an SSIM of 0.3512 and a peak signal-to-noise ratio (PSNR) of 18.3625. Compared with 3dCLSTM, introducing WindGRU improved the SSIM by 4.8% on the 10-min dataset, with a more evident improvement under relatively high wind-speed conditions. These results indicate that wind-aware volumetric spatiotemporal modeling can support ultra-short-term 3D cumulus cloud forecasting and provide a useful technical basis for solar irradiance nowcasting. Full article