Search Results (49)

Slip ring–brush assemblies are widely used in satellite mechanisms to transmit power and signals across rotating interfaces. Under authentic space environments—vacuum, radiation-dominated thermal exchange, and long-duration operation—the coupled effects of mechanical contact dynamics, electrical conduction, intermittent separation, and arcing can accelerate wear and degrade reliability. This paper presents a surface-resolved multiphysics model for multi-track slip rings with staggered brushes. The ring surface is discretized on a circumferential–axial grid and endowed with correlated 3D roughness, enabling interference-based asperity contact. Brush normal dynamics (mass–spring–damper) convert runout and micro-vibration into normal-force ripple and separation events. Electrical conduction is modeled by a parallel admittance network combining pressure-dependent micro-contact conduction and an event-based arc channel activated by separation, opening velocity, and current density with stochastic ignition. A 2D thermal model with ADI integration accounts for Joule/friction heating, radiative cooling, and optional hub conduction. Wear evolves via an Archard-type mechanical term and an arc-energy-driven erosive term. A FAST–MACRO multiscale scheme (20 s FAST, 100 h MACRO with periodic recalibration) enables tractable long-horizon wear prediction while preserving arc statistics. Baseline simulations for a 28 V bus demonstrate rare but nonzero arc activity and predict spatially non-uniform wear at the micrometer scale after 100 h. Full article

To address uneven surface hardness distribution in 65Mn external tooth friction plates after furnace quenching and disc mold tempering, we adopted an integrated quenching and forming process, using an internal-circulation mold. By simultaneously implementing pressure forming and quenching within the internal-circulation mold, the hardness uniformity of the friction plate during forming was improved, effectively suppressing warping deformation. A multi-field coupled model of the friction plate quenching in the internal-circulation mold was established to simulate the dynamic evolution of the temperature field, the microstructural transformation, and the stress field, thus obtaining the complete heat treatment response of the martensitic transformation. The experimentally observed microstructure agreed well with the simulation results. Data analysis showed that after quenching in the internal-circulation mold, the surface hardness difference of a single friction plate was reduced from 3 HRC to 0.9 HRC, and the end face runout decreased from 0.1–0.15 mm to no more than 0.06 mm, significantly improving the product’s dimensional accuracy and performance consistency. Full article

Rockfall hazard assessment and mitigation design relies heavily on three-dimensional trajectory modeling, in which the coefficient of restitution (COR) is a governing parameter controlling rebound, energy dissipation, and runout distance. In practice, COR values are commonly selected from generalized tables based on slope material type, introducing significant epistemic uncertainty and limiting predictive accuracy. This study presents a comparative evaluation of large-scale field rockfall experiments and 3-D numerical simulations conducted at a former aggregate quarry in Boise, Idaho, to assess the performance of tabulated restitution coefficients. Concrete blocks of controlled shape (spheres, cubes, and rectangular prisms) and mass (17–68 kg) were instrumented with inertial sensors and released from two slope configurations. High-resolution UAV-based LiDAR was used to reconstruct slope geometry, while dynamic cone penetrometer and friction tests were performed to characterize spatial variability in slope material stiffness. These data were incorporated into RocFall3 to simulate block trajectories using spatially varying COR values. Initial models assuming zero rotational velocity and tabulated COR ranges failed to reproduce observed runout distances, dispersion patterns, and modes of motion, particularly for non-spherical blocks. Incorporating field-measured initial rotational velocities significantly improved agreement between modeled and observed trajectories, by correcting the unrealistic sliding mode of motion previously observed. However, quantitative discrepancies in deposition and dispersion persisted, highlighting limitations associated with simplified slope geometry and the loss of small-scale surface features during LiDAR surface reconstruction. The results demonstrate that restitution behavior is strongly shape-dependent and that realistic initial conditions are essential for physically meaningful simulations. The findings underscore the need for site-specific, material-informed approaches to COR estimation and for improved integration of high-fidelity field data into physics-based rockfall models. Full article

This study addresses the challenge of predicting runout distance of rainfall-induced shallow landslides by integrating deep learning and explainable machine learning. Using the June 2024 landslide disaster at the Fujian-Guangdong-Jiangxi border as a case study and remote sensing images as the data source, we developed an improved U-Shaped Convolutional Neural Network model (RAC-Unet) combining Deep Residual Structure, Atrous Spatial Pyramid Pooling, and Convolutional Block Attention Module modules. The model identified 34,376 shallow landslides and built a dynamic parameter database with 8875 samples, which was used for data-driven model training. After comparing models, Extreme Gradient Boosting was chosen as the best (R² = 0.923), with its performance confirmed by Wilcoxon analysis and good generalization in external validation (R² = 0.877). SHapley Additive Explanations analysis revealed how factors like the area of the sliding source zone (SA), length/width ratio of the sliding source zone (SLWR), and average slope of the source zone (SS) affect landslide runout, a simplified model using the three parameters SA, SLWR, and SS was constructed (R² = 0.862). Compared to traditional models, this integrated framework solves the pre-disaster impact range estimation problem, deepens understanding of shallow landslide dynamics, and enables accurate pre- and post-disaster predictions. It provides comprehensive support for disaster risk assessment and emergency response in southeastern hilly areas. Full article

Long-runout landslides usually cause a significant loss of life and property because of their hypermobility and immense energy to travel long distances at high velocities, attracting a global focus on the dynamics and mechanism of long-runout landslides. In the past few decades, a great number of past studies on long-runout landslides have seen a surge in a range of innovative ideas and vigorous debates contributing to the advancement of understanding the dynamics and mechanism of the hypermobility of long-runout landslides. As a consequence, a review of the dynamics of long-runout landslides has been conducted by comprehensively and systematically summarizing the data and achievements of long-runout landslides over the past few decades in terms of the phenomenon and characteristics, mobility, dynamic process, dynamic mechanism, and models of long-runout landslides. This review would be of great significance in providing a comprehensive reference in understanding the dynamics of long-runout landslides. Full article

The accurate motion of roller-bearing-supported rings is critically influenced by shape and positional tolerances, which are often underestimated in conventional modeling approaches. The aim of this study is to develop and validate a multibody dynamic framework capable of quantifying the impact of roundness and positional errors on the motion accuracy of roller-bearing-supported rings. Shape errors are modeled using Fourier series and incorporated into a high-fidelity multibody simulation environment. Experimental validation using laser triangulation reveals a maximum runout error of 72.9 μm, compared to a numerically predicted value of 88.6 μm, resulting in a quantified numerical overestimation of 21.5%. Parametric studies investigated the effects of harmonic order, error amplitude, and combined error scenarios on key performance metrics, including trajectory runout and initial offset displacement. Results reveal that the trajectory errors range between 0.29 mm and 0.63 mm for shape error orders and can escalate to 2.84 mm for high amplitude errors, demonstrating the critical role of error order and amplitude. Furthermore, combined simulations show that bearing position errors exert a more pronounced effect on radial accuracy than shape deviations alone. The proposed approach enables precision design evaluation and tolerance optimization in high-accuracy applications, including robotics, aerospace mechanisms, and optical alignment systems. Full article

The estimation of runout distances for long return period avalanches is vital in zoning schemes for mountainous countries. There are two broad methods to estimate snow avalanche runout distance. One involves the use of a physical model to calculate speeds along the incline, with runout distance determined when the speed drops to zero. The second method, which is used here, is based on empirical or statistical models from databases of extreme runout for a given mountain range or area. The second method has been used for more than 40 years with diverse datasets collected from North America and Europe. The primary reason for choosing the method used here is that it is independent of physical models such as avalanche dynamics, which allows comparisons between methods. In this paper, data from diverse datasets are analyzed to explain the relation between them to give an overall view of the meaning of the data. Runout is formulated from nine different datasets and 738 values of extreme runout, mostly with average return periods of about 100 years. Each dataset was initially fit to 65 probability density functions (pdf) using five goodness-of-fit tests. Detailed discussion and analysis are presented for a set of extreme value distributions (Gumbel, Frechet, Weibull). Two distributions had exemplary results in terms of goodness of fit: the generalized logistic (GLO) and the generalized extreme value (GEV) distributions. Considerations included both the goodness-of-fit and the heaviness of the tail, of which the latter is important in engineering decisions. The results showed that, generally, the GLO has a heavier tail. Our paper is the first to compare median extreme runout distances, the first to compare exceedance probability of extreme runout, and the first to analyze many probability distributions for a diverse set of datasets rigorously using five goodness-of-fit tests. Previous papers contained analysis mostly for the Gumbel distribution using only one goodness-of-fit test. Given that climate change is in effect, consideration of stationarity of the distributions is considered. Based on studies of climate change and avalanches, thus far, it has been suggested that stationarity should be a reasonable assumption for the extreme avalanche data considered. Full article

Suyukou Gully, located on the eastern slope of the Helan Mountains in northwest China, is a typical debris-flow-prone catchment characterized by a steep terrain, fractured bedrock, and abundant loose colluvial material. The area is subject to intense short-duration convective rainfall events, which often trigger destructive debris flows that threaten the Suyukou Scenic Area. To investigate the dynamics and risks associated with such events, this study employed the FLO-2D two-dimensional numerical model to simulate debris flow propagation, deposition, and hazard distribution under four rainfall return periods (10-, 20-, 50-, and 100-year scenarios). The modeling framework integrated high-resolution digital elevation data (original 5 m DEM resampled to 20 m grid), land-use classification, rainfall design intensities derived from regional storm atlases, and detailed field-based sediment characterization. Rheological and hydraulic parameters, including Manning’s roughness coefficient, yield stress, dynamic viscosity, and volume concentration, were calibrated using post-event geomorphic surveys and empirical formulations. The model was validated against field-observed deposition limits and flow depths, achieving a spatial accuracy within 350 m. Results show that the debris flow mobility and hazard intensity increased significantly with rainfall magnitude. Under the 100-year scenario, the peak discharge reached 1195.88 m³/s, with a maximum flow depth of 20.15 m and velocities exceeding 8.85 m·s⁻¹, while the runout distance surpassed 5.1 km. Hazard zoning based on the depth–velocity (H × V) product indicated that over 76% of the affected area falls within the high-hazard zone. A vulnerability assessment incorporated exposure factors such as tourism infrastructure and population density, and a matrix-based risk classification revealed that 2.4% of the area is classified as high-risk, while 74.3% lies within the moderate-risk category. This study also proposed mitigation strategies, including structural measures (e.g., check dams and channel straightening) and non-structural approaches (e.g., early warning systems and land-use regulation). Overall, the research demonstrates the effectiveness of physically based modeling combined with field observations and a GIS analysis in understanding debris flow hazards and supports informed risk management and disaster preparedness in mountainous tourist regions. Full article

Understanding rockfall behavior is essential for accurately predicting hazards in both natural and engineered environments, yet prior research has predominantly focused on spherical rocks or single-impact scenarios, leaving critical gaps in highlighting the dynamics of non-spherical rocks and multiple impacts. This study addresses these shortcomings by investigating the influence of rock shape and release angle on motion, energy dissipation, and impact behavior. To achieve this, an innovative approach rooted in the Video-Assisted Rockfall Kinematics Analysis (VARKA) procedure was introduced, integrating a custom-designed apparatus, controlled experimental setups, and sophisticated data analysis techniques. Experiments utilizing a pendulum-based release system analyzed various scenarios involving different rock shapes and release angles. These tests provided comprehensive motion data for multiple impacts, including trajectories, translational and angular velocities, and the coefficient of restitution (COR). Results revealed that non-spherical rocks exhibited significantly more erratic trajectories and greater variability in COR values compared to spherical rocks. The experiments demonstrated that ellipsoidal and octahedral shapes had substantially higher variability in runout distances than spherical rocks. COR values for ellipsoidal shapes spanned a wide range, in contrast to the tighter clustering observed for spherical rocks. These findings highlight the pivotal influence of rock shape on lateral dispersion and energy dissipation, reinforcing the need for data-driven approaches to enhance and complement traditional physics-based predictive models. Full article

This study investigates co-seismic landslides triggered by the 1 January 2024 Mw 7.6 Noto Peninsula earthquake in Japan using LiDAR differentiation and a modified Savage–Hutter model. By analyzing pre- and post-earthquake high-resolution topographic data from 13 landslides in a geologically homogeneous area of the peninsula, we characterized distinct landslide morphologies and dynamic behaviours. Our approach combined static morphological analysis from LiDAR data with simulations of granular flow mechanics to evaluate landslide mobility. Results revealed two distinct landslide types: those with clear erosion-deposition zonation and complex landslides with discontinuous topographic changes. Landslide dimensions followed power-law relationships (H = 7.51L^0.50, R² = 0.765), while simulations demonstrated that internal deformation capability (represented by the μ parameter) significantly influenced runout distances for landslides terminating on low-angle surfaces but had minimal impact on slope-confined movements. These findings highlight the importance of integrating both static topographic parameters and dynamic flow mechanics when assessing co-seismic landslide hazards, particularly for predicting potential runout distances on gentle slopes where human settlements are often located. Our methodology provides a framework for improved landslide susceptibility assessment and disaster risk reduction in seismically active regions. Full article

Shallow landslides are often unpredictable and seriously threaten surrounding infrastructure and the ecological environment. Traditional landslide prediction methods are time-consuming, labor-intensive, and inaccurate. Thus, there is an urgent need to enhance predictive techniques. To accurately predict the runout distance of shallow landslides, this study focuses on a shallow soil landslide in Tongnan District, Chongqing Municipality. We employ a genetic algorithm (GA) to identify the most hazardous sliding surface through multi-iteration optimization. We discretize the landslide body into slice units using the dynamic slicing method (DSM) to estimate the runout distance. The model’s effectiveness is evaluated based on the relative errors between predicted and actual values, exploring the effects of soil moisture content and slice number on the kinematic model. The results show that under saturated soil conditions, the GA-identified hazardous sliding surface closely matches the actual surface, with a stability coefficient of 0.9888. As the number of slices increases, velocity fluctuations within the slices become more evident. With 100 slices, the predicted movement time of the Tongnan landslide is 12 s, and the runout distance is 5.91 m, with a relative error of about 7.45%, indicating the model’s reliability. The GA-DSM method proposed in this study improves the accuracy of landslide runout prediction. It supports the setting of appropriate safety distances and the implementation of preventive engineering measures, such as the construction of retaining walls or drainage systems, to minimize the damage caused by landslides. Moreover, the method provides a comprehensive technical framework for monitoring and early warning of similar geological hazards. It can be extended and optimized for all types of landslides under different terrain and geological conditions. It also promotes landslide prediction theory, which is of high application value and significance for practical use. Full article

When rivers impinge on the steep bluffs of valley walls, dynamic changes stem from a combination of fluvial and mass wasting processes. This study identifies the geomorphic changes, drivers, and timing of a landslide adjacent to the Apalachicola River at Alum Bluff, the tallest natural geological exposure in Florida at ~40 m, comprising horizontal sediments of mixed lithology. We used hydrographic surveys from 1960 and 2010, two sets of LiDAR from 2007 and 2018, historical aerial, drone, and ground photography, and satellite imagery to interpret changes at this bluff and river bottom. Evidence of slope failure includes a recessed upper section with concave scarps and debris fans in the lower section with subaqueous features including two occlusions and a small island exposed from the channel bottom at lower water levels. Aerial photos and satellite images indicate that the failure occurred in at least two phases in early 2013 and 2015. The loss in volume in the 11-year interval, dominantly from the upper portion of the bluff, was ~72,750 m³ and was offset by gains of ~14,760 m³ at the lower portion of the bluff, suggesting that nearly 80% of the material traveled into the river, causing changes in riverbed morphology from the runout. Despite being along a cutbank and next to the scour pool of a large meandering river, this failure was not driven by floods and the associated lateral erosion, but instead by rainfall in noncohesive sediments at the upper portion of the bluff. This medium-magnitude landslide is now the second documented landslide in Florida. Full article

Bedding rock landslides, characterized by their distinct geological structure, are widely distributed and highly susceptible to sliding under external disturbances, resulting in catastrophic events. This study aims to unravel the geomechanical mechanisms governing rainfall-induced instability through an integrated investigation of a representative landslide in Xing’an Village, Chongqing. Employing multidisciplinary approaches, including field monitoring, geotechnical testing, and dynamic numerical modeling, we systematically revealed two critical failure zones: a front failure zone and a rear potential instability zone. Under rainstorm conditions, the safety factor for both zones was 1.02, indicating a marginally unstable state. The DAN-W simulations indicate that the potential instability zone at the rear of the landslide experienced complete failure within 12 s under heavy rainfall, with a maximum run-out distance of 20 m, a maximum velocity of 4.32 m/s, and a maximum deposition thickness of 8.3 m, which could potentially bury the buildings at the toe of the landslide. The low strength and permeability of the mudstone-dominated Badong Formation, characterized by interbedded mudstone, siltstone, and sandstone within the Middle Triassic geological system, provides a fundamental prerequisite for the landslide. Rainwater infiltration into the mudstone layers degraded its mechanical properties, and excavation at the slope base ultimately triggered the landslide initiation. These findings can provide theoretical support for preventing and managing similar bedding rock landslides with similar geological backgrounds. Full article

Understanding the dynamic behavior of landslides is essential for effective risk assessment. This study examines the Yanguan landslide, which occurred on 29 October 2017, in the Three Gorges Reservoir (TGR) region of China. Due to its unique capability in modeling discontinuum behaviors during landslide fragmentation, the discrete element method was utilized to analyze the movement characteristics of this landslide. The investigation began with a field survey to assess the geological features and failure mechanism of the landslide, which indicates that the landslide was likely triggered by prolonged variations in reservoir water levels and heavy rainfall preceding the event. Following this, a three-dimensional numerical model of the landslide was constructed using pre- and post-event terrain data. The accuracy of the numerical model was validated by comparing its simulation results with field survey data. Finally, the landslide’s movement behavior and energy transformation were analyzed based on the validated model. This work can enhance landslide risk assessment by quantifying dynamic parameters critical for impact prediction, further provide a scientific basis for the study of the landslides in the TGR area, and contribute to disaster prevention. Full article

Predicting rockfall dynamics is essential for effective risk management and mitigation in mining and civil engineering, where uncontrolled rockfalls can have serious safety implications. This study explores machine learning (ML) approaches to model rockfall behavior, using experimentally derived data to predict key parameters: translational and angular velocity, coefficient of restitution (COR), and runout distance. Rockfall behavior is complex, influenced by factors such as rock shape and release angle, which create irregular, nonlinear patterns that challenge traditional modeling techniques. Three ML models—K-Nearest Neighbors (KNNs), Perceptron, and Deep Neural Networks (DNNs)—were initially tested for predictive accuracy. This study found that the Perceptron model could not capture the nonlinear intricacies of rockfall dynamics, while DNNs, though theoretically capable of handling complexity, faced issues with overfitting and interpretability due to limited data. KNNs emerged as the most effective model, offering a balance of accuracy and interpretability by using instance-based predictions to reflect localized patterns in rockfall behavior. Each parameter was modeled individually, leveraging KNNs’ strength in handling the dataset’s unique characteristics without excessive computational requirements or extensive preprocessing. The results demonstrate that KNNs effectively predicts rockfall trajectories across diverse shapes and release angles, enhancing its practical application for safety and preventive strategies. This study contributes to the understanding of rockfall mechanics by providing an interpretable, adaptable model that meets the challenges posed by small, high-dimensional datasets and complex physical interactions. Full article