Search Results (6,246)

To systematically investigate the combined effects of moisture content, confining pressure, and loading rate on the mechanical properties of weakly cemented soft rock, this study focuses on the Jurassic coal measures from the Hoxtolgay coalfield in Xinjiang. A series of uniaxial and triaxial compression tests were conducted under varying moisture states, loading velocities, and confining pressures. Complementary X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brazilian splitting tests were performed to analyze the microstructural evolution and tensile failure characteristics. The experimental results demonstrate that moisture content acts as the primary governing factor for mechanical degradation; increased hydration promotes clay mineral swelling and attenuates inter-granular cementation, leading to a continuous reduction in both compressive and tensile strengths, as well as the elastic modulus. Conversely, confining pressure consistently enhances these macroscopic mechanical parameters by restricting lateral deformation. While the loading rate alters the mechanical response, its impact is secondary compared to the definitive effects of moisture and stress constraints. Furthermore, by utilizing established stress–strain-based indices, the study quantitatively evaluates the brittleness characteristics, confirming that hydration fundamentally drives the rock mass from a brittle state toward ductility. This research elucidates the coupled degradation mechanisms of highly sensitive soft rock, providing a theoretical foundation for stability design and risk assessment in underground geotechnical engineering. Full article

Pressure fluctuations caused by a jet in crossflow (JICF) can induce cavitation and potentially damage wall surfaces. In mercury targets for a pulsed spallation neutron source, where cavitation damage progresses due to thermal shock, mercury is confined within a vessel that incorporates a double-wall structure—comprising a narrow channel and a main flow channel—to form parallel flows and suppress damage. However, as the damage progressed, penetration holes were formed in the inner wall separating these flows, and characteristic damage patterns were observed that suggest accelerated damage progression caused by JICF, in which a jet flows from the narrow channel into the main channel. The mechanism underlying this phenomenon has not been fully clarified. Therefore, the flow field and pressure fluctuations around the penetration hole were evaluated using PIV measurements in a water loop and numerical simulations of single-phase flow, with varying jet velocity and jet width. The results revealed that inflow through the penetration in the inner wall generates JICF, which produces vortices downstream of the inflow jet and induces pressure fluctuations that may be associated with cavitation. Full article

This study addresses a two-dimensional anomalous solute transport process within a two-zone fractal porous medium. A mathematical formulation is developed to characterise transport phenomena in a non-homogeneous porous domain. The medium consists of two interacting regions: one containing mobile fluid and the other containing immobile fluid, between which mass transfer occurs. In the mobile-fluid region, solute transport is governed by the convection–diffusion equation. In contrast, the immobile-fluid region is described using a first-order kinetic model. The problem of solute injection through a designated boundary point is formulated and numerically implemented. The effects of anomalous transport behaviour on solute migration and filtration characteristics are examined. The study further evaluates the pressure field, filtration velocity distribution, and solute concentration in both zones. Full article

To explore the spectral characteristics of near-field vibration signals from multi-hole millisecond-delay blasting in open-pit mines and the modulation effect of delay time on blasting energy distribution, field blasting vibration tests with multi-gradient delays were conducted taking an open-pit coal mine in Xinjiang as the engineering background. Particle Swarm Optimization (PSO) optimized Variational Mode Decomposition (VMD) and Hilbert-Huang Transform (HHT) were introduced for the refined processing and frequency band energy ratio analysis of the measured signals, and field vibration control tests were subsequently carried out. The results show that compared with the traditional Empirical Mode Decomposition (EMD), the PSO-optimized VMD can effectively overcome the mode aliasing phenomenon. By extracting the high-frequency Intrinsic Mode Function (IMF7) that characterizes the instantaneous detonation impulse, the actual delay time was successfully inverted to be 10.47 ms. The inter-hole delay time significantly affects the time-frequency distribution of vibration energy. Under the 25 ms delay condition, the energy ratio of the high-frequency band is the highest, and the low-frequency energy accumulation degree is the lowest, which is most conducive to shortening the vibration duration and accelerating energy attenuation. Control tests further confirmed that adopting a 17 ms delay in the near-slope area can effectively control the peak particle velocity (PPV) in the near field, while adopting a 23 ms delay in the middle and far areas can further reduce the low-frequency energy concentration. The research results demonstrate a dynamic matching strategy for millisecond delays based on spatial distance differences, which has important guiding significance for realizing safe and efficient blasting vibration control in open-pit mines. Full article

Background: Human physiology-based biomarkers, such as transcortical long-latency reflexes (LLRs) and movement control performance, are measurements used to evaluate human performance. We developed a method to assess human performance variables using a custom-designed visuomotor control device with the capability to examine performance accuracy and neurophysiological responses to unexpected perturbations. We assessed the internal consistency and reproducibility of this device during a shoulder tracking task including the performance accuracy and the transcortical long-latency reflexes during unexpected perturbations. Methods: 86 healthy young adults (49 females, right-handed, mean age 25.8 ± 9.5 years) were assessed for internal consistency across varying resistance and velocity conditions. We next determined test–retest reliability among 31 participants (17 females, right-handed, mean age 24.13 ± 3.8 years). We then determined the predictability of the LLRs and performance error during perturbations using the receiver operating characteristic analysis (ROC). Results: Our results supported excellent internal consistency, fair-to-good test–retest reliability for task performance accuracy, and fair-to-good transcortical LLR responses to perturbations (McDonald’s omega > 0.9; intraclass correlation coefficients (ICCs, 0.63–0.82)). Tracking accuracy, changes in movement velocity, and infraspinatus LLRs were effective predictors of perturbation conditions (receiver operating characteristics: AUC 0.72–0.90). Conclusions: These findings support that performance-based biomarkers have moderate-to-good reliability and neurophysiology-based biomarkers have fair-to-good reliability when assessing human shoulder performance among healthy adults. Studies are currently underway to determine if these measures are reproducible across other joint movements and among people with musculoskeletal and central nervous system injury. Full article

Maintaining stable molding quality in hydraulic injection molding machines is difficult because the internal state of molten resin cannot be directly observed and varies with material properties and operating conditions. This difficulty is intensified by variations in hydraulic characteristics caused by oil temperature changes. This study proposes an adaptive feedforward control (AFC) framework that improves injection velocity tracking while utilizing AFC learning parameters as indicators of resin state. AFC is implemented as a multi-frequency feedforward controller whose parameters are updated through repetitive injection cycles. To overcome the limited learning duration within a single injection shot, a shot-to-shot compensation mechanism accumulates and transfers learning results across consecutive shots. Experiments are conducted on a hydraulic injection molding machine using polypropylene materials with different viscosities. The results show that the converged AFC learning parameters vary systematically with material changes and correspond to differences in molded product appearance. Furthermore, by adjusting the cylinder temperature of another material, the AFC parameters converge to values close to those of a reference material, resulting in similar molded products. These findings demonstrate that AFC learning parameters reflect variations in resin state and can serve as practical state indicators for aligning molding conditions. Full article

Nonlinear friction in the mechanical transmission system of machine tools induces transient stagnation of the feed axis as its velocity crosses zero, thereby giving rise to contouring errors in multi-axis machining and significantly degrading machining accuracy. To address this issue, a feedforward compensation strategy is proposed based on a simplified static friction model (SSFM) with dual-segment and dual-parameter characteristics. The nonlinear friction is represented by a combination of a linear segment and an exponential segment, while the model incorporates two essential parameters that characterize the maximum friction force and the negative damping effect. Experimental results from two-axis circular trajectory tests show that the proposed SSFM reduces contour errors by approximately 73.4% and 79.2% at 600 mm/min and 2100 mm/min, respectively. To improve compensation under high-speed conditions, an acceleration-dependent dynamic correction is further introduced to establish the SDFM. The results show that the maximum contour error is further reduced to 1.44 μm and 1.49 μm at 3600 mm/min and 5000 mm/min, respectively. Compared with many existing reduced-order or hybrid friction models that rely on more parameters or more complex identification procedures, the proposed method provides a more compact and compensation-oriented modeling strategy for the velocity-reversal region of CNC feed systems. Full article

The synchronous continuum robot (SCR) was developed to emulate biological structures, such as animal tails and elephant trunks, based on continuum robot principles. By synchronizing disk motions, the SCR generates biologically inspired continuous movements while maintaining precise trajectory control. However, its synchronization-based architecture limits adaptability during physical interaction due to rigid trajectory-following characteristics. To address this limitation, this paper proposes a sensorless variable admittance control (VAC)-based compliant motion generation framework for the SCR. A dynamic model-based sensorless disturbance observer is designed to estimate external torques without additional force sensors. To compensate for uncertainties inherent in the cable-driven transmission mechanism, an adaptive term is incorporated into the parameter identification process, improving disturbance estimation accuracy. Based on the estimated external torques, admittance parameters are adaptively modulated according to joint angles, angular velocities, and robot posture, enabling interaction-aware motion speed regulation. Furthermore, the proposed method simultaneously enforces constraints on both joint angles and angular velocities through the adaptive regulation of target positions and velocities, ensuring safe and physically feasible motion. Experimental results under various interaction scenarios demonstrate reliable contact-independent force estimation and effective compliant motion generation. The proposed framework provides an integrated solution for robust force estimation, adaptive compliance control, and simultaneous constraint enforcement in mechanically synchronized continuum robots. Full article

Classical soil mechanics models are inadequate for predicting the traction of wheeled vehicles under high-velocity off-road conditions due to the complex dynamic soil response. To address this, this study proposes a velocity-segmented dynamic compression-shear model for aeolian sandy soil, enhancing classical theories with velocity-dependent corrections for the 0–10 m/s range. A theoretical patterned wheel–soil interaction model is developed, incorporating lug effects via an equivalent radius. Furthermore, a comprehensive vehicle traction model is established by integrating the soil model with a dynamic equilibrium iteration method that couples suspension dynamics, pitch attitude, and axle load distribution. Validation results demonstrate that the single-wheel traction theoretical model achieves an error of less than 18%, while the full vehicle traction model reaches a 73% prediction accuracy for drawbar pull and sinkage, as verified through soil bin tests and full-vehicle experiments. This research provides theoretical framework for the real-time and accurate prediction of wheeled-vehicle traction performance on unprepared terrain, offering significant improvements for high-velocity off-road mobility analysis. Full article

The evolution of the flow of a viscous, electrically conductive, incompressible fluid on a rotating wall in the presence of a magnetic field is studied. The wall forms an arbitrary angle with the axis of rotation. The unsteady flow is induced by longitudinal oscillations of the wall and a suddenly applied magnetic field directed normal to the wall. An analytical solution to the three-dimensional unsteady magnetohydrodynamic equations is presented for the case of infinitely high fluid conductivity. The velocity field and induced magnetic field in the flow of a viscous, electrically conductive fluid are determined. A number of special cases of wall motion are considered. Based on the obtained results, the influence of the magnetic field on the characteristics of the waves emitted by the wall is investigated. Full article

Refined urban flood risk assessment serves as a fundamental safeguard for urban sustainability. However, most studies based on scenario analysis method tend to rely on a single risk evaluation criterion, with limited consideration of applicability differences arising from underlying computational principles. Furthermore, as flood events are inherently dynamic spatial–temporal processes, most studies often overlook the three-dimensional characteristics of flood risk, particularly the connectivity of risk in physically adjacent spaces. To address these issues, this paper proposes a comprehensive flood risk assessment framework that integrates the spatial–temporal characteristics of disaster-causing factors. An improved analysis method for grid-scale flood assessment is proposed based on the comprehensive mechanical analysis method and the drowning factor. In addition, a quantitative approach for characterizing the spatial aggregation of urban flood risk is established using risk thresholds and aggregation area thresholds. These methods are then integrated through a combination weighting–cluster analysis framework for comprehensive flood risk assessment. The results show that the improved analysis method can better reflect the change in risk of flow velocity and water depth combined. Spatiotemporally, the Yinshan Road and western section of the Dongzhong Road, exhibiting high localized risk, moderate overall risk, high risk on the time scale and high spatial agglomeration status, are comprehensively assessed as extremely high-risk flooded zones. The proposed framework effectively characterizes the spatial–temporal distribution of disaster-causing factors, providing a scientific basis for disaster prevention and contributing to urban sustainability. Full article

In intelligent vehicle navigation, efficient sensor data processing and accurate system stabilization is critical to maintain robust performance, especially when GNSS signals are unavailable or unreliable. Classical calibration methods for Inertial Measurement Units (IMUs), such as discrete and system-level calibration, fail to capture time-varying, non-linear, and non-Gaussian noise characteristics. Likewise, Kalman filters typically assume static measurement noise levels for non-holonomic constraints (NHCs), resulting in suboptimal performance in dynamic environments. Furthermore, zero-velocity detection plays a vital role in preventing error accumulation by enabling reliable zero-velocity updates during motion stops, but classical thresholding approaches often lack robustness and precision. To address these limitations, we propose a novel multitask deep neural network (MTDNN) architecture that jointly learns IMU calibration, adaptive noise level estimation for NHC, and zero-velocity detection solely from raw IMU data. This shared-encoder design is utilized to minimize computational overhead, enabling real-time deployment on resource-constrained platforms such as Raspberry Pi. The model is trained using post-processed GNSS-RTK ground truth trajectories obtained from both a proprietary dataset and the publicly available 4Seasons dataset. Experimental results confirm the proposed system’s superior accuracy, efficiency, and real-time capability in GNSS-denied conditions. Full article

To investigate the effects of distribution characteristics of microscopic voids (including the connectivity degree, pore-throat morphology, and size) on the permeability performance of open-graded friction course (OGFC) asphalt mixtures with steel slag as the anti-skid wearing course, two-dimensional computed tomography (CT) images of OGFC steel slag asphalt mixture specimens were first obtained via X-ray technology. The MATLAB R2022b-based image subtraction algorithm was then adopted to identify the interconnected voids inside the specimens to quantitatively characterize the morphological differences in interconnected voids in OGFC steel slag asphalt mixtures with different gradations. Furthermore, Finite Element simulation by ANSYS 2021 R1 was conducted to explore the influences of the diversion angle of interconnected voids on the water flow characteristics of OGFC steel slag asphalt mixtures, involving the variation laws of water flow velocity, water pressure and flow path in the diversion structure, thereby analyzing the resultant effects on the permeability performance of the mixtures. The results show that the combination of X-ray CT scanning and image processing technology enables more convenient, accurate and intuitive characterization of the internal void distribution characteristics of the mixtures. It was found that the pore-throat properties, including size, length, quantity and equivalent diameter, are the dominant factors restricting the permeability capacity of OGFC steel slag asphalt mixtures. As the diversion angle increases from 20° to 60°, the pressure gradient increases by up to 103.92%. After passing through the diversion section, the flow velocity increases by approximately four times. The streamline density at the channel axis is 4.2–4.5 times that near the channel wall. This study realizes the rapid extraction of void characteristics and the identification of key influencing factors on the permeability performance of OGFC steel slag asphalt mixtures, an achievement that cannot be attained by the previous macroscopic research on the permeability performance of such mixtures. Full article

Distributed acoustic sensing (DAS) has been widely applied to injection–production profile monitoring in horizontal wells because it provides continuous full-wellbore coverage, real-time acquisition, and straightforward long-term deployment. In practical downhole operations, however, DAS measurements are frequently compromised by optical-signal attenuation, loss of fiber–casing/formation coupling, and environmental noise. Meanwhile, the mechanisms governing flow-induced acoustic responses remain insufficiently understood, which continues to impede quantitative diagnosis and interpretation of injection–production profiles based on DAS data. To address these challenges, this study performed controlled laboratory-scale physical simulation experiments of single-phase flow in a horizontal wellbore, systematically investigating DAS acoustic responses under two wellbore diameters (25 mm and 50 mm) and a range of flow velocities. Power spectral density (PSD) was derived using the fast Fourier transform to identify flow-sensitive characteristic frequency bands, and frequency-band energy (FBE) was further used to establish an optimal quantitative relationship with flow velocity. The results show that: (1) DAS energy is dominated by low-frequency components (<100 Hz), with the total energy increasing nonlinearly as flow velocity rises, accompanied by a progressive broadening of the characteristic bands; (2) the feature bands identified using an adaptive method based on energy difference statistics applied to PSD frequency-domain features exhibit a higher signal-to-noise ratio and greater physical clarity than traditional wide frequency bands; furthermore, by employing a feature band merging strategy, the distribution characteristics of flow energy can be captured more comprehensively; and (3) FBE exhibits a strong nonlinear dependence on flow velocity, with a power-law model delivering the best theoretical fit, whereas a cubic model (FBE ∝ V³) achieves high accuracy and robustness for practical applications. The proposed workflow—“PSD peak identification–characteristic band delineation–FBE regression”—establishes a methodological foundation for quantitative DAS-based monitoring of horizontal-well injection–production profiles in both laboratory and field settings, and it provides a basis for subsequent intelligent monitoring and interpretation under multiphase-flow conditions. Full article

Flip-flow screens are widely used for the efficient separations of wet fine materials. To explore the separation characteristics of the particle and screen plate in the flip-flow screening process, a flip-flow plate impact experimental system was built. The experimental system was based on a spherical inertial measurement device and a semi-industrial flip-flow screen system. In this study, we first derive the impact mechanics equation of the flip-flow screen plate on the particle and analyze the influence of the main parameters on the maximum impact force. Subsequently, we investigated the kinematic characteristics of the particle impacted by the screen plate at different moving positions, the variation of the centerline acceleration mechanism, and determined the angular velocity in the collision process. Additionally, we further clarified the alteration in the rules of translational and rotational kinetic energy of the particles in the collision process. This study addresses a research gap in the phenomenological modelling of particulate screening process. At the same time, it provides theoretical support for the accurate control of the flip-flow screening process. Full article