Search Results (13,285)

The Damietta–Port Said coast, Nile Delta, has experienced extreme morphological change over the past four decades due to sediment reduction due to Aswan High Dam and continued anthropogenic pressures. Using multi-temporal Landsat (1985–2025) and high-resolution RapidEye and PlanetScope imagery with 50 m-spaced transects, the study documents major shoreline shifts: the Damietta sand spit retreated by >1 km at its proximal apex while its distal tip advanced by ≈3.1 km southeastward under persistent longshore drift. Sectoral analyses reveal typical structure-induced patterns of updrift accretion (+180 to +210 m) and downdrift erosion (−50 to −330 m). To improve predictive capability beyond linear DSAS extrapolation, Nonlinear Autoregressive Exogenous (NARX) and Bidirectional Long Short-Term Memory (BiLSTM) neural networks were applied to forecast the 2050 shoreline. BiLSTM demonstrated superior stability, capturing nonlinear sediment transport patterns where NARX produced unstable over-predictions. Furthermore, coupled wave–flow modeling validates a sustainable management strategy employing successive short groins (45–50 m length, 150 m spacing). Simulations indicate that this configuration reduces longshore current velocities by 40–60% and suppresses rip-current eddies, offering a sediment-compatible alternative to conventional breakwaters and seawalls. This integrated remote sensing, hydrodynamic, and AI-based framework provides a robust scientific basis for adaptive, sediment-compatible shoreline management, supporting the long-term resilience of one of Egypt’s most vulnerable deltaic coasts under accelerating climatic and anthropogenic pressures. Full article

Offshore aquaculture requires net cages that remain stable under strong currents and during submersion and emergence operations. In this study, we proposed a submersible net cage structure equipped with vertical buoyancy columns as an alternative to the conventional horizontal floating-frame cage and evaluated its stability using a net geometry and load analysis system (NaLA system). Model-scale cages were tested in a recirculating flume tank at two current velocities, and the three-dimensional cage geometry was reconstructed using the multicamera through direct linear transformation method to validate the simulated cage inclination. The NaLA system accurately reproduced the measured geometry and time-varying inclination. After validation, stability was compared over a range of current velocities by tracking the cage inclination during the emergence phase. When mooring lines were attached to the top of the cage, the conventional floating-frame cage exhibited a smaller inclination than the buoyancy-column cage. However, relocating the mooring attachment point on the columns significantly improved the stability; attaching the moorings near the bottom of the columns generated the smallest final inclination and yielded a higher stability than the conventional cage. The buoyancy columns can outperform those of conventional designs when paired with an appropriate mooring configuration, thus offering a promising structure for applications under harsh offshore conditions. Full article

The present study investigates the generalized dispersion of a solute in an incompressible MHD flow via a rectangular channel with injectable or suctioned walls. The mathematical model of dispersion suggests a distinct type of mass flux expressed as a fractional partial differential equation based on the time-fractional Caputo derivative. The mass flow in the model under investigation is determined by both the concentration gradient and its historical evolution. A constant external magnetic field is provided transverse to the flow direction. The analysis and discussion of the analytical solution for the advection velocity are performed in relation to the Hartmann number and the suction/injection Reynolds number. To determine the solute concentration in space and time, the unstable fractional convection–diffusion equation is analytically solved. The polynomial in the geographic variable y that has coefficients that depend on the spatial variable x and the time t is the analytical solution of the concentration. The effects of the fractional order of the Caputo derivative, Reynolds number, Hartmann number, and Peclet number on the advection–diffusion process are examined using numerical simulations of the analytical solution of the solute concentration. Full article

To address the increasing vulnerability of unmanned aerial vehicle (UAV) lightweight airframe structures to high-velocity fragment impacts in complex operational environments, this study combines high-velocity impact penetration tests, quasi-static strength tests, fracture-surface microanalysis, and finite-element simulation to systematically reveal the formation mechanism of typical penetration damage and its influence on residual strength. Results show that such penetration induces damage such as adiabatic-shear local melting zones, spall cracks, and grain-boundary separation, significantly weakening static strength and shifting the fracture mode from ductility- to brittleness-dominated. A modified fracture-mechanics criterion with higher prediction accuracy than the traditional net-section criterion is proposed, and a high-precision simulation model based on explicit–quasi-static coupling is established, which well reproduces damage morphology and tensile-failure processes. Compared with conventional manned aircraft structures, UAV airframes characterized by thinner skins and higher lightweighting ratios exhibit more pronounced sensitivity to penetration-induced micro-defects, making rapid residual-strength assessment essential for operational recovery and field-level repair decision-making. The research reveals the damage mechanism and provides an engineering-applicable residual-strength assessment method, offering a reliable theoretical basis and simulation tool for rapid UAV damage evaluation and fast-turnaround repair planning for civil and industrial UAV platforms. Full article

This paper proposes a novel motion-aware framework to address key challenges in 4D millimeter-wave radar detection for autonomous driving. While existing methods struggle with sparse point clouds and dynamic object characterization, our approach introduces three key innovations: (1) A Bird’s Eye View (BEV) fusion network incorporating velocity vector decomposition and dynamic gating mechanisms, effectively encoding motion patterns through independent XY-component convolutions; (2) a gradient-aware multi-task balancing scheme using learnable uncertainty parameters and dynamic weight normalization, resolving optimization conflicts between classification and regression tasks; and (3) a two-phase progressive training strategy combining multi-frame pre-training with sparse single-frame refinement. Evaluated on the TJ4D benchmark, our method achieves 33.25% mean Average Precision (mAP)_3D with minimal parameter overhead (1.73 M), showing particular superiority in pedestrian detection (+4.16% AP) while maintaining real-time performance (24.4 FPS on embedded platforms). Comprehensive ablation studies validate each component’s contribution, with thermal map visualization demonstrating effective motion pattern learning. This work advances robust perception under challenging conditions through principled motion modeling and efficient architecture design. Full article

To enhance the assessment accuracy of tunnel face instability risks of active collapse during shield tunneling, this study establishes a novel unified analytical framework that couples the effects of unsaturated transient seepage induced by excavation drainage with soil stratification and heterogeneity. Grounded in unsaturated effective stress theory, the framework explicitly incorporates matric suction into the Mohr–Coulomb failure criterion via suction stress and apparent cohesion. By employing a horizontal two-layer nonhomogeneous soil model and solving the one-dimensional vertical Richards’ equation, an analytical solution for the face drainage boundary is derived to quantify the spatiotemporal evolution of suction stress and apparent cohesion. Subsequently, the critical support pressure is evaluated using the upper bound theorem of limit analysis, incorporating a horizontal layer-discretized rotational failure mechanism and the power balance equation. The validity of the proposed framework is confirmed through comparative analyses. Parametric studies reveal that in the upper hard and lower soft strata, the critical support pressure decreases and converges over time, indicating that unsaturated transient seepage exerts a significant influence in the short term that stabilizes over the long term. Additionally, sand–silt stratum exhibits lower overall stability and higher sensitivity to groundwater levels and temporal factors compared to silt–clay stratum. Conversely, silt–clay stratum displays a non-monotonic evolution with increasing cover-to-diameter ratios (C/D), reaching a minimum critical support pressure at approximately C/D = 1.1. Regarding heterogeneity, the internal friction angle of the lower layer exerts dominant control over the critical support pressure compared to seepage velocity, while the influence of other strength parameters remains secondary. These findings provide a theoretical basis for the time-dependent design of tunnel face support pressure under excavation drainage conditions. Full article

In this study, a calculation method for two-phase nonequilibrium flow in solid rocket motor nozzles is established, and an in-depth investigation into the nonequilibrium flow within the nozzle is conducted. Based on NEPE high-energy propellant, a simplified reaction mechanism model is established and validated using the full-component sensitivity analysis method for chemical nonequilibrium flow in the nozzle, consisting of 16 components and 22 steps. The nonequilibrium and frozen flow in the nozzle are simulated, and it is found that in nonequilibrium flow, the chemical reactions result in a 22.4% increase in the flow field temperature and an approximate 4.13% improvement in specific impulse. In addition, the impacts of different total pressure conditions on the nonequilibrium flow in the nozzle are studied, in which the increase in pressure enhances the overall temperature, but the change in velocity and Mach number are negligible. Finally, a discrete phase model is adopted in the nonequilibrium flow simulation to predict the evolution of aluminum oxide particles with different sizes within the nozzle. The results indicate that the presence of particles can enhance nozzle total thrust while reducing the specific impulse. As the particle size increases, both the nozzle thrust and specific impulse decrease, with the specific impulse being more significantly affected by particle size variations due to the variation in the gas-phase mass flow rate. Full article

To address the insufficient near-wall prediction capability of the traditional Partially Averaged Navier–Stokes (PANS) model in simulating curvature flows, a new nonlinear PANS model with near-wall correction was developed in this study. The model, referred to as the CLS PANS model, is constructed based on Craft’s nonlinear stress formulation and incorporates additional dissipation source and length-scale correction terms to enhance accuracy in curved, rotating, and separated flow fields. To evaluate its applicability and reliability, the new nonlinear PANS model was applied to three representative cases: Taylor–Couette flow, flow past a circular cylinder, and internal flow in a centrifugal pump. Numerical results were systematically compared with experimental data, Direct Numerical Simulation (DNS) results, and results from conventional Reynolds-Averaged Navier–Stokes and k-ε PANS models. The results show that the new nonlinear PANS model can accurately predict complex flow structures such as Taylor vortices and herringbone streaks with lower computational cost, demonstrating improved scale-resolving capability and near-wall performance. For flow past a circular cylinder, the predicted drag coefficient, Strouhal number, and velocity distribution in the wake agree well with experiments. In the centrifugal pump case, the model effectively captured the low-speed and separated flow regions near the blade pressure surfaces, yielding results consistent with experimental observations. Overall, the new nonlinear PANS model achieves a favorable balance between accuracy and efficiency and exhibits strong potential for simulating curvature- and rotation-dominated turbulent flows. Full article

Grain size is critical for metallic material performance, yet conventional ultrasonic methods rely on strong model assumptions and exhibit limited adaptability. We propose a deep learning architecture that uses multimodal ultrasonic features with spatial coding to predict the grain size distribution of GH4099. A-scan signals from C-scan measurements are converted to time–frequency representations and fed to an encoder–decoder model that combines a dual convolutional compression network with a fully connected decoder. A thickness-encoding branch enables feature decoupling under physical constraints, and an elliptic spatial fusion strategy refines predictions. Experiments show mean and standard deviation MAEs of 1.08 and 0.84 μm, respectively, with a KL divergence of 0.0031, outperforming attenuation- and velocity-based methods. Input-specificity experiments further indicate that transfer learning calibration quickly restores performance under new conditions. These results demonstrate a practical path for integrating deep learning with ultrasonic inspection for accurate, adaptable grain-size characterization. Full article

Blasting-induced vibrations from new tunnel construction pose a significant threat to the structural safety of existing tunnel linings due to dynamic stress concentration. To address this, this study establishes a transient-response analytical model for composite lining tunnels using wave function expansion and a combination of the Duhamel integral and Fourier transform methods. Through a case study of the Hongshan South Road Tunnel, the research systematically quantifies the influence of critical factors such as load rise time, lining thickness, and material stiffness. Numerical results reveal that under blasting P-wave action, the inner vault of the secondary lining exhibits the most significant dynamic stress concentration, identifying it as the primary vulnerable zone. Furthermore, peak dynamic stress and vibration velocity increase sharply as the load rise time decreases, indicating that short-duration, high-intensity impacts present the greatest hazard. To mitigate these effects, the study identifies several optimization strategies: increasing the thickness of the initial support and employing high-modulus materials effectively reduce stress peaks. Specifically, maintaining the elastic modulus ratio of the surrounding rock to the initial support at approximately 2.0 provides an optimal balance for enhancing blast resistance. The findings suggest that tunnel design should prioritize optimizing the stiffness of the initial support and utilizing grouting to reinforce the surrounding rock. This research provides a robust theoretical framework and specific parameter optimization directions for the seismic and blast-resistant design of composite lining tunnels. Full article

Drone localization is essential for various purposes such as navigation, autonomous flight, and object tracking. However, this task is challenging when satellite signals are unavailable. This paper addresses database-free vision-only localization of flying drones using optimal window template matching and velocity fusion. Assuming the ground is flat, multiple optimal windows are derived from a piecewise linear segment (regression) model of the image-to-real world conversion function. The optimal window is used as a fixed region template to estimate the instantaneous velocity of the drone. The multiple velocities obtained from multiple optimal windows are integrated by a hybrid fusion rule: a weighted average for lateral (sideways) velocities, and a winner-take-all decision for longitudinal velocities. In the experiments, a drone performed a total of six medium-range (800 m to 2 km round trip) and high-speed (up to 14 m/s) maneuvering flights in rural and urban areas. The flight maneuvers include forward-backward, zigzags, and banked turns. Performance was evaluated by root mean squared error (RMSE) and drift error of the GNSS-derived ground-truth trajectories and rigid-body rotated vision-only trajectories. Four fusion rules (simple average, weighted average, winner-take-all, hybrid fusion) were evaluated, and the hybrid fusion rule performed the best. The proposed video stream-based method has been shown to achieve flight errors ranging from a few meters to tens of meters, which corresponds to a few percent of the flight length. Full article

Targeting the thinly bedded and strongly heterogeneous tight sandstone gas reservoirs of the Shaximiao Formation in the Yingshan area of the Sichuan Basin, this study establishes an integrated workflow that combines high-fidelity 3D seismic processing with quantitative interpretation to address key challenges such as insufficient resolution of conventional seismic data under complex near-surface conditions and difficulty in depicting sand-body geometries. On the processing side, a 2D-3D integrated amplitude-preserving high-resolution strategy is applied. In contrast to conventional workflows that treat 2D and 3D datasets independently and often sacrifice true-amplitude characteristics during static correction and noise suppression, the proposed approach unifies first-break picking and static-correction parameters across 2D and 3D data while preserving relative amplitude fidelity. Techniques such as true-surface velocity modeling, coherent-noise suppression, and wavelet compression are introduced. As a result, the effective frequency bandwidth of the newly processed data is broadened by approximately 10–16 Hz relative to the legacy dataset, and the imaging of small faults and narrow river-channel boundaries is significantly enhanced. On the interpretation side, ten sublayers within the first member of the Shaximiao Formation are correlated with high precision, yielding the identification of 41 fourth-order local structural units and 122 stratigraphic traps. Through seismic forward modeling and attribute optimization, a set of sensitive attributes suitable for thin-sandstone detection is established. These attributes enable fine-scale characterization of sand-body distributions within the shallow-water delta system, where fluvial control is pronounced, leading to the identification of 364 multi-phase superimposed channels. Based on attribute fusion, rock-physics-constrained inversion, and integrated hydrocarbon-indicator analysis, 147 favorable “sweet spots” are predicted, and six well locations are proposed. The study builds a reservoir-forming model of “deep hydrocarbon generation–upward migration, fault-controlled charging, structural trapping, and microfacies-controlled enrichment,” achieving high-fidelity imaging and quantitative prediction of tight sandstone reservoirs in the Shaximiao Formation. The results provide robust technical support for favorable-zone evaluation and subsequent exploration deployment in the Yingshan area. Full article

Scrap steel is known to influence the fluid flow characteristics of the melt pool in converter steelmaking. However, few studies have considered the effects of the scrap steel charging structure. In this study, a physical model of a 1:8.8 steel–scrap–gas three-phase flow converter was established to investigate the effects of scrap steel state, distribution, material type and shape on the fluid flow characteristics of the converter melt pool. The velocity distribution within the molten pool was measured using particle image velocimetry, while mixing time under various operating conditions was determined using the stimulus–response method. Considering the melting behaviour of scrap steel and the gas utilisation rate comprehensively, the results indicate that when scrap steel is arranged in a uniform position at the bottom of the converter—comprising 90% medium scrap in rectangular scrap and 10% heavy scrap in thin-plate form—and the gas flow rate is 750 m³/h, the overall dynamic conditions of the melt pool are optimal. At this time, the mixing time is 68.2 s (a reduction of up to 45.4%), average velocity is 0.117 m/s (a maximum increase of 207.9%) and turbulent energy dissipation rate is 0.0266 m²/s³ (a maximum increase of 141.8%). Finally, a relationship was established between stirring power and mixing time at different scrap steel charging structures, providing a methodological reference and data support for optimising the charging structure of scrap steel and efficiently using scrap steel in converters. Full article

Accurate reconstruction of train collision accidents is essential for understanding impact conditions, assessing crashworthiness, and supporting safety improvements. This study proposes a surrogate-based optimization framework for reconstructing structural damage in train collisions from post-accident observations. The pre-impact kinematic state, expressed by a six-dimensional vector of relative offsets, rotations, and impact velocity, is formulated as an inverse problem in which a Sum of Squared Relative Deviations (SSRD) between measured and simulated residual deformations serves as the objective function. A reduced two-vehicle finite element (FE) model is developed to capture the dominant impact dynamics, an Optimal Latin Hypercube Design is used to sample the parameter space, and a Kriging surrogate model is constructed to approximate the response. A simulated annealing algorithm is applied to search for the global minimum. The framework is demonstrated on a real high-speed rear-end collision of electric multiple units. The Kriging model achieves a coefficient of determination of about 0.85, and the optimized kinematic state yields FE-predicted residual deformations that agree with field measurements at key locations to within about 5%. The results show that the method can efficiently reconstruct physically plausible collision scenarios and provide insight into parameter sensitivity and identifiability for railway safety analysis. Full article

Accurate measurement of sea-surface winds is critical for climate science, physical oceanography, and the rapidly expanding offshore wind energy sector. Buoy-based platforms—moored meteorological buoys, drifters, and floating LiDAR systems (FLS)—provide practical alternatives to fixed offshore structures, especially in deep water where bottom-founded installations are economically prohibitive. Yet these floating platforms are subject to continuous pitch, roll, heave, and yaw motions forced by wind, waves, and currents. Such six-degree-of-freedom dynamics introduce multiple error pathways into the measured wind signal. This paper synthesizes the current understanding of motion-induced measurement errors and the techniques developed to compensate for them. We identify four principal error mechanisms: (1) geometric biases caused by sensor tilt, which can underestimate horizontal wind speed by 0.4–3.4% depending on inclination angle; (2) contamination of the measured signal by platform translational and rotational velocities; (3) artificial inflation of turbulence intensity by 15–50% due to spectral overlap between wave-frequency buoy motions and atmospheric turbulence; and (4) beam misalignment and range-gate distortion specific to scanning LiDAR systems. Compensation strategies have progressed through four recognizable stages: fundamental coordinate-transformation and velocity-subtraction algorithms developed in the 1990s; Kalman-filter-based multi-sensor fusion emerging in the 2000s; Response Amplitude Operator modeling tailored to FLS platforms in the 2010s; and data-driven machine-learning approaches under active development today. Despite this progress, key challenges persist. Sensor reliability degrades under extreme sea states precisely when accurate data are most needed. The coupling between high-frequency platform vibrations and turbulence remains poorly characterized. No unified validation framework or benchmark dataset yet exists to compare methods across platforms and environments. We conclude by outlining research priorities: end-to-end deep-learning architectures for nonlinear error correction, adaptive algorithms capable of all-sea-state operation, standardized evaluation protocols with open datasets, and tighter integration of intelligent software with next-generation low-power sensors and actively stabilized platforms. Full article