Search Results (5,009)

This study investigates crack healing of AISI 1020 steel using localized induction heating with a pancake coil. A wire-cut slit sample and a repetitive-bent sample containing fine cracks were subjected to induction heating. Geometrical changes in the slit and cracks before and after heating were evaluated. Healing of fine cracks and local melting of the slit tip were observed. Numerical simulations were conducted to understand the current flow, current density distribution and Joule heating behavior within the samples. Results showed that current detours around cracks and concentrates at crack tips during induction heating. This enables the ability of induction heating to selectively locate and treat cracks effectively. Localized induction heating using a pancake coil enhances the crack-healing effectiveness by providing a non-singular current flow direction within the material. It also offers the flexibility to treat a specific localized region in a component. While localized induction heating demonstrates strong potential for crack-healing applications, its effectiveness is primarily limited to fine surface cracks. Full article

Omnidirectional acoustic sources play a critical role in accurate acoustic measurements, particularly in assessing parameters such as reverberation time and sound insulation. Traditionally, dodecahedral loudspeakers have been the standard for these purposes due to their geometric symmetry and uniform radiation patterns. However, recent developments have explored alternative geometries to enhance performance and expand application potential. This study presents the design and implementation of an omnidirectional source based on an icosidodecahedron geometry, which introduces a more complex mathematical formulation but offers promising acoustic characteristics. The proposed source is not only evaluated in terms of its theoretical and practical advantages, but it is also a self-fabrication initiative to strengthen the laboratory infrastructure of the Sound Engineering program in Bogotá, Colombia. Finally, a series of objective measurements is conducted to validate the performance of the source in realistic listening scenarios. Full article

This paper introduces an iceberg-based digital risk twin (DRT) framework for the health monitoring of complex engineering systems. The proposed model transforms multidimensional sensor and contextual data into a structured, interpretable three-dimensional geometry that captures both observable and latent risk components. Each monitored parameter is represented as a vertical geometric sheet whose height encodes a normalized risk level, producing an evolving iceberg structure in which the visible and submerged regions distinguish emergent anomalies from latent degradation. A formal mathematical formulation is developed, defining the mappings from the risk vector to geometric height functions, spatial layout, and surface composition. The resulting parametric representation provides both analytical tractability and intuitive visualization. A case study involving an aircraft fuel system demonstrates the capacity of the DRT to reveal dominant risk drivers, parameter asymmetries, and temporal trends not easily observable in traditional time-series analysis. The model is shown to integrate naturally into AI-enabled health management pipelines, providing an interpretable intermediary layer between raw data streams and advanced diagnostic or predictive algorithms. Owing to its modular structure and domain-agnostic formulation, the DRT approach is applicable beyond aviation, including power grids, rail systems, and industrial equipment monitoring. The results indicate that the iceberg representation offers a promising foundation for enhancing explainability, situational awareness, and decision support in the monitoring of complex engineering systems. Full article

The phaeozem in Northeast China is rich in soil organic carbon (SOC). However, the excessive and inefficient application of chemical fertilizers, particularly nitrogen fertilizers, has primarily led to a decrease in soil pH in this region. Currently, the relationship between soil pH and the stability of soil organic carbon (SOC) remains ambiguous. This study, conducted over 13 years of field experiments, focused on soils exhibiting varying degrees of pH resulting from different nitrogen application rates. The research employed aggregate classification, ¹³C nuclear magnetic resonance spectroscopy, and analysis of microbial community composition to investigate the alterations in the SOC stabilization mechanisms under varying nitrogen application levels. Our results demonstrated that the decline in soil pH led to reductions in macroaggregates (>2 mm) and the soil aggregate destruction rate (PAD) by 4.8–14.6%, and in soil aggregate unstable agglomerate index (E_LT) by 9.7–13.4%. The mean weight diameter (MWD) and geometric mean diameter (GMD) exhibited significant declines (p < 0.05) with decreasing pH levels. According to the ¹³C NMR analysis, the SOC was predominantly composed of O-alkyl carbon and aromatic carbon. At a pH of 5.32, the Alip/Arom values decreased, while the molecular structure of SOC became more complex under different levels of pH. In addition, the increase in [Fe(Al)-OC] (31.4–71.9%) complex indicates a shift in the stability of organic carbon from physical protection to organic mineral binding. Declining soil pH significantly reduced the diversity of soil microbial communities and promoted a shift toward copiotrophic microbial groups. Overall, declining soil pH resulted in a decline in soil aggregate stability and an increase in SOC aromaticity. This drove the shift in the stabilization mechanism of SOC in the black soil ecosystem of meadows in Northeast China from physical protection to chemical stability. Full article

With the development of the Internet of Things (IoT) and smart industry, the demand for high-precision indoor positioning is becoming increasingly urgent. Ultra-ideband (UWB) technology has become a research hotspot due to its centimeter-level ranging accuracy, good penetration, and high multipath resolution. However, in complex environments, it still faces problems such as high cost of anchor node layout, gross errors in observation data, and difficulty in eliminating systematic errors such as electronic time delay. To address the aforementioned problems, this paper proposes a comprehensive UWB indoor positioning scheme. By constructing virtual reference stations to enhance the observation network, the geometric structure is optimized and the dependence on physical anchors is reduced. Combined with a gross error elimination method under short-baseline constraints and a double-difference positioning model including virtual observations, it systematically suppresses systematic errors such as electronic delay. Additionally, a quality control strategy with velocity constraints is introduced to improve trajectory smoothness and reliability. Static experimental results show that the proposed double-difference model can effectively eliminate systematic errors. For example, the positioning deviation in the Xdirection is reduced from approximately 2.88 cm to 0.84 cm, while the positioning accuracy in the Ydirection slightly decreases. Overall, the positioning accuracy is improved. The gross error elimination method achieves an identification efficiency of over 85% and an accuracy of higher than 99%, providing high-quality observation data for subsequent calculations. Dynamic experimental results show that the positioning trajectory after geometric enhancement of virtual reference stations and velocity-constrained quality control is highly consistent with the reference trajectory, with significantly improved trajectory smoothness and reliability. In summary, this study constructs a complete technical chain from data preprocessing to result quality control, effectively improving the accuracy and robustness of UWB positioning in complex indoor environments, and exhibits promising engineering application potential. Full article

Drone-view mixed reality (MR) in the Architecture, Engineering, and Construction (AEC) sector faces significant self-localization challenges in low-texture environments, such as bare concrete sites. This study proposes an adaptive sensor fusion framework integrating thermal and visible light (RGB) imagery to enhance tracking robustness for diverse site applications. We introduce the Effective Inlier Count ($N_{eff}$) as a lightweight gating mechanism to evaluate the spatial quality of feature points and dynamically weigh sensor modalities in real-time. By employing a $20\times 16$ grid-based spatial filtering algorithm, the system effectively suppresses the influence of geometric burstiness without significant computational overhead on server-side processing. Validation experiments across various real-world scenarios demonstrate that the proposed method maintains high geometric registration accuracy where traditional RGB-only methods fail. In texture-less and specular conditions, the system consistently maintained an average Intersection over Union (IoU) above 0.72, while the baseline suffered from complete tracking loss or significant drift. These results confirm that thermal-RGB integration ensures operational availability and improves long-term stability by mitigating modality-specific noise. This approach offers a reliable solution for various drone-based AEC tasks, particularly in GPS-denied or adverse environments. Full article

Autonomous navigation in cluttered and dynamic industrial environments remains a major challenge for mobile robots. Traditional occupancy-grid and geometric planning approaches often struggle in such unstructured settings due to partial observability, sensor noise, and the frequent presence of moving agents (machinery, vehicles, humans). These limitations seriously undermine long-term reliability and safety compliance—both essential for Industry 4.0 applications. This paper introduces S3PM, a lightweight entropy-regularized framework for simultaneous mapping and path planning that operates directly on dense 3D point clouds. Its key innovation is a dynamics-aware entropy field that fuses per-voxel occupancy probabilities with motion cues derived from residual optical flow. Each voxel is assigned a risk-weighted entropy score that accounts for both geometric uncertainty and predicted object dynamics. This representation enables (i) robust differentiation between reliable free space and ambiguous/hazardous regions, (ii) proactive collision avoidance, and (iii) real-time trajectory replanning. The resulting multi-objective cost function effectively balances path length, smoothness, safety margins, and expected information gain, while maintaining high computational efficiency through voxel hashing and incremental distance transforms. Extensive experiments in both real-world and simulated settings, conducted on a Raspberry Pi 5 (with and without the Hailo-8 NPU), show that S3PM achieves 18–27% higher IoU in static/dynamic segmentation, 0.94–0.97 AUC in motion detection, and 30–45% fewer collisions compared to OctoMap + RRT* and standard probabilistic baselines. The full pipeline runs at 12–15 Hz on the bare Pi 5 and 25–30 Hz with NPU acceleration, making S3PM highly suitable for deployment on resource-constrained embedded platforms. Full article

A systematic density functional theory (DFT) and time-dependent DFT (TD-DFT) investigation of aluminum phosphide (Al_xP_x) nanoparticles with diverse dimensionalities and geometries is presented. Starting from a cubic-like Al₄P₄ building block, a series of one-dimensional (1D) elongated, two-dimensional (2D) exotic, and extended sheet-like nanostructures was constructed, enabling a unified structure–property analysis across size and topology. Optical absorption and infrared (IR) vibrational spectra were computed and correlated with geometric motifs, revealing pronounced shape-dependent tunability. Compact and highly interconnected 2D architectures exhibit red-shifted absorption and enhanced vibrational polarizability, whereas elongated or low-connectivity motifs lead to blue-shifted optical responses and stiffer vibrational frameworks. Benchmark comparisons indicate that CAM-B3LYP excitation energies closely reproduce reference EOM-CCSD trends for the lowest singlet states. Binding energy and HOMO-UMO gap analyses confirm increasing thermodynamic stability with size and dimensionality, alongside topology-driven electronic modulation. These findings establish AlP nanostructures as highly tunable platforms for optoelectronic and vibrationally active applications. Full article

To address the limitations of traditional timber mortise-and-tenon joints, particularly their low pull-out resistance and rapid stiffness degradation under cyclic loading, this study proposes a novel integrated sleeve mortise-and-tenon steel–timber composite beam–column joint. Building upon prior experimental validation and numerical model verification, a comprehensive parametric study was conducted to systematically investigate the influence of key geometric parameters on the seismic performance of the joint. The investigated parameters included beam sleeve thickness (1–10 mm), sleeve length (150–350 mm), bolt diameter (4–16 mm), and the dimensions and thickness of stiffeners. The results indicate that a sleeve thickness of 2–3 mm yields the optimal overall performance: sleeves thinner than 2 mm are prone to yielding, while those thicker than 3 mm induce stress concentration in the timber beam. A sleeve length of approximately 250 mm provides the highest initial stiffness and a ductility coefficient exceeding 4.0, representing the best seismic behavior. Bolt diameters within the range of 8–10 mm produce full and stable hysteresis loops, effectively balancing load-carrying capacity and energy dissipation; smaller diameters lead to pinching failure, whereas larger diameters trigger premature plastic deformation in the timber. Furthermore, stiffeners with a width of 40 mm and a thickness of 2 mm effectively enhance joint stiffness, promote a uniform stress distribution, and mitigate local damage. The optimized joint configuration demonstrates excellent ductility, stable hysteretic behavior, and a high load capacity, providing a robust technical foundation for the design and practical application of advanced steel–timber composite connections. Full article

Accurately characterizing the structural state of membrane reflector antennas (MRA) remains challenging due to the difficulty in determining stress distribution through geometric measurement alone. Although photogrammetry provides high-precision geometric data, it falls short of capturing mechanical pre-tension and is notably influenced by gravity, which limits its utility in guiding surface accuracy adjustments. This paper proposed an integrated approach combining photogrammetry with a nonlinear finite element method (NFEM) to achieve high-fidelity imaging and effective shape adjustment of electrostatically formed MRA, explicitly accounting for gravity effects during ground-based measurement and shape control. The proposed method establishes a mechanical model that incorporates real-world geometric data under gravity and performs force–shape matching to reconcile discrepancies between physical and simulation models. Experimental validation demonstrates that the gravity-corrected NFEM model closely aligns with the physical antenna, with a deviation in surface accuracy within 9.9%. Using this refined model, we successfully optimized electrode voltages and cable tensions, improving the surface accuracy of the physical model from an initial 0.7033 mm to 0.5723 mm. This work provides a reliable and efficient strategy for the shape control and adjustment of membrane space structures under gravity, with potential applications in large deployable antennas, solar sails, and other tension-controlled flexible systems. Full article

Gas-inducing reactors (GIRs) are widely used in applications where external gas recycling is unsafe or operationally restricted, yet quantitative design guidelines for impeller–stator geometry remain scarce, despite its strong influence on gas dispersion and retention. This study investigates the effects of stator blade number and blade thickness on gas holdup in a double-impeller GIR using a three-dimensional Euler–Euler CFD framework. Stator configurations with 12–48 blades and blade thicknesses of 1.5–45 mm were examined and validated against experimental data, with gas holdup predictions agreeing within 5–10%. The results show that the stator open-area fraction (ϕ_A) is the dominant geometric parameter governing the balance between radial dispersion and axial confinement. High-ϕ_A stators (fewer, thinner blades) enhance bulk recirculation and bubble residence time, increasing gas holdup by up to ~20% relative to dense stator designs, whereas low-ϕ_A stators suppress macro-circulation, promote axial gas transport, and reduce holdup despite higher local dissipation near the rotor–stator gap. A modified gas-holdup correlation incorporating ϕ_A is proposed, yielding strong agreement with CFD and experimental data (R² = 0.96). Torque analysis further reveals competing effects between impeller gassing, which lowers hydraulic loading, and increased flow resistance at low ϕ_A, which elevates torque. Overall, the results provide quantitative guidance on how stator blade number and thickness influence gas holdup, enabling informed stator design and optimization in GIRs to improve gas dispersion through rational geometric selection rather than trial and error approaches. Full article

In building measurement using terrestrial laser scanners (TLSs), acquired 3D point clouds (3DPCs) often contain significant reflection artifacts caused by reflective glass surfaces. Such reflection artifacts significantly degrade the performance of downstream applications. This study proposes a novel strategy, called GRASS, to remove these reflection artifacts. Specifically, candidate glass points are identified based on multi-echo returns caused by glass components. These potential glass regions are then refined through planar segmentation using geometric constraints. Then, we trace laser beam trajectories to identify the reflection affected zones based on the estimated glass planes and scanner positions. Finally, reflection artifacts are identified using dual criteria: (1) Reflection symmetry between artifacts and their source entities across glass components. (2) Geometric similarity through a 3D deep neural network. We evaluate the effectiveness of the proposed solution across a variety of 3DPC datasets and demonstrate that the method can reliably estimate multiple glass regions and accurately identify virtual points. Furthermore, both qualitative and quantitative evaluations confirm that GRASS outperforms existing methods in removing reflection artifacts by a significant margin. Full article

Understanding particle transport in rough-walled fractures is essential for predicting flow behavior, clogging, and permeability evolution in natural and engineered subsurface systems. This study develops a fully coupled CFD–DEM framework to investigate how self-affine fractal roughness, represented by the Joint Roughness Coefficient (JRC), governs fluid–particle interactions across multiple scales. Nine fracture geometries with controlled roughness were generated using a fractal-based surface model, enabling systematic isolation of roughness effects. The results show that increasing JRC introduces a hierarchy of geometric perturbations that reorganize the flow field, amplify shear and velocity-gradient fluctuations, and enhance particle–wall interactions. Particle migration exhibits a nonlinear response to roughness due to the competing influences of disturbance amplification and the formation of preferential high-velocity pathways. Furthermore, roughness-controlled scaling relations are identified for mean particle velocity, residence time, and energy dissipation, revealing JRC as a fundamental parameter linking geometric complexity to transport efficiency. Based on these findings, a unified mechanistic framework is established that conceptualizes fractal roughness as a multiscale geometric forcing mechanism governing hydrodynamic heterogeneity, particle dynamics, and dissipative processes. This framework provides new physical insight into transport behavior in rough fractures and offers a scientific basis for improved prediction of clogging, proppant placement, and transmissivity evolution in subsurface engineering applications. Full article

The particle exhaust system plays a pivotal role in fusion reactors and is essential for ensuring both the feasibility and sustained operation of the fusion reaction. For the successful development of such a system, density control is of great importance and some key design parameters include the neutral gas pressure and the resulting particle fluxes. This study presents a simplified conductance-based model for estimating neutral gas pressure distributions in the particle exhaust system of fusion reactors, focusing specifically on the sub-divertor region. In the proposed model, the pumping region is represented as an interconnected set of reservoirs and channels. Mass conservation and conductance relations, appropriate for all flow regimes, are applied. The model was benchmarked against complex 3D DIVGAS simulations across representative operating scenarios of the Wendelstein 7-X (W7-X) stellarator. Despite geometric simplifications, the model is capable of predicting pressure values at several key locations inside the particle exhaust area of W7-X, as well as various types of particle fluxes. The developed model is computationally efficient for large-scale parametric studies, exhibiting an average deviation of approximately 20%, which indicates reasonable predictive accuracy considering the model simplifications and the flow problem complexity. Its application may assist early-stage engineering design, pumping performance improvement, and operational planning for W7-X and other future fusion reactors. Full article

Slender structures such as minarets are highly susceptible to earthquake-induced damage in seismically active regions. Although various methods, including analytical and observational techniques, have been employed to study the seismic response of reinforced concrete (RC) minarets, the use of scaled physical models and shaking table testing remains limited. This research examines the numerical feasibility of employing scaled physical models for shaking table investigations of RC minarets under realistic laboratory constraints. A representative RC minaret with a height of 33.2 m was selected and a geometric scale ratio of 1:10 length was adopted. Established physical modeling approaches were evaluated through numerical implementation, with particular attention to similitude requirements, material properties, and laboratory limitations. Within this framework, the Artificial Mass Model (AMM) and the Neglected Gravity Model (NGM) were examined as candidate strategies for scaled modeling. Both approaches necessitate the use of a material with a reduced modulus of elasticity or an increased mass density relative to the prototype material. To satisfy these requirements, two micro-concrete mixes, designated as Mix-1 and Mix-2, incorporating partial replacement of the binder with lower-stiffness constituents such as plaster gypsum and fly ash, were developed and characterized. Numerical results indicate that both the AMM and NGM approaches are viable for modeling slender RC minaret structures. Although the AMM provides slightly higher accuracy in reproducing the prototype dynamic response, the NGM offers greater practical applicability by eliminating the need for additional artificial mass. Overall, this study presents a preliminary numerical feasibility assessment that supports the selection of appropriate physical modeling strategies and provides a rational basis for the subsequent execution of shaking table experiments. Full article