Search Results (1,208)

The potential for structural health monitoring (SHM) in fibre-reinforced polymers (FRPs) using electrical resistance measurements (ERMs) has gained increasing attention, particularly in carbon fibre-reinforced polymers (CFRPs). Most existing studies are limited to single-axis measurements on coupon-scale specimens, whereas industrial applications demand scalable solutions capable of monitoring large areas, with more complex sensing configurations. Structural health monitoring (SHM) of carbon fibre-reinforced polymers (CFRPs) using electrical resistance measurements offers a low-cost, scalable sensing approach. However, predicting surface resistance between arbitrarily placed electrodes on unidirectional (UD) CFRP laminates remains challenging due to anisotropic conductivity and geometric variability. This study introduces a practical analytical model based on two geometry-dependent parameters, effective width and effective distance, to estimate resistance between any two electrodes arbitrarily placed on UD CFRP laminates with 0° or 90° fibre orientations. Validation through finite element (FE) simulations and experimental testing demonstrates good matching, confirming the model’s accuracy across various configurations. Results show that the dominant electrical current path aligns with the fibre direction due to the material’s anisotropic conductivity, allowing simplification to a single-axis resistance model. The proposed model offers a reliable estimation of surface resistance and provides a valuable tool for electrode array configuration design in CFRP-based SHM. This work contributes to enabling low-cost and scalable electrical sensing solutions for the real-time monitoring of composite structures in aerospace, automotive, and other high-performance applications. Full article

The sensitivity analysis for the design of an automotive push-rod suspension is discussed. Conventional iterative design cycles rely heavily on repeated CAD and Finite Element Method (FEM) analyses. Here, the initial design is based on an alternative and uncommon approach. A pre-CAD diagram of the entire vehicle (for FSAE competition) integrated with drivers is fully parameterized. A series of simulations in which the virtual driver inputs are repeated while the geometry of the suspension varies is executed. A database with isolated geometric effects on suspension loads and performance is obtained. By employing multivariate regression techniques, specifically Response Surface Methodology (RSM), the complex (often nonlinear) relationship between design inputs and structural outputs is mapped. The geometric inputs for this optimization include the coordinates that define the lengths and angles of the suspension triangles, the kingpin angle, the hub length, and aerodynamic downforce coefficients. The key performance indicators analyzed include corner exit speed loss, load and force distribution on the tires and main suspension joints, and the roll and pitch angles of the chassis. This methodology allows for the rapid identification of an optimal design configuration, avoiding trial and error and reducing development time and costs. The proposed framework demonstrates how RSM can enable the configuration of an optimal push-rod design with enhanced performance characteristics and improved manufacturing efficiency. Different case studies based on the mentioned input–output are analyzed to validate the approach in a practical manner. Full article

Scaffolds are widely recognized as implantable alternatives in the field of tissue engineering. Among various scaffold structures, grid structures are commonly used due to their simple design and ease of fabrication. However, grid structures have a critical demerit of low mechanical stiffness compared to its own mechanical property (used material’s compressive stiffness), as the limited contact area between strands prevents effective load distribution. Several structural designs, such as triply periodic minimal surface (TPMS), modified honeycomb, and Kagome structures, have been proposed to improve compressive stiffness. Despite their mechanical advantages, these structures are limited by complex design and manufacturing processes. In this study, we propose a Bead-Chain-Shaped (BCS) scaffold, which maintains the simplicity of grid structures while enhancing compressive stiffness through the printing process alone. To optimize the printing process and enhance fabrication efficiency, we developed an artificial intelligence (AI)-based process optimization model that correlates printing parameters (pressure, printing speed, and delay time) with the resulting geometric accuracy while maintaining the designed geometry, and predicts the optimal printing conditions for the predesigned Bead-Chain-shaped (BCS) geometry. The model was then used to extract these optimal printing conditions, enabling precise dimensional control and improving overall fabrication accuracy of the Bead-Chain-Shaped (BCS) scaffold dimensions. Under the optimized printing conditions, the BCS scaffolds achieved compressive stiffness values of 61.8, 75.9, and 91.6 MPa for BCS 5545, 6040, and 6535, respectively, corresponding to increases of 11.9%, 37.3, and 65.7% compared to the control scaffold (55.3 MPa). Numerical analysis confirmed that compressive stiffness increases as strand-to-strand contact area increases. Furthermore, in vitro cell proliferation assays demonstrated no significant difference in cell proliferation compared to conventional structures (grid-structure scaffold), indicating that the proposed design does not inhibit cellular growth. These results highlight the potential of the proposed Bead-Chain-Shaped (BCS) scaffold as a promising candidate for bone tissue engineering, offering both enhanced mechanical stiffness and fabrication efficiency. Full article

Atomically precise control of metal adatoms on metal surfaces is critical for designing novel low-dimensional materials, and the Sn-Cu(111) system is of particular interest due to the potential of stanene in topological physics. However, conflicting reports on Sn-induced superstructures on Cu(111) highlight the need for clarifying their geometric and electronic properties at low temperatures. We employed scanning tunneling microscopy (STM), low-energy electron diffraction (LEED), angle-resolved photoemission spectroscopy (ARPES), and density functional theory (DFT) to investigate submonolayer (<0.25 ML) Sn adsorption on Cu(111) at 100 K. We confirmed a p(2 × 2) Sn/Cu(111) superstructure with one Sn atom per unit cell and found that Sn preferentially occupies three-fold hcp sites. ARPES measurements of the band structure—including a ~0.3 eV local gap between two specific bands at the ${\overline{\Gamma }}_2$ point in a metallic overall electronic structure—were in good agreement with the DFT results. Notably, the STM-observed p(2 × 2) morphology differs from the honeycomb-like or buckled stanene structures reported on Cu(111), which highlights the intricate interactions between adatoms and the substrate. Full article

Key detection performance metrics, particularly resolution, are largely determined by the design parameters of ultrasonic arrays. The structural design of the transducer strongly influences critical indicators, including side lobe levels, beam directivity, and focal spot size. To improve parameter selection, this study proposes a multi-objective optimization strategy specifically tailored for cylindrical surface ultrasonic transducers. The geometric parameters of the array and the variables influencing resolution performance are mapped in a nonlinear manner. The NSGA-II algorithm is employed to perform extremum seeking optimization on a trained BPNN, generating a Pareto-optimal solution set by specifying main-lobe width, side-lobe intensity, and sound-pressure uniformity as optimization objectives. For validation, the geometric configurations derived from this solution set are applied in acoustic field simulations. Simulation results demonstrate that the dynamic aperture exhibits clear regularity when the array settings meet millimeter-level resolution requirements. These findings support real-world engineering applications and provide valuable insights for enhancing the geometric design of cylindrical ultrasonic arrays. Full article

The labyrinth weir is an effective hydraulic structure, offering high discharge efficiency and economic advantages, making it a suitable option for dam construction or rehabilitation projects. Owing to its complex geometry, significant research efforts have been dedicated to enhancing its hydraulic performance. Since the beginning of this century, Computational Fluid Dynamics (CFD) has emerged as a vital approach, complementing traditional methods in the design of hydraulic structures. This study employs CFD ANSYS FLUENT to examine the discharge coefficient of a semicircular labyrinth weir, featuring a cyclic arrangement and a half-round crest profile. The numerical models and simulations address two-phase flow (air and water) under incompressible and free-surface conditions. The CFD ANSYS FLUENT approach used is multiphase flow modeling using the Volume of Fluid method to track the free water surface. For turbulence effects, it is complemented with the standard k-ε model and the Semi-Implicit Method for Pressure Linked Equations algorithm for pressure–velocity coupling. In addition, for boundary conditions, the flow velocity was defined as the inlet to the channel and atmospheric pressure as the outlet, and the walls of the channel and weir are considered solid, stationary, and non-sliding walls. The model was validated with experimental data reported in the literature. The results indicate that the semicircular labyrinth weir achieves greater discharge capacity when the headwater ratio H_T/P increases for H_T/P ≤ 0.25. A regression analysis mathematical model was also developed, using the H_T/P ratio, to predict the discharge coefficient for 0.05 ≤ H_T/P ≤ 1. Relative to other geometrical configurations, the semicircular labyrinth weir demonstrated a discharge capacity that was up to 88% higher than that of the trapezoidal labyrinth weir. Both weir and cycle efficiency were assessed, and maximum weir efficiency was observed when H_T/P ≤ 0.1, while cycle efficiency peaked at H_T/P ≤ 0.25. The geometric configuration under analysis demonstrated greater economic efficiency by providing a reduced total length and enhanced discharge capacity relative to trapezoidal designs, especially when the sidewall angle α is considered as α ≤ 12°. The study concludes by presenting a design sequence detailing the required concrete volume for construction, which is subsequently compared to the specifications of a trapezoidal labyrinth weir. Full article

This study presents an analysis of two titanium nitride-based coatings, TiCN and TiN:Ag, deposited on Ti6Al4V alloy by physical vapour deposition (PVD). The investigation focused on the characterisation of surface geometric structure and wettability, tribological parameters, and osseointegration. The purpose of the study was to obtain a better understanding of the interactions between the implant surface and the surrounding tissues and body fluids, which are essential for ensuring long-term durability. The results revealed significant differences in surface stereometric parameters between the coatings, with TiN:Ag exhibiting higher roughness values. These variations were reflected in the wettability tests, where the coating with a more developed surface topography (TiN:Ag) demonstrated contact angle values approximately 15% higher than those of TiCN. In contrast, tribological tests indicated superior performance of the TiCN coating, which exhibited lower coefficients of friction in artificial saliva at pH 5.8 and 6.8—reduced by 20% and 36%, respectively, compared with TiN:Ag. The findings confirmed that surface topography exerts a decisive influence on both wettability and tribological behaviour of the coatings, aspects that must be considered in their design for implantological applications. Full article

This study examines the effect of an AlTiN/TiSiXN two-layer coating on the tribological performance of HS6-5-2C steel under dry friction conditions. Tribological assessments were conducted using a tribometer and a calotester with a ball-on-disc configuration, involving HS6-5-2C steel discs (both uncoated and coated with AlTiN/TiSiXN) and 100Cr6 steel balls. Analyses, including surface topography, microstructure, and chemical composition, were performed utilising confocal microscopy, atomic force microscopy, and scanning electron microscopy with energy dispersive spectroscopy. The hardness and elastic modulus of the coating and substrate were determined through nanoindentation techniques. The coating exhibited a hardness of approximately 38 GPa and high elasticity, substantially enhancing the tribological characteristics of the system. Notably, the coated specimens exhibited friction coefficients approximately 10% lower than those of the uncoated steel, while wear on the coated discs was reduced by more than 90% in comparison to their uncoated counterparts. Wear rate evaluations of the counter-samples indicated a slightly increased wear of the balls—approximately 21%—when in contact with the coated discs, which can be attributed to the high hardness of the coating. These results substantiate the superior efficacy of the AlTiN/TiSiXN coating in improving wear resistance and reducing friction. Full article

Biomimetic superhydrophobic surfaces have become a focal point of recent research, driven by their promise in diverse applications. Among these, the lotus and rose effects are of particular interest due to their contrasting adhesion characteristics. Given that superhydrophobicity is closely related to the hierarchical structures of these surfaces, investigating the effects of two-level roughness on superhydrophobicity is crucial. In our previous work, we introduced a wetting parameter (W_Roughness), strongly correlated with the geometric characteristics of surface roughness, to elucidate the superhydrophobic behavior of solid surfaces. This parameter predicts the existence of a critical wetting parameter (W_Roughness,c) during the Wenzel–Cassie transition. For two-level surface roughness composed of primary and secondary roughness, the W_Roughness of the two-level surface is influenced by the geometric characteristics of both primary and secondary roughness. Furthermore, when secondary roughness is added to a primary roughness surface in the Wenzel state, the resulting two-level roughness can exhibit various superhydrophobic states, such as the Wenzel state, Wenzel–Cassie transition, or Cassie state, depending on the characteristics of the secondary roughness. To further investigate the influence of two-level roughness on superhydrophobicity, molecular dynamics (MD) simulations were also conducted. Full article

The automatic reconstruction of existing buildings has gained momentum through the integration of Building Information Modeling (BIM) into architecture, engineering, and construction (AEC) workflows. This study presents a hybrid methodology that combines deep learning with surface-based techniques to automate the generation of 3D models and 2D floor plans from unstructured indoor point clouds. The approach begins with point cloud preprocessing using voxel-based downsampling and robust statistical outlier removal. Room partitions are extracted via DBSCAN applied in the 2D space, followed by structural segmentation using the RandLA-Net deep learning model to classify key building components such as walls, floors, ceilings, columns, doors, and windows. To enhance segmentation fidelity, a density-based filtering technique is employed, and RANSAC is utilized to detect and fit planar primitives representing major surfaces. Wall-surface openings such as doors and windows are identified through local histogram analysis and interpolation in wall-aligned coordinate systems. The method supports complex indoor environments including Manhattan and non-Manhattan layouts, variable ceiling heights, and cluttered scenes with occlusions. The approach was validated using six datasets with varying architectural characteristics, and evaluated using completeness, correctness, and accuracy metrics. Results show a minimum completeness of 86.6%, correctness of 84.8%, and a maximum geometric error of 9.6 cm, demonstrating the robustness and generalizability of the proposed pipeline for automated as-built BIM reconstruction. Full article

A shallow, seasonal snowpack is rarely homogeneous in depth, layer characteristics, or surface structure throughout an entire winter. Aerodynamic roughness length ($z_0$) is typically considered a static parameter within hydrologic and atmospheric models. Here, we present observations showing $z_0$ as a dynamic variable that is a function of snow depth ($d_s$). This has a significant impact on sublimation modeling, especially for shallow snowpacks. Terrestrial LiDAR data were collected at nine different study sites in northwest Colorado from the 2019 to 2020 winter season to measure the spatial and temporal variability of the snowpack surface. These data were used to estimate the geometric $z_0$ from 91 site visits. Values of $z_0$ decrease during initial snow accumulation, as the snow conforms to the underlying terrain. Once the snowpack is sufficiently deep, which depends on the height of the ground surface roughness features, the surface becomes more uniform. As melt begins, $z_0$ increases, when the snow surface becomes more irregular. The correlation value of $z_0$ was altered by human disturbance at several of the sites. The $z_0$ versus $d_s$ correlation was almost constant, regardless of the initial roughness conditions that only affected the initial $z_0$. Full article

Tunnels, as symmetric structures, are critical components of transportation infrastructure, particularly in mountainous regions. However, tunnel linings are prone to spalling after long-term service, posing significant safety risks. Although 3D laser scanning enables remote measurement of tunnel linings, existing surface fitting methods face challenges such as insufficient accuracy and high computational cost in quantifying spalling parameters. To address these issues, this study leverages the symmetrical geometry of tunnels to propose a curvature variance-based threshold segmentation method using limited point cloud data. First, the tunnel center axis is accurately determined via Sequential Quadratic Programming and the Quasi-Newton method. Noise and outliers are then removed based on geometric properties. Triangular meshes are constructed, and curvature variance is used as a threshold to extract spalling regions. Finally, surface reconstruction is applied to quantify spalling extent. Experiments in both laboratory and fire-damaged tunnel environments demonstrate that the method accurately extracts and quantifies lining spalling, with an average error of approximately 9.70%. This study underscores the potential of the proposed approach for broad application in tunnel inspection, as it will provide a basis for assessing the structural safety of tunnel linings. Full article

This study is an initial study for the development of a large truss-type deployable mesh antenna, and it involved the development process of a 1.5 m-scale deployable mesh antenna. The geometric characteristics of the reflector were considered for the initial net design. Based on the antenna’s operating frequency, the L-band, the surface root mean square (RMS) error and focal length/diameter (F/D) ratio of the reflector were calculated. Design requirements for the antenna’s weight, stowed/deployed dimensions, and fundamental frequency were established. The material properties of each component were applied to the design model, and the geometric dimensions were verified to ensure that the weight and stowed/deployed design were fulfilled. The fundamental frequency requirements under stowed/deployed conditions were verified through modal analysis, and the structural deformation of the ring truss was confirmed through load analysis. The reflector antenna was assembled to the ring truss with the net and mesh, according to the assembly procedure. The curvature of the reflector surface was shaped by adjusting the bolt length of the tension control device. Using V-Stars, a specialized surface error measurement device, the surface RMS error requirements for the reflector were confirmed to be satisfied. Finally, the development verification of the antenna was completed by performing repeated deployment and a thermal vacuum test. Full article

The external floating roof of a large storage tank directly covers the liquid surface as the liquid level rises and falls, enhancing the tank’s safety and environmental performance. It is fabricated from thin SA516 Gr.70 steel plates, with a carbon equivalent of 0.37% calculated according to AWS standards, using single-sided butt welding. Such plates are susceptible to welding-induced deformations, resulting in irregular warping of the bottom plate. Current research on floating roofs for storage tanks mostly relies on idealized models that assume no deformation, thereby neglecting the actual deformation characteristics of the floating roof structure. To address this, the present study develops an automated modeling approach that reconstructs a three-dimensional floating roof model based on measured deformation data, accurately capturing the initial irregular geometry of the bottom plate. This method employs parametric numerical reconstruction and automatic finite element model generation techniques, enabling efficient creation of the irregular initial deformation caused by welding of the floating roof bottom plate and its automatic integration into the finite element analysis process. It overcomes the inefficiencies, inconsistent accuracy, and challenges associated with traditional manual modeling when conducting large-scale strength analyses under in-service conditions. Based on this research, a strength analysis of the deformed floating roof structure was conducted under in-service conditions, including normal floating, extreme rainfall, and outrigger contact scenarios. An idealized geometric model was also established for comparative analysis. The results indicate that under the normal floating condition, the initial irregular deformation increases the local stress peak of the floating roof bottom plate by 19%, while the maximum positive and negative displacements increase by 22% and 83%, respectively. Under extreme uniform rainfall conditions, it raises the stress peak of the bottom plate by 24%, with maximum positive and negative displacements increasing by 21% and 28%, respectively. Under the extreme non-uniform rainfall condition, it significantly elevates the stress peak of the bottom plate by 227%, and the maximum positive and negative displacements increase by 45% and 47%, respectively. Under the outrigger bottoming condition, it increases the local stress peak of the bottom plate by 25%, with maximum positive and negative displacements remaining similar. The initial irregular deformation not only significantly amplifies the stress and displacement responses of the floating roof bottom plate but also intensifies the deformation response of the top plate through structural stiffness weakening and deformation coupling, thereby reducing the safety margin of the floating roof structure. This study fills the knowledge gap regarding the effect of welding-induced irregular deformation on floating roof performance and provides a validated workflow for automated modeling and mechanical assessment of large-scale welded steel structures. Full article

Research on surfaces generated by curves plays a central role in linking differential geometry with physical applications, especially following Hasimoto’s transformation and the development of Hasimoto-inspired surface models. In this work, we introduce a new class of such surfaces, referred to as RM Hasimoto surfaces, constructed by employing the rotation-minimizing (RM) Darboux frame along both timelike and spacelike curves in Minkowski 3-space ${\mathbb{E}}_1^3$. In contrast to the classical Hasimoto surfaces defined via the Frenet or standard Darboux frames, the RM approach eliminates torsional difficulties and reduces redundant rotational effects. This leads to more straightforward expressions for the first and second fundamental forms, as well as for the Gaussian and mean curvatures, and facilitates a clear classification of key parameter curves. Furthermore, we establish the associated evolution equations, analyze the resulting geometric invariants, and present explicit examples based on timelike and spacelike generating curves. The findings show that adopting the RM Darboux frame provides greater transparency in Lorentzian surface geometry, yielding sharper characterizations and offering new perspectives on relativistic vortex filaments, magnetic field structures, and soliton behavior. Thus, the RM framework opens a promising direction for both theoretical studies and practical applications of surface geometry in Minkowski space. Full article