Search Results (447)

To clarify the influence of gouge-induced altered layers on fracture initiation and load-bearing capacity of pipelines, X70 pipeline steel is taken as the research object. The geometry and partition of the altered layer are first determined by means of a micro-Vickers hardness array and a threshold criterion, and its mechanical parameters are then obtained from small-scale tensile tests. The altered layer is subsequently embedded into a finite element model of a gouged pipe as an independent material domain, and the Gurson–Tvergaard–Needleman (GTN) damage model is employed to simulate damage evolution and crack propagation under pure internal pressure and combined internal pressure and tensile loading. The results indicate that, compared with the base metal, the yield strength and ultimate tensile strength of the altered layer increase by about 39% and 47%, respectively, while the elongation to failure decreases from 16% to 1.8%, exhibiting a typical “high-strength–low-ductility” behavior. When the altered layer is considered, the fracture initiation location under pure internal pressure shifts from the base metal to the altered layer, and the burst pressure decreases from 19 MPa to 16.5 MPa. Under the combined internal pressure and tensile loading, the peak load changes little, whereas the ultimate displacement is reduced by about 26.5%, leading to a marked loss of pipeline ductility. These findings demonstrate that the gouge-induced altered layer has a significant effect on the fracture initiation pressure, failure mode, and load-bearing characteristics of gouged pipes. Modeling it as an independent material domain in finite element analysis can more realistically capture the failure behavior and safety margin of gouged pipelines, thereby providing a more reliable theoretical basis for improving integrity assessment criteria for externally damaged pipelines. Full article

Coronary artery disease (CAD) remains the leading cause of cardiovascular mortality worldwide. Accurate and non-invasive quantification of coronary hemodynamics, namely in the right coronary artery (RCA), is essential for clinical decision-making but remains challenging due to the complex interaction among vessel geometry, pulsatile flow, and blood rheology. This study presents and validates a transparent computational framework for non-invasive fractional flow reserve (FFR) estimation using patient-specific RCA geometries reconstructed from coronary computed tomography angiography (CCTA) using SimVascular 27-03-2023. The proposed workflow integrates realistic boundary conditions through a Womersley velocity profile and a three-element Windkessel outlet model, coupled with a viscoelastic blood rheology formulation (sPTT) implemented via user-defined functions (UDFs). This work integrates all clinically relevant conditions of invasive FFR assessment into a single patient-specific computational framework, while delivering results within a time frame compatible with clinical practice, representing a meaningful methodological advance. The methodology was applied to seven patient-specific cases, and the resulting non-invasive FFR values were compared with both invasive wire-based measurements and commercial HeartFlow^® outputs (Mountain View, CA, USA). Under hyperemic conditions, the computed FFR values showed strong agreement with invasive references, with a mean relative error of 8.4% ± 6.3%, showing diagnostic consistency similar to that of HeartFlow^® (8.3% ± 8.1%) for the selected dataset. These findings demonstrate the ability of the proposed CFD-based pipeline to accurately replicate physiological coronary behavior under hyperemia. This novel workflow provides a fully on-site, open-source, reproducible, and cost-effective framework. Ultimately, this study advances the clinical applicability of non-invasive CFD tools for the functional assessment of CAD, particularly in the RCA. 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

Modifications in leaf architecture disrupt optical properties and internal light-scattering dynamics. Accurate modeling of leaf-scale light scattering is therefore essential not only for understanding how disease affects the availability of light for chlorophyll absorption, but also for evaluating its potential as an early optical marker for plant disease detection prior to visible symptom development. Conventional ray-tracing and radiative-transfer models rely on high-frequency approximations and thus fail to capture diffraction and coherent multiple-scattering effects when internal leaf structures are comparable to optical wavelengths. To overcome these limitations, we present a GPU-accelerated finite-difference time-domain (FDTD) framework for full-wave simulation of light propagation within plant leaves, using anatomically realistic dicot and monocot leaf cross-section geometries. Microscopic images acquired from publicly available sources were segmented into distinct tissue regions and assigned wavelength-dependent complex refractive indices to construct realistic electromagnetic models. The proposed FDTD framework successfully reproduced characteristic reflectance and transmittance spectra of healthy leaves across the visible and near-infrared (NIR) ranges. Quantitative agreement between the FDTD-computed spectral reflectance and transmittance and those predicted by the reference PROSPECT leaf optical model was evaluated using Lin’s concordance correlation coefficient. Higher concordance was observed for dicot leaves ($C_b=0.90$) than for monocot leaves ($C_b=0.79$), indicating a stronger agreement for anatomically complex dicot structures. Furthermore, simulations mimicking an early-stage fungal infection in a dicot leaf—modeled by the geometric introduction of melanized hyphae penetrating the cuticle and upper epidermis—revealed a pronounced reduction in visible green reflectance and a strong suppression of the NIR reflectance plateau. These trends are consistent with experimental observations reported in previous studies. Overall, this proof-of-concept study represents the first full-wave FDTD-based optical modeling of internal light scattering in plant leaves. The proposed framework enables direct electromagnetic analysis of pre- and post-penetration light-scattering dynamics during early fungal infection and establishes a foundation for exploiting leaf-scale light scattering as a next-generation, pre-symptomatic diagnostic indicator for plant fungal diseases. Full article

Radiative cooling (RC) offers a passive pathway to reduce surface and system temperatures by emitting thermal radiation through the atmospheric window, yet its daytime effectiveness is often constrained by geometry, angular solar exposure, and practical integration limits. This work experimentally investigates the use of passive non-imaging optics, specifically compound parabolic concentrators (CPCs), as enhancers of RC performance under realistic conditions. A three-tier experimental methodology is followed. First, controlled indoor screening using an infrared lamp quantifies the intrinsic heat gain suppression of a commercial RC film, showing a temperature reduction of nearly 88 °C relative to a black-painted reference. Second, outdoor rooftop experiments on aluminum plates assess partial RC coverage, with and without CPCs, under varying orientations and tilt angles, revealing peak daytime temperature reductions close to 8 °C when CPCs are integrated. Third, system-level validation is conducted using a modified GUNT ET-202 solar thermal unit to evaluate the transfer of RC effects to a water circuit absorber. While RC strips alone produce modest reductions in water temperature, the addition of CPC optics amplifies the effect by factors of approximately three for ambient water and nine for water at 70 °C. Across all configurations, statistical analysis confirms stable, repeatable measurements. These results demonstrate that coupling commercially available RC materials with non-imaging optics provides consistent and measurable performance gains, supporting CPC-assisted RC as a scalable and retrofit-friendly strategy for urban and building energy applications while calling for longer-term experiments, durability assessments, and techno-economic analysis before deriving definitive deployment guidelines. Full article

The fire performance of existing reinforced concrete (RC) bridge decks strengthened by external prestressing systems is investigated, with particular attention to the vulnerability of externally applied tendons under realistic fire scenarios. Fire exposure represents a critical condition for such retrofitted structures, as the structural response is strongly influenced by load level, prestressing effectiveness, and thermal degradation of the strengthening system. A comprehensive assessment framework is proposed, combining thermal and mechanical analyses applied to representative highway overpass bridges. The thermal input adopted for the analyses is first validated through computational fluid dynamics (CFD) simulations, aimed at evaluating temperature development in typical RC beam–girder grillage decks subjected to fire from below. The CFD study considers variations in clearance height and span length and confirms that, in the case of hydrocarbon tanker accidents with fuel spilled on the roadway, conventional fire curves commonly adopted in the literature provide a reliable and conservative representation of both the temperature levels reached and their rate of increase within structural elements, thus supporting their use for rapid and simplified assessments. The validated thermal input is then employed in an analytical fire safety procedure applied to several realistic bridge case-studies. A parametric investigation is carried out by varying deck geometry, span length, reinforcement layout, and the presence of external prestressing retrofit, allowing the evaluation of the reduction in bending capacity and the time-dependent degradation of mechanical properties under fire exposure. The results highlight the critical role of external prestressing in fire scenarios, showing that significant loss of prestressing effectiveness may occur within the first minutes of fire, potentially leading to critical conditions even at service load levels. Finally, a multi-hazard assessment is performed by combining fire effects with pre-existing aging-related deterioration, such as reinforcement corrosion and long-term prestressing losses, demonstrating a marked increase in failure risk and, in the most severe cases, the possibility of premature collapse under dead loads. Full article

This work presents an experimental assessment of radiative cooling (RC) films and compound parabolic concentrator (CPC) optics integrated into systems relevant for smart cities: LED street luminaires and small photovoltaic (PV) and thermoelectric (TE) modules used as energy-harvesting (EH) sources for IoT devices. Using commercial RC film and simple 2D/3D CPC geometries, we conducted outdoor measurements under realistic conditions. For a commercial LED luminaire, several configurations were compared (painted aluminum reference, full RC coverage of the head, partial RC strips above the LED and driver, and RC combined with CPCs), recording surface temperatures during daytime and nighttime operation. In parallel, single-junction PV cells and Peltier-type TE generators were mounted on aluminum plates in three configurations: reference, RC-coated, RC + 3D-CPC. Their surface temperatures and open-circuit (OC) voltages were monitored in daylight. Across all campaigns, RC consistently reduced device or surface temperatures by a few degrees Celsius compared to the reference, with larger reductions under higher irradiance. For PV and TE modules, thermal differences produced small but measurable increases in OC voltage—percent-level for PV, millivolt-level for TE. CPCs generally preserved or slightly enhanced the cooling effect in some configurations, acting as incremental modifiers rather than primary drivers. The experiments are deliberately exploratory and provide initial experimental evidence that RC integration can be beneficial in real devices. They establish an empirical baseline for future work on long-term, multi-season campaigns, electrical characterization, optimized materials/optics, and system-level prototypes in smart-city lighting and IoT EH applications. Full article

Urban underpasses are critical flood-prone hotspots during extreme rainfall, posing significant threats to urban resilience and infrastructure safety. However, a scale gap persists between catchment-scale hydrological models, which often oversimplify local geometry, and high-fidelity hydrodynamic models, which typically lack realistic boundary conditions. To bridge this gap, this study develops a multi-scale framework that integrates the Storm Water Management Model (SWMM) with 3D Computational Fluid Dynamics (CFD). The framework employs a unidirectional integration (one-way forcing), utilizing SWMM-simulated runoff hydrographs as dynamic inlet boundaries for a detailed CFD model of a 2.8 km underpass in Shanghai. Simulations across six design rainfall events (2- to 50-year return periods) revealed two distinct flooding mechanisms: a systemic response at the hydraulic low point, governed by cumulative inflow; and a localized response at entrance concavities, where water depth is rapidly capped by micro-topography. Informed by these mechanisms, an intensity-graded drainage strategy was developed. Simulation results show significant differences between different drainage strategies. Through this framework and optimized drainage system design, significant water accumulation within the underpass can be prevented, enhancing its flood resistance and reducing the severity of disasters. This integrated framework provides a robust tool for enhancing the flood resilience of urban underpasses and offers a basis for the design of proactive disaster mitigation systems. Full article

This study proposes a reinforcement learning-based link selection method for Low Earth Orbit satellite networks, aiming to reduce handover frequency while extending link duration under highly dynamic orbital environments. The proposed approach relies solely on basic satellite positional information, namely latitude, longitude, and altitude, to construct compact state representations without requiring complex sensing or prediction mechanisms. Using relative satellite and terminal geometry, each state is represented as a vector consisting of azimuth, elevation, range, and direction difference. To validate the feasibility of policy learning under realistic conditions, a total of 871,105 orbit based data samples were generated through simulations of 300 LEO satellite orbits. The reinforcement learning environment was implemented using the OpenAI Gym framework, in which an agent selects an optimal communication target from a prefiltered set of candidate satellites at each time step. Three reinforcement learning algorithms, namely SARSA, Q-Learning, and Deep Q-Network, were evaluated under identical experimental conditions. Performance was assessed in terms of smoothed total reward per episode, average handover count, and average link duration. The results show that the Deep Q-Network-based approach achieves approximately 77.4% fewer handovers than SARSA and 49.9% fewer than Q-Learning, while providing the longest average link duration. These findings demonstrate that effective handover control can be achieved using lightweight state information and indicate the potential of deep reinforcement learning for future LEO satellite communication systems. Full article

Accurate estimation of internal temperature is essential for safe operation and state estimation of lithium-ion batteries, yet it usually cannot be measured directly and requires physically grounded electro-thermal models. High fidelity 3D simulations capture geometry-dependent heat transfer behavior but are too computationally intensive for real-time use, whereas common lumped models cannot represent internal gradients. This work presents an integrated geometry-resolved workflow that combines detailed 3D finite volume thermal modeling with systematic reduction to a compact multi-node thermal network and its coupling with an equivalent circuit electrical model. A realistic 3D model of the Panasonic NCR18650B cell was reconstructed from computed tomography data and literature parameters and validated against published axial and radial thermal conductivity measurements. The automated reduction yields a five-node thermal network preserving radial temperature distribution, which was coupled with five parallel Battery Table-Based blocks in MATLAB/Simulink R2024b to capture spatially distributed heat generation. Experimental validation under dynamic loading is performed using measured surface temperature and terminal voltage, showing strong agreement (surface temperature MAE ≈ 0.43 °C, terminal voltage MAE ≈ 16 mV). The resulting model enables physically informed estimation of internal thermal behavior, is interpretable, computationally efficient, and suitable for digital twin development. Full article

Graphene oxide (GO) is a carbon-based nanomaterial explored for agricultural and forestry uses, but plant responses are strongly subject to both the dose and the route of exposure. We summarized recent studies with defined graphene oxide (GO) exposures by seed priming, foliar delivery, and root or soil exposure, while comparing annual crops with woody forest plants. Mechanistic progress points to a shared physicochemical basis: surface oxygen groups and sheet geometry reshape water and ion microenvironments at the soil–seed and soil–rhizosphere interfaces, and many reported shifts in antioxidant enzymes and hormone pathways likely represent downstream stress responses. In crops, low-to-moderate doses most consistently improve germination, root architecture, and tolerance to salinity or drought stress, whereas high doses or prolonged root exposure can cause root surface coating, oxidative injury, and photosynthetic inhibition. In forest plants, evidence remains limited and often relies on seedlings or tissue culture. For forest plants with long life cycles, processes such as soil persistence, aging, and multi-seasonal carry-over become key factors, especially in nurseries and restoration substrates. The available data indicate predominant root retention with generally limited root-to-shoot translocation, so residues in edible and medicinal organs remain insufficiently quantified under realistic-use patterns. This review provides a scenario-based framework for crop- and forestry-specific safe-dose windows and proposes standardized endpoints for long-term fate and ecological risk assessment. Full article

Coarse-Grained Reconfigurable Architectures (CGRAs) execute compute-intensive kernels on a reconfigurable processing mesh. Transparent CGRAs extend this model by generating configurations at runtime and storing them in a dedicated cache, removing compiler dependence and enabling adaptive behavior. Although prior work has explored mapping strategies and mesh scaling, the feasibility of the configuration cache remains unaddressed, as it is commonly treated as a generic storage block. This paper presents a feasibility study of configuration cache organizations and a design-space exploration of Transparent CGRAs, introducing a parameterized cache geometry model that relates cache parameters to the processing mesh and configuration structure. The model enables realistic estimates of area, latency, and energy at the digital system level and is applied to three Transparent CGRAs from the literature and five additional designs covering a wide range of spatial and temporal organizations. The results show that mesh scaling must be balanced with cache feasibility: wide I/O paths and large configurations lead to impractical caches, whereas well-proportioned meshes achieve competitive performance with modest overheads. Under the proposed exploration, selected expanded meshes outperform a two-issue out-of-order processor by up to 1.4× while increasing area by only 14.8% and energy by 2%. These findings demonstrate that Transparent CGRAs are viable, but their scalability depends on a realistic configuration cache design. The proposed parameterized cache model provides a structured and reproducible basis for analyzing transparency overheads and guiding future CGRA designs. Full article

This study investigates radio frequency (RF) wave propagation in sandstone and limestone cave environments, emphasizing the use of LiDAR-derived three-dimensional (3D) models to characterize cave geometry and support waveguide-based propagation analysis incorporating skin depth effects. RF transmission and reception measurements were conducted under line-of-sight (LOS) conditions across frequency bands from Low Frequency (LF) to Ultra-High Frequency (UHF). Comparative results reveal distinct attenuation behaviors governed by differences in cave geometry and electrical properties. The sandstone cave, with a more uniform geometry and relatively higher electrical conductivity, exhibits lower attenuation across most frequency bands, whereas the limestone cave shows higher attenuation due to its irregular structure. LiDAR-based 3D models are employed to extract key geometric parameters, including cavity dimensions, wall roughness, and wall inclination, which are incorporated into the proposed analytical framework. The model is further validated using experimental field measurements, demonstrating that the inclusion of LiDAR-derived geometry and skin depth effects enables a more realistic representation of underground RF propagation for communication system analysis. Full article

Filament winding (FW) is a widely used automated manufacturing method for cylindrical composite structures. However, conventional modeling approaches often rely on oversimplified geometries, neglecting essential features such as fiber overlaps and gaps, which can affect the accuracy of subsequent mechanical analysis. In this work, we present a computational methodology for the accurate geometric reconstruction of FW components, based on the numerical calculation of fiber trajectories and their automated integration into CAD models. The proposed approach provides realistic geometrical representations that capture the actual fiber paths, enabling more reliable finite element simulations. Comparative results between the proposed method and traditional modeling techniques highlight key differences in stiffness prediction, demonstrating the importance of realistic geometric input for the mechanical analysis of filament-wound structures. Full article

Measuring technology is used in various ways in the logistics industry for defect inspection and loading optimization. Recently, in the context of the fourth industrial revolution, research has focused on measurement automation combining AI, IoT technologies, and measuring equipment. The 3D scanner used for field logistics measurements offers high performance and can handle large volumes quickly; however, its high unit price limits adoption across all lines. Entry-level sensors are challenging to use due to measurement reliability issues: their performance varies with changes in object location, shape, and logistics environment. To bridge this gap, this study proposes a systematic framework for geometry measurement that enables reliable length and width estimation using only a single entry-level distance sensor. We design and build a conveyor-belt-based data acquisition setup that emulates realistic logistics transfer scenarios and systematically varies transfer conditions to capture representative measurement disturbances. Based on the collected data, we perform robust feature extraction tailored to noisy, condition-dependent signals and train an artificial neural network to map sensor observations to geometric dimensions. We then verified the model’s performance in measuring object length and width using test data. The experimental results show that the proposed method provides reliable measurement results even under varying transfer conditions. Full article