Search Results (15,792)

The partial discharge (PD) caused by insulation defects of gas-insulated switchgear (GIS) threatens the secure and stable operation of power systems. Traditional PD pattern recognition methods exhibit limitations due to incomplete information utilization and unresolved correlations among characteristic parameters. Based on the partial discharge mechanisms of GIS, this paper establishes a GIS partial discharge simulation model using the finite element time-domain (FETD) method. The propagation rules and influence factors of ultra-high-frequency (UHF) signals are studied. Furthermore, a PD pattern recognition method based on a deep convolutional neural network (CNN) is proposed. Research results indicate that UHF signals generated by GIS partial discharge are significantly influenced by pulse current waveforms and discharge quantity. The peak-to-peak amplitude of the electric field (Epp) increases linearly with the current amplitude, while it decreases nonlinearly with increasing pulse width. The UHF signal remains a certain value while the pulse width exceeds a critical threshold (4 ns). The proposed CNN-based approach, utilizing full-wave UHF signals, overcomes the shortcomings of traditional methods reliant on manually extracted discrete feature parameters. Compared to other network architectures and optimization algorithms, the ConvNeXt-AdamW model demonstrates superior performance, achieving an average PD pattern recognition accuracy exceeding 96%. Full article

Conventional Fiber-Reinforced Polymer (FRP) materials may exhibit certain performance uncertainties in harsh environments, limiting their reliability for structural strengthening. To address this, Basalt Fiber-Reinforced Polymer (BFRP) plates fabricated with silicate-modified epoxy resin are proposed for the flexural strengthening of reinforced concrete (RC) beams. The research aims to evaluate their short-term strengthening performance and establish a reliable calculation method for flexural capacity. Four-point bending tests were conducted to investigate the effects of BFRP plate thickness and end anchorage configuration on failure modes, flexural capacity, and ductility. Finite element simulations incorporating interfacial bond–slip behavior reproduced typical debonding failures, followed by a comprehensive parametric analysis. Based on the experimental and numerical results, a modified BFRP plate strain formula at debonding was proposed to establish a calculation method for the flexural capacity of BFRP-strengthened beams governed by debonding failure. The results indicate that beams without end anchorage were prone to interfacial debonding, where increasing the plate thickness from 0.5 mm to 2 mm raised the flexural capacity gain from 4.5% to 15% but intensified the ductility reduction from 42.9% to 64.9%. Conversely, applying mechanical anchorage improved the ductility index by over 20% compared to unanchored counterparts. The adopted FRP–concrete bond–slip constitutive model accurately characterizes interfacial debonding behavior, and the proposed flexural capacity model demonstrates high accuracy with overall deviations within 5%. It can be concluded that the novel BFRP plates exhibit strengthening behavior comparable to existing FRP systems. Effective end anchorage further enhances flexural capacity and prevents brittle failure. The proposed debonding strain formula for the novel BFRP system offers a reliable basis for capturing the critical onset of interfacial failure. Building upon this, the developed flexural capacity model provides a reliable theoretical basis for the design and assessment of RC beams strengthened with the novel BFRP plates. Full article

High-entropy amorphous materials are attracting increasing attention due to their excellent corrosion resistance and radiation tolerance in nuclear environments. In this study, novel Al₂Cr₁₆Fe₅₀V₂₀Ti₁₂ high-entropy alloy (HEA) coatings with thicknesses of 900 nm and 1400 nm were synthesized via magnetron sputtering and systematically evaluated for their structural, electrochemical, and mechanical performance. X-ray diffraction confirmed the amorphous nature of the coatings, while scanning electron microscopy revealed a denser, defect-free, and more uniform morphology in the thicker coating. Electrochemical testing in a 3.5 wt.% NaCl solution demonstrated a tenfold reduction in corrosion current density and nearly a twofold increase in charge transfer resistance for the 1400 nm coating, attributed to its improved passive film stability. Finite element modeling validated the experimental load–displacement behavior and revealed well-confined and uniformly distributed stress and strain fields within the coating. These findings establish the 1400 nm Al₂Cr₁₆Fe₅₀V₂₀Ti₁₂ coating as a promising candidate for protective applications in chloride-rich and radiation-intense nuclear systems. Full article

This study examines the seismic stability of an underground powerhouse cavern located in the Lesser Himalayan region of Nepal. Both static and seismic loading conditions are analyzed using the finite element method (FEM) and the distinct element method (DEM). Rock mass properties are derived from field investigations and laboratory testing, while empirical correlations are applied to estimate rock mass strength and deformation modulus. Pseudo-static analyses are performed using the FEM-based software Rock and Soil-2-Dimensionsl (RS²) Version 11.027, and dynamic analyses are conducted using the DEM-based software Universal Distinct Element Code (UDEC) Version 5.0 to evaluate deformation and stress redistribution around the cavern. Seismic fragility curves are developed to quantify the probability of damage under varying seismic intensities. Results indicate that a peak ground acceleration (PGA) of 0.25 g increases cavern wall deformation by approximately 15–20 mm compared to static conditions. Fragility analysis shows a probability exceeding 68% for slight damage, while the probability of collapse remains low at approximately 1.7%. Seismic loading also significantly alters stress redistribution along the cavern boundary. Overall, the combined use of numerical modeling and fragility analysis provides a probabilistic framework for assessing seismic risk in underground caverns, offering valuable insights for the design and safety evaluation of hydropower projects in seismically active Himalayan regions. Full article

In the field of structural health monitoring systems, sensors serve as the fundamental components for assessing infrastructure integrity. The rationality of their spatial configuration significantly influences the precision of structural performance assessment, the efficacy of damage detection algorithms, and the operational reliability of the system throughout its designated lifecycle. A robust optimization methodology for the placement of multi-type sensors is proposed in this study, explicitly formulated to mitigate the negative impact of sensor malfunctions during long-term operation. First, a rigorous evaluation framework for sensor placement schemes is established based on Bayesian inference and the minimization of information entropy, thereby quantifying the uncertainty inherent in parameter identification. Then, a probabilistic model of sensor failure is developed utilizing the Weibull distribution to capture time-dependent reliability characteristics, combined with a modified information entropy calculation method that mathematically assimilates these failure probabilities into the optimization objective. Finally, a heuristic search strategy is employed to achieve the robust optimal placement of multi-type sensors, efficiently navigating the complex combinatorial search space. In contrast to deterministic information entropy (DIE) methodologies, which assume ideal sensor functionality, the robust information entropy (RIE) approach comprehensively accounts for the stochastic nature of sensor failures and their impact on the information content of the monitoring network, thereby significantly augmenting the robustness and redundancy of the sensor configuration. Validations utilizing a numerical frame structure and a finite element bridge model demonstrate that the RIE method effectively integrates the sensor failure probability model to yield robust optimal placement schemes, minimizing the risk of information loss and ensuring reliable structural health monitoring throughout the engineering lifecycle. Full article

Pitting corrosion is a prevalent and highly detrimental form of localized corrosion, which can severely compromise the local load-bearing capacity of metallic materials and, in extreme cases, trigger structural failure. In response to the pronounced susceptibility of Q235 galvanized steel plates to localized pitting under the extreme service conditions of the South China Sea—characterized by high temperature, high salinity, high humidity, and coupled chemical corrosive effects—this study conducts a systematic investigation combining experimental characterization and numerical simulation. First, a novel accelerated pitting corrosion apparatus was designed and developed, and chloride ion cyclic corrosion (CICC) tests were performed on Q235 galvanized steel plates. The morphology and temporal evolution of pitting damage were comprehensively characterized. Subsequently, based on a coupled Cellular Automata (CA) and Finite Element Analysis (FEA) framework, a corrosion evolution model termed CAFE (Cellular Automata-Finite Element) was established. This model elucidates the initiation, growth, and corrosion product evolution of pitting pits under varying temperature and salinity conditions and further quantifies the spatial distributions of stress and temperature fields in the vicinity of pitting sites. Finally, experimental results were employed to validate the rationality and effectiveness of the proposed electro-thermo-mechanical-chemical (ETMC) multi-field coupling model. The results demonstrate that temperature and salinity are the dominant environmental parameters governing the evolution of localized pitting corrosion rates. A strong agreement between numerical predictions and experimental observations is achieved in both qualitative trends and quantitative metrics. Notably, the model reveals that under elevated current-driving conditions, localized plastic deformation plays a critical role in promoting pit propagation and accelerating the pitting corrosion process. Full article

Underground steel pipes are an essential component of the water and energy supply chains, and assessing their damage with standard techniques implies a temporary interruption in their use, often at a high cost to the operators. Evaluating the damage outside of the pipe would minimize these interruptions. In this work, we propose a new approach to investigating corrosion by taking advantage of the reduction in the steel’s magnetic permeability resulting from it. To enhance these variations, the pipe is excited by a static magnetic field produced by a rectangular loop, inducing magnetization in the pipe that will be weaker where corrosion is present. The secondary magnetic fields produced by this magnetization are measured using an array of triaxial magnetic sensors. A desktop study using finite-element modelling confirmed the feasibility of the approach and informed the design of a first prototype. Scans of test pipes over a custom measurement bench show that corroded zones, as well as welding joints, generate significant anomalies with a strong signal-to-noise ratio, easily identified using simple signal processing techniques. These results confirm the viability of this non-invasive magnetostatic methodology. Full article

Autonomous trucks can be used in different loading combinations, including different axle configurations, tire types, and lateral wander mode scenarios. In this research, four different truck types have been selected with varying gross weights and axle configurations. The four different truck types include a 5-axle long-haul semi-truck, a 6-axle electric autonomous truck, a 6-axle autonomous truck platoon leader, and a 5-axle autonomous truck platoon follower. Furthermore, three different tire footprint scenarios, consisting of a conventional dual wheel assembly, a wide base tire, and a new generation wide base tire, have been used. In order to utilize the possibility of lateral wander programmed into the autonomous trucks, three different lateral wander models, including zero lateral wander, a human-driven probabilistic lateral wander, and an optimum uniform wander mode, have been used. Finite element analysis has been employed to incorporate the effects of various scenarios on a conventional pavement section. Results showed improved pavement life with the use of uniform wander mode, where trucks T1 and T2 improved the pavement life by 47% and 56%, respectively, when compared to truck T3. Furthermore, the use of uniform wander mode decreases rutting and fatigue damage by 36% and 28%, respectively, on average for all scenarios. The use of new generation wide-base tires is recommended, since it reduces damaging strains by 38% when compared to the dual tire configuration. Full article

Nowadays, direct-drive systems are widely used in the actuators of computer numerical control machine tools. Linear motors are widely used in high-end computer numerical control machine tools due to their high positioning accuracy, good dynamic response, and simple transmission structure. First, a high-thrust-density concentrated magnetic linear permanent magnet vernier motor is proposed in this paper, which is designed by machine learning and optimized through an artificial intelligence optimization algorithm, to improve the air-gap magnetic density of the motor and improve the thrust density of the motor in principle; compared with traditional linear permanent magnet synchronous motors, the thrust density is increased by 40%. Second, using finite element calculations, a regression machine learning algorithm is proposed, which involves introducing a regression machine learning algorithm (called extreme learning machine (ELM)) to solve the computational modeling problem; compared with traditional ELM networks, it has faster training speed and higher stability. By mapping the nonlinear complex relationship between input structural factors and output motor performance, the superiority of the intelligent optimization algorithm is confirmed by comparative verification. Full article

Porous tubular structures are of significant interest in engineering due to their exceptional potential for lightweight design, energy absorption, and multifunctional integration. Inspired by the unique net architecture of natural luffa sponges, this study introduces a novel design approach for such structure, namely bio-inspired Voronoi Tube (BVT). This design employs Voronoi tessellation patterns, parametrically controlled through the spatial distribution of seed points and integrates iterative optimization algorithms, to achieve precise coordinated regulation over the randomness and continuity of the resulting spatial network, closely mimicking the biological paradigm. Then, specimens are fabricated via additive manufacturing and then quasi-statically compressed axially, followed by systematic mechanical testing of the base material. The experimental results are analyzed to reveal the BVT structure’s mechanical responses and simultaneously validate finite-element simulation model. Subsequently, a systematic numerical analysis is performed to further understand the deformation mechanisms of the BVT structure and the influence of key geometric parameters. The results indicate that the iteratively optimized BVT structure successfully replicates the characteristic energy absorption behavior of the natural luffa sponge, confirming the effectiveness of the bio-inspired design. A rise in diameter from 0.6 mm to 1.0 mm results in a 78.32% increase in the specific energy absorption (SEA). Under identical mass conditions, tailored adjustments to the geometry can enhance the SEA by up to 34.57%. Full article

This study establishes a numerical model for the implantation of drug-coated balloons (DCBs) in blood vessels, in order to quantify drug (paclitaxel) transfer and diffusion within stenotic vessels and provide a reference for clinical surgical plans. Objective: To study the change in paclitaxel concentration over time in the blood vessel after the implantation of a DCB in vessels with different degrees of stenosis, and thereby determine the optimal balloon dilation time. Method: Using finite element modeling and numerical simulation techniques, a model was established to study the rules of paclitaxel concentration change over time in vessels with different degrees of stenosis. Results: Based on the simulation prediction, the longer the balloon dilation time during implantation, the more paclitaxel enters the vessel wall. After the balloon is withdrawn, the paclitaxel gradually diffuses evenly throughout the vessel, and the paclitaxel concentration gradually decreases over time. Conclusion: Under the simulation conditions, the optimal balloon dilation times for vessels with stenosis rates of 10%, 30%, and 50% should be 20 s, 60 s, and 80 s, corresponding effective duration of 6 weeks, 6 weeks and 4 weeks, respectively. Full article

In this paper, a low-cost manual micro- and nano-displacement adjustment mechanism is proposed, based on the principle of flexible hinge transmission and micro-displacement scaling. The manual micro- and nano-displacement platform consists of a micrometer input platform, a nano-output platform, a differential head, and a strain displacement sensor. Firstly, a micro-displacement reduction mechanism based on a flexible beam triangular mechanism and a compact asymmetric flexible beam guiding mechanism are proposed, and a theoretical model is established for static mechanical characteristics, such as the displacement reduction multiplier, guiding stiffness, maximum stress, etc., and this is analyzed and verified by finite element simulation. The software and hardware system of the strain displacement sensor is designed and developed, and the calibration experiments of the strain displacement sensor are completed. Finally, the micro-displacement reduction times, resolution, stability, repeat positioning accuracy, load capacity and travel of the manual micro–nano-displacement platform were analyzed and experimented. The results show that when the input range of the micrometer input platform is 0–1 mm, the travel of the nano-output platform is about 0–16 μm; when a differential head with a step resolution of 2 μm is used to input 2 μm micro-displacement, the minimum displacement output of the nano-output platform is about 35.4 nm; the theoretical and simulated values of the reduction multiple of the micro–nano-displacement are 57.29 and 56.69, respectively; the calibration experiment is performed by the self-developed strain sensors, and capacitive displacement sensors measured the reduction multiples of 57.74 and 62.67, respectively, with high consistency; the vibration range of the platform after the displacement adjustment is about ±30 nm, and the load of 0–300 g has less influence on the output characteristics of the platform. Full article

Large flange shafts are the core load-bearing and connecting components of high-end equipment, and their welding multi-physical fields directly affect the quality and service safety of the components. Traditional experiments and finite element methods suffer from long cycles and low efficiency, which can hardly meet the demand for rapid prediction. Aiming at the fast and accurate prediction of welding temperature, deformation and residual stress, this study combines thermal–mechanical coupled finite element simulation with machine learning to construct and compare a variety of prediction models. A dataset is built based on simulation data from 100 groups of process parameters. Overfitting is reduced through strategies including early stopping and dropout, and models such as MLP, RF, RBF-SVR, TabNet, XGBoost, and FT-Transformer are established and verified through 10-fold cross-validation. The results show that the MLP model performs best in the prediction of temperature, deformation and residual stress, and is in good agreement with the simulation values. The prediction errors of the peak temperature of the weld and base metal are below 5%, and the errors of deformation and residual stress are controlled within 10%. The average error of peak residual stress is about 6 MPa, and the deviation of most samples is less than 5 MPa. The RF model ranks second in accuracy, with an average error of about 6.5 MPa for peak residual stress, showing a satisfactory interpretability and engineering applicability. RBF-SVR and TabNet can meet basic prediction requirements. Under the small-sample condition in this work, XGBoost and FT-Transformer present relatively large errors and a weak generalization ability, making it difficult to achieve high-precision prediction. The MLP model established in this paper can effectively reproduce the evolution of welding multi-physical fields and supports the rapid prediction and process optimization of large flange shaft welding. The generalization ability and practical performance of the model can be further improved by expanding the dataset and experimental verification in the future. Full article

The applicability of two different joining processes for producing lap joints from high-strength steel sheets is investigated, reflecting their increasing use in advanced lightweight structures with demanding performance requirements. The work is primarily focused on the joining-by-forming process known as double-flush riveting, evaluated in two variants: one utilizing forged holes and the other employing machined holes. The performance of these two variants is compared with conventional fusion-based resistance spot welding using lap joints fabricated from 2 mm high-strength low-alloy S500MC steel sheets under varying geometric and process conditions, with support from finite element modelling. Results indicate that both double-flush riveting variants produce similar joint cross-sectional geometries, but the machined hole variant simplifies sheet preparation and eliminates the need for a progressive tooling system. Tensile lap-shear and peel test results reveal that double-flush riveted joints with forged holes exhibit superior strength, attributed to strain hardening in the forged regions. Furthermore, for nuggets and rivets of equivalent size, both double-flush riveting variants surpass resistance spot welding in terms of the mechanical strength of the final joints. These results suggest that double-flush riveting represents a promising alternative for assembling high-strength steel sheets in lightweight structural applications. Full article

Railway transport is increasingly promoted as a sustainable and low-carbon mode of transportation. However, track-induced vibration propagation remains a significant challenge, particularly in metro systems situated near residential areas, where vibrations can transmit through the infrastructure into nearby buildings, disturbing residents and damaging structures. This study aimed to evaluate the cause of the significantly different vibration impact on nearby buildings caused by two nominally identical adjacent slab tracks on a metro line in Austria. Controlled weight drop tests were carried out in both track directions, and accelerations were measured to characterize wave transmission and energy dissipation. The data were processed using frequency response functions and Short-Time Fourier Transform to extract time–frequency signatures, modal parameters, and propagation delays. A three-dimensional finite element model of the railway superstructure was then calibrated against the experimental modal properties and transfer functions and used to simulate cracking or stiffness loss in the sleeper–slab region. The simulations reproduced the observed increase in slab acceleration and underground strain energy, linking the anomalous vibration transmission to hidden stiffness loss rather than to global design differences. Overall, the study demonstrates that combining impact testing, advanced signal processing, and calibrated finite element modelling provides an effective framework for diagnosing track defects and guiding the design and maintenance of more sustainable, low-vibration urban rail infrastructure. Full article