Search Results (1,385)

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components. Full article

In quantum key distribution (QKD), public discussion over the authenticated classical channel inevitably leaks information about the raw key to a potential adversary, which must later be mitigated by privacy amplification. To limit this leakage, a one-time pad (OTP) has been proposed to protect message exchanges in various settings. Building on the security proof of Tomamichel and Leverrier, which is based on a non-asymptotic framework and considers the effects of finite resources, we extend the analysis to the OTP-protected scheme. We show that when the OTP key is drawn from the entropy pool of the same QKD session, the achievable quantum key rate is identical to that of the reference protocol with unprotected error-correction exchange. This equivalence holds for a fixed security level, defined via the diamond distance between the real and ideal protocols modeled as completely positive trace-preserving maps. At the same time, the proposed approach reduces the computational requirements: for non-interactive low-density parity-check codes, the encoding problem size is reduced by the square of the syndrome length, while privacy amplification requires less compression. The technique preserves security, avoids the use of QKD keys between sessions, and has the potential to improve performance. Full article

The residual stresses induced by the curing process for carbon fiber-reinforced polymers (CFRPs) lead to inevitable deformation, which seriously affects manufacturing accuracy, especially for large-sized CFRP components. Furthermore, deformation may cause damage or failure of components during subsequent assembly. More attention needs to be paid to improving the curing accuracy for large-sized CFRP components. In this study, a normal direction compensation algorithm based on node deformation is proposed based on the mold profile. Firstly, a finite element model was constructed to simulate the curing process of CFRPs and validated with a small-sized test piece. The results showed the effectiveness of the simulated and compensation methods. Secondly, to meet the precision requirements of large-sized CFRP components, a neural network was used to establish a mapping between curing process parameters and curing deformation, and the parameters were then optimized with a genetic algorithm for subsequent analysis. Finaly, a 15 m CFRP component was used to explore the effect of compensation methods in reducing curing deformation of large-sized CFRP components. The verified results showed that the maximum deformation after compensation was 0.99 mm in the normal direction, which was superior to the 1.258 mm compensation in the connecting direction. Full article

Drilling fluid loss is a common and complex downhole problem that occurs during drilling in deep fractured formations, which has a significant negative impact on the exploration and development of oil and gas resources. Establishing a drilling fluid loss model for the quantitative analysis of drilling fluid loss is the most effective method for the diagnosis of drilling fluid loss, which provides a favorable basis for the formulation of drilling fluid loss control measures, including the information on thief zone location, loss type, and the size of loss channels. The previous loss model assumes that the drilling fluid is driven by constant flow or pressure at the fracture inlet. However, drilling fluid loss is a complex physical process in the coupled wellbore circulation system. The lost drilling fluid is driven by dynamic bottomhole pressure (BHP) during the drilling process. The use of a single-phase model to describe drilling fluids ignores the influence of solid-phase particles in the drilling fluid system on its rheological properties. This paper aims to model drilling fluid loss in the coupled wellbore–-fracture system based on the two-phase flow model. It focuses on the effects of well depth, drilling pumping rate, drilling fluid density, viscosity, fracture geometric parameters, and their morphology on loss during the drilling fluid circulation process. Numerical discrete equations are derived using the finite volume method and the “upwind” scheme. The correctness of the model is verified by published literature data and experimental data. The results show that the loss model without considering the circulation of drilling fluid underestimates the extent of drilling fluid loss. The presence of annular pressure loss in the circulation of drilling fluid will lead to an increase in BHP, resulting in more serious loss. Full article

Angle crack defects significantly affect compressor blade radial deformation characteristics, posing critical challenges for reliability assessment under operational uncertainties. This study proposes a novel osprey optimization algorithm (OOA)-optimized Kriging and radial basis function (RBF) method (OOA-KR) for the efficient reliability evaluation of blade radial clearance with angle crack defects. The approach integrates Kriging’s uncertainty quantification capabilities with RBF neural networks’ nonlinear mapping strengths through an adaptive weighting scheme optimized by OOA. Multiple uncertainty sources including crack geometry, operational temperature, and loading conditions are systematically considered. A comprehensive finite element model incorporating crack size variations and multi-physics coupling effects generates training data for surrogate model construction. Comparative studies demonstrate superior prediction accuracy with RMSE = 0.568 and R² = 0.8842, significantly outperforming conventional methods while maintaining computational efficiency. Reliability assessment achieves 97.6% precision through Monte Carlo simulation. Sensitivity analysis reveals rotational speed as the most influential factor (S = 0.42), followed by temperature and loading parameters. The proposed OOA-KR method provides an effective tool for blade design optimization and reliability-based maintenance strategies. Full article

The fracture behavior of a locking pin used in the penultimate-stage blades of a 600 MW steam turbine in a thermal power plant was investigated through microstructural and microhardness characterization, fracture surface and energy-dispersive spectroscopy (EDS) analysis, as well as finite element load simulation. The microhardness values measured on the cross-section of the service pins ranged from 528 to 541 HV0.1, showing little difference from the unused pins. Scanning electron microscopy analysis revealed that approximately 70% of the fracture surfaces exhibited an intergranular “rock candy” morphology. The results indicate that pin failure was primarily caused by the combined effects of fretting wear and stress corrosion cracking (SCC). Specifically, vibration at the blade root, impeller, and pins due to start–stop cycles and load variations led to fretting wear, forming pits approximately 75 μm in size. Under the combined effects of weakly corrosive wet steam environments and shear stresses, SCC initiated at the high stress concentration points of these pits. Early crack propagation primarily followed original austenite grain boundaries, while later stages mainly extended along martensite plate boundaries. As cracks advanced, the cross-sectional area gradually decreased, causing the effective shear stress to increase until it exceeded the shear strength, ultimately leading to fracture. These findings not only provide a scientific basis for enhancing the reliability of steam turbine locking pins and extending their service life, but also contribute to a broader understanding of the failure mechanisms of key components operating under corrosive and fluctuating load environments. Full article

The development of electromechanical linear actuators (EMLAs) aims at compactness, energy efficiency, and high reliability. Conventional design methods often rely on costly prototypes and individual considerations of mechanics, electromagnetics, and control dynamics. This leads to long development cycles, inadequate treatment of nonlinear effects, and suboptimal performance. To address these challenges, our paper introduces a novel hybrid design methodology, integrating Analytical Modeling, Finite Element Analysis (FEA), Genetic Algorithms (GAs), and targeted experiments. Analytical Modeling provides rapid sizing, FEA combined with a GA refines geometry, and targeted experiments quantify nonlinear effects (friction, wear, thermal variability, and dynamic resonances). Unlike conventional methods, the integration is performed within iterative loops, using empirical data to refine simulation assumptions. As a result, development time is reduced by 30% and nonlinear effects are precisely addressed. The method is demonstrated on an automotive-grade EMLA. Its design is based on a claw-pole Permanent Magnet Stepper Motor, a trapezoidal lead screw, and an open-loop control with Hall effect end-position detection. After applying the method, the EMLA delivers more than 40 N of push force and achieves 600,000 actuations under the required conditions, making it suitable for various applications. Full article

A thermodynamically consistent model framework to describe ion transport in nanopores is presented. The continuum model unifies electro-diffusion and selective ion transport and extends the classical Poisson–Nernst–Planck (PNP) system for an idealized incompressible mixture by including finite ion size and solvation effects. Special emphasis is placed on the consistent modeling of the selectivity filter within the pore. It is treated as an embedded domain in which the constituents can change their chemical properties and mobility. Using this framework, we achieve good agreement with an experimentally observed current–voltage (IV) characteristic for an L-type selective calcium ion channel for a range of ion concentrations. In particular, we show that the model captures the experimentally observed anomalous mole fraction effect (AMFE). As a result, we find that calcium and sodium currents depend on the surface charge in the selectivity filter, the mobility of ions and the available space in the channel. Our results show that negative charges within the pore have a decisive influence on the selectivity of divalent over monovalent ions, supporting the view that AMFE can emerge from competition and binding effects in a multi-ion environment. Furthermore, the flexibility of the model allows its application in a wide range of channel types and environmental conditions, including both biological ion channels and synthetic nanopores, such as engineered membrane systems with selective ion transport. Full article

In developing a mathematical model of a railway track, the question of determining the dimensions of the modeling domain inevitably arises. If the modeling area is too small, boundary effects may significantly influence the results, reducing their accuracy. Conversely, excessively large areas can increase computational complexity without substantial improvements in accuracy. An optimal choice of dimensions enables the balancing of computational costs and accuracy. Solving this problem is non-trivial, as it depends on numerous factors, primarily the type of mathematical model and the problem being addressed. In most cases, preference is given to minimal domain sizes that ensure the approach’s adequacy. The aim of this study is to justify the dimensions of the modeling domain by addressing such tasks as load scaling, introducing additional boundary conditions, and making relevant assumptions. The main object of the study is the minimum adequate longitudinal length of the track for the spatial model. The research is based on the analytical application of modern approaches in the theory of elasticity. The results are analyzed using mathematical methods, such as modeling the railway track through the propagation of elastic waves and finite element modeling. These findings can be applied to a wide range of problems related to the mathematical modeling of the stress–strain state of railway tracks. Full article

Land is a finite global resource supporting the growing population with food, shelter, recreation, and other environmental benefits. The United States has over 10% of global arable land, contributing to domestic and global food security. The number of farms in the United States has steadily declined with a relatively stable average farm size. Increasing population growth, pressure on food production and environmental sustainability are concerns for cropland decline and food security. This study analyzed the effects of competing land use, agricultural innovation and technology, climate change, and government policy on cropland. Seven decades (1945–2017) of United States Department of Agriculture (USDA) Census of Agriculture datasets were used as a case study to analyze drivers of cropland changes. The total amount of cropland recorded a 13% reduction in 2017 from 1945. Cropland used for pasture decreased by 72%, representing the most substantial proportional decline among the cropland categories. Competing land uses to cropland such as rural parks and wildlife increased over 1000%, urbanized land increased by 395%, and land designated for defense and industrial areas rose by 13% by 2017. The divergence between total factor productivity and farm inputs suggests that productivity gains were driven primarily by technological advancements rather than increased resource use. Linkages were drawn from several studies on climate change and population growth’s negative impact on cropland, whereas government policies and priorities can either influence cropland decline or increase, based on how the policies are structured. This study underscores a strategic planning approach that incorporates technological innovation, climate adaptation, and sustainable land management to balance agricultural output with competing land needs without compromising food security for the growing global population. Full article

In this study, we formulate and analyze a susceptible–infected–susceptible (SIS) dynamic on metapopulation networks, where each node has a finite carrying capacity and the motion of individuals is modulated by vacant space at the destination. We obtain that the vacancy-dependent mobility pattern results in various asymptotic population distributions on heterogeneous metapopulation networks. The resulting population distributions have remarkable impact on the behavior of SIS dynamics. We show that, for the given total number of individuals, higher heterogeneity in population distributions facilitates epidemic spreading in terms of both a smaller epidemic threshold and larger macroscopic incidence. Moreover, we analytically obtain a sufficient condition that the disease-free equilibrium becomes unstable and an endemic state arises. Contrary to the absence of an epidemic threshold in the standard diffusion case without excluded-volume effects, the finite carrying capacity induces a nonzero epidemic threshold under certain conditions in the limit of infinite network sizes with an unbounded maximum degree. Our analytical results agree well with numerical simulations. Full article

The size and flexibility of offshore turbine blades manufactured from composite materials have continuously increased in recent years. In this context, accurate and efficient aeroelastic analyses are important for designing and safely assessing long, flexible blades. Existing linear beam models need to be revised to offer accurate estimates of the geometric nonlinear effects triggered by large displacements. Nonlinear, geometrically exact beam models that have already been extensively used for the above purpose are generally difficult to converge and inefficient. We propose a novel co-rotational beam model for the nonlinear analysis of wind turbine blades. The method adopts vector complement to resolve rotation vector singularity problems. A complete anisotropic cross-sectional stiffness matrix and Timoshenko beam elements are introduced to capture full coupling effects. The method also considers the anisotropy and taper effects caused by the non-uniformity of chord length and material distributions. We established the nonlinear aeroelastic model of the DTU 10 MW turbine, and the results showed that the taper effect dramatically reduced the blade torsion angle by up to 31.44% under rated wind speed. Meanwhile, static beam experiments demonstrate that the accuracy error of the current method is only 1.78%, which is significantly lower than the 17.8% error of the conventional finite element beam method. Full article

Background/Objectives: Chronic retinal diseases usually require repetitive local dosing. Depending on factors such as dosing frequency, mode of administration, and associated costs, this can result in poor patient compliance. A better alternative involves using controlled-release drug delivery systems to reduce the frequency of intravitreal dosing and extend drug release. However, reaching the market stage is a time-consuming process. Methods: In this study, we employed two computational approaches to model and estimate the parameters governing the diffusion-controlled drug release from bi-layered microspheres. The case study involved microspheres composed of a chitosan core and a polycaprolactone (PCL) shell. The model drugs were bovine serum albumin and bevacizumab (an agent that slows neovascularization due to retinal disorders). Drug release from the microspheres is described by a mathematical model that was solved numerically using the finite difference and the finite element approaches. The parameter estimation was performed by nonlinear least-squares regression. Results: We used the estimated parameters to simulate the cumulative release under various conditions and optimize the device design to guide future experimental efforts and improve the duration of release beyond a target daily therapeutic release rate from the microspheres. Conclusions: We investigated the effects of polymeric layer sizes on drug release and provided recommendations for optimal sizes. We provide straightforward computational tools for others to reuse in designing bi-layered microspheres for intravitreal drug delivery needs in the treatment of chronic ocular neovascularization. Full article

This study investigates the structural performance of bolted T-joints in steel elements exposed to elevated temperatures, with a focus on the influence of fire-resistant coatings. A total of 36 T-joint specimens were tested under four different temperature levels (300 °C, 450 °C, 600 °C, and 900 °C), incorporating three IPE section sizes and three fire-resistant paint thicknesses (200 µm, 400 µm, and 600 µm). The experimental program aimed to evaluate the combined effects of temperature, cross-sectional geometry, and coating thickness on the axial load-bearing capacity and deformation characteristics of T-joints. To examine the influence of web geometry, T-sections were designed in accordance with Eurocode 3, and the flange-to-web thickness ratios (tf/tw) were varied between 1.52 and 1.58. Results showed that applying 200 µm and 400 µm coatings at 300 °C and 450 °C improved the axial load capacity by approximately 10% and 20%, respectively, compared to uncoated specimens. However, effective fire protection at higher temperatures (600 °C and 900 °C) required a minimum coating thickness exceeding 400 µm. Finite Element Models developed using ABAQUS (2017) were designed to predict post-fire load–displacement behavior, stiffness degradation, and failure modes. Predictions were validated against experimental results, with deviations ranging from 0.97% to 9.73% for maximum load and 1.18% to 42.13% for energy dissipation, confirming the model’s reliability in simulating the thermo-mechanical response of steel joints under fire exposure. Full article

This study examines the thinning behavior of AA3003-H14 aluminum alloy during single point incremental sheet forming (SPIF) through a combination of experimental trials and finite element analysis (FEA) using LS-DYNA. A full factorial experimental design was implemented to assess the effects of wall angle (45°, 55°, 65°) and step size (0.25 mm, 0.50 mm, 0.75 mm) on sheet thinning at various forming depths. Thickness measurements were analyzed using a two-way analysis of variance to determine the significance of process parameters and their interactions. Numerical simulations predicted thickness reduction, effective plastic strain, and von Mises stress distributions, with deviations from experimental results generally remaining below 10%. The findings indicate that wall angle has a dominant influence on thinning, while step size exhibits a moderate effect. The validated FEA model accurately captures localized deformation behavior, offering a predictive tool for optimizing SPIF parameters. This work enhances the understanding of AA3003 thinning mechanisms and supports process improvements for broader industrial adoption of SPIF. Full article