Engineering reports : open access最新文献

This article extends the unit interval of the Kumaraswamy distribution to an unbounded interval. This generalization enables the application of the proposed model to a wider range of scenarios, all while maintaining explicit closed-form expressions. This model is derived by applying a power transformation to a logistic random variable, resulting in the flexibility to fit a wide range of real-world risk evaluation scenarios. Given the unpredictable nature of lifetime data, this tractable model is uniquely valuable, as it can accommodate both symmetrical and asymmetrical data by effectively capturing four classic hazard rate shapes, that is, increasing, decreasing, bathtub, and upside-down bathtub, as well as more atypical forms like decreasing-increasing-decreasing. The model's foundation is established through an analysis of its analytical properties, including reliability functions, density and hazard rate shapes, quantile and quantile-based skewness measures, and moments supported by graphical illustrations. Parameter inference is conducted using eight distinct estimation methods, with a comprehensive simulation study demonstrating their performance. The model's practical utility is then highlighted through its application to two real-world engineering data sets. These applications materialize the claim that the proposed model outperforms 18 established generalized families of distributions. The study concludes with a summary of key findings and implications.

To address the challenges of limited operational resilience and insufficient support for critical loads in distribution networks during extreme disturbances, this study proposes a two-stage robust optimization method for configuring grid-connected energy storage systems (GES). A two-stage optimization model is first formulated for GES configuration. The first stage aims to minimize the investment and operational costs by determining the optimal siting and sizing of GES. The second stage focuses on minimizing load loss through the optimal scheduling of GES charging and discharging operations. The Column and Constraint Generation (C&CG) algorithm is used to solve the optimization problem, thereby improving the system's capability to respond to and withstand multiple disturbance scenarios. Furthermore, a two-level resilience assessment framework is proposed to evaluate the effectiveness of GES configurations in supporting distribution grid operations under fault conditions. Case studies are conducted on the IEEE 33-node distribution system. The results show that the proposed method enhances power quality while maintaining economic efficiency, significantly strengthens the resilience of both overall and critical loads, and demonstrates strong engineering adaptability and scalability for broader deployment.

This study investigates the elastic behavior and dynamic mechanical response of bio-inspired composite sandwich cores fabricated from polylactic acid (PLA) reinforced with varying concentrations of particulate hemp powder. Motivated by the need for sustainable lightweight structures with tailored mechanical properties, two distinct cellular architectures were developed: honeycomb inspired core (HIC) and lotus root inspired core (LIC). Three material compositions were examined—pure PLA, PLA with 2.5 wt% hemp powder, and PLA with 5 wt% hemp powder—to evaluate the influence of particulate natural filler content on structural performance. All specimens were manufactured using fused deposition modeling (FDM) additive manufacturing technology, enabling precise replication of complex bio-inspired geometries. Mechanical characterization was performed using both experimental testing and finite element analysis. the alternate dynamic method (ADM) employing an OROS OR34 dynamic signal analyzer was utilized to determine transverse shear modulus, natural frequencies, and damping characteristics. Results demonstrated that the HIC configuration without hemp powder exhibited superior stiffness, achieving a shear modulus of 126.2 MPa and a natural frequency of 1120 Hz with minimal damping (damping ratio 0.0069), attributed to its higher core density of 172.94 kg/m³. The incorporation of particulate hemp powder, particularly at 5%, resulted in significant stiffness reductions of 49% in HIC and 27% in LIC designs due to decreased material density and the softening effect of the particulate filler. However, this reduction in stiffness was counterbalanced by enhanced damping behavior, with LIC configurations consistently demonstrating higher damping ratios and superior energy dissipation capabilities. Finite element simulations using ANSYS exhibited strong correlation with experimental results, particularly for LIC geometries, with prediction errors ranging from 2.84% to 5.90%. The study concludes that lotus-inspired cellular structures, despite exhibiting slightly lower absolute stiffness, provide more stable and predictable mechanical performance, making them advantageous for lightweight engineering applications requiring both moderate structural rigidity and enhanced vibration damping. Future investigations may explore multi-material printing strategies, optimized cellular geometries, or nano-particulate integration to further enhance performance.

High-strength galvanized steel wire (HSGSW), as a critical load-bearing component in bridge cable systems, is highly susceptible to environmental corrosion during long-term service, posing significant threats to the safety and durability of bridge structures. To address this issue, this study systematically investigates the corrosion behavior and mechanical performance degradation of HSGSW in the presence of N,N′-dimethylethanolamine (N,N′-DMEA), an organic corrosion inhibitor. Electrochemical accelerated corrosion tests combined with weight loss measurements were conducted to quantitatively evaluate the inhibition efficiency of N,N′-DMEA under varying concentrations and exposure durations. SEM and EDS were employed to characterize the microstructural evolution and surface chemical composition of corroded specimens. The effects of corrosion inhibition treatment on the mechanical degradation of HSGSW were further analyzed based on load–displacement curves obtained from tensile tests. The results indicate that N,N′-DMEA forms a protective adsorption film on the steel surface, significantly enhancing corrosion resistance, with an optimal inhibitor concentration of 0.08 mol·L⁻¹. As corrosion progresses, the corrosion products evolve into a dark, porous structure primarily composed of Fe, leading to the formation of localized pits and inducing stress concentration, which alters the fracture mode from a typical cup-and-cone morphology to a mixed splitting–milling fracture. Inhibitor concentrations not exceeding 0.08 mol·L⁻¹ show a positive correlation with inhibition efficiency, while increased current density results in reduced efficiency. Notably, under equivalent corrosion conditions, specimens treated with the inhibitor exhibited significantly higher ultimate tensile strength than untreated ones, with an estimated service life extension of approximately 150%. This study provides a novel technical approach for the corrosion protection of HSGSW used in bridge cables and offers valuable engineering guidance for ensuring the long-term safe operation of cable-supported bridges.

Brain tumors remain a major neurological challenge, where timely and accurate diagnosis is critical for improving patient outcomes. Although several reviews have examined machine learning (ML) and deep learning (DL) techniques for brain tumor analysis, most existing surveys either focus on a single methodological family or lack a comparative perspective across emerging computational paradigms. This review addresses that gap by providing an integrated analysis of ML, Convolutional Neural Networks (CNNs), Transformer-based models, Generative Adversarial Networks (GANs), and hybrid ensemble frameworks for tumor detection, classification, and segmentation using magnetic resonance imaging (MRI). Unlike prior reviews, we systematically evaluate the clinical applicability, dataset limitations, and reproducibility concerns of these models while identifying unresolved issues such as interpretability, data scarcity, and domain generalization. Furthermore, we synthesize trends in multimodal learning, federated frameworks, and explainable AI, offering actionable insights for translating research advances into clinical practice. This critical perspective highlights not only the state of the art but also the pathways required for developing robust, transparent, and clinically viable artificial intelligence (AI)-driven diagnostic systems.

Wire arc additive manufacturing (WAAM) has emerged as a cost-effective and scalable approach for producing large and complex metallic components. However, its industrial deployment faces persistent challenges in process stability, real-time quality assurance, and data transparency. This review provides a comprehensive analysis of the individual applications of artificial intelligence (AI) and Blockchain technologies in WAAM, emphasizing their distinct contributions and future potential for convergence. AI techniques such as artificial neural networks (ANN), support vector machines (SVM), deep learning (DL), adaptive neuro-fuzzy inference systems (ANFIS), and reinforcement learning (RL) are critically examined for their roles in process modeling, defect prediction, adaptive control, and toolpath optimization. Concurrently, Blockchain's decentralized and tamper-proof framework is analyzed for its capacity to enhance data integrity, certification, traceability, and supply chain transparency within WAAM ecosystems. A patent landscape analysis identifies AI-related and blockchain-related filings, reflecting the rapid global expansion of intelligent and secure additive manufacturing research. Despite these advancements, current studies predominantly address these technologies independently, with limited integration between intelligent decision-making and secure data management. The review highlights key research gaps, methodological constraints, and offers actionable directions toward developing hybrid AI–Blockchain frameworks tailored for autonomous, traceable, and industry-ready WAAM systems.

Accurate and timely health status assessment of power converter systems is crucial for ensuring the reliability and safety of power equipment. Conventional health assessment methods for power converters often rely on static models or fixed-weight Health Index (HI), which lack adaptability to evolving degradation patterns and fail to prioritize recent operational data, limiting prediction accuracy and timeliness. In this study, a rolling prediction framework is proposed for health status assessment of key components in power converter systems, which is built upon an adaptively weighted HI and rolling Support Vector Regression (SVR). First, the HI is constructed from multiple degradation-related features, where an inverse standard deviation weighting scheme is applied to dynamically capture the relative contribution of each feature, yielding an adaptive and interpretable HI. Then, a rolling prediction mechanism is introduced using an SVR model to characterize the nonlinear relationship between raw features and the HI. In this framework, the training set is continuously updated through a sliding time window, while exponentially decaying weights are applied to emphasize more recent data. Finally, two experiments on circuit breakers and Insulated-Gate Bipolar Transistors (IGBT) are conducted to demonstrate the effectiveness of the proposed method.

This research formulates a two-phase mathematical model to investigate the dynamics of a Maxwell dusty fluid across a linearly stretching surface embedded within a Darcy–Forchheimer porous medium, influenced by a magnetic field and varying thermal conductivity. Dusty fluid flows are significant in industries such as oil transportation, gas cleaning, and car exhaust control. The governing partial differential equations are reduced to a system of ordinary differential equations using similarity transformations and solved numerically via the bvp4c solver in MATLAB. The model's reliability is verified by comparing its results with previously published results. Parametric analysis reveals that increasing the magnetic field strength, Maxwell fluid parameter, and Forchheimer number decreases the velocities of both the fluid and dust phases, while increasing the temperature. The dusty-phase temperature is more sensitive to thermal conductivity and fluid–particle interactions. The local Nusselt number increases with thermal conductivity but drops with magnetic and Maxwell parameters, implying a lower heat transfer rate. These findings provide a deeper scientific understanding of how viscoelastic particulate flows transmit heat and momentum.

This study presents a novel approach for sandy soil stabilization through the alkali activation of recycled construction glass powder, aimed at mitigating wind erosion. The investigation commenced with a comprehensive evaluation of the physical and chemical properties of the activated glass waste, followed by laboratory tests, including wind tunnel experiments, particle size analysis, compaction, unconfined compressive strength, x-ray spectroscopy, FTIR, SEM, permeability, and vane shear tests, on samples prepared with varying sodium hydroxide (NaOH) solution as an alkaline activator, glass powder contents, and spraying rates. Results indicated that a molar concentration of 3 M containing 25 g/L of glass powder applied at 2 L/m² produced a protective layer of 7.5–8 mm, reducing wind erosion to nearly undetectable levels. Thermal assessments confirmed the stability of the geopolymerization process at temperatures up to 50°C, while enhanced mechanical performance was evidenced by increased surface shear strength and a characteristic brittle failure mode under unconfined compressive loading. These findings validate the efficacy of alkali-activated recycled glass powder as a sustainable solution for environmental management and infrastructure protection.

Unmanned aerial vehicle (UAV) roles and applications are rapidly growing, extending their variety and functionality. However, the battery dependency considerably limits the UAV's flight duration and coverage. To overcome associated challenges, a wireless power transfer (WPT) system has emerged as a viable solution, eliminating human assistance in battery depletion. Nevertheless, lateral misalignment in such systems can significantly degrade performance. In this regard, multiple-input single-output (MISO) systems have shown potential in addressing this challenge. This paper, therefore, proposes the development and modeling of a MISO WPT system with high robustness that also addresses the lateral displacement issues encountered in UAV powering. Initially, a defected ground structure-based resonator is designed with a 50-by-50 mm² area. Subsequently, two coupled resonators at 55 mm formed the WPT system. Performance validation under perfect alignment and lateral misalignment revealed the system's efficiency to reach 98% and decrease to 33% under ±25 mm shift, accordingly. The obtained results leave room to realize a MISO WPT system with two resonators composing a transmitter and a single receiver. Furthermore, the 1.25 mm isolating substrate was embedded between adjacent resonators on the transmitter to mitigate interference. The developed MISO WPT system demonstrated stable efficiency exceeding 50% under lateral misalignment.