Search Results (2,924)

Promoting the development of aerospace vehicles toward structural–functional integration and intelligent sensing is a key strategy for achieving lightweight, high-reliability, and autonomous operation and maintenance of next-generation aircraft. However, traditional external sensors face significant limitations because of their bulky size, installation challenges, and incompatibility with aerodynamic surfaces. These issues are particularly pronounced on complex, high-curvature substrates, where achieving conformal bonding is difficult, thus restricting their application in critical components. In this study, aerosol jet printing (AJP) was employed to directly fabricate silver nanoparticle-based temperature sensors with real-time monitoring capabilities on the surface of high-curvature stainless steel sleeves, which serve as typical engineering components. This approach enables the in situ manufacturing of high-precision conformal sensors. Through optimized structural design and thermal treatment, the sensors exhibit reliable temperature sensitivity. Microscopic characterization reveals that the printed sensors possess uniform linewidths and well-defined outlines. After gradient sintering at 250 °C, a dense and continuous conductive path is formed, ensuring strong adhesion to the substrate. Temperature-monitoring results indicate that the sensor exhibits a nearly linear resistance response (R² > 0.999) across a broad detection range of 20–200 °C. It also demonstrates high sensitivity, characterized by a temperature coefficient of resistance (TCR) of 2.15 × 10^−3/°C at 20 °C. In repeated thermal cycling tests, the sensor demonstrates excellent repeatability and stability over 100 cycles, with resistance fluctuations kept within 0.5% and negligible hysteresis observed. These findings confirm the feasibility of using AJP technology to fabricate high-performance conformal sensors on complex surfaces, offering a promising strategy for the development of intelligent structural components in next-generation aerospace engineering. Full article

Accurate prediction of ultimate tensile strength (UTS) is central to the design and optimization of heat-treated steels but is traditionally achieved through costly and iterative experimental trials. This study presents a transparent, physics-aware machine learning (ML) framework for predicting UTS using an open-access steel database. A curated dataset of 1255 steel samples was constructed by combining 18 chemical composition variables with 7 processing descriptors extracted from free-text heat-treatment records and filtering them using physically justified consistency criteria. To avoid information leakage arising from repeated measurements, model development and evaluation were conducted under a group-aware validation framework based on thermomechanical states. A Random Forest (RF) regression model achieved robust, conservative test-set performance (R² ≈ 0.90, MAE ≈ 40 MPa), with unbiased residuals and realistic generalization across diverse composition–processing conditions. Performance robustness was further examined using repeated group-aware resampling and strength-stratified error analysis, highlighting increased uncertainty in sparsely populated high-strength regimes. Model interpretability was assessed using SHAP-based feature importance and partial dependence analysis, revealing that UTS is primarily governed by the overall alloying level, carbon content, and processing parameters controlling transformation kinetics, particularly bar diameter and tempering temperature. The results demonstrate that reliable predictions and physically meaningful insights can be obtained from publicly available data using a conservative, reproducible machine-learning workflow. Full article

This paper proposes a multiphysics-based optimal design process for a 750 W axial-flux ferrite consequent-pole (AFCP) pump motor aimed at reducing permanent magnet usage. To mitigate the high computational cost associated with repetitive numerical analyses, a metamodel (surrogate model)-based optimization framework is adopted. A consequent-pole (CP) structure is applied to an initial ferrite axial-flux permanent magnet (AFPM) motor, and ten key design variables are selected for optimization. The electromagnetic performance corresponding to variations in these variables is evaluated using three-dimensional finite element analysis (3D FEA), and the resulting dataset is used to construct metamodels. In AFPM motors incorporating ferrite permanent magnets and a CP structure, electromagnetic performance, thermal saturation, and structural stability collectively limit reliable operation. Therefore, a multiphysics-based evaluation is essential. The optimal design is assessed through electromagnetic, thermal, and structural finite element analyses. According to the 3D FEA results, the optimal model achieves a 46.85% reduction in permanent magnet volume while improving efficiency by 0.75%, reaching 95.53%, compared to the initial model. The torque ripple and peak-to-peak cogging torque are reduced by 28.81% and 31.37%, reaching 0.08 Nm and 0.06 Nm, respectively. In addition, the total harmonic distortion (THD) of the back-electromotive force waveform decreases from 12.4% to 2.53%. Stable operating characteristics are confirmed through demagnetization, thermal, and structural analyses, demonstrating that the proposed optimal design process successfully achieves both permanent magnet reduction and overall performance improvement in ferrite-based AFCP motors. Full article

The calculation of the limiting efficiency and structural optimization of solar cells based on the detailed balance principle is systematically investigated in this study. Through modeling and numerical simulations of various cell architectures, the theoretical efficiency limits of these structures under AM1.5G (Air Mass 1.5 Global) spectrum were quantitatively evaluated. Through a comprehensive consideration of the effects of bandgap and composition, the Al_0.03Ga_0.97As/Ge (1.46 eV/0.67 eV) cell configuration was determined to achieve a high theoretical efficiency of 43.0% for two-junction cells while maintaining satisfactory lattice matching. Furthermore, the study proposes that incorporating a Ga_0.96In_0.04As (8.3 nm)/GaAs_0.77P_0.23 (3.3 nm) strain-balanced multiple quantum wells (MQWs) structure enables precise bandgap engineering, modulating the effective bandgap to the optimal middle-cell value of 1.37 eV, as determined by graphical analysis for triple junctions. This approach effectively surpasses the efficiency constraints inherent in conventional bulk-material III–V semiconductor solar cells. The results demonstrate that an optimized triple-junction solar cell with MQWs can theoretically achieve a conversion efficiency of 51.5%. This study provides a reliable theoretical foundation and a feasible technical pathway for the design of high-efficiency solar cells, especially for the emerging MQW-integrated III–V semiconductor tandem cells. Full article

The wave-making characteristics and resistance performance of a naval vessel are fundamental to its hydrodynamic design, directly impacting its speed, stealth, and energy efficiency. To reveal the performance trade-offs inherent in different design philosophies, a systematic comparative study on the hydrodynamic performance of two representative mainstream naval destroyers from China and the United States was conducted using Computational Fluid Dynamics (CFD). Full-scale three-dimensional models of both vessels were established based on publicly available data. Their flow fields in calm water were numerically simulated at both economical (18 knots) and maximum (30 knots) speeds using an unsteady Reynolds-Averaged Navier–Stokes (RANS) solver, the Volume of Fluid (VOF) method for free-surface capturing, and the SST k-ω turbulence model. The performance differences were meticulously compared through qualitative observation of wave patterns, quantitative measurements (such as the transverse width of the wave-making region), and analysis of resistance data. Numerical results indicated that the wave-making generated by the vessel of the United States was more pronounced during steady navigation. To validate the reliability of the CFD results, supplementary towing tank tests were performed using a small-scale model (1.1 m in length) of the vessel from China. The test speed (1.5 m/s) was scaled to correspond to the full-scale ship speed through dimensional analysis. The experimental data showed good agreement with the simulation results, jointly confirming the aforementioned performance trade-off. This study clearly demonstrates that, at the economic speed, the design of the mainstream vessel from China tends to prioritize superior wave stealth performance at the expense of higher resistance, whereas the mainstream vessel from the U.S. exhibits the characteristics of lower resistance coupled with more significant wave-making features. These findings provide an important theoretical basis and data support for the future multi-objective optimization design of surface vessels concerning stealth, speed, and comprehensive energy efficiency. Full article

Wireless Sensor Networks (WSNs) are pivotal for data acquisition, yet reliability is severely constrained by routing voids induced by sparsity, uneven energy, and high dynamicity. To address these challenges, the Hybrid Acoustic-Optical Adaptive Void-handling Protocol (HAO-AVP) is proposed to satisfy the requirements for highly reliable communication in complex underwater environments. First, targeting uneven energy, a reinforcement learning mechanism utilizing Gini coefficient and entropy is adopted. By optimizing energy distribution, voids are proactively avoided. Second, to address routing interruptions caused by the high dynamicity of topology, a collaborative mechanism for active prediction and real-time identification is constructed. Specifically, this mechanism integrates a Markov chain energy prediction model with on-demand hop discovery technology. Through this integration, precise anticipation and rapid localization of potential void risks are achieved. Finally, to recover damaged links at the minimum cost, a four-level progressive recovery strategy, comprising intra-medium adjustment, cross-medium hopping, path backtracking, and Autonomous Underwater Vehicle (AUV)-assisted recovery, is designed. This strategy is capable of adaptively selecting recovery measures based on the severity of the void. Simulation results demonstrate that, compared with existing mainstream protocols, the void identification rate of the proposed protocol is improved by approximately 7.6%, 8.4%, 13.8%, 19.5%, and 25.3%, respectively, and the void recovery rate is increased by approximately 4.3%, 9.6%, 12.0%, 18.4%, and 24.2%, respectively. In particular, enhanced robustness and a prolonged network life cycle are exhibited in sparse and dynamic networks. Full article

In recent years, green hydrogen production via water electrolysis driven by renewable energy sources has garnered increasingly significant attention. Among the various water electrolysis technologies, proton-exchange membrane water electrolysis (PEMWE) distinguishes itself owing to the unique advantages, including the compact architecture, high efficiency, rapid dynamic response, and high purity of the generated hydrogen. The membrane electrode assembly (MEA) serves as the core component of a PEM electrolyzer. And only a high-performance and stable MEA can provide a reliable platform for investigating the mass transport behavior within the porous transport layer (PTL). In this study, the MEA fabrication method was optimized by varying the ionomer-to-carbon (I/C) ratio, coating strategy, and anode Ir mass loading. As a result, the cell voltage was reduced from 1.679 V to 1.645 V at 1.0 A cm⁻², with a small degradation of 1.3% over 70 h of operation. Based on the optimized MEA, the effects of the structure and porosity of PTL on the mass transport behavior were further analyzed. After the PTL parameter optimization, the cell voltage was further reduced to 1.630 V at 1.0 A cm⁻², while a high-speed camera captured bubble dynamics in real time, showing the fast detachment of small oxygen bubbles. The integrated electrochemical and visualization results provide a useful guideline to designing both MEA and PTL for efficient PEMWE. Full article

This study aims to enhance the security of unmanned aerial vehicles (UAVs) within the Internet of Drones (IoD) ecosystem by detecting and preventing Denial-of-Service (DoS) attacks. We introduce DroneDefender, a web-based intrusion detection system (IDS) that employs machine learning (ML) techniques to identify anomalous network traffic patterns associated with DoS attacks. The system is evaluated using the CIC-IDS 2018 dataset and utilizes the Random Forest algorithm, optimized with the SMOTEENN technique to tackle dataset imbalance. Our results demonstrate that DroneDefender significantly outperforms traditional IDS solutions, achieving an impressive detection accuracy of 99.93%. Key improvements include reduced latency, enhanced scalability, and a user-friendly graphical interface for network administrators. The innovative aspect of this research lies in the development of an ML-driven, web-based IDS specifically designed for IoD environments. This system provides a reliable, adaptable, and highly accurate method for safeguarding drone operations against evolving cyber threats, thereby bolstering the security and resilience of UAV applications in critical sectors such as emergency services, delivery, and surveillance. Full article

Accurate registration of optical and synthetic aperture radar (SAR) images is a fundamental prerequisite for multi-source remote sensing data fusion and analysis. However, due to the substantial differences in imaging mechanisms, optical–SAR image pairs often exhibit significant radiometric discrepancies and spatially varying geometric inconsistencies, which severely limit the robustness of traditional feature or region-based registration methods in cross-modal scenarios. To address these challenges, this paper proposes an end-to-end Optical–SAR Registration Network (OSR-Net) based on multi-constraint joint optimization. The proposed framework explicitly decouples cross-modal feature alignment and geometric correction, enabling robust registration under large appearance variation. Specifically, a multi-modal feature extraction module constructs a shared high-level representation, while a multi-scale channel attention mechanism adaptively enhances cross-modal feature consistency. A multi-scale affine transformation prediction module provides a coarse-to-fine geometric initialization, which stabilizes parameter estimation under complex imaging conditions. Furthermore, an improved spatial transformer network is introduced to perform structure-preserving geometric refinement, mitigating spatial distortion induced by modality discrepancies. In addition, a multi-constraint loss formulation is designed to jointly enforce geometric accuracy, structural consistency, and physical plausibility. By employing a dynamic weighting strategy, the optimization process progressively shifts from global alignment to local structural refinement, effectively preventing degenerate solutions and improving robustness. Extensive experiments on public optical–SAR datasets demonstrate that the proposed method achieves accurate and stable registration across diverse scenes, providing a reliable geometric foundation for subsequent multi-source remote sensing data fusion. Full article

The growing global population has increased the demand for reliable security systems, especially in areas with limited or unstable energy infrastructure. Renewable energy sources, particularly solar panels, offer an effective solution to ensure continuous operation of cameras and sensors on security poles in such regions. This study analyzes data from a solar-powered security pole and develops Maximum Power Point Tracking (MPPT) algorithms to improve system efficiency. The original design, which relied solely on a buck converter, lacked flexibility. To address this, a buck–boost converter capable of operating in both buck and boost modes was designed, and the proposed algorithms were implemented and tested on this converter. Classical MPPT techniques, including Perturb and Observe (P&O) and Incremental Conductance (IC), were evaluated for their performance. Additionally, under partial shading conditions, metaheuristic approaches such as Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) were examined and compared. The performance of all algorithms was assessed in terms of energy efficiency and system adaptability. This study aims to contribute to renewable energy-based solutions by developing flexible and high-performance energy management systems for applications with limited energy access, such as security poles in rural areas. Full article

Soft errors and Hardware Trojans (HTs) constitute major reliability concerns, and in combination they can pose an even greater threat to circuit security. The main aim of this research is to develop and implement a reliability-based HT and to identify the optimal regions for its injection, enabling the creation of challenging benchmarks for evaluating detection techniques. In this context, a reliability-based HT is designed and evaluated using different components to achieve the required time overhead. Next, a method that combines the generation and propagation of Single-Event Transients (SETs), while accounting for both masking effects and the design’s timing constraints, is employed to efficiently identify the most vulnerable and critical gates. The sensitive gates selected for HT insertion exhibit 50–70% vulnerability to soft errors. At the same time, their insertion and the resulting path delay overhead must not violate the design’s timing constraints, and the additional area must remain below 10% of the total area. These three conditions ensure that the inserted HTs remain stealthy and, therefore, challenging to detect. The experimental results demonstrate that selecting this category of gates is highly effective, as it leads to a significant increase in the number of soft errors and, consequently, aggravates circuit vulnerability with minimal impact on the design. On average, the targeted gates exhibit a 130% increase in sensitivity, and the overall Soft Error Rate (SER) increases by 78%, confirming the importance of providing robust benchmarks to combat potential attacks of this kind. Full article

To address the diverse requirements of satellite communication applications involving medium-/low-rate reliable links and high-rate high-capacity services, an integrated S/Ka dual-band dual-reflector antenna is proposed as an effective solution. Owing to the stringent spatial constraints of satellite platforms, the longer operating wavelengths in the S-band lead to oversized feed horns in the integrated antenna design, which induces severe secondary aperture blockage, thus degrading aperture efficiency and impeding practical mechanical layout implementation. To alleviate this critical drawback, the proposed antenna achieves multi-band aperture reuse by deploying an array with four miniaturized S-band radiating elements around a broadband Ka-band feed horn. A frequency-selective surface (FSS)-based sub-reflector is further designed to effectively enhance the effective aperture size for the S-band operation, while ensuring unobstructed electromagnetic propagation in the Ka-band, thus enabling simultaneous dual-band high-gain radiation. After comprehensive electromagnetic simulation and parametric optimization for the antenna feed and the FSS sub-reflector, experimental measurements verify that the S-band left-hand and right-hand circularly polarized (LHCP/RHCP) channels achieve more than 20.2 dBic gains with more than 6° half-power beamwidths (HPBWs), and the Ka-band channel yields gains exceeding 41.2 dBic, with HPBWs greater than 0.8°. Full article

The reliable joining of dissimilar stainless steel and carbon steel remains a critical challenge in Metal Inert Gas (MIG) welding due to complex thermal–metallurgical interactions and the formation of brittle phases at the weld interface. In this study, a Taguchi-based design of experiments was employed to systematically optimize MIG welding parameters for SUS201/S20C dissimilar joints using a SUS201 filler wire, with particular attention to the welding current, voltage, travel speed, and electrode stick-out. The welding process was performed using an automatic welding robot. Tensile specimens were tested on a universal testing machine. Microstructural analysis was performed using a metallurgical microscope. The microstructure reveals that the development of the carbon side’s large ferrite and the stainless steel side’s δ-ferrite both significantly degrade joint quality. Among all process parameters, electrode stick-out is identified as the most influential parameter governing both tensile and bending performance, highlighting a critical process sensitivity that has received limited attention in prior studies. Optimized parameter combinations are required to maximize tensile and flexural responses. The highest tensile strength is 450.96 MPa. These findings advance the understanding of parameter–microstructure–property relationships in dissimilar MIG welding. Future work applying numerical welding simulations and advanced evaluation techniques is recommended. Full article

As a reliable peak-shaving power source, coal-fired boilers’ flexible operation technology has become a key support for achieving the low-carbon transition. To enhance the peak-shaving capacity of the boiler, it is urgent to explore the transient mechanisms of flow, combustion, and heat transfer under dynamic conditions. In this study, the heat transfer characteristics of the burner under varying load conditions and the combustion characteristics in boilers under low and dynamic load conditions are investigated by CFD numerical simulation technology based on a 10 MW coal-fired test bench. The results indicate that at load rates of 2%/min and 4%/min, heat flux density remains mostly consistent across the upper wall of the furnace. At 6%/min, the heat flux near dense pulverized coal flow exceeds that near fresh coal flow. At 60% load, the flow fields are symmetrical, optimizing flame filling and distribution. As the load drops to 40%, the upper flow field begins to distort, and by 20% load, turbulence and uneven temperature distribution arise. At 20% load, the one-layer burner demonstrates superior flow field stabilization compared to the two-layer configuration, with particle concentration remaining lower near the wall above the burner but higher in the cold ash hopper, while high-temperature zones predominantly concentrate in the furnace center with minimal areas exceeding 1900 K. A boiler designed for concentration separation enhances airflow and decreases wall particle concentration at 20% load, resulting in a more uniform temperature distribution with high-temperature zones further from the walls. Full article

With the increasing penetration of renewable energy, power systems are facing greater uncertainty and volatility, which poses significant challenges for Virtual Power Plant scheduling. Existing research mainly focuses on optimizing economic efficiency but often overlooks system reliability and the impact of forecasting deviations on scheduling, leading to suboptimal performance. Thus, this paper presents a reliability-cost bi-objective cooperative optimization model based on a dual-swarm particle swarm algorithm: it introduces positive and negative imbalance price penalty factors to explicitly describe the economic costs of forecast deviations, constructs a reliability evaluation system covering PV, EVs, air-conditioning loads, electrolytic aluminum loads, and energy storage, and solves the multi-objective model via algorithm design of “sub-swarms specializing in single objectives + periodic information exchange”. Simulation results show that the method ensures stable intraday operation of VPPs, achieving 6.8% total cost reduction, 12.5% system reliability improvement, and 14.8% power deviation reduction, verifying its practical value and application prospects. Full article