Search Results (72,107)

With the growth in global energy demand and increasing concern over the environmental issues associated with fossil fuels, magnetic confinement fusion (MCF) has gained widespread attention as a clean and sustainable energy solution. The superconducting magnet systems in MCF devices operate under liquid helium temperature of 4.2 K and strong magnetic fields, requiring structural materials to possess exceptional high strength, high toughness, and non-magnetic properties. This paper reviews recent research advances in cryogenic high-strength and high-toughness austenitic stainless steels (ASSs) for MCF devices, focusing on modified grades like 316LN and JK2LB used in the International Thermonuclear Experimental Reactor (ITER) project, as well as China’s CHN01 steel developed for the China Fusion Engineering Test Reactor (CFETR) project. The mechanical properties at 4.2 K (including yield strength (R_p0.2), fracture toughness (K(J)_Ic), and elongation at fracture (e)), microstructural evolutions, weldability, and manufacturing challenges of these materials are systematically analyzed. Finally, the different technical approaches and achievements in material development among Japan, the United States, and China are compared, the current limitations of these materials in terms of weld integrity and manufacturability are discussed, and future research directions are outlined. Full article

Soil salinization has become a critical constraint on agricultural productivity and eco-logical sustainability in arid regions. The accurate mapping of its spatial distribution is essential for sustainable land management. Although many studies have used satellite remote sensing combined with machine learning or convolutional neural networks (CNN) for soil salinity monitoring, most CNN approaches rely on single-scale convolution kernels. This limits their ability to simultaneously capture fine local detail and broader spatial patterns. In this study, we developed a multi-scale deep learning framework to enhance salinity prediction accuracy. We target the root-zone soil salinity in the Wei-Ku Oasis. Sentinel-2 multispectral imagery and Sentinel-1 radar backscatter data, together with topographic, climatic, soil texture, and groundwater covariates, were integrated into a unified dataset. We implemented the workflow using the Google Earth Engine (GEE; earthengine-api 0.1.419) and Python (version 3.8.18) platforms, applying the Sequential Forward Selection (SFS) algorithm to identify the optimal feature subset for each model. A multi-branch convolutional neural network (MB-CNN) with parallel 1 × 1 and 3 × 3 convolutional branches was constructed and compared against random forest (RF), 1 × 1-CNN, and 3 × 3-CNN models. On the validation set, MB-CNN achieved the best performance (R² = 0.752, MAE = 0.789, RMSE = 1.051 dS∙m⁻¹, nRMSE = 0.104), showing stronger accuracy, lower error, and better stability than the other models. The soil salinity inversion map based on MB-CNN revealed distinct spatial patterns consistent with known hydrogeological and topographic controls. This study innovatively introduces a multi-scale convolutional kernel parallel architecture to construct the multi-branch CNN model. This approach captures environmental characteristics of soil salinity across multiple spatial scales, effectively enhancing the accuracy and stability of soil salinity inversion. It provides new insights for remote sensing modeling of soil properties. Full article

As technology advances, small unmanned aerial vehicles (UAVs) are being engineered for increasingly versatile missions. The Multiple Environment Deployable Aerial Item Delivery (MEDAID) team, composed of 16 senior undergraduate aerospace engineering students, developed the XM-24 Orca as part of a capstone design project. This single-use UAV is designed to deliver medical supplies to soldiers in contested or remote environments. Capable of being ground or air-launched, the Orca incorporates spring-loaded swinging wings to meet a compact 610 mm stowed width requirement, a defining challenge in this project, allowing integration with existing drone platforms. The design effort was driven by key requirements: the ability to carry two 2.3 kg medical aid canisters, achieve a range of at least 370 km, sustain endurance for at least 4 h, and execute a dash speed of 51.4 m/s. This unique combination of mission requirements including airborne launch and wing deployment, extended range, and payload delivery necessitated an innovative design previously undocumented in the literature. The design was developed through rigorous computational analysis, refined through wind tunnel testing, and validated through a series of ground-based and flight tests. This paper documents unique design challenges and innovative solutions that offer guidance for future development efforts. Full article

Titanium (Ti) alloy sheets are important mechanical and structural components. However, thickness deviations may occur during the production of Ti-alloy sheets, significantly compromising product quality and structural safety. Eddy current testing (ECT) is a common method for measuring the thickness deviation of metal sheets. Nevertheless, conventional ECT methods often rely on complex calibration procedures or iterative inversion algorithms, thereby limiting their applicability. It was found that when low-frequency ECT excitation is used, such that the eddy current penetration depth exceeds three times the maximum target thickness of the Ti-alloy sheet, the tangent of the ECT coil impedance phase exhibits a linear relationship with the thickness. Based on this observation, by analyzing the low-frequency ECT response of Ti-alloys and separating the real and imaginary parts of the impedance under approximate conditions, a phase feature model was developed. The model effectively describes the linear dependence of the phase tangent on the thickness of the Ti-alloy sheet, offering a succinct characterization. The measurement method based on this model thereby allows for direct thickness calculation from the measured coil impedance without requiring master-curve calibration or iterative computation. Experiments were conducted using a custom-designed ECT coil and impedance analyzer to measure different Ti-alloy specimens. The results indicate that the measurement error was less than 3.5%. This research provides a theoretical foundation as well as a straightforward engineering solution for online, high-speed thickness measurement of Ti-alloy sheets. Full article

Laser Powder Bed Fusion (LPBF) technology represents one of the most promising additive manufacturing methods, enabling the production of components with high geometric complexity and a wide range of industrial and biomedical applications. In this study, the influence of both standard and high-productivity process parameters on the microstructure, porosity, surface roughness, and hardness of three commonly used materials, stainless steel 316L, aluminum alloy AlSi10Mg, and titanium alloy Ti6Al4V, was analyzed. The investigations were carried out on samples fabricated using the EOS M290 system, and their characterization was performed with scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), porosity analysis by point counting, Vickers hardness measurements, and optical profilometry. The obtained results revealed significant differences depending on the alloy and the applied parameters. For stainless steel 316L, the high-productivity variant led to grain refinement and stronger crystallographic orientation, albeit at the expense of increased porosity (0.11% vs. 0.05% for the standard variant). In the case of AlSi10Mg alloy, high-productivity parameters enabled a substantial reduction in porosity (from 0.82% to 0.27%) accompanied by an increase in hardness (from 115 HV1 to 122 HV1), highlighting their particular suitability for engineering applications. For the Ti6Al4V alloy, a decrease in porosity (from 0.17% to 0.07%) was observed; however, the increase in mechanical anisotropy resulting from a stronger texture may limit its application in cases requiring isotropic material behavior. The presented research confirms that optimization of LPBF parameters must be strictly tailored to the specific alloy and intended application, ranging from industrial components to biomedical implants. The results provide a foundation for further studies on the relationship between microstructure and functional properties, as well as for the development of hybrid strategies and predictive models of the LPBF process. Full article

In this paper, different physiochemical and biological indicators were tested to determine and compare the water quality of the Zaalklapspruit ecologically engineered wetland before and after a veldfire. Five sampling sites and a reference site 2.2 km upstream of an acid mine drainage (AMD)-decanting coal mine were selected and sampled before and after the veldfire. The “black box” method was also employed to determine the percentage change in the selected in- and outflow variables before and after the veldfire. After the veldfire, Al was reduced by 97.43%. The same trend was observed for Fe, which decreased by 99.65% at the outflow, and Mn and sulphate levels decreased by 98.41% and 68.16%. Possible pathways of the reduction in acid mine drainage impacts on the wetland were identified after the veldfire, including the increase in waterflows during the wet season causing a dilution factor, and phycoremediation by macroalgae drifting mats that accumulate metals and ash slurry from the burned-out macrophyte plant material that may have increased the wetland’s alkalinity. A comprehensive framework for the digital twinning and monitoring of the effects of natural disasters on wetlands is also presented. Full article

Research on the development of prefabricated foundations has been quite extensive to date, while studies on prefabricated concrete raft foundations and their connection methods remain relatively scarce. This study proposes a novel type of prefabricated raft foundation and its corresponding grouted plug-in connection. The connection comprises two prefabricated units and achieves connection via steel inserts and grouting in pre-slots, possessing numerous advantages such as convenient construction, fast installation, and high construction quality. To verify the performance of the connection node and the bearing capacity of the foundation, based on the engineering practice of prefabricated raft foundations, this study fabricated a full-scale specimen composed of three prefabricated units of the raft foundation, conducted a stacking load test on it, and carried out finite element analysis afterwards. The main conclusion is that severe flexural failure occurred near the grouted plug-in connection of the prefabricated units when the specimen failed, implying that the node region has sufficient bearing capacity. The ultimate bending moments of the specimen obtained from the experiment and finite element analysis are 736.5 kN·m and 859.5·kN m, respectively, with a difference of 14%, indicating a good agreement between them. Ignoring the effect of the upper steel reinforcements, the calculated section bending capacity of the prefabricated unit is 892.8·kN m; the ultimate bending moment of the test specimen reached 0.83 of the section bending capacity of the prefabricated unit, indicating that the proposed raft foundation and its connection method have good bending bearing capacity. Full article

Product variety in manufacturing has increased significantly, driven by technological advancements and growing demand for customisation. To meet diverse customer preferences, companies often overextend their portfolios without fully considering the resulting impact on manufacturing effectiveness. This study investigates how product variety is associated with quality conformance in continuous process manufacturing, an area underexplored in the existing literature, which predominantly focuses on discrete or assembly-based operations. Utilising production data from a large chemical manufacturer, logistic regression analysis was applied to examine how variety-related engineering parameters relate to the probability of non-conformance at the big-bag level. The analysis shows that ramp-ups, especially those associated with major changeovers, and short production uptimes are correlated with an increased likelihood of quality issues. Very infrequent production also appears to increase quality risks. Contrary to learning-curve expectations, products with medium and low production intensity showed lower odds of non-conformance than high-intensity products. These findings indicate the implications of product variety in continuous manufacturing environments. By identifying variety parameters that appear to contribute to quality risks, this study offers initial guidance for production planners and product portfolio managers aiming to balance product variety with quality conformance and overall manufacturing effectiveness. Full article

This paper describes a laboratory set-up designed to support hands-on learning of heat transfer principles in aerospace engineering education. Developed within the framework of experiential and project-based learning, the set-up enables students to experimentally characterize the convective coefficient of a cooling fan and the thermo-optical properties of aluminum plates with different surface coatings, specifically their absorptivity and emissivity. A custom-built, LED-based radiation source (the ESAT Sun simulator) and a calibrated temperature acquisition system are used to emulate and monitor radiative heating under controlled conditions. Simplified physical models are developed for both the ESAT Sun simulator and the plates that capture the dominant thermal dynamics via first-order energy balances. The laboratory workflow includes real-time data acquisition, curve fitting, and thermal model inversion to estimate the convective and thermo-optical coefficients. The results demonstrate good agreement between the model predictions and observed temperatures, which supports the suitability of the set-up for education. The proposed activities can strengthen the student’s understanding of convective and radiative heat transport in aerospace applications while also fostering skills in data analysis, physical and numerical reasoning, and system-level thinking. Opportunities exist to expand the material library, refine the physical modeling, and evaluate the long-term pedagogical impact of the educational set-up described here. Full article

A segment of the machine tool industry in Taiwan specializes in manufacturing equipment tailored to the semiconductor sector. Due to the stringent quality requirements inherent in semiconductor manufacturing processes, the machine tools employed in these processes are also subject to correspondingly rigorous quality standards. In fact, a machine tool is assembled from hundreds of individual components, each of which must meet the required standards to ensure that the final product adheres to overall quality requirements. Similarly, each component possesses multiple quality characteristics, all of which must individually satisfy the specified criteria to guarantee that the component achieves the required quality level. Clearly, without a comprehensive evaluation model, ensuring final product quality is difficult. To address this practical issue, this study employed the Process Capability Index (PCI), the most widely used process capability index in the industry, and based on statistical verification principles, constructed a quality assessment and analysis model applicable to products with multiple quality characteristics. This approach enables process engineers to simultaneously evaluate all product quality characteristics and determine whether they meet the desired quality standards. For products that do not meet the expected quality standards, improvement directions are proposed, and improvement decisions are made based on cost and economic benefits, thereby ensuring final product quality. This study concludes with a real-world case study to illustrate the application of the proposed model, making it easier for relevant industries to apply the model. Full article

Under the dual pressures of global climate change and accelerated urbanization, the impacts of flood disasters on urban systems are becoming increasingly pronounced. Enhancing regional resilience has emerged as a critical factor in achieving sustainable urban development. Compared with existing methods such as CRITIC–Entropy, PCA–AHP, or SWMM-based resilience evaluations, grounded in urban resilience theory, this study takes Fangshan District in Beijing as empirical research to construct a post-flood disaster resilience evaluation index system spanning five dimensions (ecological, social, engineering, economic, and institutional) and leverages the integrated DEMATEL-TOPSIS-AISM model to synergistically identify key drivers, evaluate performance, and uncover internal hierarchies, thereby overcoming the limitations of existing research approaches. The findings indicate that the DEMATEL analysis identified the frequency of heavy rainfall (a12 = 0.889) and the proportion of flood disaster information databases (c51 = 1.153) as key driving factors. The TOPSIS assessment reveals that Fangshan District exhibits the strongest resilience in the economic dimension (Relative Closeness C = 0.21200), while the institutional dimension is the weakest (C = 0.00000), the AISM model constructs a hierarchical topology from a cause–effect priority perspective, elucidating the causal relationships and transmission mechanisms among factors across different dimensions. This study pioneers a novel perspective for urban resilience assessment, thereby establishing a theoretical foundation and practical references for enhancing flood resilience and advancing resilient city development. Full article

In the coring process of ocean drilling, conventional vertical holes face many difficulties, such as the high cost of single holes and limited acquisition of geological information, which cannot meet the demand for fine delineation of strata around drill holes. For this reason, based on wire-line coring and directional drilling technology, a continuous core tool for directional drilling has been designed, which can efficiently and accurately obtain cores in seabed strata and improve perceptions of target geological bodies. In this paper, the structure and working principle of a directional coring drilling tool (DCDT) were introduced in detail, and the ultimate deflecting capacity of a hollow single bend sub (HSBS) and the power demand of a positive displacement motor (PDM) were calculated. Then, an experiment platform was established to test the performance of the DCDT prototype. The test results showed that a total core length of 5.15 m was obtained among hybrid drilling processes, and the maximum core recovery rate was 91.67%. In slide drilling processes, the core recovery rate was only 55–60%, and the calculated build-up rate reached 7.5°/30 m. Through simulation and experiments, the key components of DCDTs were verified. This research will promote the optimization of DCDTs and accelerate engineering applications. Full article

Mitigating ground vibrations from sources like vehicles and construction operations poses significant challenges, often relying on computationally intensive numerical methods such as Finite Element Methods (FEM) or Boundary Element Methods (BEM) for analysis. This study addresses these limitations by developing and evaluating Machine Learning (ML) methodologies for the rapid and accurate prediction of Insertion Loss (IL), a critical parameter for assessing the effectiveness of open trenches as vibration barriers. A comprehensive database was systematically generated through high-fidelity numerical simulations, capturing a wide range of geometric, elastic, and physical configurations of a stratified geotechnical system. Three distinct ML strategies—Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF)—were initially assessed for their predictive capabilities. Subsequently, a Meta-RF stacking ensemble model was developed, integrating the predictions of these base methods. Model performance was rigorously evaluated using complementary statistical metrics (RMSE, MAE, NMAE, R), substantiated by in-depth statistical analyses (normality tests, Bootstrap confidence intervals, Wilcoxon tests) and an analysis of input parameter sensitivity. The results clearly demonstrate the high efficacy of Machine Learning (ML) in accurately predicting IL across diverse, realistic scenarios. While all models performed strongly, the RF and the Meta-RF stacking ensemble models consistently emerged as the most robust and accurate predictors. They exhibited superior generalization capabilities and effectively mitigated the inherent biases found in the ANN and SVM models. This work is intended to function as a proof-of-concept and offers promising avenues for overcoming the significant computational costs associated with traditional simulation methods, thereby enabling rapid design optimization and real-time assessment of vibration mitigation measures in geotechnical engineering. Full article

To address the difficulty of directly detecting internal stresses in high-temperature forgings during dual-manipulator control experiments and the significant safety risks associated with high-temperature environments, this study developed an experimental device to simulate the deformation behavior of such forgings. First, numerical simulations of the elongation process were conducted using DEFORM V11 software to examine the deformation mechanisms of high-temperature forgings. Quantitative results for axial deformation, maximum deformation velocity, and deformation force ranges were obtained, which defined the operational specifications and functional requirements of the device. Second, the mechanical structure and hydraulic system were designed based on engineering principles. The dynamic response characteristics of the simulation device under conventional PID and fuzzy PID control were compared through simulations, and the feasibility of the fuzzy PID control strategy was experimentally verified. Finally, a joint simulation model of the high-temperature forging deformation simulation device and the dual forging manipulator clamping system was established. This model was used to analyze the dynamic response of the simulated workpiece under typical cooperative conditions of dual manipulators and to assess the accuracy of the simulation process during clamping. The results confirmed the practical applicability of the device. Overall, the developed simulation device can effectively reproduce the deformation behavior of high-temperature forgings under ambient conditions, providing a safe and reliable platform for studying coordinated control strategies of dual forging manipulators. Full article

Diesel engines remain the foundation for obtaining mechanical energy in sectors where autonomy and reliability are required; however, predictive diagnostics under real-world conditions remain challenging. The purpose of this scoping review is the investigation and systematization of published scientific data on the application of vibration methods for monitoring the technical condition of diesel engines in industrial or controlled laboratory conditions. Based on numerous results of publication analysis, sensor configurations, diagnosed components, signal analysis methods, and their application for assessing engine technical condition are considered. As methods for determining vibration parameters, time-domain and frequency-domain analysis, adaptive decompositions, and machine and deep learning algorithms predominate; high accuracy is more often achieved under controlled conditions, while confirmations of robustness on industrial installations are still insufficient. Key limitations for the application of vibration monitoring methods include the multicomponent and non-stationary nature of signals, a high level of noise, requirements for sensor placement, communication channel limitations, and the need for on-site processing; meanwhile, the assessment of torsional vibrations remains technically challenging. It is concluded that field validations of vibroacoustic data, the use of multimodal sensor platforms, noise-immune algorithms, and model adaptation to the specific environment are necessary, taking into account fuel quality, transient conditions, and climatic factors. Full article