Search Results (116)

Aluminum–hydrogen compounds have drawn considerable interest for applications in solid-state hydrogen storage. The structural, hydrogen storage, electronic, mechanical, phonon, and thermodynamic properties of XAl₂H₂ (X = Ca, Sr, Sc, Y) hydrides are investigated using density functional theory. These hydrides exhibit negative formation energies in the hexagonal phase, indicating their thermodynamic stability. The gravimetric hydrogen storage capacities of CaAl₂H₂, SrAl₂H₂, ScAl₂H₂, and YAl₂H₂ are calculated to be 1.41 wt%, 0.94 wt%, 1.34 wt%, and 0.93 wt%, respectively. Analysis of the electronic density of states reveals metallic characteristics. Furthermore, the calculated elastic constants satisfy the Born stability criteria, confirming their mechanical stability. Additionally, through phonon spectra analysis, dynamical stability is verified for CaAl₂H₂ and SrAl₂H₂ but not for ScAl₂H₂ and YAl₂H₂. Finally, we present temperature-dependent thermodynamic properties. This research reveals that XAl₂H₂ (X = Ca, Sr, Sc, Y) materials represent promising candidates for solid-state hydrogen storage, providing a theoretical foundation for further studies on XAl₂H₂ systems. Full article

As a core component in renewable energy systems for grid regulation, hydropower units are increasingly exposed to flow conditions that elevate the risk of cavitation and erosion, posing significant challenges to the safe operation of flow-passage components. In this study, model testing and computational fluid dynamics (CFD) simulations are employed to investigate the hydraulic performance and cavitation behavior of a bulb turbine operating under rated head conditions and varying cavitation numbers. The analysis focuses on how changes in cavitation intensity affect flow characteristics and efficiency within the runner region. The results show that as the cavitation number approaches its critical value, the generation, growth, and collapse of vapor cavities increasingly disturb the main flow, causing a marked drop in blade hydraulic performance and overall turbine efficiency. Cavitation predominantly occurs on the blade’s suction side near the trailing edge rim and in the clearance zone near the hub, with bubble coverage expanding as the cavitation number decreases. A periodic inverse correlation between surface pressure and the cavitation area is observed, reflecting the strongly unsteady nature of cavitating flows. Furthermore, lower cavitation numbers lead to intensified pressure pulsations, aggravating flow unsteadiness and raising the risk of vibration. Full article

Despite the shale revolution triggering global shale oil and gas exploration, our understanding of the sedimentary environments of deep-water organic-matter-rich shale remains unclear. The sedimentary environment and facies of some siliceous shales at the bottom of the Longmaxi Formation in the Weiyuan area of the Sichuan Basin, China, were therefore analyzed. Nano-resolution petrological characterization and genesis analysis of the siliceous shales studied were conducted using nano-resolution petrologic image datasets. We identified these siliceous shales as microbial mats formed by deep-water traction current sedimentation. The microbial mats’ formation and burial diagenesis processes were divided into seven stages. The silt-grade bioclastic carpet deposits initially, colonizing mud-grade siliceous microbes and forming the siliceous microbial mat. Subsequently, carbohydrate-rich microbes thrive in sediment voids, forming the carbohydrate-rich microbial mat. Additionally, SOM undergoes four stages of burial diagenesis process, progressing from kerogens to pre-oil bitumen generation and ultimately transforming into porous pyrobitumen and nonporous pyrobitumen. This study will improve the understanding of deep-water traction current sedimentation and has implications for guiding shale gas exploration and development. Full article

The alloying elements usually lead to the precipitation of second phases in steel, readily forming at grain boundaries, and the type and distribution of these phases significantly influence the mechanical properties of the matrix. In the present contribution, the austenitic matrix fcc-Fe, the precipitate NbN, and the interface properties between them are investigated by first-principles calculations in detail. The effects of vacancy and alloying element content on the interface performance are examined. The results indicate that the density of states (DOS) of the former is primarily contributed by the Fe d-orbitals, and both exhibit elastic anisotropy. Under a tensile strain of 20%, the maximum tensile strength of fcc-Fe reaches 32.6 GPa. For NbN, the maximum tensile strength comes to 29 GPa at a strain of 10%, after which the stress rapidly decreases with the increasing of strain. In the meantime, the uneven distribution of electron cloud density increases in both. Regarding the interface, the introduction of vacancies enhances atomic interaction and improves interface stability by altering electron cloud distribution. As the Co doping content increases, the covalent interactions between atoms strengthen at the interface, enhancing interface stability. However, excessive V doping may reduce the interface stability. Furthermore, when the vacancies coexist with alloying elements, the stronger covalent characteristics are observed due to shortened bond lengths and positive bond population values. These insights provide a data foundation and theoretical basis for designing high-performance austenitic stainless steels. Full article

The Karamaili Granite Belt (KGB) in the southern margin of the Eastern Junggar is the most important tin metallogenic belt in the southwestern Central Asian Orogenic Belt. The plutons in the western part have a close genetic relationship with tin mineralization. The zircon U-Pb ages of the Kamusite, Laoyaquan, and Beilekuduke plutons are 315.1 ± 3.4 Ma, 313.6 ± 2.9 Ma, and 316.5 ± 4.6 Ma, respectively. The plutons have high silica (SiO₂ = 75.53%–77.85%), potassium (K₂O = 4.43%–5.42%), and alkalis (K₂O + Na₂O = 8.17%–8.90%) contents and low ferroan (Fe₂O₃^T = 0.90%–1.48%), calcium, and magnesium contents and are classified as metaluminous–peraluminous, high-potassium, calc-alkaline iron granite. The rocks are enriched in Rb, Th, U, K, Pb, and Sn and strongly depleted in Ba, Sr, P, Eu, and Ti. They have strongly negative Eu anomalies (δEu = 0.01–0.05), 10,000 Ga/Al = 2.87–4.91 (>2.6), showing the geochemical characteristics of A-type granite. The zircon U/Pb ratios indicate that the above granites should be I- or A-type granite, which is generally formed under high-temperature (768–843 °C), low-pressure, and reducing magma conditions. The high Rb/Sr ratio (a mean of 48 > 1.2) and low K/Rb ratio (53.93–169.94) indicate that the tin-bearing plutons have undergone high differentiation. The positive whole-rock εNd(t) values (3.99–5.54) and the relatively young Nd T_2DM model ages (616–455 Ma) suggest the magma is derived from partially melted juvenile crust, and the underplating of basic magma containing mantle materials that affected the source area. The results indicate the KGB was formed in the tectonic transition period in the late Carboniferous subduction post-collision environment. Orogenic compression influenced the tin-bearing plutons in the western part of the KGB, forming highly differentiated and reduced I, A-type transition granite. An extensional environment affected the plutons in the eastern sections, creating A-type granite with dark enclaves that suggest magma mixing with little evidence of tin mineralization. Full article

In this study, a comprehensive experimental framework was developed to quantitatively characterize the pore structure of a deep coal-rock (DCR; reservoirs below [3000 m]) gas reservoir. Experimentally, petrological and mineral characteristics were determined by performing proximate analysis and scanning electron microscopy (SEM) as well as by measuring vitrinite reflectance and maceral components. Additionally, physisorption and high-pressure mercury injection (HPMI) tests were conducted to quantitatively characterize the nano- to micron-scale pores in the DCR gas reservoir at multiple scales. The DCR in the NQ Block is predominantly composed of vitrinite, accounting for approximately 77.75%, followed by inertinite. The pore space is predominantly characterized by cellular pores, but porosity development is relatively limited as most of such pores are extensively filled with clay minerals. The isothermal adsorption curves of CO₂ and N₂ in the NQ Block and the DJ Block exhibit very similar variation patterns. The pore types and morphologies of the DCR reservoir are relatively consistent, with a significant development of nanoscale pores in both blocks. Notably, micropore metrics per unit mass (pore volume (PV): 0.0242 cm³/g; and specific surface area (SSA): 77.7545 m²/g) indicate 50% lower gas adsorption potential in the DJ Block. In contrast, the PV and SSA of the mesopores per unit mass in the NQ Block are relatively consistent with those in the DJ and SF Blocks. Additionally, the peak mercury intake in the NQ Block occurs within the pore diameter < 20 nm, with nearly 60% of the mercury beginning to enter in large quantities only when the pore size exceeds 20 nm. This indicates that nanoscale pores are predominantly developed in the DCR of the NQ block, which aligns with the findings from physical adsorption experiments and SEM analyses. Overall, the development characteristics of multi-scale pores in the DCR formations of the NQ Block and the eastern part of the Basin are relatively similar, with both total PV and total SSA showing an L-shaped distribution. Due to the disparity in micropore SSA, however, the total SSA of the DJ Block is approximately twice that of the NQ Block. This discovery has established a robust foundation for the subsequent exploitation of natural gas resources in DCR formations within the NQ Block. Full article

Background: Cashmere is one of the important economic products of goats, and the KRTAP gene family, as an important family of regulatory genes in the growth process of cashmere fiber, largely affects the quality of cashmere. Methods: In this study, the KRTAP6-3 gene was identified and located on goat chromosome 1 using a goat genome homology search combined with a phylogenetic tree approach. The Longdong cashmere goat KRTAP6-3 gene variation and its effect on cashmere quality were explored by using the polymerase chain reaction single-stranded conformation polymorphism (PCR-SSCP) technique, in situ hybridization, and the allele presence/absence model. Results: The results identified a total of six SNPs in KRTAP6-3, three of which were located in the coding region and two of which were synonymous mutations, in addition to 45- bp deletion sequences detected in alleles C and F. Moreover, the KRTAP6-3 mRNA showed a strong expression signal in the cortical layer of the primary and secondary follicles in the inner root sheaths, as well as in the cells of the hair papillae and the matrices during the anagen phase, and signaling at the sites described above is attenuated during the telogen phase. The presence of allele C was associated with increased MFD (mean fiber diameter) (p < 0.01). The MFD of goats with allele C genotype (genotype AC) was significantly higher (p < 0.05) than that of goats without allele C genotype (genotypes AA and AB). Conclusions: This indicates that genetic variation in the KRTAP6-3 gene in goats is significantly associated with cashmere traits and can serve as a candidate gene for molecular markers of cashmere traits. Full article

A novel interactive maintenance method for space station in-orbit devices using scene semantic segmentation technology is proposed. First, a wearable and handheld system is designed to capture images from the astronaut in the space station’s front view scene and play these images on a handheld terminal in real-time. Second, the proposed system quantitatively evaluates the environmental lighting condition in the scene by calculating image quality evaluation parameters. If the lighting condition is not proper, a prompt message will be given to the astronaut to remind him or her to adjust the environment illumination. Third, our system adopts an improved DeepLabV3+ network for semantic segmentation of these astronauts’ forward view scene images. Regarding the improved network, the original backbone network is replaced with a lightweight convolutional neural network, i.e., the MobileNetV2, with a smaller model scale and computational complexity. The convolutional block attention module (CBAM) is introduced to improve the network’s feature perception ability. The atrous spatial pyramid pooling (ASPP) module is also considered to enable an accurate calculation of encoding multi-scale information. Extensive simulation experiment results indicate that the accuracy, precision, and average intersection over the union of the proposed algorithm can be better than 95.0%, 96.0%, and 89.0%, respectively. And the ground application experiments have also shown that our proposed technique can effectively shorten the working time of the system user. Full article

The application of intelligent video monitoring for natural resource protection and management has become increasingly common in recent years. To enhance safety monitoring during the grazing prohibition and rest period of grassland, this paper proposes a multi-object segmentation algorithm based on mid-infrared images for all-weather surveillance. The approach integrates the Image Timing Features–Gaussian Mixture Model (ITF-GMM) and Convolutional-UNet (Con-UNet) to improve the accuracy of target detection. First, a robust background modelling, i.e., the ITF-GMM, is proposed. Unlike the basic Gaussian Mixture Model (GMM), the proposed model dynamically adjusts the learning rate according to the content difference between adjacent frames and optimizes the number of Gaussian distributions through time series histogram analysis of pixels. Second, a segmentation framework based on Con-UNet is developed to improve the feature extraction ability of UNet. In this model, the maximum pooling layer is replaced with a convolutional layer, addressing the challenge of limited training data and improving the network’s ability to preserve spatial features. Finally, an integrated computation strategy is designed to combine the outputs of ITF-GMM and Con-UNet at the pixel level, and morphological operations are performed to refine the segmentation results and suppress noises, ensuring clearer object boundaries. The experimental results show the effectiveness of proposed approach, achieving a precision of 96.92%, an accuracy of 99.87%, an intersection over union (IOU) of 94.81%, and a recall of 97.75%. Furthermore, the proposed algorithm meets real-time processing requirements, confirming its capability to enhance small-target detection in complex outdoor environments and supporting the automation of grassland monitoring and enforcement. Full article

Under extremely low-head conditions, the performance and stability of pump-turbine units are strongly influenced by the flow distortion caused by variations in guide vane opening. In this study, a pump-turbine model—representative of a domestic pumped storage power station—was investigated through a combination of experimental observations and three-dimensional unsteady numerical simulations employing the SST k-ω turbulence model. The analysis focused on characterizing the variations in turbulence kinetic energy, pressure pulsations, and impeller force fluctuations as the guide vane opening was altered. The results reveal that, with increasing guide vane opening, the turbulence kinetic energy within the impeller region is notably reduced. This reduction is primarily attributed to a decrease in energy losses along the suction surfaces of the blades and within the straight pipe section of the tailwater tunnel. Simultaneously, pressure pulsations were detected at multiple locations including the volute inlet, the blade-free zone, downstream of the conical pipe, and along the inner surface of the shaft tube. While most regions experienced a decline in pressure pulsation intensity with larger openings, the bladeless zone exhibited a significant increase. Moreover, force analysis at four distinct guide vane settings indicated that an opening of 41 mm resulted in relatively uniform fluctuations in the impeller forces. This uniformity suggests that an optimal guide vane configuration exists, which minimizes uneven stress distributions and enhances the operational stability of the pump-turbine under extremely low-head conditions. These findings offer valuable insights for the design and operational optimization of pump-turbine systems in pumped storage power stations. Full article

Low-carbon micro-alloyed steel has become a wire material with great potential for further development due to its excellent comprehensive performance; however, there is still a lack of insight into the evolution of its electrical conductivity during annealing treatment after undergoing deformation. In this present contribution, we systematically explored the intrinsic correlation between the microstructural characteristics (including grain size evolution, dislocation density change, etc.) and performance indexes of cold-rolled high-conductivity high-strength steels and their mechanisms, using the annealing temperature, a key process parameter, as a variable. Characterization methods were used to comprehensively investigate the variation rule of the electrical conductivity of low-carbon micro-alloyed steels containing Ti-Nb elements under different annealing temperatures, as well as their influencing factors. The results show that for the ultra-low-carbon steel (0.002% C), the dislocation density continuously decreases with the increasing annealing temperature. Both experimental steels underwent complete recrystallization at 600 °C, with grain growth increasing at higher temperatures (with ultra-low-carbon steel being finer than low-carbon steel (0.075% C)). Dislocation density in ultra-low-carbon steel decreased steadily, whereas low-carbon steel exhibited an initial decline followed by an increase due to carbon-rich precipitate pinning. The yield ratio decreased with the annealing temperature, with optimal performance being at 700 °C for ultra-low-carbon steel (lowest resistivity: 13.75 μΩ/cm) and 800 °C for low-carbon steel (best conductivity: 14.66 μΩ/cm). Yield strength in ultra-low-carbon steel was dominated by grain and precipitation strengthening, while low-carbon steel relied more on precipitation and solid solution strengthening. Resistivity analysis confirmed that controlled precipitate size enhances conductivity. Full article

The industrial sector accounts for approximately 65% of global energy consumption, with projections indicating a steady annual increase of 1.2% in energy demand. In the context of growing concerns about climate change and the need for sustainable energy solutions, solar thermal energy has emerged as a promising technology for reducing reliance on fossil fuels. With its ability to provide high-efficiency heat for industrial processes at temperatures ranging from 150 °C to over 500 °C, solar thermal power generation offers significant potential for decarbonizing energy-intensive industries. This review provides a comprehensive analysis of various solar thermal technologies, including parabolic troughs, solar towers, and linear Fresnel reflectors, comparing their effectiveness across different industrial applications such as process heating, desalination, and combined heat and power (CHP) systems. For instance, parabolic trough systems have demonstrated optimal performance in high-temperature applications, achieving efficiency levels up to 80% for steam generation, while solar towers are particularly suitable for large-scale, high-temperature operations, reaching temperatures above 1000 °C. The paper also evaluates the economic feasibility of these technologies, showing that solar thermal systems can achieve a levelized cost of energy (LCOE) of USD 60–100 per MWh, making them competitive with conventional energy sources in many regions. However, challenges such as high initial investment, intermittency of solar resource, and integration into existing industrial infrastructure remain significant barriers. This review not only discusses the technical principles and economic aspects of solar thermal power generation but also outlines specific recommendations for enhancing the scalability and industrial applicability of these technologies in the near future. Full article

Three-dimensional reconstruction entails the development of mathematical models of three-dimensional objects that are suitable for computational representation and processing. This technique constructs realistic 3D models of images and has significant practical applications across various fields. This study proposes a rapid and precise multi-view 3D reconstruction method to address the challenges of low reconstruction efficiency and inadequate, poor-quality point cloud generation in incremental structure-from-motion (SFM) algorithms in multi-view geometry. The methodology involves capturing a series of overlapping images of campus. We employed the Scale-invariant feature transform (SIFT) algorithm to extract feature points from each image, applied the KD-Tree algorithm for inter-image matching, and Enhanced autonomous threshold adjustment by utilizing the Random sample consensus (RANSAC) algorithm to eliminate mismatches, thereby enhancing feature matching accuracy and the number of matched point pairs. Additionally, we developed a feature-matching strategy based on similarity, which optimizes the pairwise matching process within the incremental structure from a motion algorithm. This approach decreased the number of matches and enhanced both algorithmic efficiency and model reconstruction accuracy. For dense reconstruction, we utilized the patch-based multi-view stereo (PMVS) algorithm, which is based on facets. The results indicate that our proposed method achieves a higher number of reconstructed feature points and significantly enhances algorithmic efficiency by approximately ten times compared to the original incremental reconstruction algorithm. Consequently, the generated point cloud data are more detailed, and the textures are clearer, demonstrating that our method is an effective solution for three-dimensional reconstruction. Full article

Mobile manipulators have the potential to replace manual labor in various scenarios. However, current mobile base designs have limitations when it comes to accommodating complex movements that involve both high-altitude tasks and ground-based composite tasks. This paper presents a new design for the mobile manipulator base, which utilizes a time-sharing drive with gears and differential wheels. Additionally, a new foldable mechanical gear-track system has been developed, enabling the robot to effectively operate on both the ground and the mechanical gear-tracks. To address the challenges of power distribution and localization caused by the mechanical characteristics of the designed track, this study proposes a precise pose estimation method for the robot on the mechanical gear-track, along with a compliance control method for the gears. Furthermore, a segmented multi-sensor fusion navigation approach is introduced to meet the high-precision motion control requirements at the entrance of the designed track. Experimental results demonstrate the effectiveness of the proposed new mobile manipulator base, as well as its corresponding control methods. Full article

Sand screenout is a critical challenge in hydraulic fracturing, affecting both the construction process and operational safety. This paper proposes a sand screenout warning model that integrates a combinatorial neural network and physical approaches to enhance both the speed and accuracy of sand screenout warnings. Firstly, the combined neural network uses a Transformer to capture key features during fracturing construction from historical data, and the extracted features are input to the Gated Recurrent Unit (GRU) for temporal prediction and the Crested Porcupine Optimizer (CPO) to further optimise the GRU-Transformer hyperparameters of the model. Additionally, the physical model improves the conventional inverse slope method by incorporating a threshold and sliding module, which enhances slope calculation and warning accuracy. The results showed that for fracturing pressure prediction, the proposed CPO-GRU-Transformer model obtained an RMSE value of 0.842 MPa, MAE of 0.613 Mpa, and $R^2$ of 0.971, a smaller RMSE and MAE and a larger $R^2$ than the three pressure prediction models, namely LSTM, GRU, and CPO-GRU. The proposed sand screenout warning model has been applied in the field construction of the U shale gas area in the Sichuan Basin. The warning points of the model proposed in this study were advanced by 73.5 s on average compared with the manual warning points in the three validated fracturing segments, with a successful warning rate of 85.71%, which greatly avoids the possibility of sand screenout and provides a method of fast calculation speed and high prediction accuracy, providing an early warning of sand screenout. Full article