Search Results (523)

Deep drilling and horizontal wells, as important means of unconventional oil and gas development, face problems with the high energy consumption but low removal efficiency of traditional well washing equipment, the uneven cleaning of horizontal well intervals, and an insufficient degree of automation. This paper proposes a novel hydraulic drive well washing device which consists of two main units. The wellbore cleaning unit comprises a hydraulic drive cutting–flushing module, a well cleaning mode-switching module, and a filter storage module. The unit uses hydraulic and mechanical forces to perform combined cleaning to prevent mud and sand from settling. By controlling the flow direction of the well washing fluid, it can directly switch between normal and reverse washing modes in the downhole area, and at the same time, it can control the working state of corresponding modules. The assembly control unit includes the chain lifting module and the arm assembly module, which can lift and move the device through the chain structure, allow for the rapid assembly of equipment through the use of a mechanical arm, and protect the reliability of equipment through the use of a centering structure. The device converts some of the hydraulic power into mechanical force, effectively improving cleaning and plugging removal efficiency, prolonging the downhole continuous working time of equipment, reducing manual operation requirements, and comprehensively improving cleaning efficiency and energy utilization efficiency. Full article

Photovoltaic panels have become a promising solution for generating renewable energy and reducing our reliance on fossil fuels by capturing solar energy and converting it into electricity. The effectiveness of this conversion depends on several factors, such as the quality of the solar panels and the amount of solar radiation received in a specific region. This makes accurate solar irradiance forecasting essential for planning and managing efficient solar power systems. This study examines the application of machine learning (ML) models for accurately predicting global horizontal irradiance (GHI) using a three-year dataset from six distinct photovoltaic stations: NELHA, ULL, HSU, RaZON+, UNLV, and NWTC. The primary aim is to identify optimal shared features for GHI prediction across multiple sites using a 30 min time shift based on autocorrelation analysis. Key features identified for accurate GHI prediction include direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and solar panel temperatures. The predictions were performed using tree-based algorithms and ensemble learners, achieving $R^2$ values exceeding 95% at most stations, with NWTC reaching 99%. Gradient Boosting Regression (GBR) performed best at NELHA, NWTC, and RaZON, while Multi-Layer Perceptron (MLP) excelled at ULL and UNLV. CatBoost was optimal for HSU. The impact of time-shifting values on performance was also examined, revealing that larger shifts led to performance deterioration, though MLP performed well under these conditions. The study further proposes a stacking ensemble approach to enhance model generalizability, integrating the strengths of various models for more robust GHI prediction. Full article

In this study, the combined influence of vibration direction, feature selection strategy, and the support vector machine (SVM) kernel on the classification accuracy of unbalance faults was investigated. Experiments were carried out on a Jeffcott rotor test rig at a constant speed and under three operating conditions. The overlapping sliding window method was used for raw sample expansion. Features extracted from time domain signals and from the order and power spectra obtained in the frequency domain were ranked using the Kruskal–Wallis algorithm. Based on the feature-ranking results, the three most discriminative features for each domain–axis combination, as well as all nine most discriminative features for each axis in a hybrid manner, were fed into SVM classifiers with different kernels, and their performance was evaluated using ten-fold cross-validation. Classification using vibration signals in the vertical direction had higher accuracy rates than those using signals in the horizontal direction for the feature sets obtained in the same domains. According to the statistical results, feature set selection had a much greater impact on classification accuracy than SVM kernel choice. Power spectrum-based features allowed higher classification accuracies in all SVM algorithms compared to both the time domain features and the order spectrum-based features for detecting unbalance faults. Increasing the number of features or employing hybrid feature selection did not result in a consistent or significant enhancement in overall classification performance. Selecting the right SVM kernel shapes both the model’s flexibility and its fit to the chosen feature space; when this fit is inadequate, classification accuracy may decrease. Consequently, by selecting the appropriate vibration direction, feature set, and SVM kernel, an improvement of up to 67% in unbalance fault classification accuracy was achieved. Full article

Outer-membrane vesicles (OMVs), naturally secreted by Gram-negative bacteria, have gained recognition as a versatile platform for the development of next-generation vaccines. OMVs are essential contributors to bacterial pathogenesis, horizontal gene transfer, cellular communication, the maintenance of bacterial fitness, and quorum sensing. Their intrinsic immunogenicity, adjuvant properties, and scalability establish OMVs as potent tools for combating infectious diseases and cancer. Recent advancements in genetic engineering and biotechnology have further expanded the utility of OMVs, enabling the incorporation of multiple epitopes and antigens from diverse pathogens. These developments address critical challenges such as antigenic variability and co-infections, offering broader immune coverage and cost-effective solutions. This review explores the unique structural and immunological properties of OMVs, emphasizing their capacity to elicit robust immune responses. It critically examines established and emerging engineering strategies, including the genetic engineering of surface-displayed antigens, surface conjugation, glycoengineering, nanoparticle-based OMV engineering, hybrid OMVs, and in situ OMV production, among others. Furthermore, recent advancements in preclinical research on OMV-based vaccines, including synthetic OMVs, OMV-based nanorobots, and nanodiscs, as well as emerging isolation and purification methods, are discussed. Lastly, future directions are proposed, highlighting the potential integration of synthetic biology techniques to accelerate research on OMV engineering. Full article

Taking the shield construction of Xiamen Metro Line 2 tunnel side-crossing the Tianzhushan overpass and under-crossing the Shen-Hai Expressway as the engineering background, FLAC3D 6.0 software was used to examine the deformation of adjacent structures based on shield construction parameters in upper-soft and lower-hard strata. The reliability of the numerical simulation results was verified by comparing measured and predicted deformations. The study results indicate that deformation of the pile will occur during the construction of the tunnel shield next to the pile foundation. The shape of the pile deformation curve in the horizontal direction is significantly influenced by the distance from the pile foundation to the adjacent tunnel’s centerline, as well as by soil bin pressure, grouting layer thickness, and stress release coefficient. During the tunnel shield construction beneath the expressway, increasing the soil bin pressure, the grouting layer thickness, and reducing the stress release coefficient can effectively minimize surface deformation and differential settlement on both sides of the deformation joints between the bridge and the roadbed. The practice shows that, by optimizing shield construction parameters in upper-soft and lower-hard strata, the deformation of nearby bridges and pavements can be kept within allowable limits. This is significant for reducing construction time and costs. The findings offer useful references for similar projects. Full article

To investigate the sintering shrinkage behavior of multigeneration, multilayer zirconia materials using geometrical measurements. Seven zirconia CAD/CAM materials were analyzed, comprising two mono-generation zirconia (HTML: Katana Zr, HTML Plus, 3Y-TZP; UTML: Katana Zr, UTML, 5Y-TZP) and five strength-gradient multilayer zirconia (AIDI: optimill 3D PRO Zir; PRIT: Priti multidisc ZrO₂ multicolor; UPCE: Explore Esthetic; ZCPC: IPS e.max ZirCAD Prime; ZYML: Katana YML) materials. Cubes (10 × 10 × 10 mm³) were milled in varying positions within the disks. Geometrical measurements were applied before and after dense sintering using a micrometer screw gauge, light microscopy, as well as surface scans and shrinkages were calculated. Data were analyzed using Kolmogorov–Smirnov, five-way ANOVA followed by the Scheffé post hoc test, and partial eta squared, as well as the Kruskal–Wallis test, including Bonferroni correction (p < 0.05). The highest influence on the shrinkage was exerted by the zirconia material (^η_P² = 0.893, p < 0.001), followed by the test method (^η_P² = 0.175, p < 0.001), while the vertical and horizontal position and measurement point showed no impact on the shrinkage results (p = 0.195–0.763) in the global analysis. Depending on the test method, the pooled shrinkage values of all tested zirconia materials varied between 17.7 and 20.2% for micrometer screw gauge, 17.7 and 20.1% for light microscopy, and 17.8 and 21.1% for surface scan measurements. The shrinkage values measured in the upper, middle, and lower multilayered vertical direction did not differ significantly in the global analysis for the multilayer materials. Therefore, a uniform shrinkage of these strength-gradient multilayer zirconia materials within clinically relevant restorations can be assumed. Full article

The horizontal tube falling film heat exchangers (HTFFHEs), which exhibit remarkable advantages such as high efficiency in heat and mass transfer, low resistance, and a relatively simple structural configuration, have found extensive applications. Complex flow phenomena and the coupled processes of heat and mass transfer take place within it. Given that the heat and mass transfer predominantly occur at the gas-liquid interface, the flow characteristics therein emerge as a significant factor governing the performance of heat and mass transfer. This article elaborates on the progress of experimental and simulation research approaches with respect to flow characteristics. It systematically reviews the influence patterns of various operating parameters, namely parameters of gas, solution and internal medium, as well as structural parameters like tube diameter and tube spacing, on the flow characteristics, such as the flow regime between tubes, liquid film thickness, and wettability. This review serves to furnish theoretical underpinnings for optimizing the heat and mass transfer performance of the horizontal tube falling film heat exchanger. It is further indicated that the multi-dimensional flow characteristics and their quantitative characterizations under the impacts of different airflow features will constitute the focal research directions for horizontal tube falling film heat exchangers in the foreseeable future. Full article

Greenhouses offer optimal environments for crop cultivation during the winter months. The rationale for this study was identified as the synergistic exchange of air between the soil, the wall, and the indoor environment within the greenhouse (referring to the coupling law of the temperature fields of the three elements in space and time, including the direction of heat transfer and the consistency of the temperature zoning), thereby maintaining a more optimal temperature. However, there is a paucity of research on the impact of different spans on the thermal environment in solar greenhouses and even fewer studies on the synergistic law of changes in soil-wall indoor air in solar greenhouses with different spans. In this study, two solar greenhouses with different spans were analyzed through a combination of experiments as follows: K-means classification optimized using the grey wolf optimizer (GWO), computational fluid dynamics (CFD) simulations, and long short-term memory (LSTM) prediction models. The two solar greenhouses, designated as S1 and S2, had spans of 11 m and 10 m, respectively. The results are as follows: In two greenhouses when the span and temperature were the same, the indoor air temperature and soil temperature of the S1 greenhouse were lower than those of the S2 greenhouse; there was an isothermal layer in the north wall of greenhouses S1 and S2 (a stable area where the temperature change over time is less than 0.5 °C), the horizontal distance between the isothermal layer on the inside of the greenhouse wall and the inside of the wall was more than 400 mm, and that of the outside of the greenhouse wall was more than 200 mm; within the solar greenhouse, this study identified that heat was emitted from the inner surface of the wall (at 0 mm from the inner surface) toward the outer surface of the wall (at 0 mm from the outer surface), as well as at a horizontal distance of 200 mm from the inner surface of the wall. The temperature data from 0:00 to 8:00 at night were selected for the purpose of analyzing the temperature synergistic change in soil-wall indoor air in the S1 greenhouse. The temperature change can be classified into four categories according to K-means classification, which was optimized based on the grey wolf algorithm. The categories were as follows: high-temperature region, medium-high temperature region, medium-low temperature region, and low-temperature region. The low-temperature region spanned the range of X = (800, 3000) mm, and its height range was Y = (−150, 1200) mm. The CFD model and LSTM prediction model have been shown to be superior, and the findings of this study offer a theoretical basis for the optimization of thermal environment control in solar greenhouses. Full article

A small-scale supersonic flight experiment vehicle is being developed at Muroran Institute of Technology as a flying testbed for verification of innovative technologies for high-speed atmospheric flights, which are essential to next-generation aerospace transportation systems. Its baseline configuration M2011 with a cranked-arrow main wing with an inboard and outboard leading edge sweepback angle of 66 and 61 degrees and horizontal and vertical tails has been proposed. Its aerodynamics caused by attitude motion are required to be clarified for six-degree-of-freedom flight capability prediction and autonomous guidance and control. This study concentrates on characterization of such aerodynamics caused by rolling rates in the subsonic regime. A mechanism for rolling a wind-tunnel test model at various rolling rates and arbitrary pitch angle is designed and fabricated using a programmable stepping motor and an equatorial mount. A series of subsonic wind-tunnel tests and preliminary CFD analysis are carried out. The resultant static derivatives have sufficiently small scatter and agree quite well with the static wind-tunnel tests in the case of a small pitch angle, whereas the static directional stability deteriorates in the case of large pitch angles and large nose lengths. In addition, the resultant dynamic derivatives agree well with the CFD analysis and the conventional theory in the case of zero pitch angle, whereas the roll damping deteriorates in the case of large pitch angles and proverse yaw takes place in the case of a large nose length. Full article

Facing engineering challenges of real-time and high-precision recognition of strongly heterogeneous formations during directional drilling, it is crucial to address the issues of sparse lithology geological labels and multi-source lithology identification from LWD data. This paper proposes a real-time intelligent recognition method for strongly heterogeneous formations based on multi-parameter logging while drilling and drilling engineering, which can effectively guide directional drilling operations. Traditional supervised learning methods rely heavily on extensive lithology labels and rich wireline logging data. However, in LWD applications, challenges such as limited sample sizes and stringent real-time requirements make it difficult to achieve the accuracy needed for effective geosteering in strongly heterogeneous reservoirs, thereby impacting the reservoir penetration rate. In this study, we comprehensively utilize LWD parameters (six types, including natural gamma and electrical resistivity, etc.) and drilling engineering parameters (four types, including drilling rate and weight on bit, etc.) from offset wells. The UMAP algorithm is employed for nonlinear dimensionality reduction, which not only integrates the dynamic response characteristics of drilling parameters but also preserves the sensitivity of logging data to lithological variations. The K-means clustering algorithm is employed to extract the deep geo-engineering characteristics from multi-source LWD data, thereby constructing a lithology label library and categorizing the training and testing datasets. The optimized CatBoost machine learning model is subsequently utilized for lithology classification, enabling real-time and high-precision geological evaluation during directional drilling. In the Hugin Formation of the Volve field in the Norwegian North Sea, the application of UMAP demonstrates superior data separability compared with PCA and t-SNE, effectively distinguishing thin reservoirs with strong heterogeneity. The CatBoost model achieves a balanced accuracy of 92.7% and an F1-score of 89.3% in six lithology classifications. This approach delivers high-precision geo-engineering decision support for the real-time control of horizontal well trajectories, which holds significant implications for the precision drilling of strongly heterogeneous reservoirs. Full article

Compared with traditional gas reservoirs, ultra-deep and ultra-high-pressure tight sandstone gas reservoirs are characterized by well-developed faults and fractures, strong heterogeneity and stress sensitivity, and complex in situ stress distribution. Traditional three-dimensional geological models and numerical models ignore the variation characteristics of reservoir in situ stress during the production process, it affects the accuracy of the subsequent fracturing modification design and development plan formulation. Therefore, based on the integrated method of geological engineering, this article first carried out high-temperature and high-pressure stress sensitivity tests on reservoir rock samples and fitted the stress-sensitive mathematical model to clarify the influence of high temperature and high pressure on permeability. Then, aiming at the problem of four-dimensional in situ stress variation caused by the coupling of the seepage field and stress field during the exploitation of tight sandstone gas reservoirs, combined with the results of well logging interpretation, rock physical property analysis, and mechanical experiments, based on the three-dimensional geological model and geomechanical model of the gas reservoir and coupled with the stress-sensitive characteristics of the reservoir, a four-dimensional in situ stress model for the reservoir of tight sandstone gas reservoirs was established. The prediction of the variation law of four-dimensional in situ stress during the production process was carried out. Finally, the influence of considering stress sensitivity on reservoir production was simulated. The results show the following: ① The production process has a significant impact on the magnitude and distribution of four-dimensional in situ stress. With the decrease in pore pressure, both the maximum horizontal principal stress and the minimum horizontal principal stress decrease. ② In the area near the production well, the direction of in situ stress will significantly deflect over time. ③ In an ultra-deep and ultra-high-pressure environment, the gas reservoir is affected by the stress-sensitive effect. The stable production time of the gas well is reduced by two years, and the cumulative gas production decreases by 5.01 × 10⁸ m³. The research results provide the temporal stress field distribution results for the simulation and prediction of the secondary fracturing of old wells and the commissioning fracturing of new wells in the target well area. Full article

Structural design in Europe should strongly follow EN 1998-1 or so called Eurocode 8 (EC8), for a seismic resistance assessment of structures. Eurocode 8 recommends two linear methods and two nonlinear methods. The nonlinear methods require some knowledge about the nonlinear behavior of beams and joints in the structure, which makes the linear methods preferable. An alternative method of the seismic loading representation is to use artificial accelerograms with the same or similar spectra as the response spectrum used for modal spectrum analysis. Using an artificial diagram, three approaches in finite element methods exist: explicit time integration, implicit time integration, and modal dynamics. A typical six-story steel structure is modeled using the finite element method, and all linear methods are examined in both horizontal directions. The structure is examined by the modal response spectrum method using sufficient modes, as well as with and without the residual mode. The results are compared, and conclusions concerning the efficiency and precision of methods are deduced. Time history loading by accelerograms reveals higher dynamics and stress in the structural response than the modal response spectrum and lateral forces methods. The time history analysis methods have almost no difference in accuracy, and the modal dynamics method is the cheapest one. Full article

Motion direction detection is an essential task for both computer vision and neuroscience. Inspired by the biological theory of the human visual system, we proposed a learnable horizontal-cell-based dendritic neuron model (HCdM) that captures motion direction with high efficiency while remaining highly robust. Unlike present deep learning models, which rely on extension of computation and extraction of global features, the HCdM mimics the localized processing of dendritic neurons, enabling efficient motion feature integration. Through synaptic learning that prunes unnecessary parts, our model maintains high accuracy in noised images, particularly against salt-and-pepper noise. Experimental results show that the HCdM reached over 99.5% test accuracy, maintained robust performance under 10% salt-and-pepper noise, and achieved cross-dataset generalization exceeding 80% in certain conditions. Comparisons with state-of-the-art (SOTA) models like vision transformers (ViTs) and convolutional neural networks (CNNs) demonstrate the HCdM’s robustness and efficiency. Additionally, in contrast to previous artificial visual systems (AVSs), our findings suggest that lateral geniculate nucleus (LGN) structures, though present in biological vision, may not be essential for motion direction detection. This insight provides a new direction for bio-inspired computational models. Future research will focus on hybridizing the HCdM with SOTA models that perform well on complex visual scenes to enhance its adaptability. Full article

Maritime unmanned aerial vehicles (UAVs) equipped with inertial navigation systems (INS) are prone to error accumulation, which can lead to excessive positioning errors and hinder their ability to perform long distance missions. To address this issue, this study first constructs a directed graph network for a flight area based on start and end points as well as error correction points. A multi-objective route planning model is then developed for a UAV, aiming to minimize both the flight distance and the number of positioning corrections. Considering the UAV’s turning radius, a trajectory length calculation model based on 3D Dubins curves is designed. Subsequently, a forward labeling-based multi-objective path planning algorithm is proposed to develop an optimization model. Experimental results demonstrate that the proposed method can effectively constrain the UAV’s horizontal and vertical positioning errors within 2.5 m, while optimally balancing flight distance and positioning accuracy to ensure the successful execution of long-range maritime UAV missions. The comparative results demonstrate that, while satisfying the positioning error requirements, our proposed method achieves a reduction of over 1.5% in total flight distance for maritime UAVs compared to the NSGA-II algorithm. Full article

The accuracy of identifying dynamic characteristics of super-tall buildings under typhoon conditions, as well as their correlation with the vibration amplitude, remains unclear, limiting the effective assessment of the structural performance and optimization of wind-resistant designs. To address this issue, the measured wind-generated vibration responses of Shanghai World Finance Center during the passage of Typhoon “Rumbia” were derived using data obtained from the health monitoring system of a super-tall building in Shanghai. The first and second inherent frequencies, as well as the damping ratio of the structure, were ascertained through the employment of the curve method and the standard deviation method. Based on this, a comparison and analysis were carried out regarding the variation patterns of the first and second inherent frequencies and the damping ratio with reference to the vibration amplitude. Vibration modes were identified using frequency domain analysis. The results of the natural frequency identification were compared to those from the Peak Picking method to see how well the curve method and the standard deviation method worked at finding modal parameters. Ultimately, an assessment of the super-tall building’s performance during the impact of the typhoon was conducted. The results demonstrate that the curve method and the standard deviation method can accurately identify the inherent frequency and damping ratio of the structure, with the curve method revealing a more pronounced regularity of the modal parameters. For the structure, in the horizontal and longitudinal directions, the first and second inherent frequencies exhibit a negative correlation with amplitude, while the damping ratio shows a positive correlation with amplitude. Moreover, as the floor level rises, the vibration modes in both directions of the structure steadily increase. During the impact of Typhoon “Rumbia”, the building’s performance complied with the requirements set by comfort standards. These analytical results not only provide valuable references for the wind-resistant design and vibration control of super-tall buildings but also offer critical support for condition assessment and damage identification within structural health monitoring systems. Full article