Search Results (925)

Micro-hydropower technologies are increasingly attracting attention due to their potential to contribute to sustainable energy generation. With the growing global demand for electricity, it is essential to research and innovate in the development of devices capable of harnessing hydroelectric potential through such technologies. In this context, the Archimedes screw generator (ASG) stands out as a device that potentially offers significant advantages for micro-hydropower generation. This study aimed, through a simplified yet effective method, to analyze and determine the simultaneous effects of the number of blades, inclination angle, and flow rate on the torque, mechanical power, and efficiency of an ASG. Computational Fluid Dynamics (CFD) was employed to obtain the torque and perform the hydrodynamic analysis of the devices, in order to compare the results of the optimal geometric and operational characteristics with previous studies. This proposal also helps guide future work in the preliminary design and evaluation of ASGs, considering the geometric and flow conditions that take full advantage of the available water resources. Under the specific conditions analyzed, the most efficient generator featured three blades, a 20° inclination, and an inlet flow rate of 24.5 L/s, achieving a mechanical power output of 117 W with an efficiency of 71%. Full article

Floating vertical−axis wind turbines present unique advantages for deep−water offshore deployments, but their basin model testing encounters significant challenges in aerodynamic load simulation due to Reynolds scaling effects. While Froude−scaled experiments accurately replicate hydrodynamic behaviors, the drastic reduction in Reynolds numbers at the model scale leads to substantial discrepancies in aerodynamic forces compared to full−scale conditions. This study proposed two methodologies to address these challenges. Fully physical model tests adopt a “physical wind field + rotor model + floating foundation” approach, realistically simulating aerodynamic loads during rotor rotation. Semi−physical model tests employ a “numerical wind field + rotor model + physical floating foundation” configuration, where theoretical aerodynamic loads are obtained through numerical calculations and then reproduced using controllable actuator structures. For fully physical model tests, a blade reconstruction framework integrated airfoil optimization, chord length adjustments, and twist angle modifications through Taylor expansion−based sensitivity analysis. The method achieved thrust coefficient similarity across the operational tip−speed ratio range. For semi−physical tests, a cruciform−arranged rotor system with eight dynamically controlled rotors and constrained thrust allocation algorithms enabled the simultaneous reproduction of periodic streamwise/crosswind thrusts and vertical−axis torque. Numerical case studies demonstrated that the system effectively simulates six−degree−of−freedom aerodynamic loads under turbulent conditions while maintaining thrust variation rates below 9.3% between adjacent time steps. These solutions addressed VAWTs’ distinct aerodynamic complexities, including azimuth−dependent Reynolds number fluctuations and multidirectional force coupling, which conventional methods fail to accommodate. The developed techniques enhanced the fidelity of floating VAWT basin tests, providing critical experimental validation tools for emerging offshore wind technologies. Full article

This paper presents a robust and computationally efficient fault classification framework for wind energy conversion systems (WECS), built upon a Robust Random Vector Functional Link Network (Robust-RVFLN) and validated through real-time simulations on a Real-Time Digital Simulator (RTDS). Unlike existing studies that depend on high-dimensional feature extraction or purely data-driven deep learning models, our approach leverages a compact set of five statistically significant and physically interpretable features derived from rotor torque, phase current, DC-link voltage, and dq-axis current components. This reduced feature set ensures both high discriminative power and low computational overhead, enabling effective deployment in resource-constrained edge devices and large-scale wind farms. A synthesized dataset representing seven representative fault scenarios—including converter, generator, gearbox, and grid faults—was employed to evaluate the model. Comparative analysis shows that the Robust-RVFLN consistently outperforms conventional classifiers (SVM, ELM) and deep models (CNN, LSTM), delivering accuracy rates of up to 99.85% for grid-side line-to-ground faults and 99.81% for generator faults. Beyond accuracy, evaluation metrics such as precision, recall, and F1-score further validate its robustness under transient operating conditions. By uniting interpretability, scalability, and real-time performance, the proposed framework addresses critical challenges in condition monitoring and predictive maintenance, offering a practical and transferable solution for next-generation renewable energy infrastructures. Full article

Diesel engines power much of the heavy-duty equipment used in underground mines, where exhaust emissions pose acute environmental and occupational health challenges. However, predicting the amount of air required to dilute these emissions is difficult because exhaust mass flow and pollutant concentrations vary nonlinearly with multiple operating parameters. We apply deep learning to predict the total exhaust mass flow and carbon monoxide (CO) concentration of a six-cylinder gas–diesel (dual-fuel) turbocharged KAMAZ 910.12-450 engine under controlled operating conditions. We trained artificial neural networks on the preprocessed experimental dataset to capture nonlinear relationships between engine inputs and exhaust responses. Model interpretation with Shapley additive explanations (SHAP) identifies torque, speed, and boost pressure as dominant drivers of exhaust mass flow, and catalyst pressure, EGR rate, and boost pressure as primary contributors to CO concentration. In addition, symbolic regression yields an interpretable analytical expression for exhaust mass flow, facilitating interpretation and potential integration into control. The results indicate that deep learning enables accurate and interpretable prediction of key exhaust parameters in dual-fuel engines, supporting emission assessment and mitigation strategies relevant to underground mining operations. These findings support future integration with ventilation models and real-time monitoring frameworks. Full article

The aim of this study was to evaluate the effects of Flos lonicerae and Baikal skullcap extracts (PE) on laying performance, antioxidant capacity, immune function, follicular development, estrogen secretion, ovarian metabolomics, and cecal microbiota in aged laying hens. The total number of 70-week-old XinYang Black-Feathered laying hens was 240. These hens were randomly divided into two groups, with each group consisting of six replicates of 20 birds. Control (CON) group was fed a basal diet, whereas the PE group received the same basal diet supplemented with 500 mg/kg of PE. The duration of the experiment was 10 weeks. The findings indicated that the supplementation of PE improved laying performance, antioxidant capacity, and immune function. This was reflected by significant increases (p < 0.05) in laying rate, feed conversion ratio, antioxidant indicators (such as glutathione peroxidase, total antioxidant capacity, and catalase), and immunoglobulin levels. Additionally, there were notable decreases (p < 0.05) in the malondialdehyde levels and pro-inflammatory markers. Moreover, the PE group exhibited a greater number of large yellow and white follicles, as well as higher serum estrogen levels, compared to the CON group (p < 0.05). 16S rRNA sequencing revealed that PE supplementation altered the composition of the cecal microbiota by increasing Ruminococcus_torques_group, Butyricoccus and Christensenellaceae_R-7_group abundances and decreasing Bacteroides, Prevotellaceae_UCG-001 and Megamonas abundances (at genus level), which are primarily associated with short-chain fatty acid production. Ovarian metabolomic analysis showed that the major metabolites altered by PE supplementation were mainly involved in follicular development, estrogen biosynthesis, anti-inflammatory and antioxidant properties. Moreover, changes in both the cecal microbiota (at genus level) and ovarian metabolites were strongly correlated with laying performance, antioxidant status, and immune function. In conclusion, PE supplementation improved laying performance in aged hens by enhancing antioxidant, immune, and ovarian functions, promoting follicular development and estrogen secretion, and modulating the gut microbiota and ovarian metabolites. These findings will offer novel insights into the mechanisms that underlie egg production in the ovaries of aged poultry. Full article

The harvesting of dragon fruit remains challenging due to uneven clamping forces, high fruit damage rates, and low redundancy in conventional end-effectors. To address these issues, we developed a novel embracing end-effector with a Variable-Span Arch (VSA) structure. The VSA design enables adaptive clamping force distribution and effective torsional fruit separation, significantly reducing static pressure damage. Theoretical modeling, mechanical testing, and field experiments were conducted to evaluate its performance. Results show that the proposed end-effector achieves a 95% harvesting success rate, with an average picking time of 15 s per fruit, and can output a maximum torque of 18 kgf·cm, which is sufficient for dragon fruit detachment. These findings demonstrate that the VSA-based embracing end-effector offers a low-damage, efficient, and robust solution for dragon fruit harvesting, providing practical guidance for robotic applications in tropical fruit production. Full article

Aiming at the problem that the well depth parameters in existing intelligent drilling technology can not be obtained underground, a multi-branch parallel neural network is proposed to solve the problem of downhole well depth tracking, and its effectiveness is verified by a field example. After analyzing and correcting the quality of the logging data collected on site by using DBSCAN (a density clustering algorithm), five parameters of WOB, rotating speed, displacement, pump pressure, and torque are selected to predict and calculate the downhole mechanical ROP. Adjust the structure of a traditional artificial BP neural network and design a multi-branch parallel neural network, change the basic architecture of the original hierarchical operation, make full use of the operation efficiency of a computer to realize parallel operation, and adopt the method of point-to-point depth comparison when evaluating the well depth tracking effect. The results indicate that the MAE and mechanical drilling rate evaluation values obtained were 1.18 and 0.873, respectively. The multi-branch parallel neural network achieved a 66.55% improvement in MAE compared to the original BP neural network, while the R² evaluation method showed a 61.82% increase. The average point-by-point comparison error in the example calculation was 0.012 m, with a maximum error of 0.268 m. This result can serve as a fundamental basis for judging changes in well depth during the drilling process. Full article

Friction stir welding (FSW) is a leading technique for joining high-strength steel. This study investigates the relationship between weld power, heat generation (HG), cooling medium, and parent austenite grain (PAG) size during both FSW and submerged FSW (SFSW) processes on 11Cr-1.6W-1.6Ni Martensitic Stainless Steel. Weld power and HG were determined by measuring plunge force and tool torque at various tool rotation rates (350–550 rpm). Additionally, the PAG size and microstructural phases in the base metal (BM), thermo-mechanically affected zone (TMAZ), and stir zone (SZ) were examined using scanning electron microscopy (SEM), electron backscattered diffraction (EBSD), and X-ray diffraction (XRD). The results indicated that the SFSW of martensitic steel required a plunge force twice that of the FSW process, along with greater weld power. The heat generated during SFSW was 130% higher than in FSW at 550 rpm. Despite this, the peak temperatures in the SZ were lower in SFSW as a result of the surrounding water’s high heat absorption. This difference in thermal behavior significantly affected the microstructure. While FSW resulted in a complete phase transformation to fine PAG, SFSW showed only minimal or partial transformation and a higher strain rate. Consequently, the SZ and TMAZ in SFSW exhibited a higher hardness than in FSW. Full article

This paper presents an integrated downhole risk prevention and control system designed to enhance safety, efficiency and sustainability in deep-well drilling operations. The system incorporates advanced measurement processing, risk evaluation, and intelligent data transmission technologies for real-time monitoring of nine key drilling parameters, such as downhole drilling pressure, bending moment, and torque, etc. Bench tests and field trials demonstrated the system’s reliability in accurately capturing and transmitting data under high-pressure, high-temperature conditions. For instance, it successfully monitored bottom-hole pressure up to 61.4 MPa and temperature to 120.8 °C, allowing for early detection of abnormal events such as pressure kicks and torsional stick-slip. The system was laboratory-tested to withstand bottom-hole pressures up to 61.4 MPa and temperatures of 120.8 °C. During field trials, the tool operated safely under actual downhole conditions of approximately 59.2 MPa and 115 °C, which are within its rated limits. The system also facilitated automated controlled actions, including mud weight and pump rate control, to prevent incidents. These results underscore the system’s potential to significantly improve real-time and intelligent process control, minimize operational risks, and advancing the sustainability of drilling practices. The approach marks a step forward in intelligent drilling technologies, supporting proactive decision-making in energy extraction. Future work will extend this system to ultra-deep and high-temperature wells while integrating advanced AI-based analytics for further optimization. Full article

Horizontal wells in shale formations, such as those in the South Pars gas field (Iran) and the Bakken shale (Canada/USA), are essential for production from ultralow-permeability reservoirs but remain limited by poor hole cleaning, high torque, and unstable fluid transport. This study integrates real-time field data from South Pars with Drillbench simulations in the Bakken to develop practical strategies for improving drilling efficiency. A water-based mud system (9–10.2 ppg, 29–35 cP) supplemented with 2 wt.% sulphonated asphalt was applied to mitigate shale hydration, enhance cuttings transport, and preserve near-wellbore injectivity. Field implementation in South Pars demonstrated that adjusting drillstring rotation to 90 RPM and circulation rates to 1100 GPM reduced torque by ~70% (24 to 7 klbf·ft) and increased the rate of penetration (ROP) by ~25% (8 to 10 m/h) across a 230 m interval. Simulations in the Bakken confirmed these improvements, showing consistent torque and pressure trends, with cuttings transport efficiency above 95%. Inducing controlled synchronous whirl further improved sweep efficiency by ~15% and stabilized annular velocities at 0.7 m/s. Overall, these optimizations enhanced drilling efficiency by up to 25%, reduced operational risks, and created better well conditions for field development and EOR applications. The results provide clear, transferable guidelines for designing and drilling shale wells that balance immediate operational gains with long-term reservoir recovery. Full article

Under no-load conditions, the wet clutch of vehicles generates drag torque across a wide speed range, which increases power loss in the transmission system and significantly impacts its efficiency and reliability. To address the clutch drag issue over a wide speed range, this study first establishes a low-speed drag torque model that simultaneously considers the viscous friction effects in both the complete oil film region and the oil film rupture zone of the friction pair clearance. Subsequently, by solving the fluid-structure interaction dynamics model of the friction plates, the collision force between high-speed friction pairs and the resulting friction torque are determined, forming a method for calculating high-speed collision-induced drag torque. Building on this, a unified drag torque model for wet clutches across a wide speed range is developed, integrating both viscous and collision-induced drag torques. The validity of the wide-speed-range drag torque model is verified through experiments. The results indicate that as oil temperature and friction pair clearance increase, the drag torque decreases and the rotational speed corresponding to the peak drag torque is reduced, while the onset of collision phenomena occurs earlier. Conversely, with an increase in oil supply flow rate, the drag torque rises and the rotational speed corresponding to the peak drag torque increases, but the onset of collision phenomena is delayed. Finally, with the optimization objectives of minimizing the peak drag torque in the low-speed range and the total drag torque at the maximum speed in the high-speed range, an optimization design model for the surface grooves of the clutch friction plates is constructed. An optimized groove pattern is obtained, and its effectiveness in suppressing drag torque across a wide speed range is experimentally validated. Full article

To overcome the issues of excessive cutting force, poor chip segmentation, and premature tool wear during the drilling of Ti-6Al-4V titanium alloy. This study established the cutting edge motion trajectory function and instantaneous dynamic cutting thickness equation for ultrasonic vibration-assisted drilling through kinematic analysis. Based on this, an analytical model of drilling force was formulated, integrating tool geometry, cutting radius scale effects, dynamic chip thickness, and drilling depth. In parallel, a finite element model was constructed to achieve visual simulation analysis of chip deformation and cutting force. Finally, the accuracy of the model was verified through experiments, with a comprehensive analysis performed on how cutting parameters affect thrust force. The findings indicate that the average absolute prediction errors of thrust force and torque between the analytical model and finite element simulations were 7.87% and 6.26%, respectively, confirming the model’s capability to accurately capture instantaneous force and torque variations. Compared to traditional drilling methods, the application of ultrasonic vibration assistance resulted in reductions of 40.8% in thrust force and 41.7% in torque. The drilling force exhibited nonlinear growth as the spindle speed and feed rate were elevated, while it declined with greater vibration frequency and amplitude as drilling depth increased. Furthermore, the combined effect of optimized vibration parameters enhanced chip fragmentation, producing short discontinuous chips and effectively preventing entanglement. Overall, this research provides a theoretical and practical foundation for optimizing ultrasonic vibration-assisted drilling and improving precision hole making in titanium alloys. Full article

The conventional sliding mode control (SMC) strategy for direct torque control of switched reluctance motors suffers from severe chattering and prolonged dynamic response. Accordingly, an enhanced SMC strategy is proposed to mitigate motor chattering and suppress torque ripple. On the basis of the conventional exponential approximation rate, a compensation factor and a fractional order are incorporated. Meanwhile, the sigmoid function, characterized by superior smoothness, is employed to replace the sign function that induces severe chattering, thereby attenuating the motor torque ripple. At the same time, in response to the challenge of parameter tuning arising from motor nonlinearity and the abundance of parameters, the sparrow search algorithm (SSA) is employed to optimize the controller parameters. The motor control models before and after the improvement are constructed in MATLAB/Simulink, and the sparrow search algorithm (SSA) is employed to optimize the controller parameters for both cases. Comparative results indicate that the improved control strategy and parameter optimization method can effectively suppress motor torque ripple and enhance the dynamic response characteristics of the system under various operating conditions and rotational speeds. Full article

To address the issue of poor yaw stability in distributed-drive electric pickup trucks at medium-to-high speeds, particularly under the influence of continuously varying tire forces and road adhesion coefficients, a novel Kalman filter-based method for estimating the road adhesion coefficient, combined with a Tube-based Model Predictive Control (Tube-MPC) algorithm, is proposed. This integrated approach enables real-time estimation of the dynamically changing road adhesion coefficient while simultaneously ensuring vehicle yaw stability is maintained under rapid response requirements. The developed hierarchical yaw stability control architecture for distributed-drive electric pickup trucks employs a square root cubature Kalman filter (SRCKF) in its upper layer for accurate road adhesion coefficient estimation; this estimated coefficient is subsequently fed into the intermediate layer’s corrective yaw moment solver where Tube-based Model Predictive Control (Tube-MPC) tracks desired sideslip angle and yaw rate trajectories to derive the stability-critical corrective yaw moment, while the lower layer utilizes a quadratic programming (QP) algorithm for precise four-wheel torque distribution. The proposed control strategy was verified through co-simulation using Simulink and Carsim, with results demonstrating that, compared to conventional MPC and PID algorithms, it significantly improves both the driving stability and control responsiveness of distributed-drive electric pickup trucks under medium- to high-speed conditions. Full article

In consequent-pole permanent magnet (CPPM) machines, the configuration where PM poles and iron poles are alternately arranged causes distortion in the air-gap magnetic field. This results in significant differences in magnetic circuit characteristics compared to conventional PM machines. To address the requirements of reducing torque ripple and enhancing average output torque, the cogging torque and optimization methods for CPPM machines were investigated. A general analytical model for cogging torque was established. This model accounts for asymmetric pole configurations and is particularly well-suited for analyzing CPPM machines. The mechanism through which the consequent-pole (CP) structure improves the utilization rate of PM material was explored, and the parameters influencing the main flux were analyzed. By replacing PMs with soft magnetic materials, the conventional topology of a 12-slot/8-pole surface-inserted PM machine with stator skewing was directly converted into a CP topology. Performance optimization was conducted based on this original scheme. This approach ensures manufacturing convenience while maximizing the sharing of identical components. Simulation results demonstrate that, compared to the benchmark machine, the optimized CPPM machine uses only 60.16% of the PM material while producing 88.19% of the electromagnetic torque, resulting in a 46.61% increase in torque generated per unit volume of PM material. Finally, the benchmark and optimized CPPM prototypes were fabricated, and their torque output capabilities were tested. The finite element simulation results and the measured data show good consistency, validating the correctness of the theoretical analysis and the effectiveness of the finite element model. This study provides a theoretical basis and engineering reference for the performance analysis and optimal design of CPPM machines. Full article