Search Results (266)

During the installation and operation of Pelton turbines, deviations of the jet centerline from the runner pitch circle can compromise turbine stability and efficiency. Utilizing design data from a Pelton turbine on China’s Dadu River, this study employs the SST k-ω and VOF models to investigate the flow characteristics, pressure pulsations, and force on the bucket surface under varying offset conditions. The results demonstrate that radial offset causes the jet to enter the bucket later when deflected outward and earlier when deflected inward. All forms of offset exert adverse effects on turbine performance, with axial offsets causing more severe impacts than radial ones. The maximum pressure pulsation amplitude reached 24%. Afterwards, the erosion of Pelton turbines with different grain sizes was investigated by erosion modeling. It was found that the erosion of large grain size is more serious than that of small grain size. This research provides valuable theoretical insights and an important guiding role for improving the operational stability of Pelton turbines. Full article

Warpage is among the most prevalent defects affecting injection molded parts. In this study, we aimed to develop methods to minimize warpage through mold design. Common strategies include matching the cavity geometry to the intended shape of the part, adjusting cavity dimensions to offset material shrinkage, and optimizing the cooling system and critical injection molding parameters. These optimization methods can offer significant improvements, but recently introduced methods that optimize the molded part and mold cavity shape result in higher levels of warpage reduction. In these methods, optimization of the shape of the molded part is achieved by shaping it in the opposite direction of warpage—a method known as inverse contouring. Inverse contouring of molded parts is a design technique in which mold cavities are intentionally modified to incorporate compensatory geometric deviations in regions anticipated to exhibit significant warpage. The final result after molded part ejection and warpage is a significant reduction in deviations between the warped and reference molded part geometries. In this study, a two-step approach for minimizing warpage was used: the first step was optimizing the most significant injection molding parameters, and the second was inverse contouring. In the first step, Response Surface Methodology (RSM) and Autodesk Moldflow Insight 2023 simulations were used to optimize molded part warpage based on three processing parameters: melt temperature, target mold temperature, and coolant temperature. For improved accuracy, a Computer-Aided Design (CAD) model of the warped molded part was exported into ZEISS Inspect 2023 software and aligned with the reference CAD geometry of the molded part. The maximal warpage value after the initial simulation was 1.85 mm based on Autodesk Moldflow Insight simulations and 1.67 mm based on ZEISS Inspect alignment. After RSM optimization, the maximal warpage was 0.73 mm. In the second step, inverse contouring was performed on the molded part, utilizing the initial injection molding simulation results to further reduce warpage. In this step, the CAD model of the redesigned, inverse-contoured molded part was imported into Moldflow Insight to conduct a second iteration of the injection molding simulation. The simulation results were exported into ZEISS Inspect software for a final analysis and comparison with the reference CAD model. The warpage values after inverse contouring were reduced within the range of ±0.30 mm, which represents a significant decrease in warpage of approximately 82%. Both steps are presented in a case study on an injection molded part made of polybutylene terephthalate (PBT) with 30% glass fiber (GF). Full article

In repeated-measures meta-analyses, raw data are often unavailable, preventing the calculation of the correlation coefficient r between pre- and post-intervention values. As a workaround, many researchers adopt a heuristic approximation of r = 0.7. However, this value lacks rigorous mathematical justification and may introduce bias into variance estimates of pre/post-differences. We employed Monte Carlo simulations (n = 500,000 per scenario) in Fisher z-space to examine the distribution of the standard deviation of pre-/post-differences (σ_D) under varying assumptions of r and its uncertainty (σ_r). Scenarios included r = 0.5, 0.6, 0.707, 0.75, and 0.8, each tested across three levels of variance (σ_r = 0.05, 0.1, and 0.15). The approximation of r = 0.75 resulted in a balanced estimate of σ_D, corresponding to a “midway” variance attenuation due to paired data. This value more accurately offsets the deficit caused by assuming a correlation, compared to the traditional value of 0.7. While the r = 0.7 heuristic remains widely used, our results support the use of r = 0.75 as a more mathematically neutral and empirically defensible alternative in repeated-measures meta-analyses lacking raw data. Full article

The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) power flow modeling, and integration with optimization frameworks. This paper proposes a closed-loop technical framework combining high-confidence interval prediction, second-order cone convex relaxation, and robust optimization to facilitate renewable energy integration in distribution networks via smart microgrid technology. First, a hybrid prediction model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and Quantile Regression (QR) is designed to extract multi-frequency characteristics of time-series data, generating adaptive prediction intervals that accommodate individualized decision-making preferences. Second, a second-order cone relaxation method transforms the AC power flow optimization problem into a mixed-integer second-order cone programming (MISOCP) model. Finally, a robust optimization method considering source–load uncertainties is developed. Case studies demonstrate that the proposed approach reduces prediction errors by 21.15%, decreases node voltage fluctuations by 16.71%, and reduces voltage deviation at maximum offset nodes by 17.36%. This framework significantly mitigates voltage violation risks in distribution networks with large-scale grid-connected photovoltaic systems. Full article

Articulated arm coordinate measuring machines are designed for in situ use directly in manufacturing environments, enabling efficient dimensional control outside of climate-controlled laboratories. This study investigates the influence of ambient temperature variation on the accuracy of length measurements performed with the Hexagon Absolute Arm 8312. The experiment was carried out in a laboratory setting simulating typical shop floor conditions through controlled temperature changes in the range of approximately 20–31 °C. A calibrated steel gauge block was used as a reference standard, allowing separation of the influence of the measuring system from that of the measured object. The results showed that the gauge block length changed in line with the expected thermal expansion, while the articulated arm coordinate measuring machine exhibited only a minor residual thermal drift and stable performance. The experiment also revealed a constant measurement offset of approximately 22 µm, likely due to calibration deviation. As part of the study, an uncertainty budget was developed, taking into account all relevant sources of influence and enabling a more realistic estimation of accuracy under operational conditions. The study confirms that modern carbon composite articulated arm coordinate measuring machines with integrated compensation can maintain stable measurement behavior even under fluctuating temperatures in controlled environments. Full article

Purpose: This pilot study aimed to evaluate how deviations in X-ray tube head angulation and digital sensor alignment affect the radiographic measurement of the profile angle in CAD-CAM abutments. Materials and Methods: A mandibular model was used with five implant positions (central, buccal, and lingual offsets). Custom CAD-CAM abutments were designed with identical bucco-lingual direction contours and varying mesio-distal asymmetry for the corresponding implant positions. Periapical radiographs were acquired under controlled conditions by systematically varying vertical tube angulation, horizontal tube angulation, and horizontal sensor rotation from 0° to 20° in 5° increments for each parameter. Profile angles, interthread distances, and proximal overlaps were measured and compared with baseline STL data. Results: Profile angle measurements were significantly affected by both X-ray tube and sensor deviations. Horizontal tube angulation produced the greatest profile angle distortion, particularly in buccally positioned implants. Vertical x-ray tube angulations beyond 15° led to progressive underestimation of profile angles, while horizontal tube head rotation introduced asymmetric mesial–distal variation. Sensor rotation also caused marked interthread elongation, in some cases exceeding 100%, despite vertical projection being maintained. Profile angle deviations greater than 5° occurred in multiple conditions. Conclusions: X-ray tube angulation and sensor alignment influence the reliability of profile angle measurements. Radiographs with > 10% interthread elongation or crown overlap may be inaccurate and warrant re-acquisition. Special attention is needed when imaging buccally positioned implants. Full article

To address the impact of insulation medium degradation in the arc quenching chambers of ultra-high-voltage SF₆ circuit breakers on phase-controlled switching accuracy caused by multiple operations throughout the service life, this paper proposes an adaptive switching algorithm. First, a modified formula for the breakdown voltage of mixed gases is derived based on the synergistic effect. Considering the influence of contact gap on electric field distortion, an adaptive switching strategy is designed to quantify the dynamic relationship among operation times, insulation strength degradation, and electric field distortion. Then, multi-round switching-on and switching-off tests are carried out under the condition of fixed single-arc ablation amount, and the laws of voltage–current, gas decomposition products, and pre-breakdown time are obtained. The test data are processed by the least squares method, adaptive switching algorithm, and machine learning method. The results show that the coincidence degree of the pre-breakdown time obtained by the adaptive switching algorithm and the test value reaches 90%. Compared with the least squares fitting, this algorithm achieves a reasonable balance between goodness of fit and complexity, with prediction deviations tending to be randomly distributed, no obvious systematic offset, and low dispersion degree. It can also explain the physical mechanism of the decay of insulation degradation rate with the number of operations. Compared with the machine learning method, this algorithm has stronger generalization ability, effectively overcoming the defects of difficult interpretation of physical causes and the poor engineering adaptability of the black box model. Full article

The purpose of this paper is to analyze the multi-wavelength and multi-instrument observations of two quiescent filament eruptions as well as the deflection of associated CMEs from the radial direction. The events occurred on 18 October 2017 and 9 May 2021, respectively, in the southern solar hemisphere. Both of them and associated flares were registered by the Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO) and the Solar Terrestrial Relations Observatory–Ahead (STEREO A) Observatory in different EUV wavebands. Using data from STEREO A COR1 and COR2 instruments and the Large Angle and Spectrometric Coronagraph (LASCO) onboard the Solar and Heliospheric Observatory (SOHO), we investigated morphology and kinematics of the eruptions and the latitudinal offset of the related CMEs with respect to the erupting filaments. Our observations provide the evidence that the two filament eruptions were highly non-radial. The observed deviations are attributed to the presence of low-latitude coronal holes. Full article

In modern oil and gas exploration and development, wellbore trajectory optimization and control is the key technology to improve drilling efficiency, reduce costs, and ensure safety. In the drilling operation of non-vertical wells in complex formations, the traditional static trajectory function, combined with the classical optimization algorithm, has difficulty adapting to the parameter fluctuation caused by formation changes and lacks real-time performance. Therefore, this paper proposes a wellbore trajectory optimization model based on deep reinforcement learning to realize non-vertical well trajectory design and control while drilling. Aiming at the real-time optimization requirements of complex drilling scenarios, the TD3 algorithm is adopted to solve the problem of high-dimensional continuous decision-making through delay strategy update, double Q network, and target strategy smoothing. After reinforcement learning training, the trajectory offset is significantly reduced, and the accuracy is greatly improved. This research shows that the TD3 algorithm is superior to the multi-objective optimization algorithm in optimizing key parameters, such as well deviation, kickoff point (KOP), and trajectory length, especially in well deviation and KOP optimization. This study provides a new idea for wellbore trajectory optimization and design while drilling, promotes the progress and development of intelligent drilling technology, and provides a theoretical basis and technical support for more accurate, efficient, concise, and effective wellbore trajectory optimization and design while drilling in the future. Full article

This study examines the impact of economic policy uncertainty (EPU) on agricultural land conversion. Using a newspaper-based index of EPU and a comprehensive panel dataset covering 270 prefecture-level cities in China, we estimate a city fixed effects model to explore this relationship. Our results indicate that a one-standard-deviation increase in EPU leads to a 22.2% increase in the conversion of agricultural land to urban residential, commercial, and industrial uses. This finding suggests that the surge in EPU triggered by the global financial crisis accounts for approximately 45% of the increase in agricultural land conversion. The adverse effect on agricultural land preservation mainly stems from intensified fiscal pressures and heightened demands on local governments to meet economic growth targets. To address potential endogeneity concerns, we employ the one-period lagged U.S. EPU index and its temporal variations as an instrument for China’s EPU, leveraging cross-country spillover effects. Our instrumental variable estimates confirm the validity of the land conversion effect and its underlying mechanisms. Furthermore, we find that the effects of EPU are particularly pronounced in cities located in non-eastern China and those that depend heavily on fixed asset investment for local economic development. Finally, our analysis of potential policy interventions to mitigate EPU-induced agricultural land loss suggests that strengthening market-oriented reforms and reducing province-level quotas on agricultural land conversion can effectively offset the impact of rising EPU. Full article

This study evaluates coordinate consistency in the static Korean Geodetic Datum 2002 (KGD2002) by comparing GNSS station positions derived independently from GAMIT/GLOBK and Bernese software. Using a nationwide network of approximately 3000 unified geodetic control points (UGCPs), we analyze horizontal coordinate differences (ΔN, ΔE) to identify regional patterns and potential systematic biases. The results indicate that both solutions are closely aligned with the official KGD2002 coordinates, generally within a few millimeters to sub-centimeter levels. However, small regional discrepancies are evident; for example, some provinces exhibit consistent mean northward or southward offsets on the order of 0.1–0.3 cm, and greater dispersions—up to 2 cm—are observed in peripheral regions such as Jeollanam. Notably, the Bernese solution demonstrates slightly tighter agreement, with lower standard deviations compared to GAMIT/GLOBK. The application of two distinct processing strategies within a unified static reference frame is a novel aspect of this study, revealing subtle differences attributable to network geometry, environmental factors, and software modeling approaches. The findings also underscore the limitations of KGD2002’s static nature, particularly its fixed epoch and lack of motion modeling. In response to these issues, this study discusses the rationale for transitioning to a dynamic geodetic reference frame, such as ITRF2020, to improve compatibility with international systems and account for ongoing crustal motions. Overall, the results provide a foundation for the future modernization of Korea’s spatial reference infrastructure and highlight the importance of adopting time-dependent datums in geodetic applications. Full article

The bottom-simulating reflector (BSR) serves as an important seismic indicator for identifying gas hydrate-bearing sediments. This review synthesizes global BSR observations and demonstrates that spatial relationships among BSRs, free gas, and gas hydrates frequently deviate from one-to-one correspondence. Moreover, our analysis reveals that more than 35% of global BSRs occur shallower than the bases of gas hydrate stability zones, especially in deepwater regions, suggesting that the BSRs more accurately represent the interface between the gas hydrate occurrence zone and the underlying free gas zone. BSR morphology is influenced by geological settings, sediment properties, and seismic acquisition parameters. We find that ~70–80% of BSRs occur in fine-grained, grain-displacive sediments with hydrate lenses/nodules, while coarse-grained pore-filling sediments host <20%. BSR interpretation remains challenging due to limitations in traditional P-wave seismic profiles and conventional amplitude versus offset (AVO) analysis, which hinder accurate fluid identification. To address these gaps, future research should focus on frequency-dependent AVO inversion based on viscoelastic theory, multicomponent full-waveform inversion, improved anisotropy assessment, and quantitative links between rock microstructure and elastic properties. These innovations will shift BSR research from static feature mapping to dynamic process analysis, enhancing hydrate detection and our understanding of hydrate–environment interactions. Full article

This study investigates the spread patterns of tunnel fires and examines issues related to emergency response. It focuses on the temperature characteristics, spread patterns, conditions leading to multi-source fires, and the efficacy of water mist suppression methods in heavy-haul railway tunnel fires. The research employs theoretical derivations and numerical simulations to achieve its objectives. It was discovered that, during a fire in a heavy-haul railway tunnel, the temperature inside the tunnel can exceed 500 °C. Furthermore, depending on the nature of the goods transported by the train and under specific wind speed conditions, the fire source has the potential to spread to other carriages, resulting in a multi-source fire. Using the numerical simulation software Pyrosim 2022, various wind speed conditions were simulated. The results revealed that at lower wind speeds, the smoke demonstrates a reverse flow phenomenon. Concurrently, when the adjacent carriage on the leeward side of the fire is ignited, the high-temperature reverse flow smoke, along with the thermal radiation from the flames, ignites combustible materials in the adjacent carriage on the windward side of the burning carriage. Through theoretical derivation and numerical simulation, the critical wind speed for the working conditions was determined to be 2.14 m/s. It was found that while a higher wind speed can lead to a decrease in temperature, it also increases the flame deflection angle. When the wind speed exceeds 2.4 m/s, although the temperature significantly drops in a short period, the proximity of combustible materials on the leeward side of the carriage becomes a concern. At this wind speed, the flame deflection angle causes heat radiation on the leeward side, specifically between 0.5 m and 3 m, to ignite the combustible materials on the carriage surface, resulting in fire spread and multiple fire incidents. The relationship between wind speed and the angle of deflection from the fire source was determined using relevant physics principles. Additionally, the relationship between wind speed and the trajectory of water mist spraying was established. It was proposed to optimize the position of the water mist based on its deviation, and the results indicated that under critical wind speed conditions, when the water mist spraying is offset approximately 5 m towards the upwind side of the fire source, it can act more directly on the surface of the fire source. Numerical simulation results show a significant reduction in the maximum temperature and effective control of fire spread. Under critical wind speed conditions, the localized average temperature of the fire decreased by approximately 140 °C when spraying was applied, compared to the conditions without spraying, and the peak temperature decreased by about 190 °C. This modification scheme can effectively suppress the threat of fire to personnel evacuation under simulated working conditions, reflecting effective control over fires. Additionally, it provides theoretical support for the study of fire patterns in tunnels and emergency response measures. Full article

We propose an empirical motion compensation algorithm for a better turbulence intensity (TI) measurement by Floating LiDAR systems (FLSs) with a newly introduced motion parameter, the significant tilt angle ${\theta }_{\alpha ,1/3}$, using four datasets from three different FLSs in Japan. The parameter was compared to other environmental parameters; it was confirmed to well represent various types of buoy motion. A sensitivity assessment was conducted for the error of the FLS’s standard deviation of wind speed to the buoy motion. The strong correlation obtained by the assessment suggests that the error of the FLS TI is dominated by the motion and that it is possible to offset the error by applying the relationship back to the measurement. The corrected TI shows good agreement with that of a reference fixed vertical LiDAR (VL). Moreover, the similarity of the relationships for the same type of VL mounted on different buoys implies that the correction may be VL-specific rather than FLS-specific, and, therefore, universal regardless of the FLS type. The successful validation suggests that the correction based on ${\theta }_{\alpha ,1/3}$ can be applied not only to the future campaign but also to those performed in the past to revitalize numerous existing FLS datasets. Full article

Islanded microgrids commonly use droop control methods for autonomous power distribution; however, this approach causes system frequency deviation when common loads change. This deviation can be eliminated using secondary control methods, but the core of this approach is to generate compensation values equal to the offset amount to add to the controller, thereby eliminating deviations from rated values. Such a mechanism can actually achieve the same effect by setting power reference values within the droop control method. The power references within the controller need to be adjusted dynamically, and they are associated with common load variations. Therefore, establishing a fitting relationship between the adjustment of power reference and changes in common loads can achieve better frequency regulation, keeping the system frequency operating within rated frequency ranges. These two types of data are correlated, however, due to physical parameters, the fitting between them is not strictly fixed in a mathematical sense. Thus, to find their interconnected relationships, using intelligent methods becomes crucial. This paper proposes a shallow neural network-based method to achieve fitting relationships. Moreover, to address power inputs with zero values, an input enhancement method is proposed to prevent potential gradient vanishing and ineffective learning problems. Thus, through precise matching between power commands and load consumption, the system frequency can be maintained near rated values. Various simulation scenarios demonstrate the feasibility and effectiveness of the proposed method. Full article