Search Results (1,989)

The electrical arcs generated by high-speed dynamic separation between pantograph and catenary systems pose a significant threat to the operational safety of high-speed railways. Environmental factors, particularly relative humidity and airflow, critically influence arc characteristics. This study establishes a two-dimensional pantograph–catenary arc model based on magnetohydrodynamic theory, validated through a self-developed experimental platform. Research findings demonstrate that as relative humidity increases from 25% to 100%, the core arc temperature decreases from 10,500 K to 9000 K due to enhanced heat dissipation in humid air and electron capture by water molecules; the peak arc voltage rises from 37.25 V to 48.17 V resulting from accelerated deionization processes under high humidity conditions; the average arc energy in polar regions increases from 2.5 × 10⁻⁴ J/m³ to 3.5 × 10⁻⁴ J/m³, exhibiting a saddle-shaped distribution; and the maximum arc pressure declines from 5.3 Pa to 3.7 Pa. Under airflow conditions of 10–30 m/s, synergistic effects between airflow and humidity further modify arc behavior. The most pronounced temperature fluctuations and most frequent arc root migration occur at 100% humidity with 30 m/s airflow, while the shortest travel distance and longest persistence are observed at 25% humidity with 10 m/s airflow, as airflow accelerates heat dissipation and promotes arc root alternation. Experimental measurements of arc radiation intensity and temperature distribution show excellent agreement with simulation results, verifying the model’s reliability. This study quantitatively elucidates the influence patterns of humidity and airflow on arc characteristics, providing a theoretical foundation for enhancing pantograph–catenary system reliability. Full article

The surface temperature of grain piles is sensitive to environmental fluctuations and exhibits nonlinear, multi-scale temporal patterns, making accurate prediction crucial for grain storage risk early warning. This paper proposes a decomposition–reconstruction prediction method integrating Sample Entropy (SampEn), variational mode decomposition (VMD), and a variant Long Short-Term Memory network (vLSTM). SampEn determines the optimal decomposition parameters, VMD extracts intrinsic mode functions (IMFs), and vLSTM, with peephole connections and coupled gates, conducts synchronous multi-IMF prediction. To explicitly account for environmental influences, a support vector regression (SVR) model driven by dew point temperature and vapor pressure deficit is employed to estimate the surface temperature variation $\Delta T$. This component enhances the adaptability of the framework to dynamic storage conditions. The environment-derived $\Delta T$ is then integrated with the VMD-SampEn-vLSTM output to obtain the final forecast. Experiments on real-granary data from Liaoning, China demonstrate that the proposed method reduces mean absolute error (MAE) and root mean square error (RMSE) by 25% and 14%, respectively, compared with baseline models, thus achieving a significant improvement in prediction performance. This integration of data-driven prediction with environmental adjustment significantly improves forecasting accuracy and robustness. Full article

Mean flow velocities and the corresponding turbulence fluctuation velocities were measured within the suction port of a standard twin-screw compressor using LDV and PIV optical techniques. Time-resolved velocity measurements were carried out over a time window of 1° at a rotor speed of 1000 rpm, a pressure ratio of 1, and an air temperature of 55 °C. Detailed LDV measurements revealed a very stable and slow inflow, with almost no influence from rotor movements except near the rotors, where a more complex flow formed in the suction port. The axial velocity near the rotors exhibited wavy profiles, while the horizontal velocity showed a rotational flow motion around the centre of the port. The turbulence results showed uniform distributions and were independent of the rotors’ motion, even near the rotors. PIV measurements confirmed that there is no rotor movement influence on the inflow structure and revealed complex flow structures, with a crossflow dominated by a main flow stream and two counter-rotating vortices in the X-Y plane; in the Y-Z plane, the presence of a strong horizonal stream was observed away from the suction port, which turned downward vertically near the entrance of the port. The corresponding turbulence results in both planes showed uniform distributions independent of rotor motions that were similar in all directions. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

The Organic Rankine Cycle (ORC) offers an efficient approach for harnessing low-grade thermal energy. However, ORC systems often struggle to achieve maximum output power when subject to fluctuations in sink and heat source temperatures. To address this challenge, this paper proposes a Maximum Power Point Tracking (MPPT) strategy based on the optimal evaporation pressure and superheat degree, enabling ORC systems to achieve maximum power output even under varying thermal conditions. First, a dynamic model of the ORC system is established, and the variations in key parameters under different expander and working fluid pump speeds are analyzed. Based on this analysis, the MPPT strategy is developed and its performance is verified through simulations under fluctuating sink and heat source temperatures. The results demonstrated that the ORC system must simultaneously adjust both the expander speed and the working fluid pump speed to maximize power output. Moreover, there exist optimal values of evaporation pressure and superheat degree that yield maximum system performance. Compared with the optimal evaporation pressure strategy, the proposed MPPT approach improves power generation by 14.15%. Full article

With the global expansion of hydrogen infrastructure, the safe and efficient transportation of hydrogen is becoming more important. In this study, several technical factors, including material degradation, pressure variations, and monitoring effectiveness, that influence hydrogen transportation using pipelines are examined using system dynamics. The results show that hydrogen embrittlement, which is the result of microstructural trapping and limited diffusion in certain steels, can have a profound effect on pipeline integrity. Material incompatibility and pressure fluctuations deepen fatigue damage and leakage risk. Moreover, pipeline monitoring inefficiency, combined with hydrogen’s high flammability and diffusivity, can raise serious safety issues. An 80% decrease in monitoring efficiency will result in a 52% reduction in the total hydrogen provided to the end users. On the other hand, technical risks such as pressure fluctuations and material weakening from hydrogen embrittlement also affect overall system performance. It is essential to understand that real-time detection using hydrogen monitoring is particularly important and will lower the risk of leakage. It is crucial to know where hydrogen is lost and how it impacts transport efficiency. The model offers practical insights for developing stronger and more reliable hydrogen transport systems, thereby supporting the transition to a low-carbon energy future. Full article

Background and Objectives: Circadian fluctuations in blood pressure, particularly the non-dipping pattern characterized by the absence of a nocturnal decline, are associated with an increased risk of microvascular complications. The retina, as a highly sensitive microvascular tissue, offers a valuable window into systemic hemodynamic alterations. However, the literature lacks detailed structural analyses that evaluate all retinal regions by segmenting nighttime into specific time intervals. Notably, the early morning period (04:00–08:00), during which stress hormones such as cortisol and catecholamines rise physiologically, leads to increased blood pressure that may significantly affect retinal microcirculation. This prospective study aims to assess retinal microvascular structures in dipper and non-dipper individuals using structural optical coherence tomography and to investigate their relationship with blood pressure parameters by dividing nighttime into distinct time segments. Materials and Methods: A total of 60 participants were classified as dipper (n = 26) or non-dipper (n = 34) based on 24 h ambulatory blood pressure monitoring results. Structural optical coherence tomography was used to evaluate superficial and deep capillary plexus densities in the foveal, parafoveal, and perifoveal regions, along with the area and perimeter of the foveal avascular zone (FAZ) and flow density (FD). Blood pressure values, including systolic, diastolic, mean arterial, and pulse pressure, were recorded during two nighttime intervals (00:00–04:00 and 04:00–08:00), and correlations with retinal parameters were analyzed. Results: No significant differences were observed in retinal microvascular parameters between the dipper and non-dipper groups. Deep capillary densities, particularly in the parafoveal and perifoveal regions, showed significant positive correlations with serum total protein, albumin, and very low-density lipoprotein (VLDL) levels. Furthermore, systolic and mean arterial pressures measured during the 04:00–08:00 interval demonstrated significant positive correlations with deep retinal vascular densities. The FAZ perimeter was negatively correlated with pulse pressure variability, while FD showed a negative correlation with mean arterial pressure variability. Conclusions: This prospective study is among the first to investigate the effects of circadian blood pressure patterns on retinal microvascular structures by segmenting nighttime into specific intervals and employing comprehensive structural optical coherence tomography across the entire retina. The findings suggest that retinal microvascular structure may be associated with fluctuations in blood pressure. Analyses of blood pressure measurements between 04:00 and 08:00 may offer supplementary insights into the evaluation of retinal microvascular structure. Full article

As a core component of large marine engines, the compressor delivers robust and efficient power for propulsion. This study focuses on assessing and quantifying the uncertainty in the aerodynamic performance of a transonic rotor under various operating conditions, with the aim of investigating the impact of blade manufacturing variability on performance. Monte Carlo simulation (MCS) and sensitivity analysis were initially employed to identify parameters that significantly influence airfoil performance. Subsequently, a non-intrusive polynomial chaos (NIPC) uncertainty quantification model was developed to compare the effects of tip clearance deviation and surface geometry deviation on rotor performance. The study then analyzes how the geometric deviation at the different spanwise sections affects aerodynamic performance. The results reveal that geometric deviations have a more profound influence on aerodynamic performance than blade tip clearance. The impact of geometric deviations on average pressure ratio and efficiency of the transonic compressor rotor intensifies as the air mass flow rate approaches the near-stall point, while it decreases near the choking point. Interestingly, fluctuations in pressure ratio exhibit the opposite trend. Regarding spatial distribution, deviations in the upper half of the blade span (near the tip) exert a more dramatic influence on mass flow rate and pressure ratio fluctuation. A conceivable reason is that the inlet airflow velocity increases along the radial direction of the blade, and manufacturing variations in the same magnitude produce more notable relative geometric deviations in the upper half of the blade span. Centered on the machining tolerance guidelines for transonic compressor rotors, this work recommends stricter profile tolerance requirements for the upper half of the blade span. Full article

Active control of injection quantity during start-up idle optimizes automotive diesel engine starting performance, aligning with low-carbon goals. Conventional methods rely on a calibrated demand torque map adjusted by speed, temperature, and pressure variations, requiring extensive labor for calibration and limiting energy-saving and emission improvements. To address this problem, this paper proposes a transient injection quantity active control method for the start-up process based on the variation characteristics of target speed. Firstly, the target speed variation characteristics of the start-up process are optimized by setting different accelerations. Secondly, a transient injection quantity control strategy for the start-up process is proposed based on the target speed variation characteristics. Finally, the control strategy proposed in this paper was compared with the conventional starting injection quantity control method to verify its effectiveness. The results show that the start-up idle control strategy proposed in this paper reduces the cumulative fuel consumption of the start-up process by 25.9% compared to the conventional control method while maintaining an essentially unchanged start-up time. The emissions of hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxides (NOx) exhibit peak reductions of 12.4%, 32.5%, and 62.9%, respectively, along with average concentration drops of 27.2%, 35.1%, and 41.0%. Speed overshoot decreases by 25%, and fluctuation time shortens by 23.6%. The results indicate that the proposed control method not only avoids complicated calibration work and saves labor and material resources but also effectively improves the starting performance, which is of great significance for the diversified development of automotive power sources. Full article

To address the challenge of long-term stiffness retention of subgrades in humid–hot climates, this study evaluates expansive soil stabilized with construction and demolition waste (CDW), focusing on the resilient modulus (M_r) under coupled stress states and wetting–drying histories. Basic physical and swelling tests identified an optimal CDW incorporation of about 40%, which was then used to prepare specimens subjected to controlled. Wetting–drying cycles (0, 1, 3, 6, 10) and multistage cyclic triaxial loading across confining and deviatoric stress combinations. M_r increased monotonically with both stresses, with stronger confinement hardening at higher deviatoric levels; with cycling, M_r exhibited a rapid then gradual degradation, and for most stress combinations, the ten-cycle loss was 20%–30%, slightly mitigated by higher confinement. Grey relational analysis ranked influence as follows: the number of wetting–drying cycles > deviatoric stress > confining pressure. A Lytton model, based on a modified prediction method, accurately predicted M_r across conditions (R² ≈ 0.95–0.98). These results integrate stress dependence with environmental degradation, offering guidance on material selection (approximately 40% incorporation), construction (adequate compaction), and maintenance (priority control of early moisture fluctuations), and provide theoretical support for durable expansive soil subgrades in humid–hot regions. Full article

We present a tunable diode-laser absorption spectroscopy (TDLAS) system operating at 1.5711 µm for CO₂ gas concentration measurements. The system can operate in either a traditional direct-mode (dTDLAS) sawtooth wavelength scan or a recently demonstrated wavelength-toggled single laser differential-absorption lidar (WTSL-DIAL) mode using precompensated current pulses. The use of such precompensated pulses offsets the slow thermal constants of the diode laser, leading to fast toggling between ON and OFF-resonance wavelengths. A short measurement time is indeed pivotal for atmospheric sensing, where ambient factors, such as turbulence or mechanical vibrations, would otherwise deteriorate sensitivity, precision and accuracy. Having a system able to operate in both modes allows us to benchmark the novel experimental procedure against the well-established dTDLAS method. The theory behind the new WTSL-DIAL method is also expanded to include the periodicity of the current modulation, fundamental for the calculation of the OFF-resonance wavelength. A two-detector scheme is chosen to suppress the influence of laser intensity fluctuations in time (1/f noise), and its performance is eventually benchmarked against a one-detector approach. The main difference between dTDLAS and WTSL-DIAL, in terms of signal processing, lies in the fact that while the former requires time-consuming data processing, which limits the maximum update rate of the instrument, the latter allows for computationally simpler and faster concentration readings. To compare other performance metrics, the update rate was kept at 2 kHz for both methods. To analyze the dTDLAS data, a four-parameter Lorentzian fit was performed, where the fitting function comprised the six main neighboring absorption lines centered around 1.5711 µm. Similarly, the spectral overlap between the same lines was considered when analyzing the WTSL-DIAL data in real time. Our investigation shows that, for the studied time intervals, the WTSL-DIAL approach is 3.65 ± 0.04 times more precise; however, the dTDLAS-derived CO₂ concentration measurements are less subject to systematic errors, in particular pressure-induced ones. The experimental results are accompanied by a thorough explanation and discussion of the models used, as well as their advantages and limitations. Full article

Cave systems serve as key interfaces connecting surface and underground carbon cycles, and research on their carbon dynamics provides a unique perspective for revealing the mechanisms of carbon transport and transformation in karst critical zones. In this study, we established a multi-factor monitoring framework spanning the atmosphere–soil–cave continuum and associated meteorological conditions, continuously recorded cave microclimate parameters (temperature, relative humidity, atmospheric pressure, and cave winds) and CO₂ concentrations across atmospheric–soil–cave interfaces, and employed stable carbon isotope (δ¹³C) tracing in Mahuang Cave, a typical karst cave in southwestern China, from 2019 to 2023. The results show that the seasonal amplitude of atmospheric CO₂ and its δ¹³C is small, while soil–cave CO₂ and δ¹³C fluctuate synchronously, exhibiting “high concentration-light isotope” signatures during the rainy season and the opposite pattern during the dry season. Cave CO₂ concentrations drop by about 29.8% every November. Soil CO₂ production rates are jointly controlled by soil temperature and volumetric water content, showing a threshold effect. The δ¹³C response exhibits nonlinear behavior due to the combined effects of land-use type, vegetation cover, and soil texture. Quantitative analysis establishes atmospheric CO₂ as the dominant source in cave systems (66%), significantly exceeding soil-derived contributions (34%). At diurnal, seasonal, and annual scales, carbon-source composition, temperature and precipitation patterns, ventilation effects, and cave structure interact to control the rhythmic dynamics and spatial gradients of cave microclimate, CO₂ levels, and δ¹³C signals. Our findings enhance the understanding of carbon transfer processes across the karst critical zone. Full article

Inadequate screening of features and insufficient extraction of multi-source time-series data potentially result in insensitivity to historical noise and poor extraction of features for PM_2.5 concentration prediction models. Precipitable water vapor (PWV) data obtained from the Global Navigation Satellite System (GNSS), along with air quality and meteorological data collected in Suzhou city from February 2021 to July 2023, were employed in this study. The Spearman correlation analysis and Random Forest (RF) feature importance assessment were used to select key input features, including PWV, PM₁₀, O₃, atmospheric pressure, temperature, and wind speed. Based on RF, Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP) algorithms, four PM_2.5 concentration prediction models were developed using sliding window and fusion algorithms. Experimental results show that the root mean square error (RMSE) of the 1 h PM_2.5 concentration prediction model using the RF-LSTM fusion algorithm is 4.36 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, while its mean absolute error (MAE) and mean absolute percentage error (MAPE) values are 2.63 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$ and 9.3%. Compared to the individual LSTM and MLP algorithms, the RMSE of the RF-LSTM PM_2.5 prediction model improves by 34.7% and 23.2%, respectively. Therefore, the RF-LSTM fusion algorithm significantly enhances the prediction accuracy of the 1 h PM_2.5 concentration model. As for the 2 h, 3 h, 6 h, 12 h, and 24 h PM_2.5 prediction models using the RF-LSTM fusion algorithm, their RMSEs are 5.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 6.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 9.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 12.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and 15.3 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and their corresponding MAPEs are 13.8%, 18.3%, 28.3%, 38.2%, and 48.2%, respectively. Their prediction accuracy decreases with longer forecasting time, but they can effectively capture the fluctuation trends of future PM_2.5 concentrations. The RF-LSTM PM_2.5 prediction models are efficient and reliable for early warning systems in Suzhou city. Full article

Palm trees, referred to here as vegetation cover (VC), provide essential ecosystem services in an arid Oasis. However, because of socioeconomic transformation, the rapid urban expansion of major cities and villages at the expense of agricultural lands of the Al-Ahsa Oasis, Saudi Arabia, has placed enormous pressure on the palm-growing area and led to the loss of productive land. These challenges highlight the need for robust, integrative methods to assess their impact on the agroecosystem. Here, we analyze spatiotemporal fluctuations in vegetation cover and its effect on the agroecosystem to determine the potential influencing factors. Data from Landsat satellites, including TM (Thematic mapper of Landsat 5), ETM+ (Enhanced Thematic mapper plus of Landsat 7), and OIL (Landsat 8) and Sentinel-2A imageries were used for analysis, while GeoEye-1 satellite images as well as socioeconomic data were applied for result validation. Principal Component Analysis (PCA) was applied to extract pure endmembers, facilitating Spectral Mixture Analysis (SMA) for mapping vegetation and urban fractions. The spatiotemporal change patterns were analyzed using time- and space-oriented detection algorithms. Results indicated that vegetation fraction patterns differed significantly; pixels with high fraction values declined significantly from 1990 to 2020. The mean vegetation fraction value varied from 0.79 to 0.37. This indicates that a reduction in palm trees was quickly occurring at a decreasing rate of −14.24%. Results also suggest that vegetation fractions decreased significantly between 1990 and 2020, and this decrease had the greatest effect on the agroecosystem situation of the Oasis. We assessed urban sprawl, and our results indicated substantial variability in average urban fractions: 0.208%, 0.247%, 0.699%, and 0.807% in 1990, 2000, 2010, and 2020, respectively. Overall, the data revealed an association between changes in palm tree fractions and urban ones, supporting strategic vegetation and/or agricultural management to enhance the agroecosystem in an arid Oasis. Full article

As a clean energy carrier, hydrogen is essential for global low-carbon energy transitions due to its unique combination of safe transport properties and energy density. This investigation employs computational fluid dynamics (ANSYS Fluent) to systematically characterize hydrogen dispersion through soil media from buried pipelines. The research reveals three fundamental insights: First, leakage orifices smaller than 2 mm demonstrate restricted hydrogen migration regardless of directional orientation. Second, dispersion patterns remain stable under both low-pressure conditions (below 1 MPa) and minimal thermal gradients, with pipeline temperature variations limited to 63 K and soil fluctuations under 40 K. Third, dispersion intensity increases proportionally with higher leakage pressures (exceeding 1 MPa), greater soil porosity, and larger particle sizes, while inversely correlating with burial depth. The study develops a predictive model through Sequential Quadratic Programming (SQP) optimization, demonstrating exceptional accuracy (mean absolute error below 10%) for modeling continuous hydrogen flow through moderate-porosity soils under medium-to-high pressure conditions with weak inertial effects. These findings provide critical scientific foundations for designing safer hydrogen transmission infrastructure, establishing robust risk quantification frameworks, and developing effective early-warning systems, thereby facilitating the practical implementation of hydrogen energy systems. Full article