Search Results (671)

This study employs both experimental and numerical simulation methods to systematically investigate the influence of sudden expansion diameter ratios on methane–air premixed flame propagation, explosion overpressure, and the evolution of turbulent structures. The results show that with the increase in the diameter ratio, the flame propagation velocity and explosion overpressure present a nonlinear trend of first increasing, then decreasing, and then increasing. Specifically, when the diameter ratio is 1.5, an optimal balance between turbulence enhancement and energy dissipation is achieved, and the overpressure attenuation rate is 47.61%. However, when the diameter ratio increases to 2.0, the turbulence intensity significantly escalates, the peak flame propagation speed increases by 81%, the peak explosion overpressure increases by 69%, and the overpressure attenuation efficiency decreases, which brings greater safety challenges. Moreover, when the diameter ratio is between 1.5 and 2.0, the turbulence intensity of the premixed gas explosion flow field is significantly increased, and the stable “tulip flame” propagation velocity range is extended from 16~35 m/s to 16~42 m/s. When the diameter ratio is 2.0, a distinctive four-vortex structure is formed, with strong turbulent mixing and fast energy dissipation. The vortex structure evolves with the diameter ratio, transitioning from a symmetric and stable double-vortex form to a complex multi-vortex system. The research results provide theoretical support for the prevention of explosions. Full article

Distributed temperature sensing (DTS) has been widely used in downhole dynamic monitoring. How to analyze its data and accurately interpret the flow profile using DTS data are still great challenges. Quantitative interpretation of downhole temperature measurements requires the development of an integrated flow and thermal model capable of handling multi-phase flow. The model must strike a balance between computational efficiency and achieving the highest possible accuracy. The finite difference method can solve the relevant problems well. The flow model and thermal model of reservoirs and wellbores are established. Combined with the single-phase flow theory, the coupling prediction model of wellbore and reservoir temperature is established through appropriate boundary and constraint conditions. The problem was solved iteratively using the finite difference method, and the coupled temperature prediction model’s reliability was confirmed through comparison with numerical simulation results. Based on the forward model, the sensitivity analysis of the influencing factors is carried out in this study which provides a theoretical basis for the inversion model. Taking the flow rate as the inversion parameter, the injection profile interpretation model based on DTS logging data is constructed. Four optimization methods are used in the inversion model which can balance the computational efficiency and model accuracy. The DTS data are preprocessed by the Kalman filter, and the inversion and interpretation evaluation of X injection well is carried out by the LSO-MCMC combined optimization algorithm. The results show that the method has high reliability in the interpretation accuracy of injection profile, and the inverted flow profile meets practical application requirements, confirming the method’s accuracy and effectiveness. Full article

Wave energy converters (WECs) have significant potential for renewable energy generation, but early-stage design processes often require lengthy simulations. This study focuses on the pre-design selection of the rated power for a heaving point-absorber WEC. Addressing the gap in simplified methodologies, this study evaluates the wave energy resource, selects operational sea-states, and assesses device performance using time-domain simulations and linear potential flow theory. The results revealed that a WEC rated at 87% below peak power can capture 91% of the total available energy, achieving a balance between energy efficiency and cost-effectiveness. Furthermore, a simplified method to estimate rated power based on a constant ratio between mean and RMS power is proposed, offering significant potential for early-stage design applications. Future work should validate this approach across diverse WEC types and wave climates. Full article

The internal conditions of the high-temperature molten pool in an electro-fused magnesia furnace (EFMF) are difficult to measure, and the temperature distribution–energy conservation relationship in the EFMF cannot be effectively evaluated. Assuming that the feeding speed is constant, the heat absorbed by the newly added raw materials is equal to the rated power minus the heating power required to maintain thermal balance. Therefore, the EFMF can be approximately described by a steady-state model. In order to analyze the state of the molten pool of EFMF at different smelting stages, this study first constructed a three-dimensional steady-state multi-physics field numerical simulation model. The calculations show that the equivalent resistance of the molten pool varies approximately between 1 m$\Omega$ and 0.4 m$\Omega$. Furthermore, the equivalent reactance produced by the whole conductive circuit is almost of the same order as the resistance. The Reynolds number of the convection inside the molten pool exceeds ${10}^5$, which means that the flow inside the molten pool is forced convection dominated by the Lorentz force. Moreover, the turbulence makes the temperature uniformity of the molten pool (the temperature gradient near the solid–liquid interface is approximately within 300 K/m) far greater than that of the unmelted raw materials with very low thermal conductivity (the average temperature gradient reaches over 1000 K/m); the respective proportions of arc power and Joule heating power can be predicted by the model. When the molten pool size is small, the proportion of Joule heating power is high, reaching about 20% of the rated power (3700 kVA); as the molten pool size increases, the convection effect is relatively weakened, and the proportion of Joule heating power also decreases accordingly, only 5% to 10%; the model prediction and experimental estimation results are in good agreement, which makes it feasible to conduct a quantitative analysis of the power distribution in different smelting stages. Full article

The Haidian District was, historically, rich in water resources. However, with urban development, the groundwater levels have declined, and most rivers have lost their ecological baseflows. To restore the aquatic ecosystems, the district has implemented a cyclic water network and advanced water replenishment projects. Nonetheless, the existing replenishment strategies face challenges, such as an insufficient scientific basis, lack of data, and high energy consumption. There is an urgent need to develop a scientifically robust ecological water replenishment system and optimize pump station scheduling to enhance water resource management efficiency. This study addresses the ecological water replenishment needs of seasonal rivers by integrating the Literature method, Rainfall-Runoff method, and R2cross method to develop a comprehensive approach for calculating the ecological flow and water depth. The proposed method simultaneously meets the ecological functionality and landscape requirements of seasonal rivers. Additionally, the SWMM model is employed to design intelligent pump station scheduling rules, optimizing the replenishment efficiency and energy consumption. Through field measurements and data collection, the ecological water demands of the river channels in different areas are assessed. Using a hydrodynamic model, the dynamic variations in the ecological flow and water depth are simulated. For the Cuihu, Daoxianghu, and Yongfeng areas, this study reveals that the current replenishment volume is insufficient to meet the landscape and ecological needs of the rivers. Most rivers require a 20–30% increase in water levels, with the Dazhai qu needing a substantial rise from 0.17 m to 0.3 m, representing an increase of 76%. Additionally, the results demonstrate that intelligent pump station scheduling can significantly reduce operating costs and energy consumption by dynamically adjusting the replenishment timing and flow rates. This approach optimizes the intervals between equipment activation and deactivation, thereby balancing ecological and energy-saving goals. This research not only provides technical support for the precise calculation of ecological replenishment volumes and the intelligent management of pump stations, but also offers scientific references for water resource management in similar regions. The findings will enhance the ecological functions and landscape quality of the rivers in the Haidian District while promoting refined and intelligent regional water resource management. Moreover, this study presents innovative solutions and theoretical foundations for water resource regulation under the backdrop of climate change. Full article

Autonomous driving (AD) significantly reduces road accidents, providing safer transportation while optimizing traffic flow for greater efficiency and smoothness. However, ensuring safe decision-making in dynamic and complex highway environments, especially during lane-changing maneuvers, remains a challenge. Reinforcement Learning (RL) has become a promising method for developing decision-making systems in AD, particularly Deep Reinforcement Learning (DRL). In this study, we focus on highway lane-change behaviors and propose a novel DRL algorithm, called Huber-regularized Reward-threshold Adaptive Double Deep Q-Network (HRA-DDQN). First, a reward function optimally balances speed, safety, and the necessity of lane changes, ensuring efficient and safe maneuvering in highway scenarios. Second, the dynamic target network update strategy triggered by reward difference is introduced into HRA-DDQN, which enhances the model’s adaptability to varying traffic conditions. Finally, a hybrid loss function, combining Huber loss with L2 regularization, is implemented in HRA-DDQN to improve robustness against outliers and mitigate overfitting. Simulation results demonstrate that the proposed decision framework significantly enhances both driving efficiency and safety, outperforming other methods by yielding higher rewards, lower collision rates, and more stable lane-changing decisions. Full article

Eccentric silo is an extremely common type of silo, but it is still unclear how to accurately control the discharge by adjusting eccentric orifices, limiting the application and development of eccentric silo. In this study, the rice particle discharging process on silos with different eccentricities was simulated by the discrete element method (DEM), and the influence mechanism of orifice eccentricity on silo discharge rate was analyzed. The results show that eccentricity has a direct influence on the particle volume fraction and vertical velocity that determine the discharge rate of the silo. In fully eccentric silo, it is not easy for particle flow to achieve balance, particles will pass through outlet with more kinetic energy. Moreover, continuous force network cannot be formed between particles with shear resistance, resulting in weak interlocking action between particles. The orientation of particle in fully eccentric silo is more vertical, especially near the silo wall, which will produce larger local particle volume fraction above the orifice. When the eccentricity exceeds the critical eccentricity, the sparse flow area on the discharge orifice becomes larger, and the particle acceleration area increases accordingly. Research findings may offer valuable insights for the accurate control of discharge rate of eccentric silo, as well as for optimizing silo design. Full article

Plant water use can have a profound impact on the regional water cycle and water balance. A great deal of research has been conducted in this area in recent years. However, plant nighttime sap flow and non-growing season water use have rarely been addressed. These two components should not be neglected in accurately predicting the water use of urban landscape trees and large-scale plantation forests. In this study, the thermal diffusion probe (TDP) method was used to observe the water use of Fraxinus pennsylvanica Marshall, a common tree species in northern China. Continuous observations of sap flow were made from November 2020 to September 2021, while meteorological conditions in the region were recorded. We analyzed the sap flow changes in different months and their responses to environmental factors at the daily scale. The results showed a clear circadian rhythm phenomenon of sap flow during the growing season, with strong correlations between nighttime sap flow and daytime sap flow, as well as environmental factors. Transpiration and refilling stem water storage were also observed at night. In the non-growing season, the average whole day sap flow rate is less than 0.5 cm/h. The difference in average sap flow rate between daytime and nighttime is less than 0.3 cm/h. At the daily scale, temperature (Ta), relative humidity (RH), and vapor pressure deficit (VPD) were the main influences on nighttime sap flow. Solar radiation had a significant effect on the overall water use strategy of the trees. Full article

Coal gangue slurry filling technology is an effective way of utilizing coal gangue solid waste resources rationally, and its fluidity and sedimentation behavior have an essential influence on filling performance. However, evaluation and optimization methods for the fluidity and sedimentation performance of coal gangue slurry filling materials (CSFMs) are still scarce. In order to solve this problem, based on the fractal grading theory, this paper carried out an experimental study on the influence of the fractal dimension on the flow characteristics of CSFMs, revealed the impact of the fractal dimension on the flow performance of slurry, and constructed a CSFM flow performance evaluation and optimization model based on the fractal dimension. At the same time, the influence of the fractal dimension on solid mass fraction and particle distribution in the CSFM sedimentation process was analyzed using a sedimentation experiment. Combined with fitting analysis and model construction, a CSFM sedimentation performance evaluation method based on fractal dimension D was proposed. The results show that (1) the slump, expansion, and yield stress of CSFMs increased first and then decreased with the increase in the fractal dimension, and the bleeding rate of CSFMs decreased with the rise in the fractal dimension. The analysis of the consistency coefficient of CSFMs shows that the increase in the proportion of fine particles will increase the consistency coefficient. (2) The fitting analysis indicates that the fractal dimension D of CSFMs is negatively correlated with the sedimentation performance P_S. The change in D is most significant in the range of 2.3 to 2.4, where the slurry’s stability is poor. When D exceeds 2.5, the slurry’s stability improves significantly. (3) Based on the evaluation of flow performance and settlement performance, the flow performance and settlement performance of CSFMs with fractal dimensions between 2.50 and 2.59 achieve the best balance, which ensures the reliability of long-distance transportation and construction quality. The research results can provide a reference for the pipeline transportation of whole gangue slurry and have important practical significance for realizing the large-scale disposal of gangue solid waste and green mining of coal mines. Full article

This study numerically investigates the thermal performance of a refrigerant-based battery thermal management system (BTMS) under various operating conditions. A validated numerical model is used to examine the effects of cooling channel rib configurations (rib spacing and rib angles) and refrigerant parameters (mass flow rate and saturation temperature) on battery thermal behavior. Additionally, the impact of discharge C-rates is analyzed. The results show that a rib spacing of 11 mm and a rib angle of 60° reduce the maximum battery temperature by 0.8 °C (cooling rate of 2%) and improve temperature uniformity, though at the cost of a 130% increase in pressure drop. Increasing the refrigerant mass flow rate lowers the maximum temperature by up to 10%, but its effect on temperature uniformity diminishes beyond 20 kg/h. A lower saturation temperature enhances cooling but increases internal temperature gradients, while a higher saturation temperature improves uniformity at the expense of a slightly higher maximum temperature. Under high discharge rates (12C), the system’s cooling capacity becomes limited, leading to significant temperature rises. These findings provide insights that can aid in optimizing BTMS design to balance cooling performance, energy efficiency, and temperature uniformity. Full article

Mining activities generate substantial amounts of waste rock, which are often disposed of in waste rock piles. Drainage from these piles can pose serious environmental risks. It is crucial to reliably predict drainage properties in order to effectively manage them. In previous work, we developed a machine learning model to predict waste rock drainage quantity using weather monitoring data as the input and drainage flow rate as the output. However, this model lacked physical constraints, limiting its interpretability, reliability, and applicability. In this study, we introduced a new machine learning model designed with physical constraints to improve the predictions of drainage quantity. This new model incorporates a weather refining sub-model and integrates physical constraints to enhance the overall reliability of the model predictions. The weather refining sub-model transforms primary weather features (total precipitation and temperature) into secondary features (rainfall, snowmelt, and evaporation) through established mathematical relationships. These secondary features were then used as inputs for the machine learning model to predict drainage quantity. To embed physical principles within the machine learning model, we integrated a water balance equation into the neural network architecture and modified the loss function accordingly. In addition, we included an adjustable bias term to optimize the balance between model performance and interpretability. Compared with our previous model, the incorporation of physical constraints into the machine learning model improved the accuracy of the drainage quantity predictions. More importantly, this approach ensures that the model outputs adhere to physical laws, thereby enhancing its interpretability, reliability, and applicability. Full article

This article aims to increase the intensity of mass transfer between gas and liquid in counterflow gas–liquid flow, one of the key problems in designing mass transfer equipment. For this purpose, analytical and experimental studies were carried out to evaluate the main features of operating processes in a vortex counterflow apparatus. In particular, the presented research substantiates the possibility of achieving several theoretical stages of concentration change in a single atomizing stage of the vortex counterflow mass transfer apparatus. The corresponding experimental stand was developed to carry out experimental studies. Afterward, the efficiency of the vortex counterflow mass transfer apparatus was evaluated. The model was based on material balance and flow rate equations, allowing for the determination of mass transfer and intensity ratio. After comparing the analytical expressions with the experimental results, the regression dependence for evaluating the main parameter of the proposed mathematical model was obtained. An increase in steam consumption led to increased steam velocities, affecting the droplets. This fact proved an increase in the intensity of mass transfer processes. The studies substantiated the achievement of several theoretical stages of concentration change and increased the efficiency of a vortex counterflow mass transfer apparatus. From a practical viewpoint, the experimental studies confirmed that when the height and radius ratio is less than 0.6–0.7, it is possible to create a plane vortex countercurrent motion of gas and liquid flows with a significant increase in peripheral gas velocities along the radius of the vortex chamber. Full article

Low-orbit satellite communication networks have gradually become the research focus of fifth-generation (5G) beyond and sixth generation (6G) networks due to their advantages of wide coverage, large communication capacity, and low terrain influence. However, the low earth orbit mega satellite network (LEO-MSN) also has difficulty in constructing stable traffic transmission paths, network load imbalance and congestion due to the large scale of network nodes, a highly complex topology, and uneven distribution of traffic flow in time and space. In the service-based architecture proposed by 3GPP, the introduction of service function chain (SFC) constraints exacerbates these challenges. Therefore, in this paper, we propose GDRL-SFCR, an end-to-end routing decision method based on graph neural network (GNN) and deep reinforcement learning (DRL) which jointly optimize the end-to-end transmission delay and network load balancing under SFC constraints. Specifically, this method constructs the system model based on the latest NTN low-orbit satellite network end-to-end transmission architecture, taking into account the SFC constraints, transmission delays, and network node loads in the end-to-end traffic transmission, uses a GNN to extract node attributes and dynamic topology features, and uses the DRL method to design specific reward functions to train the model to learn routing policies that satisfy the SFC constraints. The simulation results demonstrate that, compared with graph theory-based methods and reinforcement learning-based methods, GDRL-SFCR can reduce the end-to-end traffic transmission delay by more than 11.3%, reduce the average network load by more than 14.1%, and increase the traffic access success rate and network capacity by more than 19.1% and two times, respectively. Full article

Global population growth is putting pressure on the food supply, necessitating the exploration of new, alternative, and sustainable protein sources. Lupin, an underutilized legume in human nutrition, has the potential to play a significant role in addressing this challenge. However, its incorporation into the human diet requires thorough investigation, including exploring and optimizing functionalization processes to maximize its potential. This study aimed to optimize the parameters (pressure, time, and CO₂ flow) for extracting anti-technological factors (ATFs) from lupin using supercritical CO₂ (SC-CO₂) and to evaluate the effects of this extraction on both the flour and the protein isolate derived from it. Optimization revealed that the optimal SC-CO₂ conditions were a CO₂ flow rate of 4 kg/h at 400 bar for 93 min. Under these conditions, significant changes were observed in the flour composition, including a reduction in oil, polyphenols, and moisture content, along with an increase in ash content. Improved color parameters were also noted. These variations were attributed to the removal of oil and phenolic compounds during processing. Furthermore, this research demonstrated that SC-CO₂ treatment improved lupin protein isolate (LPI) purity (93.81 ± 0.31% vs. 87.42 ± 0.48%), significantly reduced oil content (8.31 ± 0.09% vs. 14.31 ± 0.32%), and enhanced color parameters. The SC-CO₂ procedure also resulted in a higher protein extraction yield (56.95 ± 0.45% vs. 53.29 ± 2.37%). However, the total extraction yield (g LPI/100 g of flour) was not affected by SC-CO₂ treatment, remaining at 24.30 ± 0.97% for the control sample and 24.21 ± 0.26% for the treated sample. The extracted oil (2.71 ± 0.11 g/100 g of flour), a co-product of the SC-CO₂ step, exhibited a fatty acid profile characterized by high levels of unsaturated fatty acids (62.8 ± 0.74 g/100 g oil), oleic acid (27.76 ± 0.77 g/100 g oil), linoleic acid (25.98 ± 0.73 g/100 g oil), and α-linolenic acid (5.32 ± 0.16 g/100 g oil), as well as a balanced ratio of essential fatty acids (n-6/n-3 = 4.89). The treatment had minimal to no effect on amino acid content or chemical score, and the protein was characterized by high amounts of essential amino acids (334 ± 3.12 and 328 ± 1.05 mg/g protein in LPI-control and LPI-SF, respectively). These findings demonstrate that both the LPI and the oil extracted using SC-CO₂ possess high nutritional quality and are suitable for human food applications. Full article

Phosphorus input into surface water is a global concern due to its role in eutrophication, which is especially critical in semi-arid regions with their challenging climatic conditions. This study evaluated the best model for estimating the phosphorus decay coefficient (k) in semi-arid lakes, using flows from the Soil Moisture Accounting Procedure (SMAP), model of Génie Rural à 4 paramètres Journalier (GR4J), and reverse water balance hydrological models. Conducted at the Orós reservoir with 37 sampling campaigns from 2008 to 2017, it compared decay rates for temperate, tropical, and semi-arid climates. Some analyses also used phosphorus concentrations measured at the reservoir inlet. Model efficiency was assessed with bias, mean relative error, mean squared error, root mean squared error, and standard deviation. from the best models, water quality classes were classified based on phosphorus concentrations with the use of a confusion matrix to calculate accuracy, precision, recall, and F1 score. The findings demonstrated that the decay rate tailored for semi-arid regions, when combined with GR4J flow data, offered the highest accuracy in estimating phosphorus concentrations (bias = 0.0012, RMSE = 0.0326, EMR = 60.6134, STD = 0.0312). In contrast, the decay rate calibrated for tropical conditions with SMAP-derived flows proved superior for classifying water quality categories (classes defined by CONAMA Resolution 357/05). Therefore, the GR4J model for semi-arid conditions stands out for concentration estimation, while the tropical decay rate with SMAP flows is preferable for effective classification of water quality status. Full article