Search Results (7,618)

A major danger that significantly raises the possibility of deep coal mining accidents is the delayed water influx from the bottom plate, which is brought on by the activation of tiny faults brought on by mining at the working face of the restricted aquifer. This study develops 17 numerical models utilizing FLAC3D simulation software 6.00.69 to clarify the activation and water inburst mechanisms of minor faults influenced by various parameters, incorporating fluid–solid coupling effects in coal seam mining. The developmental patterns of the stress field, displacement field, plastic zone, and seepage field of the floor rock layer were systematically examined in relation to four primary factors: aquifer water pressure, minor fault angle, fracture zone width, and the distance from the coal seam to the aquifer. The results of the study show that the upper and lower plates of the minor fault experience discontinuous deformation as a result of mining operations. The continuity of the rock layers below is broken by the higher plate’s deformation, which is significantly larger than that of the lower plate. The activation and water flow into small faults are influenced by many elements in diverse ways. Increasing the distance between the coal seam and the aquifer will make the water conduction pathway more resilient. This will reduce the amount of water that flows in. On the other hand, higher aquifer water pressure, a larger fracture zone, and a fault that is tilted will all help smaller faults become active and create channels for water to flow into. The gray relational analysis method was used to find out how sensitive something is. The sensitivities of each factor to water influence were ranked from high to low as follows: distance between the aquifer and coal seam (correlation coefficient 0.766), aquifer water pressure (0.756), width of the fracture zone (0.710), and angle of the minor fault (0.673). This study statistically elucidates the inherent mechanism of delayed water instillation in minor faults influenced by many circumstances, offering a theoretical foundation for the accurate prediction and targeted mitigation of mine water hazards. Full article

The wave-making characteristics and resistance performance of a naval vessel are fundamental to its hydrodynamic design, directly impacting its speed, stealth, and energy efficiency. To reveal the performance trade-offs inherent in different design philosophies, a systematic comparative study on the hydrodynamic performance of two representative mainstream naval destroyers from China and the United States was conducted using Computational Fluid Dynamics (CFD). Full-scale three-dimensional models of both vessels were established based on publicly available data. Their flow fields in calm water were numerically simulated at both economical (18 knots) and maximum (30 knots) speeds using an unsteady Reynolds-Averaged Navier–Stokes (RANS) solver, the Volume of Fluid (VOF) method for free-surface capturing, and the SST k-ω turbulence model. The performance differences were meticulously compared through qualitative observation of wave patterns, quantitative measurements (such as the transverse width of the wave-making region), and analysis of resistance data. Numerical results indicated that the wave-making generated by the vessel of the United States was more pronounced during steady navigation. To validate the reliability of the CFD results, supplementary towing tank tests were performed using a small-scale model (1.1 m in length) of the vessel from China. The test speed (1.5 m/s) was scaled to correspond to the full-scale ship speed through dimensional analysis. The experimental data showed good agreement with the simulation results, jointly confirming the aforementioned performance trade-off. This study clearly demonstrates that, at the economic speed, the design of the mainstream vessel from China tends to prioritize superior wave stealth performance at the expense of higher resistance, whereas the mainstream vessel from the U.S. exhibits the characteristics of lower resistance coupled with more significant wave-making features. These findings provide an important theoretical basis and data support for the future multi-objective optimization design of surface vessels concerning stealth, speed, and comprehensive energy efficiency. Full article

The “bauxite gas reservoir” in the Ordos Basin represents a novel exploration domain, yet the mechanisms governing its widespread aluminous fracture fillings remain unclear. This study integrates core observation, thin-section analysis, geochemical simulation, and rock physics to investigate the formation and impact of these fracture systems. Results identify a characteristic filling evolutionary sequence of “wall-lining film → oolitic/globular → plug-like → vermicular.” Geochemical simulations confirm that increasing pH and decreasing Eh driven by water–rock interactions are the key drivers for aluminous mineral precipitation. A distinct well log response model characterized by high GR, DEN, and CNL values coupled with low AC and RT is established for effective identification. Seepage experiments reveal that while Al–Si colloidal fracture fillings reduce permeability, they act as natural proppants to preserve effective flow channels, acting as a crucial high-permeability network for gas migration despite the mineral occlusion. These findings refine the accumulation theory for bauxite series reservoirs and provide geological evidence for deep tight gas exploration. Full article

Proton exchange membrane fuel cells (PEMFC) are recognized as promising next-generation energy technology. Yet, their performance is critically limited by inefficient gas transport and water management in conventional flow channels. Current rectangular gas channels (GC) restrict reactive gas penetration into the gas diffusion layer (GDL) due to insufficient longitudinal convection. At the same time, the complex multiphase interactions at the mesoscale pose challenges for numerical modeling. To address these limitations, this study proposes a novel cathode channel design featuring laterally contracted fin-shaped barrier blocks and develops a mesoscopic multiphase coupled transport model using the lattice Boltzmann method combined with the volume-of-fluid approach (LBM-VOF). Through systematic investigation of multiphase flow interactions across channel geometries and GDL surface wettability effects, we demonstrate that the optimized barrier structure induces bidirectional forced convection, enhancing oxygen transport compared to linear channels. Compared with the traditional straight channel, the optimized composite channel achieves a 60.9% increase in average droplet transport velocity and a 56.9% longer droplet displacement distance, while reducing the GDL surface water saturation by 24.8% under the same inlet conditions. These findings provide critical insights into channel structure optimization for high-efficiency PEMFC, offering a validated numerical framework for multiphysics-coupled fuel cell simulations. Full article

The removal of residual free chlorine ions from industrial wastewater is a critical step toward achieving sustainable and environmentally compliant water reuse. Excess chlorine in sludge collector water causes corrosion of process equipment, inhibits biological treatment, and leads to toxic discharge effects. In this study, an intelligent control strategy was developed for an ion-exchange-based dechlorination process to dynamically regulate chlorine concentration in the effluent stream. A pilot-scale ion-exchange filtration unit, designed with a nominal capacity of 500 L h⁻¹, was constructed using a strong-base anion-exchange resin to selectively adsorb chloride and free chlorine ions. A total of 200 experimental observations were obtained to characterize the nonlinear relationship between inlet flow rate and outlet chlorine concentration under varying operational conditions. Based on these experimental data, an Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed in MATLABR2025 to simulate and control the ion-exchange process. Two model-optimization techniques, Grid Partition + Hybrid and Subtractive Clustering + Hybrid, were applied. The subtractive clustering approach demonstrated faster convergence and superior accuracy, achieving RMSE = 0.147 mg L⁻¹, MAE = 0.101 mg L⁻¹, and R² = 0.993, outperforming the grid-partition model (RMSE ≈ 0.29, R² ≈ 0.97). The resulting ANFIS model was subsequently integrated into a MATLAB/Simulink-based intelligent control system for real-time regulation of chlorine concentration. A comparative dynamic simulation was performed between the proposed ANFIS controller and a conventional PID (Proportional-Differential-Integral) controller. The results revealed that the ANFIS controller achieved a faster response (rise time ≈ 28 s), lower overshoot (≈6%), and shorter settling time (≈90 s) compared to the PID controller (rise time ≈ 35 s, overshoot ≈ 18%, settling time ≈ 120 s). These improvements demonstrate the ability of the proposed model to adapt to nonlinear process behavior and to maintain stable operation under varying flow conditions. Full article

The construction of underground reservoirs in coal goafs is an innovative technology to alleviate the coal–water conflict in arid mining areas of northwest China. However, its widespread application is constrained by the challenge of accurately predicting water inflow, which fluctuates significantly due to the dynamic “expansion–closure” behavior of mining-induced fractures. This study focuses on the Shendong mining area, where repeated multi-seam mining occurs, and employs a coupled Finite Discrete Element Method (FDEM) and Computational Fluid Dynamics (CFD) numerical model, combined with in situ tests such as drilling fluid loss and groundwater level monitoring, to quantify the evolution of overburden fractures and their impact on reservoir water inflow during mining, 8 months post-mining, and after 7 years. The results demonstrate that the height of the water-conducting fracture zone decreased from 152 m during mining to 130 m after 7 years, while fracture openings in the key aquifer and aquitard were reduced by over 50%. This closure process caused a dramatic decline in water inflow from 78.3 m³/h to 2.6 m³/h—a reduction of 96.7%. The CFD-FDEM simulations showed a deviation of only 10.6% from field measurements, confirming fracture closure as the dominant mechanism driving inflow attenuation. This study reveals how fracture closure shifts water flow patterns from vertical to lateral recharge, providing a theoretical basis for optimizing the design and sustainable operation of underground reservoirs. Full article

Geostructures, such as foundations, embankments, retaining structures, bridge abutments, and both natural and engineered slopes, interact with the ground to ensure structural safety and functionality. One significant factor influencing these systems is climate, which continuously affects soil conditions through dynamic processes. Over the past century, climate change has intensified, increasing uncertainties regarding the safety of both existing and planned geostructures. While the impacts of climate change on geostructures are evident, effective methods to address them remain uncertain. This paper presents an approach for mitigating and adapting to climate change impacts through a stepwise geomechanical analysis and geotechnical design framework that incorporates expected climatic conditions. A novel framework is introduced that systematically integrates projected climate scenarios into geomechanical modeling, enabling climate-resilient design of geostructures. The concept establishes an interface between climate effects and geomechanical data, capturing the causal chain of climate hazards, their effects, and potential consequences. The proposed interface provides a practical tool for integrating climate considerations into geotechnical design, supporting adaptive and resilient infrastructure planning. The approach is demonstrated across different geostructure types, with a detailed slope stability analysis illustrating its implementation. Results show that the interface, reflecting processes such as water infiltration, soil hydraulic conductivity, and groundwater flow, is often critical to slope stability outcomes. Furthermore, slope stability can often be maintained through simple, timely implemented nature-based solutions (NbS), whereas delayed actions typically require more complex and costly interventions. Full article

Modeling flow field evolution accurately is important for numerous natural and engineering applications, such as pollutant dispersion in the ocean and atmosphere, yet remains challenging because of the highly nonlinear, multi-physics, and high-dimensional features of flow systems. While traditional equation-based numerical methods suffer from high computational costs, data-driven neural networks struggle with insufficient data and lack physical explainability. The physics-informed neural operator (PINO) addresses this by combining physics and data losses but faces a fundamental gradient imbalance problem. This work proposes a physics-informed fine-tuned neural operator for high-dimensional flow field modeling that decouples the optimization of physics and data losses. Our method first trains the neural network using data loss and then fine-tunes it with physics loss before inference, enabling the model to adapt to evaluation data while respecting physical constraints. This strategy requires no additional training data and can be applied to fit out-of-distribution (OOD) inputs faced during inference. We validate our method using the shallow water equation and advection–diffusion equation using a convolutional neural operator (CNO) as the base architecture. Experimental results show a 26.4% improvement in single-step prediction accuracy and a reduction in error accumulation for multi-step predictions. Full article

This study employs numerical simulation methods to systematically analyze the multiphase flow and heat transfer characteristics in a circulating fluidized bed flue gas desulfurization (CFB-FGD) reactor handling ascharite ore roasting flue gas. Based on the simulation results, key structural optimization strategies are proposed. A three-dimensional mathematical model was developed based on the Fluent 19.1 platform, and the multiphase flow process was simulated using the Eulerian-Lagrangian method. The study examined the effects of venturi tube structure, atomized water nozzle installation height, and gas injection disruptor configuration on reactor performance. Optimization strategies for key structural components were systematically evaluated. The results show that the conventional inlet structure leads to significant non-uniformity in the velocity field. Targeted adjustments to the dimensions of venturi tubes at different positions markedly improve the velocity distribution uniformity. Reducing the atomized water nozzle installation height from 1.50 m to 0.75 m increased the temperature distribution uniformity index in the middle part of the straight pipe section by 5.5%. Moreover, a gas injection disruptor was installed in the upper part of the straight pipe section of the CFB-FGD reactor. Increasing the gas injection velocity from 15 m/s to 30 m/s increased the average residence time of desulfurization sorbents by 17.0%. This increase effectively enhances gas–solid mixing within the CFB-FGD reactor. The optimization strategies described above significantly reduced the extent of flow dead zones and low-temperature regions in the CFB-FGD reactor and improved flow conditions. This study provides important theoretical and technical support for the optimization and industrial application of CFB-FGD technology for ascharite ore roasting flue gas. Full article

Aiming at the unclear mechanisms of fluid migration in nanopore-throat systems within tight oil reservoirs, this study focuses on the microscopic interactions at the oil–water interface in nanoconfined spaces. Based on molecular dynamics simulation, water-flooding models within nanopores of tight oil reservoirs under varying salinity conditions were constructed. The microscopic flow behaviors of oil and water in the pores were investigated, and the mechanism by which interfacial hydrogen bonding influences displacement efficiency under nanoconfinement was elucidated. The results demonstrate that due to the strong hydrogen bonding interactions between acetic acid and water, it is impossible to establish an effective displacement process or form stable displacement pathways within the pores. The extensive hydrogen-bonding network formed by acetic acid molecules at the oil–water interface severely restricts the transport capacity of water. Salinity exerts a nonlinear regulatory effect on hydrogen bonding. High-salinity (246.5 g/L) waterflooding shortens hydrogen bond lengths, enhances local bonding strength, and restricts the expansion of water channels; low-salinity (21.9 g/L) waterflooding mitigates ionic interference, resulting in the highest diffusion capacity of alkanes. The diffusion coefficient increases by 1.4 times compared to that under high-salinity conditions, leading to the highest degree of crude oil mobility. The research findings provide important guidance for enhanced oil recovery in tight oil reservoirs. Full article

Hospitals are among the most energy-intensive buildings, yet their heating systems often operate below optimal efficiency due to outdated controls and limited sensing. Existing facilities often provide only a few accessible measurement points, many of which are locked within proprietary master controllers and not integrated into the Building Automation System (BAS). To address these limitations, this study proposes a data-driven feature selection approach that supports the development of gray-box emulators for complex, real-world central heating plants. A year of operational and weather data from a large hospital was used to train multiple machine learning models to predict the heating demand and return water temperature of a hospital heating plant system. The model’s performance was evaluated under reduced-sensor conditions by intentionally removing unpredictable values such as the VFD speed and flow rate. XGBoost achieved the highest accuracy with full sensor data and maintained a strong performance when critical sensors were omitted. An explainability analysis using Shapley Additive Explanations (SHAP) is applied to interpret the models, revealing that outdoor temperature and time of day (as an occupancy proxy) are the dominant predictors of boiler load. The results demonstrate that, even under sparse instrumentation, an AI-driven digital twin of the heating plant can reliably capture system dynamics. Full article

This study examines the hydrological response of the Klambu Dam Catchment in Central Java, Indonesia, to climatic and land cover changes from 2000–2023, with simulations extending to 2040. Utilizing CHIRPS satellite data calibrated with six ground stations, monthly precipitation and temperature datasets were analyzed and projected via linear regression aligned with IPCC scenarios, revealing a marginal temperature decline of 0.21 °C (from 28.25 °C in 2005 to 28.04 °C in 2023) and a 17% increase in rainfall variability. Land cover assessments from Landsat imagery highlighted drastic changes: a 73.8% reduction in forest area and a 467.8% increase in mixed farming areas, alongside moderate fluctuations in paddy fields and settlements. The Thornthwaite-Mather water balance method simulated monthly discharge, validated against observed data with Pearson correlations ranging from 0.5729 (2020) to 0.9439 (2015). Future projections using Cellular Automata-Markov modeling indicated stable volumetric flow but a temporal shift, including a 28.1% decrease in April rainfall from 2000 to 2040, contracting the wet season and extending dry spells. These shifts pose significant threats to agricultural and aquaculture activities, potentially exacerbating water scarcity and economic losses. The findings emphasize integrating dynamic land cover data, climate projections, and empirical runoff corrections for climate-resilient watershed management. Full article

Landfills serve as a primary disposal method for municipal solid waste in China, with over 20,000 operational sites nationwide; however, long-term operations risk leachate leakage and groundwater contamination. Amid intensifying climate change and human activities, understanding contaminant evolution mechanisms in landfills has become critically urgent. Focusing on a representative plain-based landfill in North China, this study integrated field investigations and groundwater monitoring to establish a monthly coupled groundwater flow–solute transport model (using MODFLOW and MT3DMS codes) based on site-specific hydrogeological boundaries and multi-year monitoring data, analyzing spatiotemporal plume evolution under the coupled impacts of precipitation variability (climate change) and intensive groundwater extraction (human activities), spanning the historical period (2021–2024) and future projections (2025–2040). Historical simulations demonstrated robust model performance with satisfactory calibration against observed water levels and chloride concentrations, revealing that the current contamination plume exhibits a distinct distribution beneath the site. Future projections indicate nonlinear concentration increases: in the plume core zone, concentrations rise with precipitation, whereas at the advancing front, concentrations escalate with extraction intensity. Spatially, high-risk zones (>200 mg/L) emerge earlier under wetter conditions—under the baseline scenario (S0), such zones form by 2033 and exceed site boundaries by 2037. Plume expansion scales positively with extraction intensity, reaching its maximum advancement and coverage under the high-extraction scenario. These findings demonstrate dual drivers—precipitation accelerates contaminant accumulation through enhanced leaching, while groundwater extraction promotes plume expansion via heightened hydraulic gradients. This work elucidates coupled climate–human activity impacts on landfill contamination mechanisms, proposing a transferable numerical modeling framework that provides a quantitative scientific basis for post-closure supervision, risk assessment, and regional groundwater protection strategies, thereby aligning with China’s Standard for Pollution Control on the Landfill Site of Municipal Solid Waste and the Zero-Waste City initiative. Full article

Tsunami amplification and overland flow characteristics have been investigated using numerical modeling in a case study of the Ukai coast during the 2024 tsunami event. The tsunami wave amplification from offshore Iida Bay to Ukai has been investigated by using a hydrodynamic model. The model has been successfully validated by comparing simulated tsunami inundation with observations in Ukai. Non-breaking tsunami amplification from model simulations shows a power law, with a correlation coefficient R² of 0.97, leading to a 1.84-fold amplification at the breaking depth location. After wave breaking, tsunami amplification follows an exponential function of water depth, with a significantly slower increase rate compared to that before breaking. Tsunami travel time from the Iida Bay offshore boundary to Ukai is determined by comparing tsunami peaks at two different locations. A quick approximation of tsunami travel time using the averaged depth for shallow wave celerity results in an 8.5% error compared to hydrodynamic model simulations. Supercritical and subcritical flow characteristics in the beachfront area have been examined using a wave dynamic model. Based on the Froude number, beachfront overland flow on an asphalt ground surface with low friction results in fast supercritical flow and deeper inundation, which can have major impacts on coastal structures and sediment scour. Grass-covered ground lowers tsunami velocity to slower subcritical flow and lower the maximum inundation height which can reduce the tsunami damage. The findings will provide valuable support for coastal hazard mitigation and resilience studies. Full article

Post-earthquake fires often cause more severe losses than the earthquakes themselves, highlighting the critical role of water distribution networks (WDNs) in mitigating fire risks. This study proposed an improved assessment framework for the post-earthquake firefighting functionality of WDNs. This framework integrates a WDN firefighting simulation model into a cloud model-based assessment method. By combining seismic damage and firefighting scenarios, the simulation model derives sample values of the functional indexes through Monte Carlo simulations. These indexes integrate the spatiotemporal characteristics of the firefighting flow and pressure deficiencies to assess a WDN’s capability to control fire and address fire hazards across three dimensions: average, severe, and prolonged severe deficiencies. The cloud model-based assessment method integrates the sample values of functional indexes with expert opinions, enabling qualitative and quantitative assessments under stochastic–fuzzy conditions. An illustrative study validated the efficacy of this method. The flow- and pressure-based indexes elucidated functionality degradation owing to excessive firefighting flow and the diminished supply capacity of a WDN, respectively. The spatiotemporal characteristics of severe flow and pressure deficiencies demonstrated the capability of firefighting resources to manage concurrent fires while ensuring a sustained water supply to fire sites. This method addressed the limitations of traditional quantitative and qualitative assessment approaches, resulting in more reliable outcomes. Full article