Search Results (374)

Choking is a common method for controlling severe slugging in offshore oil and gas pipeline–riser systems. By combining experimental data with OLGA simulation, the influence of the installation position of the choke valve on control performance is analyzed. The results indicate that installing the valve near the riser top enables the elimination of slug flow at a larger valve opening, and can mitigate the pressure rise in the pipeline and facilitate valve selection for the slug control system, thus improving the safety and stability of the oil and gas transportation system. The mechanism analysis concludes that the principle for optimizing the valve installation position is to suppress liquid accumulation and liquid slug formation in the pipe section on an FPSO unit and to promote gas outflow. In a practical offshore pipeline case, the results under low-liquid-production-rate conditions are consistent with the simulated trends of the laboratory pipeline. However, in the case of the biggest production rate, the control performance at different installation positions tends to converge. The findings of this study can provide a reference for designing slug control strategies on offshore oil and gas production platforms. Full article

Freshwater accumulation is one of the most striking observations in the Beaufort Gyre (BG) region in the Arctic Ocean. A 39-year simulation, using the validated high-resolution, geometrical-fitting, unstructured grid Finite-Volume Community Ocean Model for the Arctic Ocean, aimed to investigate the contributions of coastal currents and their interannual variability to this phenomenon. The model reasonably reproduced the interannual variability of freshwater content (FWC) in the BG region. Analysis revealed the constructive role of Ekman pumping in supplying FWC, while the lateral flux generally acts to remove FWC from the region. The disparity between Ekman pumping and lateral flux drives the interannual variability of total FWC, with accumulation occurring when the downward Ekman FWC flux surpasses the net outflow-induced lateral FWC flux. Since 2007, there has been a significant increase in downward Ekman pumping, accompanied by a rise in net outflow lateral flux, indicating heightened variability of FWC in the BG region. The model results suggested that the coastal flow over the Arctic continental shelf underwent dramatic changes, especially during summer, and these changes were partially due to increased freshwater and sea ice melting. Increased lateral FWC flux during summer has become a competitive source for unprecedented seasonal freshwater accumulation in the BG region. Flow intensification over the North American coast is influenced by increased freshwater runoff, including the Firth, Kobuk, and Mackenzie Rivers. Interannual FWC variation in the Beaufort Sea could be influenced by the changes in slope flow, with the water originating in part from the Barents and Kara Seas. Full article

One of the key factors determining energy consumption and control stability in hydraulic servovalves with direct electric drive is the flow forces acting on the spool. These forces are complex in nature and consist of both steady-state and transient components, with the steady-state component exerting the dominant influence on the performance and dynamics of spool valves. In recent years, this issue has become the subject of intensive research aimed at reducing undesirable hydraulic loads while maintaining high nominal flow capacity, strong energy efficiency, and low manufacturing cost. In engineering practice, the most effective approach has proven to be the modification of the spool geometry in order to control the direction and jet angle of the outflow while keeping the valve sleeve design as simple as possible. This solution reduces the forces acting on the spool without the need to redesign the flow channels or increase production complexity. This study presents classical analytical methods used to calculate flow forces in typical spool valve designs, which serve as a reference point for subsequent investigations. Then, using CFD simulation tools, a method of flow force compensation is demonstrated for selected spool geometries, followed by a detailed comparative analysis of their effectiveness. The results may provide a foundation for developing more energy-efficient and dynamically stable direct-drive servovalve constructions. Full article

Water resources management in arid and semi-arid regions has become increasingly challenging due to climate change impacts and upstream water policies, particularly for strategic reservoirs. This study evaluates the applicability of the HEC-HMS model for simulating inflow hydrographs and supporting reservoir operation in data-scarce arid environments, focusing on Haditha Reservoir, the only major dam on the Euphrates River within Iraqi territory. An integrated hydro-meteorological and GIS-based framework was developed using 20 years of data (2004–2024), incorporating basin characteristics and reservoir operation records into the HEC-HMS model. Rainfall–runoff processes were simulated using SCS-based methods and routing techniques, followed by calibration and validation against observed inflows. The results demonstrated satisfactory model performance, with an accurate reproduction of inflow hydrographs during both calibration and validation periods. Subsequently, three reservoir operation scenarios were developed and compared with the actual operating policy (outflow curve operation, outflow structure routing operation and rule-based operation scenarios). The rule-based operation scenario showed superior performance by maintaining higher reservoir storage and water levels during dry periods compared to the existing operation, despite higher supply deficits. Overall, the findings confirm that the HEC-HMS model can be reliably applied as a decision-support tool for evaluating reservoir operation in arid and semi-arid regions under water scarcity conditions. Full article

Compound geological disaster chains pose major challenges for disaster prevention in mountainous regions due to their complex mechanisms and cascading impacts. This study investigates a landslide–debris flow–flash flood hazard chain that occurred on 21 July 2024 in the Xujia River catchment, Mianning County, Sichuan Province, China. This event is used as a representative case to improve the understanding of the formation and amplification mechanisms of breach-type debris flows through dynamic inversion constrained by sedimentary records. The objective is to reconstruct the evolution of the event and assess its downstream hazard extent. Post-disaster sedimentary and geomorphological records, including deposit distribution, channel aggradation, and flow traces, were systematically analyzed based on remote sensing interpretation, unmanned aerial vehicle surveys, and detailed field investigations. These sedimentary data were used as key constraints to estimate debris flow magnitude and mobility under different rainfall scenarios. A rainfall flood scenario-based estimation method was applied to quantify debris flow magnitude, and numerical simulations were conducted using the Rapid Mass Movement Simulation model to reproduce debris flow propagation and deposition processes. The results indicate that prolonged antecedent rainfall triggered slope failure in a tributary, leading to the accumulation of landslide-derived material and the formation of a temporary channel blockage. The subsequent breach of this blockage significantly amplified debris flow discharge, velocity, and sediment outflow, resulting in downstream hazard expansion. Simulation results constrained by sedimentary evidence show that peak discharge and solid material output under breach conditions were approximately three times higher than those of rainfall-driven scenarios under comparable rainfall frequencies. These findings demonstrate that sedimentary records provide critical constraints for the inversion of landslide debris flow disaster chain dynamics and highlight the effectiveness of post-disaster evidence based numerical assessment for hazard analysis and risk mitigation in debris flow-prone mountainous catchments. Full article

Global climate change and spatiotemporal heterogeneity in water resources exacerbate supply-demand imbalances. Accurate outflow simulation for joint reservoir group operations thus becomes critical for scientific water resources management. Existing data-driven models like the Long Short-Term Memory (LSTM) lack the robust integration of physical constraints. Traditional mechanistic methods, by contrast, lack generality and stability under complex hydrological conditions. To address this limitation, we propose MDUPLEX-KG-LSTM—a physically constrained data-driven model for reservoir outflow simulation. The model incorporates multi-round DUPLEX (MDUPLEX) data partitioning, which ensures statistical homogeneity across training, validation, and test datasets. It also features a Knowledge-Guided (KG) loss function that embeds core physical constraints: water balance, dead water level, flood season restricted water level, and inter-reservoir re-regulation mechanisms. Additionally, it adopts an LSTM network optimized via Particle Swarm Optimization (PSO) for enhanced predictive performance. We validate the model using daily hydrological data from 2010 to 2025 for three reservoirs in the Wujiaqu Irrigation District of Xinjiang, China. The model exhibits exceptional stability and predictive accuracy across key evaluation metrics: Nash–Sutcliffe Efficiency (NSE) ≥ 0.82, Pearson correlation coefficient (r) > 0.94, Root Mean Square Error (RMSE) ≤ 1.50 m³/s, and Water Balance Index (WBI) ≤ 0.016. It outperforms conventional data-driven and mechanistic models in extreme flow simulation scenarios. It also eliminates unphysical negative outflow values in all predictive results. The model achieves 100% compliance with flood control standards and an irrigation guarantee rate of no less than 86%. This study advances the development of physically constrained data-driven modeling for water resources engineering. It provides reliable methodological support for the intelligent operation of reservoir groups in smart water conservancy systems. The model also balances training cost and inference efficiency effectively. It demonstrates verified scalability for reservoir groups of varying scales, fully meeting the operational deployment requirements of smart water systems. Full article

Cavitation flow within airless spray nozzles critically influences both atomization quality and nozzle longevity. However, its highly transient and multiphase-coupled nature poses significant challenges to the predictive accuracy of turbulence models. To improve numerical simulation fidelity, this study develops an improved dynamic Wall-Adapting Local Eddy-viscosity (WALE) subgrid-scale model for Large Eddy Simulation (LES). Building on the standard WALE formulation, the model incorporates a dynamic coefficient determined via the Germano identity and a least-squares approach, which enables it to adaptively capture the turbulence modulation effects induced by cavitation. Coupled with a Volume of Fluid (VOF) multiphase flow method, this framework is employed to systematically simulate the complex internal nozzle flow under varying spray pressures, coating viscosities, and surface tensions. Results indicate that the improved dynamic WALE model increases numerical stability by approximately 15% compared with the standard model. The internal flow can be partitioned into three regions: a potential-flow acceleration region, a cavitation-induced fluctuation region, and an outlet formation region. Within the cavitation-induced fluctuation region near the wall, cavitation generates a local double-peaked velocity profile and pronounced pressure pulsations. Cavitation intensity increases approximately linearly with spray pressure but decreases with increasing viscosity and surface tension. Both the discharge coefficient and velocity coefficient decrease linearly with increasing cavitation number, indicating that moderate cavitation can enhance instantaneous throughput by altering the flow-field structure. Finally, outflow mass-flow experiments validate the numerical model’s reliability: the improved dynamic WALE model achieves prediction errors ranging from 0.47% to 11.91%, substantially outperforming the standard WALE model, which has errors ranging from 1.27% to 21.10%. Full article

The rapid expansion of impervious surfaces in urban environments has significantly increased surface runoff and flood risk. Detention basins, implemented as part of Sustainable Urban Drainage Systems (SUDSs), are widely adopted worldwide to control peak discharges and mitigate recurrent flooding. In this study, an explicit flood routing model is applied to simulate the hydraulic behaviour of an urban detention reservoir, offering a computationally efficient alternative to traditional implicit numerical schemes by avoiding iterative solution procedures. In parallel, twenty-eight machine learning (ML) models are evaluated to estimate the percentage reduction in peak discharge required to comply with local regulatory constraints. The proposed framework integrates explicit hydrological routing with data-driven modelling to support decision-making during the design of detention systems. The methodology is applied to an urban catchment in Cartagena, Colombia, comparing an uncontrolled inflow hydrograph (without SUDSs) with an attenuated outflow hydrograph produced by the detention basin. The results demonstrate a substantial reduction in peak discharge and a delay in the time to peak, fully complying with Colombian regulations that require a minimum attenuation of 30%. Among the evaluated ML models, Squared Exponential Gaussian Process Regression achieved the best performance, yielding coefficient of determination (R²) values of 0.999 in both the validation and test sets. The findings confirm the potential of machine learning techniques to quantify peak-flow reduction requirements accurately and to support the planning and design of detention reservoirs in urban environments. The proposed approach constitutes a practical, efficient, and replicable tool for sustainable urban drainage design since the results of this research can be used to design detention pond systems employing ML tools. Full article

The increasing use of engineered timber in modern architecture raises critical concerns about fire safety, particularly when combustible linings are exposed within compartments. Classical compartment fire framework, largely derived from non-combustible enclosures, may not adequately capture the dynamics introduced by materials such as cross-laminated timber (CLT). This study investigates how combustible linings influence the fluid dynamic fields of compartment fires derived from the thermal field using CFD simulations informed by experimental data. A series of configurations, from inert to fully lined compartments, were analysed to isolate the effect of burning boundaries. Results show a progressive intensification of fire conditions with additional combustible surfaces: upper-layer temperatures approach 900 °C, smoke layers thicken, and stratification becomes more pronounced. Velocity fields are similarly affected, with peak inflow and outflow velocities doubling compared to the inert case and new vortical structures emerging near burning walls. These findings highlight that exposed CLT significantly amplifies radiative and convective heat feedback, modifying both temperature distributions and flow patterns in ways not captured by the traditional framework based on the inverse opening factor. This underscores the need for performance-based fire design approaches integrating both thermal and fluid dynamic perspectives, ensuring safe implementation of timber in modern construction. Full article

This article introduces and thoroughly examines a novel deterministic compartmental model of COVID-19 dynamics. The model uniquely incorporates compartments for symptomatic and asymptomatic individuals alongside an environmental reservoir for the pathogen. It also accounts for a steady inflow of infected visitors and a steady outflow from the removed class. The mathematical soundness of the model is established by identifying the invariant region and ensuring positivity of solutions. Notably, during surges of infected visitors, certain classes maintain positive minimum values. We analytically determine endemic equilibrium points and prove the global stability of the disease-free equilibrium. Sensitivity analysis highlights the significant roles of transmission rates and asymptomatic individuals in disease spread. Simulation results corroborate the theoretical findings and provide additional insights into the model’s predictive capabilities. Full article

Landslide dams, typically composed of newly deposited, loose, and heterogeneous materials, are highly susceptible to failure induced by overtopping and seepage, particularly under extreme hydrological conditions. Accurate prediction of such breaching processes is essential for flood risk management and emergency response, yet existing models generally consider only a single failure mechanism. This study develops a mathematical model to simulate landslide dam breaching under the coupled action of overtopping and seepage erosion. The model integrates surface erosion and internal erosion processes within a unified framework and employs a stable time-stepping numerical scheme. Application to three real-world landslide dam cases demonstrates that the model successfully reproduces key breaching characteristics across overtopping-only, seepage-only, and coupled erosion scenarios. The simulated breach hydrographs, reservoir water levels, and breach geometries show good agreement with field observations, with peak outflow and breach timing predicted with errors generally within approximately 5%. Sensitivity analysis further indicates that the model is robust to geometric uncertainties, as variations in breach outcomes remain smaller than the imposed parameter perturbations. These results confirm that explicitly accounting for the coupled interaction between overtopping and seepage significantly improves the representation of complex breaching processes. The proposed model therefore provides a reliable computational tool for analyzing landslide dam failures and supports more accurate hazard assessment under multi-mechanism erosion conditions. Full article

Water diversion projects are a crucial measure for addressing eutrophication in shallow lakes worldwide. However, the impacts of different water diversion operation schemes on lake hydrodynamics and water quality can vary significantly, necessitating targeted, refined simulation assessments. This study focuses on Chaohu Lake, one of China’s most eutrophic lakes, and uses a mesoscale meteorological model coupled with a three-dimensional hydrodynamic and water quality model to conduct detailed numerical simulations. The study evaluates the effects of three water diversion operation scenarios and three subsurface flow guide dam scenarios during the ecological water replenishment period in Chaohu Lake from September to November. The simulation results indicate that all three water diversion operation scenarios improve the hydrodynamic conditions of Chaohu Lake, but there are significant differences in their effects on pollutant reduction in the lake. The retention of chemical oxygen demand (COD) in the water ranges from −36,812.1 to 472.8 tons, total nitrogen (TN) retention ranges from −22,637.2 to 3 tons, total phosphorus (TP) retention ranges from −4974 to 10.7 tons, and chlorophyll-a (Chl-a) retention ranges from −310.8 to −3.3 tons. Among the three subsurface flow guide dam schemes, all can promote the outflow of pollutants from Chaohu Lake. The combined subsurface flow guide dam scheme is the most effective, enabling an approximately 7.4% increase in pollutant export. The study demonstrates that diverting Huaihe River water through Paihe into Chaohu Lake, along with adding a combined subsurface flow guide dam in the West Lake area, can significantly improve the hydrodynamics and water quality in the West Lake area. This research provides essential technical support for the future operation of the Yangtze-to-Huaihe River Water Diversion Project and the layout of subsurface flow guide dams in Chaohu Lake, offering valuable insights for the ecological management of other shallow lakes. Full article

Complex water networks face prominent contradictions among flood control, water supply, and ecological protection, and traditional scheduling models struggle to address multi-dimensional water security challenges. To solve this problem, this study proposes a multi-objective coordinated water resources scheduling model for complex water networks, taking the Taihu Lake Basin as a typical case. First, a multi-objective optimization indicator system covering flood control, water supply, and aquatic ecological environment was constructed, including 12 key indicators such as drainage efficiency of key outflow hubs and water supply guarantee rate. Second, a dynamic variable weighting strategy was adopted to convert the multi-objective optimization problem into a single-objective one by adjusting indicator weights according to different scheduling periods. Finally, a combined solving mode integrating a basin water quantity-quality model and a joint scheduling decision model was established, optimized using the particle swarm optimization (PSO) algorithm. Under the 1991-Type 100-Year Return Period Rainfall scenario, three scheduling schemes were designed: a basic scheduling scheme and two enhanced discharge schemes modified by lowering the drainage threshold of the Xinmeng River Project. Simulation and decision results show that the enhanced discharge scheme with the lowest drainage threshold achieves the optimal performance with an objective function value of 98.8. Compared with the basic scheme, it extends the flood season drainage days of the Jiepai Hub from 32 to 43 days, increases the average flood season discharge of the Xinmeng River to the Yangtze River by 9.5%, and reduces the maximum water levels of Wangmuguan, Fangqian, Jintan, and Changzhou (III) stations by 5 cm, 5 cm, 4 cm, and 4 cm, respectively. This model effectively overcomes technical bottlenecks such as conflicting multi-objectives and complex water system structures, providing theoretical and technical support for multi-objective coordinated scheduling of water resources in complex water networks. Full article

This paper proposes a novel framework for embedding the Fixed And Variable Area Discharge (FAVAD) equation into the software EPANET 2.2 for the simulation of water distribution networks (WDNs). This framework yields a realistic model of leakage outflows that accounts for the expansion of the leak area as a function of service pressure. Without altering the source code of EPANET, this is accomplished by using node emitters and by iteratively adjusting emitter coefficients in the Matlab^® (R2023a) environment to mimic the effects of the FAVAD equation along WDN pipes. An additional benefit consists of preventing backflow occurring under negative pressure conditions in EPANET 2.2. The application to two benchmark WDNs under various leakage configurations demonstrates the robustness and the numerical efficiency of the framework, as well as the impact and benefits of the FAVAD formulation. For instance, for pipes with higher elasticity, omitting the expansion of the leak area leads to an underestimation of the total leakage rate that exceeds 30% for one of the studied cases. Furthermore, the algorithm successfully prevents leakage backflow under both demand-driven and pressure-driven analyses. Full article

In today’s volatile business environment, securing a sustainable competitive advantage hinges on retaining and effectively managing talent. While talent turnover is inevitable, strategic internal human resource (HR) transfers offer a solution to prevent talent outflow and supplement skill gaps. However, previous models for identifying internal substitutes often focus solely on individual work capabilities, neglecting the critical role of group interactions and collaborative structure. Drawing on social network theory, transactive memory systems, and person–group fit, this study proposes a graph-based analytical approach that models the organization as a complex system. Our methodology provides a holistic framework that integrates both (1) individual capabilities and (2) group-level characteristics (e.g., work-relationship networks and cluster-level similarity) to identify the most suitable substitutes. At the macroscopic level, we use an inductive graph neural network (GraphSAGE) to learn node embeddings from a work relationship network constructed from process event logs and to quantify group-level similarity. At the microscopic level, we compute dynamic collaboration intensity, frequency, and task similarity between employees over time. To validate the approach, we develop four simulation scenarios using an enriched incident management process event log and implement them in a SimPy-based simulator, benchmarking against an existing method that considers only individual factors. Across all scenarios, the proposed dual-factor model significantly outperforms the baseline in terms of efficiency, accuracy, and suitability. This research provides a practical, validated algorithm that supports evidence-based workforce management and more effective internal talent allocation. Full article