Search Results (237)

Intensity–Duration–Frequency (IDF) curves are the cornerstone of hydraulic infrastructure design, yet standard methodologies often fail to account for the complex dependence structure of rainfall characteristics and the non-stationary effects of climate change. This study develops a robust Regional Copula Framework for the Kastoria Lake basin, Greece, utilizing sub-hourly rainfall records from four meteorological stations (2007–2024). We employ a forensic data quality control process to pool 277 independent storm events. Unlike traditional approaches, our analysis demonstrates that the Generalized Extreme Value (GEV) distribution (ξ = 0.348) significantly outperforms the standard Lognormal distribution in modeling heavy-tailed rainfall intensities. The dependence between storm duration and intensity was found to be consistently negative (τ = −0.35), a structure best captured by the Rotated Gumbel (90°) copula, which physically reflects the region’s convective storm dynamics. Trend analysis revealed a statistically significant decrease in peak intensity (τ = −0.14) coupled with an increase in storm duration (τ = 0.22), a hydro-climatic shift that contrasts with increasing intensity trends reported in the wider Balkan region. These findings suggest a regime transition from flash-flood dominance to volume-critical events, necessitating updated design criteria that integrate both multivariate dependence and local climatic non-stationarity. Full article

The development of effective Intrusion Detection Systems (IDS) for Internet of Things (IoT) environments is constrained by the absence of realistic, large-scale datasets, particularly for the Message Queuing Telemetry Transport (MQTT) protocol, which is prevalent in industrial IoT. Existing datasets are frequently limited in scope, imbalanced, or do not capture MQTT-specific attack patterns, thereby impeding the training of accurate machine learning models. To address this gap, the extensible Message Queuing Telemetry Transport (eMQTT) Traffic Generator is introduced as a modular platform capable of simulating both legitimate MQTT communication and targeted denial-of-service (DoS) attacks. The framework features a scalable and reproducible architecture that incorporates protocol-aware attack modeling, automated traffic labeling, and direct export of datasets suitable for machine learning applications. The system produces standardized, configurable, repeatable, and publicly accessible datasets, thereby facilitating reproducible research and scalable experimentation. Experimental validation demonstrates that the simulated traffic aligns with established DoS behavior models. Two high-volume datasets were generated: one representing normal MQTT traffic and another emulating CONNECT-flooding attacks. Machine learning classifiers trained on these datasets exhibited strong performance, with gradient boosting models achieving over 95% accuracy in distinguishing benign from malicious traffic. This work offers a practical solution to the scarcity of datasets in IoT security research. By providing a controlled, extensible, and reproducible traffic-generation platform alongside validated datasets, eMQTT enables systematic experimentation, supports the advancement of IDS solutions, and enhances MQTT security for critical IoT infrastructures. Full article

While various methods exist for assessing urban rainwater and flood resources, there is a lack of targeted evaluation for the rainwater harvesting potential of areas equipped with sponge city facilities. This study employs the Yield Before Spillage (YBS) principle to design rainwater collection tanks for sponge facilities under different design return periods, conducting a specialized assessment of the rainwater resource potential in built-up sponge facility areas within the “Dongsheng–Kangbashi–Ejin Horo Banner” urban cluster. The results indicate that the collection potential follows the patterns of “wet year > normal year > dry year” and “Ejin Horo Banner > Kangbashi District > Dongsheng District.” A rainwater collection tank designed for a 5-year return period (p = 5a) is more applicable to the study area. The sponge facilities in the study area achieve an annual runoff volume control rate exceeding 85%, effectively alleviating drainage pressure. The conclusions demonstrate that the YBS method can effectively assess the rainwater and flood resources of sponge facilities in arid regions. Tanks designed for the three different return periods all meet the rainwater retention requirements of sponge cities across various hydrological years. In arid areas, tanks designed for lower return periods are sufficient for harnessing rainwater collection potential, offering lower costs. Full article

The configurational stability and mobilizable oil release behavior of a multiscale gel–particle cooperative nested system within tight sandstone pore structures were systematically investigated. Scanning electron microscopy (SEM), atomic force microscopy (AFM), and μCT-based three-dimensional reconstruction were employed to characterize the multiscale structural features of the system. Interfacial regulation behavior was analyzed using contact angle measurements, oil–water interfacial tension (IFT), and zeta potential tests, while core flooding experiments were conducted to evaluate seepage response and oil displacement performance. The results indicate that particle reinforcement transforms the gel pore walls from a weakly rough interface into a strongly rough and mechanically interlocked structure, with the root-mean-square surface roughness increasing from 23.6 nm to 71.4 nm. μCT quantitative analysis shows that the pore volume fraction increases from 38.6% to 52.4%, and the connectivity ratio rises from 41.2% to 68.5, leading to the formation of a more continuous pore–throat network. Interfacial property measurements reveal that the rock surface contact angle decreases from 116.3° to 60.5°, and the oil–water interfacial tension is reduced from 27 mN·m⁻¹ to 3–5 mN·m⁻¹. Meanwhile, the system–rock interface exhibits a stronger overall negative surface charge. During displacement experiments, the pressure differential at 3.0 pore volumes (PV) is only 17.0 kPa, significantly lower than that of the control gel (26.2 kPa). The oil recovery is increased to 44.8%, while the residual oil saturation decreases from 0.46 to 0.32, and the displacement efficiency improves from 36.1% to 55.6%. These results demonstrate that the multiscale gel–particle cooperative nested system establishes a stable, regulated seepage configuration in tight sandstone and enables sustained mobilization of trapped oil under relatively low-pressure gradients through the coupled regulation of wettability, interfacial tension, and interfacial electrostatics. This study elucidates a coupled mechanism of configurational stability–flow channel redistribution–continuous oil mobilization and provides a new material design and regulation strategy for efficient recovery of residual oil in tight reservoirs. Full article

This study addresses high water cut and low recovery in bottom-water sandstone reservoirs by optimizing CO₂ and N₂ foam flooding parameters. The key innovation is the pioneering application of fractal dimension to quantitatively characterize water coning morphology during composite gas flooding. A numerical simulation assessed composite gas type, injection gas ratio, sequence, speed, volume, and injection–production ratio. Fractal dimension quantified water coning. Optimal conditions were: 2:1 injection gas ratio (CO₂ then N₂ foam), 140 t/d injection speed, 0.31 PV volume, and 1:3.2 injection–production ratio. This achieved 39.52% recovery over 15 years—a 4.89% increase, adding 3.17 × 10⁴ t of oil. Fractal dimension fell to 1.672. Sensitivity analysis showed the injection gas ratio most affects oil output. The injection volume best suppresses water coning. The injection speed has low sensitivity. Key interactions exist between volume, gas type, and injection–production ratio. Injection gas ratio, volume, and injection–production ratio are crucial for development control. The proposed methodology presents a viable strategy for enhancing oil recovery in similar reservoirs, with broader implications for advancing CO₂ utilization and supporting carbon management objectives in the petroleum industry. Full article

Reservoir management, flood control, and operational planning are the benefits of dam inflow forecasting. Decomposition algorithms can decompose complex inflow data into intrinsic components and reduce noise and fluctuations, while quantum machine learning models use features such as superposition and entanglement to manage large datasets and capture nonlinear hydrological behaviors. This study used three models: random forest (RF) as a classical benchmark, hybrid quantum neural network (HQNN) as a quantum approach, and sequential variational mode decomposition with HQNN (SVMD-HQNN) that integrates decomposition and quantum learning. The modeling was applied to forecast the inflow to Khoda Afarin Dam over 16 years (2009–2024) in two scenarios that included hydrological parameters (precipitation and evaporation) and reservoir parameters (water level, volume, and surface area). The data was divided into training and testing sets in a ratio of 70:30. The results showed that SVMD-HQNN achieved higher accuracy than the other two models with RMSE = 34.51, R² = 0.93, NSE = 0.91, MAPE = 11.48%, and KGE = 0.89 in scenario (i) and RMSE = 25.74, R² = 0.95, NSE = 0.94, MAPE = 8.98%, and KGE = 0.93 in scenario (ii). In the first scenario, this approach increased the prediction accuracy by 43.71%, and in the second scenario, it increased the prediction accuracy by 45.47% compared to the HQNN model. The proposed SVMD-HQNN framework is particularly effective under climate change conditions, where inflow fluctuations and instability are significant, and provides robust and generalizable predictions for reservoirs in similar environments. Full article

The Three Gorges Reservoir (TGR) in China is one of the world’s largest hydropower projects. Interval inflow, originating from ungauged areas between the upstream gauging control stations (Zhutuo, Beibei, Wulong) and the TGR dam site, is a critical component of total reservoir inflow, but its hydrological characteristics have not been fully clarified. The accurate estimation and prediction of interval inflow are essential for reservoir safety and flood control operations. Using daily hydrological data from 2009 to 2017, we propose an integrated analytical framework combining (i) flow travel time estimation using cross-correlation analysis, (ii) multi-scale statistical characterization, and (iii) K-means clustering with bootstrap validation and algorithm comparison. This framework systematically identified hydrological regimes of interval inflow and their associated flood control risks. The key findings are as follows. (1) The optimal flow travel time from the upstream gauging stations to the dam site is 1 day (correlation coefficient $\rho =0.9809,p<0.001$), and it remains stable across different flow regimes. (2) The interval inflow exhibited a highly right-skewed distribution (mean 1279 m³/s, standard deviation 1651 m³/s) and contributed on average 10.1% to the total inflow. The contribution ratio exhibited an inverted U-shaped relationship with increasing total inflow, peaking at 11.4% when the total inflow ($Q$) was 13,014 m³/s. The quartile thresholds were 5788 m³/s, 9575 m³/s, and 16,869 m³/s (corresponding to Q1, Q2, and Q3, respectively), and the 10th and 90th percentiles (P10 and P90) were 4865 m³/s and 24,625 m³/s, respectively. (3) Five distinct hydrological patterns (C1–C5) were successfully identified, among which Cluster C4 (5.7% of days) was defined as the high-impact pattern based on reservoir operational criteria, with a mean $I$ of 6425 m³/s, a mean $R$ of 27.8% (up to 44% in extreme events), a mean flood duration of 5.8 days, a mean flood volume of 36.1 × 10⁸ m³, and a flashiness index of 1.48. (4) C4 is predominantly triggered by localized heavy rainfall, and its flashy nature implies a substantially shorter forecast lead time compared with mainstream-dominated floods, posing major challenges to real-time reservoir operations. This study demonstrates that interval inflow risk is pattern-dependent and that the proposed framework provides a scientific basis for developing pattern-specific reservoir operation strategies. The proposed framework is transferable to other large river-type reservoirs facing similar ungauged interval inflow challenges. Full article

Urban flood information enables managers to rapidly synthesize comprehensive flood event profiles, serving as critical evidence for flood control decision making. Compared with traditional methods, public data offer unprecedented spatiotemporal granularity due to its high volume, multidimensionality, and real-time nature. In this paper, we investigated public data’s usefulness and generalizability of spatial feature differences using multi-source social media data as an entry point. We selected rainstorm events that occurred in three cities located in the North China Plain, the Southeast Coastal Region, and the Western Region of China, with vastly different developmental statuses in 2023. Then, multi-platform data from the events were collected and analyzed through crawling and topic mining. The results indicate that: (1) social media data from different sources are complementary to each other and can collectively extract plenty of neglected waterlogging points to supplement official data, with a supplementary rate reaching 171% on average; and (2) social media data has significant value in spatial characterization, which means that its availability remains constant despite geographical differences and can self-adapt to local geography, inhabitant profiles and social development levels. To address the issues of limited available data and essential information lacking during the analysis process, we propose recommendations for data processing and city managers to enhance the scientific value of social media data utilized in practice. Full article

The tight gas reservoirs developed in the central Shenfu block are characterized by ultra-low porosity and permeability (typically < 10% porosity, <1 mD permeability), and high irreducible water saturation (40–60%). The frequent water blocking issue sharply reduces gas relative permeability during the production period, severely limiting well productivity. In this study, core flooding experiments using artificial cores were conducted to systematically evaluate the feasibility of CO₂ injection for enhanced gas recovery (EGR). The results show that the effectiveness of CO₂ EGR is sensitive to many factors, such as injection pressure, injection rate, total injection volume, and core permeability. The higher injection pressure and rate would improve the pressure gradient, CO₂ sweep efficiency, and EGR. An optimal total volume with the value (around 2.0 pore volumes, PV) was recommended as the amount of CO₂ injection are varied in the range of 0.5–2.5 PV. A higher permeable tight reservoir is prone to a higher nature gas recovery. The experimental findings, within the controlled conditions of this study, suggest that a flowback strategy of “slow startup and controlled depressurization” could be considered. Combining CO₂ injection with managed pressure drop of production and optimized fracturing process is proposed as a potential comprehensive strategy focused on “energy supplement, damage mitigation, and water control,” which may provide a useful reference for the efficient development of high-water-saturation tight gas reservoirs. Full article

Enhanced oil recovery (EOR) in fractured reservoirs presents significant challenges due to fluid channeling and poor sweep efficiency. In this study, a synergistic EOR system was developed with polymer-based weak gel as the primary component and foam as the auxiliary enhancer. The system utilizes a low-concentration polymer (1000 mg·L⁻¹) that forms a weakly cross-linked three-dimensional viscoelastic gel network in the aqueous phase, inheriting the core functions of viscosity enhancement and profile control from polymer flooding. Foam acts as an auxiliary component, leveraging the high sweep efficiency and strong displacement capability of gas in fractures. These two components synergistically create a multiscale enhancement mechanism of “bulk-phase stability control and interfacial-driven displacement.” Systematic screening of seven foaming agents identified an optimal formulation of 0.5% SDS and 1000 mg·L⁻¹ polymer. Two-dimensional visual flow experiments demonstrated that the polymer-induced gel network significantly improves mobility control and sweep efficiency under various injection volumes (0.1–0.7 PV) and gravity segregation conditions. Numerical simulation in a 3D fractured network model confirmed the superiority of this enhanced system, achieving a final oil recovery rate of 75%, significantly outperforming gas flooding (65%) and water flooding (59%). These findings confirm that weakly cross-linked polymer gels serve as the principal EOR material, with foam providing complementary reinforcement, offering robust conformance control and enhanced recovery potential in fracture-dominated reservoirs. Full article

Discontinuous phase flooding (such as polymer microspheres) is an important method for enhancing oil recovery. With the hydration swelling and elastic properties, a unique “migration–entrapment–remigration” discontinuous flow behavior is identified during flooding. And a more pronounced conformance control effect is observed in high-permeability flow channels and deeper reservoir regions compared to continuous phase flooding. These complex seepage mechanisms pose significant challenges to reservoir numerical simulation. Based upon a chemical reaction framework, a multi-component mathematical model comprising oil, gas, water, pre-discontinuous phase, and discontinuous phase components is developed in this study. The discontinuous phase is generated through chemical reactions involving the pre-discontinuous phase. A minimum reaction porosity is first introduced in the chemical reaction process to enhance the discontinuous phase generation in high-permeability regions. A threshold pressure is incorporated into the discontinuous phase equation for the “migration–entrapment–remigration” discontinuous flow characteristics. The model is subsequently solved using a fully implicit finite volume method. A new numerical simulator implementing this approach is developed in C++. Validation through physical experiments confirms the method’s accuracy. The discontinuous migration process of “migration–entrapment–remigration” is clearly reflected through the injection pressure fluctuations during simulation. Mechanistic models and field-scale simulations both confirm that discontinuous phase flooding significantly enhances oil recovery efficiency, outperforming both water flooding and continuous phase flooding. The novel reaction specification enhances conformance control in high-permeability channels, as demonstrated by the simulation results. The proposed model accurately captures the migration characteristics of the discontinuous phase and holds important practical value for reservoirs with discontinuous phase flooding. Full article

Declining recovery factors from mature oil fields, coupled with the technical challenges of recovering residual oil under harsh reservoir conditions, necessitate the development of advanced enhanced oil recovery (EOR) techniques. While promising, chemical EOR often faces economic and technical hurdles in high-salinity, high-temperature environments where conventional polymers like hydrolyzed polyacrylamide (HPAM) degrade and fail. This study presents a comprehensive numerical investigation that addresses this critical industry challenge by applying a rigorously calibrated simulation framework to evaluate a novel hybrid EOR process that synergistically combines an ionic liquid (IL) with HPAM polymer. Utilizing core-flooding data from a prior study that employed the same Berea sandstone core plug and Saudi medium crude oil, supplemented by independently measured interfacial tension and contact angle data for the same chemical system, we built a core-scale model that was history-matched with RMSE < 2% OOIP. The calibrated polymer transport parameters—including a low adsorption capacity (~0.012 kg/kg-rock) and a high viscosity multiplier (4.5–5.0 at the injected concentration)—confirm favorable polymer propagation and effective in -situ mobility control. Using this validated model, we performed a systematic optimization of key process parameters, including IL slug size, HPAM concentration, salinity, temperature, and injection rate. Simulation results identify an optimal design: a 0.4 pore volume (PV) slug of IL (Ammoeng 102) reduces interfacial tension and shifts wettability toward water-wet, effectively mobilizing residual oil. This is followed by a tailored HPAM buffer in diluted formation brine (20% salinity, 500 ppm), which enhances recovery by up to 15% of the original oil in place (OOIP) over IL flooding alone by improving mobility control and enabling in-depth sweep. This excellent history match confirms the dual-displacement mechanism: microscopic oil mobilization by the IL, followed by macroscopic conformance improvement via HPAM-induced flow diversion. This integrated simulation-based approach not only validates the technical viability of the hybrid IL–HPAM flood but also delivers a predictive, field-scale-ready framework for heterogeneous reservoir systems. The work provides a robust strategy to unlock residual oil in such challenging reservoirs. Full article

Rainfall-adapted irrigation (RAI), the application of controlled-release nitrogen fertilizer (CRNF), and deep placement of nitrogen fertilizer can contribute to the improvement of resource utilization efficiency. Nevertheless, the interactive effects of these factors on nitrogen loss via runoff and leaching from paddy fields remain ambiguous. Consequently, a two-year field experiment was conducted to evaluate the interactive effects of four nitrogen management strategies on nitrogen losses through runoff and leaching from paddy fields and rice yield under RAI when compared to conventional flooding irrigation (CI). Compared to CI, RAI significantly reduced total nitrogen loss via runoff (−49.8%) and leaching (−35.9%) by lowering volume of runoff and leaching. Compared to conventional nitrogen application (surface application of common urea with 240 kg N ha⁻¹), deep placement of CRNF with 192 kg N ha⁻¹ decreased floodwater nitrogen concentration, reducing total nitrogen loss by 46.8% via runoff and 50.9% via leaching. Importantly, RAI combined with deep placement of CRNF with 192 kg N ha⁻¹ minimized nitrogen losses through leaching and runoff from paddy fields and maximized grain yield (8251 kg ha⁻¹) by improving nitrogen accumulation in rice. Collectively, RAI combined with deep-placed CRNF with an 80% nitrogen rate could reduce non-point source pollution from paddy fields. Full article

Rapid urbanization and increasingly frequent extreme rainfall events have intensified stormwater challenges, underscoring the need for watershed-scale strategies that integrate blue-green infrastructure (BGI). This study evaluates the stormwater control performance of combined initial reservoir storage level regulation, river water level adjustment, and green infrastructure (GI) implementation in the 42.4 km² Baihuayong watershed of Guangzhou, China. A coupled stormwater model (SWMM) was developed, calibrated, and coupled with TELEMAC-2D to simulate schemes varying initial reservoir storage levels (30.6 m to 27.6 m), river water levels (11 m to 8 m), and GI proportions (0–45%) under 2- to 100-year rainfall events. Results show that lowering initial reservoir storage levels from 30.6 m to 27.6 m enhanced runoff reduction by ~40% and reduced discharged water volume by ~30%, though overflow mitigation remained limited. Decreasing river water levels from 11 m to 8 m reduced flooded areas by up to 8.3%, with diminishing benefits below 9 m. Increasing GI coverage from 0% to 45% reduced overflow nodes from 236 to 192 and flood extent from 10.76 ha to 9.20 ha under moderate storms, but improvements were modest during extreme events. A synergistic configuration, combining a low initial reservoir storage level (27.6 m), low river water level (8 m), and a high GI proportion (35–45%), yielded the most comprehensive improvements. These findings demonstrate the strong potential of integrated BGI for watershed-scale flood resilience and provide quantitative guidance for sponge city planning. Full article

Water inrush from flooded goaf under high hydraulic head seriously threatens deep coal mining, especially where roadways must be driven close to old workings. This study investigates the seepage and load-bearing behavior of a combined coal pillar and rigid cutoff wall system under coupled mining-excavation-seepage processes. A three-dimensional hydro-mechanical model based on Biot poroelasticity and a stress-damage-permeability relationship is developed in FLAC3D, using a field case from the Yuwu Coal Mine. Different wall thicknesses and mining stages are simulated, and pillar performance is quantified by the elastic-core volume fraction and a permeability-connectivity index. Similar-material shear tests are further carried out to examine sliding behavior at the wall–pillar interface. Simulations show that the composite system reduces peak vertical stress in the pillar by 12–20% during panel retreat (from 54.2 MPa without a wall to 47.7–45.0 MPa with 0.5–2.5 m walls), while the elastic core volume fraction increases from 16.7% to 30.4–50.4% and the minimum elastic core width improves from 0.5 m to 1.5–2.0 m. The wall provides strong lateral confinement, increasing lateral stress within the pillar by up to 50% and preventing hydraulic penetration for wall thicknesses ≥1.0 m. Shear tests reveal critical distances for safe load transfer and support the use of targeted reinforcement at the interface. The findings offer a quantitative basis for designing safe water-control structures in high-pressure goaf environments. Full article