Search Results (1,532)

Open-channel hydraulic analysis often requires repeated solution of implicit nonlinear equations and numerical integration of gradually varied flow (GVF), which can become computationally demanding in inverse, optimization, and sensitivity applications. This study develops a hydraulically informed artificial neural network (ANN) surrogate framework comprising ten independently trained models for normal and critical depths, alternative and conjugate depths, GVF-related water-surface behavior, and profile-based discharge inference. Hydraulic information is introduced through physically meaningful, and where appropriate dimensionless, variables and reference solutions derived from established governing equations or numerical hydraulic models, while ANN optimization remains data driven. Equation-generated test sets quantified surrogate fidelity, whereas HEC-RAS comparisons were treated as numerical hydraulic cross-verification rather than independent physical validation. The forward surrogates reproduced their reference mappings with high accuracy within the represented domains. Benchmarking against Random Forest, support vector regression, and Gaussian Process Regression for Models 1, 5, and 7 showed no universal algorithmic superiority; however, ANN provided a favorable trade-off among accuracy, relative-error robustness, compactness, and repeated-inference efficiency. For Model 7, ANN inference was approximately 249 times faster than conventional GVF calculation, with development cost recovered after about 1.03 × 10⁵ evaluations. Model 9 inferred discharge with a 4.75% error in the profile-based test. Observation-based assessment using 16 historical Missouri River stage–discharge events showed that direct HEC-RAS inversion yielded a MAPE of 44.71%, whereas observation-only ANN and hybrid HEC-RAS-ANN discrepancy correction reduced MAPE to 10.25% and 9.83%, respectively. The framework is therefore a computational complement to established hydraulic equations and numerical models, with broader field validation and explicit uncertainty treatment required for general deployment. Full article

To reduce aggregate gradation counting errors caused by over-segmentation and under-segmentation of stacked and clustered aggregates with mixed types and diverse spatial distributions in hydraulic concrete, this study proposes BC-GECO2, a coarse and fine aggregate segmentation and counting method. Firstly, a BAHiera feature extraction network is designed to extract multi-scale deep features through edge-aware attention. In addition, a DFG-Edge module is developed to enhance the boundary features of densely distributed aggregates by integrating wavelet transform with a gated fusion mechanism, thereby alleviating the loss of small aggregate features during downsampling. Secondly, a CSFM-GFFCA module is constructed, in which a dual-branch structure is employed to adaptively fuse adjacent-scale features, strengthen the edge responses of densely distributed small aggregates, and enhance cross-layer feature interaction. Finally, a joint optimization function combining Focal loss and counting loss is established to guide the model toward hard-to-classify pixels, especially boundary pixels, thereby improving segmentation integrity and counting accuracy. Experiments conducted on an aggregate dataset collected from practical construction sites show that, compared with the baseline GECO2 model, the proposed method improves the average segmentation IoU, Dice, and BIoU by 2.92%, 5.04%, and 2.83%, respectively, while reducing the average counting MAE and RMSE by 6.92 and 15.65, respectively. Moreover, BC-GECO2 exhibits superior robustness and generalization capability under different stacking densities and blurred-boundary scenarios, providing technical support for the intelligent development of rapid concrete gradation detection. Full article

To characterise the fracture network geometry of a fluid reservoir, fundamental parameters are used in a DFN simulation algorithm. However, evaluating the hydrodynamic behaviour of such rock bodies also requires apertures of individual fractures. Aperture is usually not treated as an independent variable; rather, it is derived from length. A common approach is a linear relationship, a = A × L, where A is the aperture coefficient. The fracture’s free volume varies with the aperture coefficient, which influences the modelled fractured porosity and permeability. Since post-tectonic fluid–rock interactions can considerably alter the original apertures, the relationship between fracture length and aperture may vary across a reservoir, complicating hydrodynamic modelling. Therefore, from a hydrodynamic perspective, the hydraulic aperture should be used instead of the physical aperture. In this paper, transmissivity data and DFN models are analysed simultaneously to estimate reliable aperture coefficients. The method is demonstrated using the fractured Mórágy granite body in SW Hungary. In the context of the radioactive waste depository project, numerous wells penetrated the fractured granite. Transmissivity data and DFN models from 238 intervals are used to calibrate aperture coefficient values. The associated porosity data are employed to construct a porosity log for each well and analyse poro-perm diagrams. Full article

Permeability prediction and hydraulic rock typing in carbonate reservoirs remain challenging because similar porosity values may correspond to markedly different flow capacities controlled by pore throat size, connectivity, and capillary accessibility. This study aims to develop an integrated workflow for permeability prediction and petrophysical–hydraulic rock typing of a heterogeneous dolomite reservoir using parameters that directly characterize the drainable pore throat network. Routine core analysis, centrifuge-derived capillary pressure curves, nuclear magnetic resonance $T_2$ spectra, electrical measurements, petrographic and SEM observations, and fractal descriptors were jointly analyzed. Capillary pressure curves were fitted with the Li–Horne model and transformed into equivalent pore throat radius distributions; characteristic radii $R_q$, Swanson and Capillary-Parachor parameters, irreducible water saturation, and fractal characteristics were calculated for subsequent regression analysis and rock typing. The conventional $k-\varphi$ relationship showed limited predictive capability, whereas models incorporating $R_{15}-R_{21}$ provided a more reliable permeability estimate. The best-performing relationship was close to $k\propto R^2\varphi$, supporting the interpretation of $R_{20}$ as a centrifuge-derived analog of the effective hydraulic radius. Comparison with FZI, Winland R35, NMR groups, and electrofacies showed that $R_{20}$-based typing produced a compact separation of samples by hydraulic quality. The proposed workflow is presented as a single-well proof of concept and requires validation in independent wells before application to field-scale geological and hydrodynamic models. Full article

Shale gas reservoirs are characterized by low permeability, nanoscale pore structures, and complex fracture networks. Multistage fractured horizontal wells are an important technology for commercial shale gas development. However, many shale gas productivity models primarily emphasize gas transport within the reservoir and fracture system, while pressure variations caused by frictional losses along the horizontal wellbore are often simplified or treated separately. To address this issue, this study develops a fully coupled multiscale dual-porosity numerical model that integrates the shale matrix, hydraulic fractures, and horizontal wellbore within a unified simulation framework. The model incorporates key physical mechanisms governing shale gas transport, including Knudsen diffusion, Langmuir adsorption–desorption, stress sensitivity, and non-Darcy flow in fractures. Meanwhile, the Darcy–Weisbach equation is introduced to describe wellbore frictional pressure losses. The reliability of the proposed model is validated through history matching with field production data from the Changning shale gas reservoir. The results demonstrate that neglecting wellbore friction losses leads to a 30–50% overestimation of horizontal well productivity, indicating that wellbore friction has a significant impact on fracture flow distribution and productivity prediction. Furthermore, an exponent factor r is introduced to characterize and evaluate non-uniform fracture placement patterns. The results show that toe-dense fracture placement can increase cumulative gas production by approximately 37.8% compared with uniform fracture placement when r = 1.10, which yields the highest cumulative gas production among the tested cases. However, the additional production benefit becomes substantially smaller after the initial increase and remains relatively stable as r further increases. This study improves the understanding of friction-induced heel-to-toe effects and provides an effective numerical approach for productivity prediction and fracture placement design in shale gas horizontal wells. Full article

Targeting the rock burst hazard induced by the hard roof key stratum during deep mining at the Mengcun Coal Mine in the Binchang mining area, this study takes the No. 403109 working face as the engineering background and systematically investigates the rockburst prevention mechanism and effectiveness of ground hydraulic fracturing through theoretical analysis, UDEC numerical simulation, and surface microseismic monitoring. The results indicate that fracturing pre-weakens the overlying key stratum, transforming its load-bearing mode from a long-beam rigid support to a segmented flexible support. This significantly reduces the cantilever length, lowers the accumulation of elastic strain energy, and enables flexible load transfer and stress redistribution in the overburden. Numerical simulations reveal that after fracturing, the breakage timing of the key stratum advances, the fragmentation size decreases, and the over-burden movement shifts from stepwise fracturing to sequential caving, with the stress concentration zone substantially narrowed. In the field, a total of 44 fracturing stages were implemented in wells MC-05L and MC-06L, creating a fracture network with an average fracture length of 317 m and an average fracture height of 55 m, achieving an effective stimulated volume ratio of 86.7%. During the mining period, microseismic events exhibited a median energy of only 868.14 J, characterized by high frequency and low energy. The average weighting interval was 13.69 m, the peak coal stress was controlled within 5.0–6.7 MPa, and the loads on roadway bolts and cables remained within safe limits. This study validates the source-control effect of ground hydraulic fracturing on working faces with strong rock burst risks in deep mining, providing a theoretical basis and engineering reference for mines with analogous conditions. Full article

To clarify how shale-reservoir heterogeneity constrains hydraulic-fracturing effectiveness in complex structural areas, this study analyzes exploration well X in the Mugan–Shoushan area, Yunnan Province, using organic geochemistry, petrology and mineralogy, reservoir-property, and rock-mechanical data from the Wufeng–Longmaxi formations. The results show pronounced vertical engineering-geological differentiation. Average clay content decreases from 42% to 8%, Average carbonate minerals increase from 16% to 50%, and quartz is anomalously enriched in the Longyi 1-1 layer of the Longmaxi Formation (Longyi 1-1; 76%). The Longyi 1-3 layer of the Longmaxi Formation has the highest porosity (9.37%) but low matrix permeability (0.013–0.019 mD); the Longyi 1-2 layer of the Longmaxi Formation is highly brittle and tight; and the Longyi 1-4 layer of the Longmaxi Formation is highly ductile and water-rich. Accordingly, four engineering geological facies are defined: Type I, organic-rich, moderately brittle, and moderately ductile composite facies; Type II, organic-rich, highly brittle, tight, and strongly stress-sensitive facies; Type III, organic-poor, highly ductile, water-rich, and strongly water-sensitive facies; and Type IV, highly brittle, fracture-developed, and high-adsorption facies. Implications for fracturing are proposed for each facies, including mixed-fluid network stimulation, acid pretreatment with controlled flowback, interval avoidance, and coordinated stimulation with adjacent main reservoirs. Full article

Hydraulic perturbations are among the earliest plant-wide consequences of drought, salinity and wounding, yet they are often treated as passive outcomes rather than as biologically interpreted inputs. This review distinguishes hydraulic state, hydraulic perturbation and hydraulic signal, and evaluates how organ-scale pressure and water-potential changes are converted into local membrane tension, wall strain, turgor and water-flux cues. We propose, as a testable model rather than an established mechanism, a distributed architecture comprising OSCA/TMEM63 and other mechanosensitive channels, cell-wall integrity pathways, aquaporin-mediated conductance control and vacuolar buffering. Evidence for the individual components is substantial, but evidence that they act together within a single physiological event is still limited. These layers are reciprocally coupled to Ca²⁺, ROS, electrical, hormonal and peptide networks. Hydraulic cues are fast, and they differ in amplitude, direction, rise time, duration, recovery and anatomical route, so they are not informationally inert. Specificity nevertheless appears to emerge from the integration of the hydraulic waveform with tissue state and coincident ionic, electrical and biochemical inputs rather than from any single variable. We compare drought, salinity and wounding; clarify the roles of roots, vasculature, bundle sheath, mesophyll and guard cells; and outline experiments that combine calibrated physical perturbations with live reporters, tissue-specific genetics and hydromechanical modeling. The key frontier is no longer to document that pressure changes occur. It is to identify the variables directly sensed, to separate instructive from permissive roles, and to test whether dynamic decoding traits improve crop resilience at acceptable carbon and growth cost. Full article

This study investigates the macromolecular interaction mechanisms between linear-anionic xanthan gum (XG) and branched-nonionic guar gum (GG) biopolymers in three mineralogically distinct soils: Bentonite Clay (BC), Zeolite Silty Soil (ZS), and Red Clay (RC). Mıxtures were prepared by dry mixing of soil powders with biopolymer powders at designated ratios (0%, 1%, 2%, 3%, and 4% by dry weight). The prepared mixtures were characterized using X-Ray Diffraction (XRD), X-Ray Fluorescence (XRF), Scanning Electron Microscopy (SEM), and standard compaction and shear strength tests. The results show that geotechnical macro-behavior is primarily influenced by polymer chain conformation and mineral interfacial reactions. In ZS-XG mixture, hydraulic conductivity increased approximately 26-fold (from 0.107 × 10⁻⁹ to 2.83 × 10⁻⁹ m/s), a phenomenon attributed to the Donnan electrostatic exclusion effect, where linear anionic XG chains repel zeolite surfaces and generate low-friction macro-flow paths. Conversely, the addition of GG to RC formed a strongly interconnected hydrogel network through hydrogen bonding with trivalent iron and magnesium oxides, resulting in a 20.8% increase in cohesion (up to 70.05 kPa). In contrast, GG addition decreased cohesion in BC and ZS. These findings confirm that sustainable biopolymer-based soil remediation depends on customizing the polymer morphology according to the properties of the soil. In engineering applications, ZS-XG mixtures should be evaluated for drainage projects requiring high permeability, whereas the RC-GG4 mixture should be considered a primary option for infiltration barriers (e.g., landfill liners) requiring low permeability and high cohesion. Full article

The oilfield gathering and transportation system is an important component of oilfield energy use and therefore provides practical opportunities for supporting the dual-carbon goals through operating-parameter optimization. This study combined field cooling trials on high-water-cut well pipelines, thermal-hydraulic calculations of the downstream gathering network, regression-based surrogate models of the dehydration-station equipment, and coordinated system-level energy accounting. A constraint-based direct-search procedure initialized from the actual field operating condition was used to identify the best feasible operating point within the examined ranges. The search was terminated when a complete update cycle produced no further reduction in energy consumption while all engineering constraints remained satisfied. The field trials showed that the investigated well pipelines could be operated below the corresponding crude-oil pour points under the tested high-water-cut conditions. For the transfer-station-to-central-station stage, the original three-pipe heat-tracing process was adjusted to electric heating and hot-water blending according to the pipeline conditions. Within the central processing station, the equipment operating temperatures were coordinated with the upstream pipeline scheme. For the investigated operating condition, the resulting best feasible scheme produced a deterministic 3.18% reduction in total standard-coal-equivalent energy consumption. The results demonstrate the engineering value of coordinating low-temperature operating boundaries, pipeline heating processes, and station operating parameters in an existing high-water-cut gathering system. Full article

Coal-bearing tight gas and deep coalbed methane (CBM) widely co-occur in China, and integrated commingled production outperforms separate development. Conventional separated simulation fails to capture coupled reservoir–wellbore gas–water flow. This study develops an integrated geomechanical coupled numerical model with multi-scale fractures and multi-phase wellbore flow: tight gas reservoirs use a stress-sensitive single-porosity model, deep CBM adopts a dual-porosity model for matrix desorption, and EDFM characterizes non-Darcy flow in hydraulic fractures. The Gray gas column and liquid column methods calculate layered bottomhole pressure according to reservoir vertical distribution, and matrix bordering solves the whole coupled system. Validated by field data of Well C-1 in Shanxi, the model yields average relative errors of 8.76% for daily gas output and 2.92% for daily water output. Sensitivity analysis on Well C-2 indicates vertical reservoir stacking controls interlayer pressure difference, and commingled gas curves show dual peaks with shifting dominant gas sources over production stages. A 3.9% rise in deep coalbed methane gas content significantly boosts mid-term peak production and cumulative gas output, making reservoir gas content the dominant geological factor governing commingled production performance. A 120.0% increase in tight gas saturation only delivers a slight uplift in cumulative production under low-porosity conditions. Elevated reservoir stress sensitivity triggers a cumulative gas production reduction of over 50%. Cumulative gas output varies proportionally with hydraulic fracture length, while fracture network width brings mismatched production improvement due to pressure drawdown funnel effects. Therefore, hydraulic fracturing operations should prioritize extending artificial fractures to expand the drainage area of commingled wells. Schemes with constant bottomhole flowing pressure and constant gas rate exert marginal influences on ultimate cumulative production and can be flexibly switched on site. To stabilize daily gas deliverability throughout the early, middle and late production stages, a bottomhole pressure drawdown rate of 0.05 MPa/d or a fixed daily gas rate of 4000 m³/d is recommended. This work provides theoretical support for optimizing commingled development of superimposed tight gas and deep CBM reservoirs. Full article

Sustainable and reliable operation of water distribution systems requires timely detection of bursts and effective control of real water losses. The digitalisation of water utilities and the increasing deployment of smart monitoring infrastructures are enabling continuous monitoring and diagnostic support, but the development of operational anomaly detection tools remains constrained by scarce labelled events and by the lack of structured workflows for designing, testing and comparing alternative solutions. This study proposes a methodology for developing and benchmarking burst detection tools in water distribution systems. The framework integrates stochastic-hydraulic synthetic data generation to create or enrich labelled datasets, feature engineering to extract temporal and spatial descriptors from hydraulic signals, baseline modelling of normal system behaviour, residual generation, anomaly identification, and systematic performance evaluation. The methodology is applied to a real water distribution network partitioned into nine district metered areas, enabling a consistent comparison of alternative strategies for normal-behaviour modelling and anomaly detection. Results show that the forecasting-based approach, including multi-horizon prediction, produces more informative residuals than the reconstruction-based approach, while XGBoost provides the best overall trade-off between sensitivity and false alarms. These findings demonstrate the value of the proposed methodology for the data-driven development of operational burst detection tools in smart water distribution systems. Full article

Natural-fracture connectivity substantially influences hydraulic-fracture interaction with pre-existing discontinuities, but its quantitative role in fracture-network propagation remains insufficiently constrained. In this study, a coupled LEFM–cohesive-zone hydraulic-fracture propagation model was developed by combining crack-tip deflection criteria, traction-separation damage evolution and fluid–solid coupling. True triaxial hydraulic-fracturing experiments were conducted on artificial fracture networks with I-, V-, Y- and X-shaped connectivity elements to evaluate the model response. The results show that connected natural fractures redirect hydraulic fractures under low horizontal stress differences, producing deflection angles of 30–50 degrees. When the stress difference exceeds 4 MPa, fracture growth becomes more strongly aligned with the maximum principal stress direction. In the true triaxial tests, the total number of connected natural fractures increased from 14 in the I-shaped network to 17 and 21 in the Y- and X-shaped networks, corresponding to increases of 21.4% and 50.0%, respectively. X-shaped networks showed the strongest sensitivity to stress difference and injection rate, while higher elastic modulus reduced fracture width and promoted longer, narrower fractures. Scale-normalized comparisons based on image-derived experimental measurements showed that the predicted propagation length, fracture width and connected-fracture number followed the experimental trend from I-shaped to Y-shaped and X-shaped networks, with relative errors within 7.1% and a mean absolute percentage error of 4.8%. These findings suggest that fracture topology strongly influences pressure transmission and multidirectional activation in the tested models, whereas field-scale extrapolation requires three-dimensional validation and transport analysis. Full article

With the rapid development of Industrial Internet-oriented smart energy systems, hydro-turbine generator units are increasingly monitored through networked sensors, industrial communication infrastructures, and edge/cloud-based condition-monitoring platforms. These Internet-connected monitoring environments provide abundant vibration data for intelligent operation and maintenance (O&M) but also introduce a challenging unsupervised cross-scenario diagnosis problem. Specifically, diagnostic models trained on labeled historical data may suffer severe performance degradation when deployed to unlabeled online data collected under different hydraulic conditions, rotational speeds, or operating conditions. Furthermore, existing domain adaptation methods, in their pursuit of distribution alignment, frequently overlook a critical bottleneck that limits generalization performance: inter-class entanglement. Specifically, under intense hydraulic background noise and cross-condition distribution shifts, features belonging to distinct fault types are highly susceptible to aliasing within the feature space. To overcome these issues, this paper proposes a Conditional Wasserstein Adversarial Network with Bi-level Prototype Disentanglement Regularization (CWAN-BPDR). First, a Conditional Wasserstein Adversarial Network (CWAN) is constructed by combining the smooth-gradient property of Wasserstein distance with conditional adversarial alignment, thereby achieving stable and fine-grained category-level domain adaptation. Furthermore, to alleviate the inter-class entanglement problem that may arise during cross-domain alignment, a Bi-level Prototype Disentanglement Regularization (BPDR) term is designed. By jointly implementing source–target prototype alignment and prototype–feature bidirectional alignment, BPDR explicitly suppresses inter-class confusion and enhances intra-class compactness and inter-class separability in the feature space. Experimental results on the JNU and NEFU datasets demonstrate that CWAN-BPDR achieves average diagnostic accuracies of 97.82% and 98.99%, respectively, while significantly mitigating label entanglement in challenging cross-operating-condition tasks. These results indicate that the proposed method can effectively transfer diagnostic knowledge acquired from labeled historical operating conditions to unlabeled online monitoring data. It can therefore serve as an offline-trained diagnostic module for Industrial Internet of Things-based condition-monitoring platforms in hydropower systems. Full article

Pronounced spatio-temporal variability of rainfall in semi-arid basins remains a central challenge for rainfall–infiltration–runoff modeling, particularly in ungauged or newly instrumented catchments where continuous soil-moisture data are unavailable. This study evaluates the comparative predictive performance of the SCS-CN and Green-Ampt (GA) infiltration methods, implemented within HEC-HMS at the sub-basin scale, using eight rainfall–runoff analysis time windows recorded during the 2020–2025 rainy seasons within a monitoring network operational since October 2019 in an instrumented semi-arid basin in Mexico. A blind-validation framework was adopted: parameters for both methods were derived a priori from tabulated sources indexed by land use, hydrologic soil group, soil textural class, and locally supported by textural analysis at three depths per sub-basin, in situ testing of saturated hydraulic conductivity, and gravimetric determination of field capacity; the initial moisture content required by GA was set equal to the measured field capacity (θ_i = θ_fc) to equate initial conditions between the two methods. Spatially distributed rainfall was captured by four monitoring stations under a one-to-one gauge–sub-basin assignment scheme, with monthly rainfall depth varying from 22.8 to 204.9 mm across the four sub-basins. Both methods reproduced observed discharge with varying levels of agreement: SCS-CN yielded very good performance (Pearson R = 0.95; Nash–Sutcliffe efficiency NSE = 0.76), whereas Green-Ampt yielded moderate correlation but unsatisfactory NSE (R = 0.70; NSE = 0.45) against the Levelogger records. Contrary to the initial expectation that the physically based GA would outperform SCS-CN, SCS-CN yielded substantially higher performance across windows, with the two simulated discharge series differing by a mean absolute deviation of 36.9 m³/s. A systematic sensitivity analysis (±6%, ±10%, ±20% perturbations) revealed an asymmetric response: SCS-CN was highly sensitive to Curve Number perturbations (mean-deviation amplitude 65.9 m³/s), whereas Green-Ampt was nearly insensitive to its compound soil-hydraulic parameterization (amplitude 3.2 m³/s), indicating a structural limitation of the physically based method under blind validation. Full article