The aim of the present study is to investigate steady two-phase slug flow pressure loss in water pipelines with a contraction, based on model experimentation. Experiments were performed in a horizontal rectangular closed conduit with a sudden contraction and two types of gradual contraction. Results reveal that although the homogeneous flow model with vena-contracta predicts the steady two-phase flow pressure loss more accurately than the slip flow model with vena-contracta, it nevertheless overestimates the two-phase flow pressure loss multiplier. The two-phase flow pressure loss multiplier is a function of liquid Reynolds number, the mass quality and the contraction type. The recovery pressure was detected downstream of a sudden contraction, for both single- and two-phase flows, implying flow contraction. The ratio of pressure loss through the gradual contractions to that in a sudden contraction is significantly greater in a two-phase flow compared to that in a single-phase flow, and increases with the increase in the air-to-water rates ratio, approaching unity. Finally, applying dimensional analysis and using SPSS mathematical software and statistical analysis, relationships were derived for the two-phase flow pressure loss multiplier and the ratio of two-phase flow pressure loss in a gradual contraction to a sudden contraction as functions of governing dimensionless parameters.
Owing to their simplicity and theoretical foundation, log-law velocity profiles have been widely used to estimate bed shear stress. Such estimates depend on both the measured velocities and the wall-normal coordinate referenced to the bed elevation. However, in natural environments, bed elevation may be uncertain because of factors such as bed microtopography and changes in bed elevation between bed surveys and velocity profile measurements. This uncertainty can introduce a systematic bias into the regression-based bed shear stress. In this study, the effects of bed-elevation uncertainty on the bed shear stress estimated using log-law velocity profiles are analytically quantified. The analytical solutions reveal notable asymmetry, whereby the overestimation of the bed shear stress induced by negative bed-elevation error is greater than the underestimation caused by positive errors of equal absolute magnitude. Fixed-bed flume velocity profiles are then used to conduct a controlled sensitivity assessment of the theoretical error mechanism, with elevation shifts applied exclusively during postprocessing rather than during physical bed evolution. These findings provide a quantitative indication of the potential bias caused by bed-elevation uncertainty in log-law-based shear stress estimation.
Geyser eruption in urban drainage is a high-velocity discharge of an air-water mixture driven by intense air-water interactions. It is sudden, destructive and strongly non-linear, and is often associated with air entrapment, surcharge flows during intense rainfall and transient hydraulic processes, posing risks to drainage assets and surface infrastructure. A representative horizontal main pipe-vertical riser configuration is modelled using a volume of fluid (VOF) two phase framework and a systematic numerical study is conducted of the inlet pressure head, dimensionless initial entrapped air index, I-a0, and the initial water-column height. Results identify a distinct eruption threshold: under the fixed geometry and boundary conditions considered in this study and within the tested parameter range, geyser eruption is triggered when H-in=H-0 >= 1.33 and Ia 0 >= 3.20. As the free surface enters the upper riser, pronounced pressure spikes at PT4 and PT5 can serve as precursors. For a given inlet pressure head and the initial entrapped air condition, increasing the initial water-column height leads to higher eruption velocities, highlighting its control over eruption intensity.
Water shortage in arid and semi-arid regions has made groundwater the primary source of water supply. However, the performance of water wells gradually declines due to physical, chemical or biological clogging. Identifying wells with the highest rehabilitation potential is of significant practical and economic importance. This study proposes advanced environmental impact assessment methodologies to prioritise water well rehabilitation. Specifically, remote sensing-based (RSB) and observation-based (OB) assessment approaches are developed to determine high-priority well revival cases. The proposed RSB methodology selects wells based on land-subsidence values derived from the differential interferometric synthetic-aperture radar technique. These values are used as indicators to classify well rehabilitation potential, considering aquifer stratification and sand pumping characteristics. In the OB assessment stage, dynamic water level, well depth, submersible pump type and well videometry data are incorporated to evaluate well conditions. A theoretical model is introduced to predict dynamic water levels and identify wells exhibiting the most significant variations in the well performance index. The results show that the proposed model effectively captures the relationships among dimensionless parameters, explaining 91.2% of the total variance, while cross-validation confirms its robustness and applicability to new data sets. Furthermore, different well clogging types were identified, and appropriate rehabilitation strategies were recommended.
Accurate short-term streamflow forecasting is essential for mitigating urban flood risks and ensuring sustainable water resource management. This study presents a comparative analysis of advanced data-driven techniques for streamflow prediction in the Gandak River basin, India, evaluating their efficacy in handling rapid hydrological fluctuations. Three sophisticated machine learning models – random forest (RF), extreme gradient boosting (XGBoost) and long short-term memory (LSTM) networks – are employed to forecast streamflow across lead times ranging from 1 to 30 days. The findings reveal that all models exhibit strong predictive capabilities for 1-day lead times, achieving high coefficients of determination (R2): 0.976 (LSTM), 0.970 (RF) and 0.964 (XGBoost). However, predictive accuracy diminishes significantly as the forecasting horizon extends. By the 30-day lead time, the LSTM network consistently outperforms the tree-based models (R2 = 0.759 compared to RF’s 0.564 and XGBoost’s 0.569). LSTM’s superior performance for medium- to long-term forecasts is attributed to its advanced architecture, which effectively captures long-term dependencies and non-linear sequential patterns in hydrological time-series data. This study demonstrates the added value of deep learning over traditional ensemble methods for extended forecasting, offering actionable insights into optimising predictive frameworks for urban flood preparedness.
