Local regulations control the additional runoff produced by urbanization processes. Sustainable urban drainage systems can mitigate the issues associated with increased runoff by employing infiltration basins, detention ponds, wet ponds, and constructed wetlands. Traditionally, the Water Level Pool Routing Method, which relies on an implicit calculation scheme, has been used to calculate outflow hydrographs in reservoirs. In this research, an explicit scheme for the Water Level Pool Routing Method has been developed. The proposed model is applied to a case study where the reservoir has a surface area of 9.12 hectares. The influence of weir width and the discharge coefficient is also analyzed. Additionally, the variation in time step does not significantly affect the response of the proposed model, demonstrating its adequacy as a novel method. The proposed model is compared to the traditional method, yielding similar results in an analyzed ornamental reservoir (low percentage reduction in peak flow). However, a case study with experimental data reveals that the proposed model provides better accuracy than the traditional method. In addition, the proposed model is more efficient as it reduces computational time compared to the implicit scheme (conventional method). Finally, the proposed model is simplified for small watersheds by applying the rational method for computing an inflow hydrograph.
The estimation of thermodynamic behavior during filling processes with entrapped air in water pipelines is a complex task as it requires solving a system of algebraic-differential equations. A lot of different numerical methods have been used for this purpose in literature including the rigid water column (RWC) model. The main advantage of the RWC model is its acceptable accuracy with very low computational load. In that context, this research presents the computation of critical points of the physical equations that describe the phenomenon. These points provide information about the final position of the air–water interface. The Newton–Raphson method was then applied to obtain a unique equation that can be used by engineers to directly compute variables such as air pocket pressure and water column length at the end of the hydraulic event. A case study was analyzed to compare the results of the mathematical model with the obtained equation for computing critical points. Both methods provided the same values for the water column length at the end of the hydraulic event. A sensitivity analysis was conducted to identify dependent and non-dependent parameters for evaluating the critical points. The proposed formulation was validated through an experimental set of data.
The water hammer phenomenon represents a significant challenge to the safe and efficient operation of pressurised water systems. This study investigates the application of hydro-pneumatic tanks (HPTs) as protective devices against transient flow events, with a particular focus on their integration into simplified modelling frameworks. Rigid and elastic water column models are examined, and their performance is evaluated through a representative case study. A multi-criteria decision matrix was employed to select a suitable simulation tool, leading to the adoption of the ALLIEVI software for implementing both modelling approaches. Comparative results indicate that the rigid water column model offers a favourable compromise between accuracy and computational efficiency under appropriate conditions. This supports its practical application in installing HPTs in design and operational scenarios. To further assess the predictive capacity of each model, a confusion matrix analysis was conducted across 57 scenarios. This approach proved effective in evaluating the models’ ability to anticipate pipeline rupture based on the initial configuration of the hydraulic installation. The elastic model achieved accuracy levels ranging from 90.7% to 100%, whereas the rigid water column model exhibited a slightly broader accuracy range, from 76.7% to 97.7%. These findings suggest that integrating machine learning techniques could enhance the rapid assessment of failure risks in water utility networks. Such tools may enable operators to determine in advance whether a given operating condition will likely lead to system failure, thus improving resilience and decision-making in managing pressurised pipeline systems.
The presence of paracetamol and ciprofloxacin in aquatic ecosystems is a cause for great concern due to their harmful effects on human health. The objectives of this investigation are to simulate an industrial-scale adsorption bed for the competitive removal of these pharmaceutical metabolites from effluents using banana-based activated carbon as the adsorbent. Aspen Adsorption simulation software (v.1) was used to model an industrial-scale packed-bed column under different conditions. Freundlich and Langmuir isothermal models were used in combination with the linear driving force (LDF) kinetic formulation. Adsorption efficiencies of 89.57% for paracetamol and 89.57% for ciprofloxacin were achieved using the Freundlich-LDF model, while the Langmuir-LDF model presented efficiencies of 89.60% for paracetamol and 89.59% for ciprofloxacin. This study used machine learning algorithms, combined with analyses of multiple statistical indicators (R2, RMSE, and MAE), to evaluate model performance. Coefficient of determination (R2) values of up to 0.99 were observed in validation and testing. The application of these mathematical models yielded high removal efficiencies, demonstrating the potential of this approach for drug-contaminated effluent remediation and for forecasting the performance of packed columns at scaled-up levels.
The outlet angle and shape of impeller blades are important parameters in centrifugal pump design. There is a lack of detailed studies related to double curvature impellers in centrifugal pumps in the current literature; therefore, an experimental and numerical analysis of double curvature impellers was performed. Six impellers were made and then assessed in a centrifugal pump test bed and simulated via 3D CFD simulation. The original impeller was also tested and simulated. One of the manufactured impellers had the same design as the original, and the other five impellers had a double curvature. Laboratory tests and simulations were conducted with three rotation speeds: 1400, 1700, and 1900 RPM. Head and performance curve equations were obtained for the pump–engine unit based on the flow of each impeller for the three rotation speeds. The results showed that a double curvature impeller improved pump head by approximately 1 m for the range of the study and performance by about 2% when compared to basic impeller. On the other hand, it was observed that turbulence models such as k-ε and SST k-ω reproduced similar physical and numerical results.
This study presents a methodology for the digitalisation process for analysing transient flow phenomena in a U-tube. It comprises several layers, including the characterisation of liquid oscillation dynamics, image segmentation for experimentally determining variations in the meniscus position, and the integration of machine learning techniques with analytical solutions. The position, velocity, and acceleration of the meniscus are obtained using image-processing methods and subsequently compared with the corresponding analytical predictions. The proposed methodology accurately represents the existing hydraulic conditions, incorporating both Newtonian and Ogawa friction models. To assess model performance, the index of agreement was employed to compare analytical and experimental results. The findings indicate a systematic error of 2.2 mm ± 3 pixels when using the Ogawa friction model, which corresponds to the best model for predicting this hydraulic behaviour. Finally, the implementation of machine learning techniques demonstrates considerable potential for predictive analysis, with statistical measures showing coefficients of determination above 0.997 and consistently low Root Mean Square Error values.
Water supply networks in both developed and developing major cities worldwide were constructed many years ago. Currently, these systems face numerous challenges, including population growth, climate change, emerging technologies, and the policies implemented by local governments. Such factors can impact the design life of water infrastructure, leading to service pressure deficiencies. Consequently, water infrastructure must be reinforced to ensure an adequate and reliable service. This research presents the development of an analytical formulation for hydraulic installations with a pumping station, enabling the calculation of requirements for a new parallel pipeline within an existing water system without altering the current pipe resistance class. To implement the proposed solution, it is essential to maintain the initial pump head by adjusting the impeller size. A construction cost assessment is also undertaken to identify the most cost-effective reinforcement strategy, acknowledging that pipe costs vary significantly with diameter and material, and are proportional to the square of the diameter. The proposed methodology is applied to a 30 km pipeline with a 10% increase in demand, showing that a new parallel pipe of the same diameter as the existing hydraulic installation must be installed to minimise construction costs. A multi-parametric analysis was conducted employing machine learning presets with 309 dataset points.
Draining operations using pressurised air can produce sub-atmospheric pressures that pose a significant risk to structural integrity, given the pipe stiffness class. This research presents a modelling strategy for predicting water velocities during the occurrence of this phenomenon. The proposed approach combines a physically based hydraulic formulation with machine learning techniques for making this prediction. A calibrated rigid water column model is first employed to reproduce the transient interaction between the expanding air phase and the draining water column. Input parameters include pipe bridge height varying from 0.5 to 3.0 m, a valve loss dimensionless coefficient ranging from 2.0 to 14.0, and an initial water column length between 163.0 and 286.3 m. Subsequently, a Monte Carlo scheme is used to generate a representative dataset. A total of 28 models were assessed, among which a wide neural network demonstrated superior predictive capability, achieving root-mean-square error values between 0.043 and 0.056 m/s and coefficients of determination ranging from 0.996 to 0.997 for the validation and testing stages, respectively. Sensitivity analyses indicate that the minor loss coefficient governs the water velocity response, whereas geometric features such as the pipe bridge height exert a comparatively minor influence.
No abstract is provided for this article.
Frequency analysis of extreme events is used to estimate the maximum rainfall associated with different return periods and is used in planning hydraulic structures. When carrying out this type of analysis in engineering projects, the hydrological distributions that best fit the trend of maximum 24 h rainfall data are unknown. This study collected maximum 24 h rainfall records from 362 stations distributed throughout Colombia, with the goal of guiding hydraulic planners by suggesting the probability distributions they should use before beginning their analysis. The generalized extreme value (GEV) probability distribution, using the weighted moments method, presented the best fits of frequency analysis of maximum daily precipitation for various return periods for selected rainfall stations in Colombia.
Air exchange in pressurized water pipelines is an essential but complex aspect of pipeline modeling and operation. Implementing effective air management strategies can yield numerous benefits, enhancing the system’s energy efficiency, reliability, and safety. This paper comprehensively evaluates an irregular profile pipeline filling procedure involving air-release through an air valve. The analysis includes real-time data tests and numerical simulations using Computational Fluid Dynamics (CFD). A Digital Twin model was proposed and applied to filling maneuvers in water installations. In particular, this research considers an often-overlooked aspect, such as filling a pipe with an irregular profile rather than a simple straight pipe. CFD simulations have proven to capture the main features of the transient event, which are suitable for tracking the air-water interface, the unsteady water flow, and the evolution of the trapped air pocket. Thus, they provide thorough and reliable information for real-time operational processes in the industry, focusing on the filling pressure and geometry of the air-valve hydraulic system. Additionally, this study provides details regarding the application of an efficient Digital Twin CFD approach, demonstrating its feasibility in optimizing the filling procedure in pipes with irregular profiles.
Pilot-scale experimental measurements and simulations were utilised to evaluate the nutrient removal efficiency of three submerged membrane bioreactor designs. This study compared setups with post- and pre-denitrification processes. A 625 L pilot plant for treating primary effluent provided the operational data necessary for calibrating the activated sludge model, specifically for chemical oxygen demand and nitrogen removal under steady-state flow. Identical influent conditions were maintained for all configurations while varying the sludge retention times (from 5 to 100 d), hydraulic retention times (ranging from 4 to 15 h), return activated sludge flow rates (between 0.1 and 3.0), and aerobic volume fractions (from 0.3 to 1.0). The pilot plant tests showed high COD and ammonia removal (above 90%) but moderate total nitrogen removal (above 70%). The simulation results successfully forecasted the effluent concentrations of COD and nitrogen for each configuration. There were noticeable variations in the kinetic parameters, such as mass transfer coefficients and biomass decay rates, related to the activated sludge model. However, increasing the sludge retention time beyond 20 d, hydraulic retention time beyond 8 h, return activated sludge rates above 2.0, or aerobic volume fractions beyond 0.4 did not significantly enhance nutrient removal. The post-denitrification setup showed a clear benefit in nitrogen removal but required a greater oxygen supply.
This research developed smart integrated hybrid renewable systems for small energy communities and applied them to a real system to achieve energy self-sufficiency and promote sustainable decentralized energy generation. It compares stand-alone (SA) and grid-connected (GC) configurations using a developed optimized mathematical model and data-driven optimization, with economic analysis of various renewable combinations (PV, Wind, PHS, BESS, and Grid) to search for the optimal solution. Four cases were developed: two stand-alone (SA1: PV + Wind + PHS, SA2: PV + Wind + PHS + BESS) and two grid-connected (GC1: PV + PHS + Grid, GC2: Wind + PHS + Grid). GC2 shows the most economical with stable cash flow (−€123.2 annually), low CO2 costs (€367.2), and 91.7% of grid independence, requiring 125 kW of installed power. While GC options had lower initial investments (between €157k to €205k), the SA configurations provided lower levelized costs of energy (LCOE) ranging from €0.039 to €0.044/kWh. The integration of pumped hydropower storage enhances energy independence, supporting peak loads for up to two days with a storage capacity of 2.17 MWh.
Water leakages are assessed with an extended period simulation (traditional analysis) in water distribution networks. However, when rapid manoeuvres regulate valves, the conventional analysis proves unsuitable for representing the water volume of leaks. Under these circumstances, the Rigid Water Column Model can be utilised to accurately compute the quantity of water volume leaks. This method has been applied to single and parallel pipelines to predict water leakages, considering the opening and closure manoeuvres of regulating valves. This research develops governing equations using the Rigid Column Model to predict water volume leaks in a general scheme of series pipelines, accounting for the opening and closure manoeuvres in regulating valves. The practical application involves two branches of a series pipeline with internal pipe diameters of 0.45 and 0.25 m and pipe lengths of 1200 and 1400 m, respectively. The results of this practical application demonstrate the accuracy of the Rigid Column Model and its relevance in real-world scenarios. For instance, the model shows that traditional analysis can overestimate water volume leaks by approximately 72% when a closure manoeuvre regulates valves for 200 s and by about 28% for 30 s, highlighting the need for a more accurate method.
This paper investigates air–water interactions during a controlled filling process of an actual water pipeline using a two-dimensional Computational Fluid Dynamics (CFD) model. The main objectives are to understand the dynamic interaction of these fluids through water inflow patterns, pressure pulses, and air-pocket dynamics based on contours. This study uses an existing cast iron pipeline 485 m in length, a nominal diameter of 400 mm, and an air valve with a nominal diameter of 50 mm. The methodology of this CFD model includes the Partial Volume of Fluid (pVoF) method for air–water interface tracking, a turbulence model, mesh sensitivity and numerical validation with pressure and velocity measurements. Results highlight the gradual pressurization of pipelines and air pocket behavior at critical points and show the thermodynamic interaction concerning heat transfer between gas and liquid. This study advances the application of CFD in actual water pipelines, offering a novel approach to air pocket management.
Rating curves are derived from the combined measurement of water levels and discharges in rivers. This curve is used to convert observed water levels into flow rates, thereby generating discharge time series. Traditionally, rating curves are computed using exponential regression, which often neglects the underlying hydraulic conditions. Consequently, such curves may provide reasonable estimates of average flow but become unreliable under extreme conditions (e.g., high water levels). This research proposes a strategy for estimating discharge at high water levels using hydraulic modelling to support designers and practitioners in interpreting the upper range of the stage–discharge relationship. The methodology was applied to assess the rating curve for high flows in the Magdalena River at the Magangué reach (Bolívar, Colombia). Daily discharge records from 1974 to 2023 were analysed. The maximum historical discharge recorded was 11,127 m3/s (in 2010), while the mean annual peak discharge was 7904 m3/s. The proposed methodology yielded Manning’s roughness coefficients ranging from 0.046 to 0.052 and achieved satisfactory performance, with a Nash–Sutcliffe Efficiency (NSE) of 0.99. Results demonstrated that the traditional regression-based method tends to underestimate maximum discharges relative to a properly calibrated upper section of the rating curve. The analysis revealed systematic underestimation by the conventional approach, with discrepancies of up to 4.2% in determining maximum discharges. These findings emphasise the importance of incorporating hydraulic modelling to refine rating curves for high-flow conditions, thereby improving the reliability of design discharges.
