The filling process in water pipelines produces pressure surges caused by the compression of air pockets. In this sense, air valves should be appropriately designed to expel sufficient air to avoid pipeline failure. Recent studies concerning filling maneuvers have been addressed without considering the behavior of air valves. This work shows a mathematical model developed by the authors which is capable of simulating the main hydraulic and thermodynamic variables during filling operations under the effect of the air valve in a single pipeline, which is based on the mass oscillation equation, the air–water interface, the polytropic equation of the air phase, the air mass equation, and the air valve characterization. The mathematical model is validated in a 7.3-m-long pipeline with a 63-mm nominal diameter. A commercial air valve is positioned in the highest point of the hydraulic installation. Measurements indicate that the mathematical model can be used to simulate this phenomenon by providing good accuracy.
Starting up water installations is typically a task that falls within the purview of water utility companies. These operations involve the presence of two separate fluids (water and air) that can be analyzed in terms of consideration two distinct behaviors (hydraulic and thermodynamic). During a filling process, trapped air pockets exhibit a trend of declining volume, generating pressure surges that are typically not addressed under current worldwide regulations. This research introduces an innovative mathematical approach based on physical equations to investigate filling operations in water installations involving trapped air, incorporating an unsteady friction model (using the Brunone friction coefficient), in combination with the rigid water column model. The validation of the proposed model is carried out in an experimental facility measuring 7.36 m in length. The proposed model is then applied to a case study involving a 460 m long pipeline with an internal pipe diameter of 150 mm, featuring an undulating profile composed of three branches, to demonstrate how the gravity term should be calculated in real-world water installations. The results showed that the proposed model, considering an unsteady friction model, is suitable for simulating the start up of water pipelines for the experimental facility analysis and the case study. The Swamee–Jain formula yielded the best results compared to other formulations for computing the friction factor.
Current design techniques for water installations are based on pressure-independent consumption. However, certain types of consumption depend on pressure, most notably water leakages, which occur in all water distribution systems. All water installations are prone to water leakages. To mitigate this issue during the design stage of water pipelines, manuals and local regulations recommend a percentage of water losses for sizing pipelines and using them with pressure-independent consumption. This research develops a practical procedure for sizing single water pipelines by considering the effect of pressure-dependent consumption, as in actual pipelines. The findings of this research offer water utilities a new perspective on pipeline design, which is essential for developing new tools to create digital twins. A new methodology for designing pipe diameters is developed in this research. A single pipeline, 2000 m long, is used for the analysis with an emitter coefficient of 0.00829 m3/s/m0.5. The proposed methodology focuses on calculating energy losses to maintain the required minimum pressure in the system. A sensitivity analysis is conducted on key parameters such as the emitter coefficient, absolute roughness, and the resistance coefficient of a regulating valve. The results of the proposed methodology are compared with the current design (using pressure-independent consumption), revealing differences of up to 28% in calculating water losses.
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.
A filling operation generates continuous changes over the shape of an air–water interface, which can be captured using a 3D CFD model. This research analyses the influence of different hydro-pneumatic tank pressures and air pocket sizes as initial conditions for studying rapid filling operations in a 7.6 m long PVC pipeline with an irregular profile, using the OpenFOAM software. The analysed scenarios were validated using experimental measurements, where the 3D CFD model was suitable for simulating them. In addition, a mesh sensitivity analysis was performed. Air pocket pressure patterns, water velocity oscillations, and the different shapes of the air–water interface were analysed.
This study evaluates the mechanical performance and predictive modeling of fine-grained soils stabilized with crushed aggregate residue (CAR) or crushed limestone waste (CLW) and Portland cement by integrating the porosity–binder index (η/Civ) and Machine Learning (ML) techniques. Laboratory testing included unconfined compressive strength (qu) and small-strain shear modulus (Go) measurements on mixtures containing 15% and 30% CAR and 3% and 6% cement, compacted at dry unit weights between 1.69 and 1.81 g·cm−3 and cured for 7 and 28 days. Results revealed that strength and stiffness increased significantly with both cement and CAR contents. The mixture with 30% CAR and 6% cement exhibited the highest mechanical performance at 28 days (qu = 1550 kPa and Go = 6790 MPa). When mixtures are compared within the same curing period, the role of CAR and cement becomes evident. At 28 days, increasing CAR from 15% to 30% led to a moderate rise in qu (from 1390 to 1550 kPa) and Go (from 6220 to 6790 MPa). Likewise, at 7 days, increasing cement from 3% to 6% at 15% CAR produced significant gains in qu (207 to 693 kPa) and Go (2090 to 4120 MPa). The porosity–binder index showed strong correlations with qu (R2 = 0.94) and Go (R2 = 0.92). The ML models further improved accuracy, achieving R2 values of 0.99 for qu and 0.97 for Go. Although the index already performed well, the additional gain provided by ML is meaningful because it reduces prediction errors and better captures nonlinear interactions among mixture variables. This results in more reliable estimates for mix design, confirming that the combined use of η/Civ and ML offers a robust framework for predicting the behavior of soil–cement–CAR mixtures.
The conventional practice in the design of storm drainage systems is based on statistically stationary load and resistance conditions that remain invariant over time. However, uncertainties in the variables affect the design accuracy and the satisfactory performance of these hydrosystems during their operation and service. To overcome this limitation, a design methodology for a storm drainage channel was proposed using a probabilistic framework that incorporates uncertainty analysis of random variables and estimates the system’s probability of failure in terms of design depth and maximum allowable velocity. This methodology employs the Monte Carlo simulation technique and offers an alternative design and analysis approach to strengthen the conventional sizing method for drainage channels in urban watersheds. Based on uncertainty criteria associated with hydraulic design, operation, and prospective changes in the watershed and the channel, appropriate dimensions were estimated regarding design depth and freeboard. The results of this study demonstrate that the annual probability of failure of a channel, when considering uncertainty, is significantly higher than the yearly exceedance probability associated with the hydrological design return period event. Therefore, the proposed methodology is appropriate for estimating the system’s capacity and potential failure risk. This methodology may also be applied to sizing other stormwater drainage structures.
Previous soil moisture conditions play an important role in the design of hydraulic structures because they are directly related to the runoff threshold associated with a return period. These represent one of the main determinants of the runoff response of a drainage basin. One of the main difficulties facing hydrologists in Colombia lies in the time spent gathering and analyzing information related to the selection of antecedent moisture conditions. In this study, complete records from 19 rainfall stations located in the Atlántico region, Colombia, were used to analyze the cumulative precipitation during the 5 days prior to the annual maximum daily precipitation associated with different return periods using the Gev, Gumbel, Pearson Type III and Log Pearson Type III probability distributions. Different interpolation methods (IDW, kriging and spline) were applied to evaluate the spatial distribution of the antecedent moisture conditions. The main contribution of this research is establishing, using a probabilistic approach, the behavior of antecedent moisture conditions in a particular region, which can be used by engineers and designers to plan water infrastructure. This probabilistic approach was applied to a case study of the Atlántico region, Colombia, where the spatial distribution of antecedent moisture conditions was calculated for several return periods. The results indicate that the better results were obtained with the IDW interpolation method, and the Pearson Type III and Gumbel distributions also showed the best fits based on the Akaike criterion.
The probabilistic analysis of streamflow and drought event durations plays a crucial role in the efficient and sustainable management of existing water resources in the region. This approach involves the collection of historical hydrological data from river gauging stations, the use of statistical and probabilistic models, and the assessment of hydrological projections at different return periods to provide valuable information for society to understand the potential impacts of extreme events. The analysis is carried out on the Sinú River in Colombia, with consideration given to both the presence and absence of the Hydropower Plant Urrá I. The results reveal that, under natural conditions, a higher number of return periods correspond to less extreme drought flows and longer temporal durations. However, when the hydropower plant is operational, the occurrence and duration of drought are influenced by the regulations implemented during energy generation. The results of this analysis can guide water resource management policies, considering the operation of the hydroelectric plant, thereby enabling decisions that enhance the resilience and sustainability of the river’s hydrological conditions and communities that depend on it.
This paper presents a mathematical model for analyzing the emptying process in a pipeline using pressurized air. The rigid water column model (RWCM) is used to analyze the transient phenomena that occur during the emptying of the pipeline. The air-water interface is also computed in the proposed model. The proposed model is applied along a 271.6-m-long PVC-steel pipeline with a 232-mm internal diameter. The boundary conditions are given by a high-pressure air tank at the upstream end and a manual butterfly valve at the downstream end. The solution was carried out in a computer modeling program. The results show that comparisons between both the computed and measured water flow oscillations and gauge pressures are very similar; hence, the model can effectively simulate the transient flow in this system. In addition, the results indicate that the proposed model can predict both the water flow and gauge pressure better than previous models.
The use of these new interaction tool implies the improvement of the awareness of the whole system and it lies in improving the sustainability and efficiency of the water systems with the integration of measurements. The research proposed a methodology, which enables improvement in the accuracy and reliability of data and it increases the performance of water systems. This study proposes a pressure-reduction strategy and the implementation of pumps as turbines (PATs), applicable in Sta Cruz, Madeira water system. The use of the developed digital twin model assures a decrease of 3.3 hm3 in water-demand volume, increasing renewable generation by micro-hydropower up to 1.2 GWh. These actions would result in savings above 1.5 M€, decreasing around 530 tons of CO2 emissions each year. The consideration of these values implies the improvement of different indicators, which allows the evaluation of different targets linked to sustainable development goals (SDGs).A digital twin is a tool, which enables a real-time simulation of the water systems and therefore, the water managers can make a decision in the management of the water system over time. The use of these new interaction tool implies the improvement of the awareness of the whole system and it lies in improving the sustainability and efficiency of the water systems with the integration of measurements. The research proposed a methodology to integrate GIS and water models, being the main goal the integration of social, economic, environmental and technical issues. This integration enables improvement in the accuracy and reliability of data and it increases the performance of water systems. This study proposes a pressure-reduction strategy and the implementation of pumps as turbines (PATs), applicable in Sta Cruz, Madeira water system. The use of the developed digital twin model assures a decrease of 3.3 hm3 in water-demand volume, increasing renewable generation by micro-hydropower up to 1.2 GWh. These actions would result in savings above 1.5 M€, decreasing around 530 tons of CO2 emissions each year. The consideration of these values implies the improvement of different indicators, which allows the evaluation of different targets linked to sustainable development goals (SDGs).
A thorough literature review was conducted on the effects of free surface oscillation in open channels, highlighting the risks of the occurrence of positive and negative surge waves that can lead to overtopping. Experimental analyses were developed to focus on the instability of the flow due to constrictions, gate blockages, and the start-up and shutdown of hydropower plants. A forebay at the downstream end of a tunnel or canal provides the right conditions for the penstock inlet and regulates the temporary demand of the turbines. In tests with a flow of 60 to 100 m3/h, the effects of a gradually and rapidly varying flow in the free surface profile were analyzed. The specific energy and total momentum are used in the mathematical characterization of the boundaries along the free surface water profile. A sudden turbine stoppage or a sudden gate or valve closure can lead to hydraulic drilling and overtopping of the infrastructure wall. At the same time, a PID controller, if programmed appropriately, can reduce flooding by 20–40%. Flooding is limited to 0.8 m from an initial amplitude of 2 m, with a dissipation wave time of between 25 and 5 s, depending on the flow conditions and the parameters of the PID characteristics.
