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The pump-turbine is the core equipment of the pumped storage system. The dynamic coupling effect between the flow channel and the water delivery system during variable-speed operation is a crucial factor that contributes to flow instability. This paper investigates the effects of water pipelines on the variable-speed operation of pump-turbines. Based on a 1D-3D unsteady numerical simulation method, the unsteady flow evolution characteristics of the entire flow passage under variable-speed conditions are studied. By integrating the SST k-ω turbulence model, the focus is on revealing the influence of the water pipeline on the flow pattern inside the runner and the vortex in the draft tube. Research indicates that the water pipeline substantially enhances the stability of the variable-speed process through the effects of inertial compensation and damping. The efficiency of the system utilizing piping is enhanced by 2.6% in comparison to a single unit, while the flow gradient has been decreased by 12%. Extend the propagation path of pressure waves to mitigate torque pulsation energy. It is essential to avoid the risks associated with structural resonance. By optimizing the speed distribution of the runner outlet surface, the disorderly development of the meridional velocity component is suppressed. This achieves the purpose of improving the vortex in the draft tube. This study systematically elucidates the effects of water pipelines on variable-speed processes, offering theoretical support for the operational optimization of variable-speed pump-turbines.

This paper introduces a novel vibration-based energy harvesting technique for leak detection in wall-mounted water pipelines utilizing piezoelectric energy harvesters. Wall-mounted pipelines pose a unique challenge due to clamps placed at shorter intervals that dampen vibration intensity. To address this, the proposed approach strategically positions sensor nodes at optimal locations to maximize energy harvesting while ensuring timely and accurate leak detection. To reduce energy consumption in sensing and computation, strategies such as duty cycling and a reduction in the number of samples have been incorporated. Due to the conflicting relationship between leak detection accuracy, delay in detecting the leak, and energy consumed by the sensor node, the approach addresses this trade-off by linking some crucial design parameters, namely the number of samples per cycle, node sleep time, delay in leak detection, required leak detection accuracy, and remaining sensor node energy. The resulting optimization problem is solved using a graphical method. Experimental data is gathered for the harvested energy from a home-grown lab testbed and various techniques are suggested to increase energy generation. Subsequently, the experimental data is utilized to solve the optimization problem by providing optimal node parameters for a selected remaining node energy after a specified number of sampling cycles, as well as a desired leak detection delay and accuracy. The results offer a solution for enhancing sensor node energy efficiency and minimizing leak detection delay, leading to improved system performance and lower long-term maintenance costs for wall-mounted water pipelines. Highlights • A novel accelerometer circuit for detecting leaks in wall-mounted water pipelines. • It powers accelerometer sensors with a piezoelectric energy harvester. • It efficiently uses limited harvested energy by optimizing sensor monitoring parameters. • It balances leak detection delay with energy consumption. • Recommendations are provided to further increase harvested energy.

The transient frequency response (TFR) based pipe leak detection method has been developed and applied to water pipeline systems with different connection complexities such as branched and looped pipe networks. Previous development and preliminary applications have demonstrated the advantages of high efficiency and non-intrusion for this TFR method. Despite of the successful validations through extensive numerical applications in the literature, this type of method has not yet been examined systematically for its inherent characteristics and application accuracy under different system and flow conditions. This paper investigates the influences of the analytical approximations and assumptions originated from the method development process and the impacts of different uncertainty factors in practical application systems on the accuracy and applicability of the TFR method. The influence factors considered for the analysis contain system properties, derivation approximations and data measurement, and the pipeline systems used for the investigation include simple branched and looped multi-pipe networks. The methods of analytical analysis and numerical simulations are adopted for the investigation. The accuracy and sensitivity of the TFR method is evaluated for different factors and system conditions in this study. The results and findings are useful to understand the validity range and sensitivity of the TFR-based method, so as to better apply this efficient and non-intrusive method in practical pipeline systems.

Maintenance-Renewal Works (MRWs) are among the most complicated planning operations of water companies and usually involve a group of experts. However, experts are often hesitant in their decision making as they often need to rely on unreliable quantitative operational data, which prompts doubts about the effectiveness of their decisions. This problem can be addressed by using linguistic (qualitative) values instead of imprecise numerical data. Hence, this study develops a qualitative-risk-based model to plan the MRW of six Iranian networks comprising pipes with unreliable operational data. The study also investigates the effectiveness of group decisions on planning pipe MRW. The results indicate that planning with imprecise numerical operational data can be improved by using a qualitative-risk-based model. Additionally, it is found that group decisions do not significantly change the pipe priorities, although they have a decisive effect on determining the pipe maintenance-renewal strategies. Furthermore, increasing the number of criteria is found to lead to more accurate results within the developed model, although increasing the number of decision-makers has no decisive effect on the results.

Underground cast-in-place pipes (CIPP, Diameter of 2’–5’) are used to transport water for the Phoenix, AZ area. These pipes have developed leaks due to their age and changes in the environment, resulting in a significant waste of water. Currently, leaks can only be identified when water pools above ground occur and are then manually confirmed through the inside of the pipe, requiring the shutdown of the water system. However, many leaks may not develop a puddle of water, making them even harder to identify. The primary objective of this research was to develop an inspection method utilizing drone-based infrared imagery to remotely and non-invasively sense thermal signatures of abnormal soil moisture underneath urban surface treatments caused by the leakage of water pipelines during the regular operation of water transportation. During the field tests, five known leak sites were evaluated using an intensive experimental procedure that involved conducting multiple flights at each test site and a stringent filtration process for the measured temperature data. A detectable thermal signal was observed at four of the five known leak sites, and these abnormal thermal signals directly overlapped with the location of the known leaks provided by the utility company. A strong correlation between ground temperature and shading before sunset was observed in the temperature data collected at night. Thus, a shadow and solar energy model was implemented to estimate the position of shadows and energy flux at given times based on the elevation of the surrounding structures. Data fusion between the metrics of shadow time, solar energy, and the temperature profile was utilized to filter the existing points of interest further. When shadows and solar energy were considered, the final detection rate of drone-based infrared imaging was determined to be 60%.

In fluid mechanics, the large pressure fluctuation caused by the rapid change of fluid flow in a closed pipeline is called water hammer. It has huge destructive power for pressurized water pipelines, which often lead to pipeline leakage or even burst. In this paper, through the analysis of the dynamic characteristics of the gas in the pressurized water pipeline, the necessity of setting the air valve is drawn. Then based on the Bentley HAMMER model, combined with the example of the reservoir water pipeline engineering, the water hammer analysis was carried out. Compared with the maximum pressure head before and after adding the air valve, it is reduced from 126.89 m to 62.11 m, a decrease of about 50%. At the same time, the negative pressure in the pipeline under transient conditions is also eliminated. The results show that the air valve used in the pressure water pipeline has a good effect on the prevention of water hammer.

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to delineate pipeline axes from surface anomalies (vegetation stress, soil discoloration, and proxies) and rigorously quantify positional uncertainty, with findings validated against RTK-GNSS (Real-Time Kinematic—Global Navigation Satellite System) surveys of an excavated trench. The combined approach yielded sub-meter accuracy (±0.3 m) with UAV data, meter-scale precision (≈±1 m) with Google Earth, and precision up to several meters (±13.0 m) with Sentinel-2, significantly improving upon inaccurate legacy maps (up to a 300 m divergence) and successfully guiding excavation to locate a pipeline segment. The methodology demonstrated seasonal variability in detection capabilities, with optimal UAV-based identification occurring during early-vegetation growth phases (NDVI, Normalized Difference Vegetation Index ≈ 0.30–0.45) and post-harvest periods. A Sentinel-2 analysis of 221 cloud-free scenes revealed persistent soil discoloration patterns spanning 15–30 m in width, while Google Earth historical imagery provided crucial bridging data with intermediate spatial and temporal resolution. Ground-truth validation confirmed the pipeline location within 0.4 m of the Google Earth-derived position. This integrated, cost-effective workflow provides a transferable methodology for enhanced pipeline mapping and establishes a vital baseline of surface signatures, enabling more effective future monitoring and proactive maintenance to detect leaks or structural failures. This methodology is particularly valuable for water utility companies, municipal infrastructure managers, consulting engineers specializing in buried utilities, and remote-sensing practitioners working in pipeline detection and monitoring applications.

Water pipeline leaks pose significant risks to urban infrastructure, leading to water wastage and potential structural damage. Existing leak detection methods often face challenges, such as heavily relying on the manual selection of frequency bands or complex feature extraction, which can be both labour-intensive and less effective. To address these limitations, this paper introduces a Frequency-Informed Transformer model, which integrates the Fast Fourier Transform and self-attention mechanisms to enhance water pipe leak detection accuracy. Experimental results show that FiT achieves 99.9% accuracy in leak detection and 98.7% in leak type classification, surpassing other models in both accuracy and processing speed, with an efficient response time of 0.25 seconds. By significantly simplifying key features and frequency band selection and improving accuracy and response time, the proposed method offers a potential solution for real-time water leak detection, enabling timely interventions and more effective pipeline safety management.

There is an increasing demand to enhance infrastructure asset management within the drinking water sector. A key factor for achieving this is improving the accuracy of pipe failure prediction models. Machine learning-based models have emerged as a powerful tool in enhancing the predictive capabilities of water distribution network models. Extensive research has been conducted to explore the role of explanatory variables in optimizing model outputs. However, the underlying mechanisms of incorporating explanatory variable data into the models still need to be better understood. This review aims to expand our understanding of explanatory variables and their relationship with existing models through a comprehensive investigation of the explanatory variables employed in models over the past 15 years. The review underscores the importance of obtaining a substantial and reliable dataset directly from Water Utilities databases. Only with a sizeable dataset containing high-quality data can we better understand how all the variables interact, a crucial prerequisite before assessing the performance of pipe failure rate prediction models.

Water is essential for life and it is an inorganic constituent of living matter. Water pipeline systems are sighted as problematic in aquatic habitats in which multiple pathogens are occupied including fungi. They have rigid cell walls containing glucans and chitin. The bodies of fungi comprise filaments called hyphae. These hyphae are split into a mat of interwoven single cells made of mycelium. Fungi can pollute the drinking water system and are responsible for biofilm formation. Biofilms are complex polymers containing many times their dry weight in water. Moisture is essential for biofilm formation. The occurrence of biofilms affects the quality of drinking water. Hence, the present study is aimed at recovering the fungi from drinking water samples and their biofilm formation in the water pipeline system. Drinking water samples such as mineral water, tap water, and RO-purified water are collected from different places. Fungi such as Aspergillus, Penicillium and Mucor were recovered from these samples and most species belong to Aspergillus and Penicillium. Further, the biofilm formation of fungi from cast iron in the pipeline system was detected using fluorescence microscopy and fluorescent in situ hybridization analysis.