Pipe breaks have significant impacts on the hydraulic and water quality performance of water distribution systems (WDSs). Therefore, it is important to evaluate these impacts for developing effective strategies to ultimately minimize the consequences of these events. However, there has been surprisingly limited research focusing on impact evaluation for pipe breaks so far. To address this gap, this paper proposes a framework to comprehensively evaluate hydraulic and water quality impacts of pipe breaks on a WDS using six quantitative metrics. These metrics primarily focus on identifying (i) break outflow volume, (ii) water shortage, (iii) nodes with reduced service quality, (iv) pipes with affected pressures, (v) pipes with reversed flow directions, and (vi) pipes with significantly increased velocities, for each breaking event within a WDS. Statistical behaviors, spatial properties, and pipe rankings of metric results are analyzed to reveal the underlying characteristics of impacts induced by pipe breaks. We illustrate the proposed framework using three WDSs with different properties. Results show that impacts of pipe breaks not only vary with pipe diameters but are also significantly influenced by pipe locations, when the break occurs, and the specific metric considered. The proposed framework greatly enhances the fundamental understanding of the underlying properties of breaking impacts on the hydraulic and water quality of WDSs, as well as the ranking of pipes based on the consequences of breaks. Such understanding offers important guidance to develop effective pipe management, resource planning, and break restoration strategies to minimize the impacts of breaking events on WDSs.

In recent years, pressure management based on district metering areas (DMAs) has played an irreplaceable role in background leakage reduction in water distribution networks (WDNs). To further improve DMA management, this paper describes a novel sectorization method to optimize the reduction of background leakage. First, the network trunk mains are optimally selected using a proposed evaluation index which contains three reference indicators and the resulting isolated regions are identified through graph theory algorithm. Meanwhile, a modified community detection algorithm is adopted to partition the oversized regions considering both elevation uniformity within DMAs and demand uniformity across DMAs. Then the optimal layout of flow meters (i.e., inlet pipes) and gate valves is achieved by solving a two-objective optimization for minimizing the number of flow meters and cumulative pressure differences compared with critical points in each DMA. Finally, pressure-reducing valves (PRVs) are installed in every inlet pipe and optimized to reduce leakage. The overall procedure is applied to a medium-sized network. The results reveal that the proposed sectorization methodology combined with pressure management can efficiently reduce leakage by controlling excess hydraulic capacity.

Erosion of a saturated sand bed by a vertically upward water jet was studied experimentally. Direct observations on the behavior of sand-bed erosion were obtained by increasing the flow rate at a small increment until fluidization developed to the surface of the sand layer. With the increase in flow rate, the sand bed underwent different stages: no cavity, a stable cavity above the jet, an unstable cavity propagating upward, and full fluidization. The mechanism for the cavity evolution was investigated, and an analytical model was developed to predict the critical flow rate for full fluidization. The model predictions compared well with the measurements in the experiments with different sand-layer thicknesses.

Apart from water quality, leakage control and energy consumption management are the most concerning challenges for water treatment plants (WTPs). The joint scheduling of pumps and pressure reducing valves (PRVs) in the water distribution network can reduce excessive pressure and distribute pressure more evenly, which achieves comprehensive reduction of leakages and energy consumption. Taking into account the main shortcomings of the commonly used methods, such as scheduling pumps or PRVs separately, or optimizing PRV settings when their position is given, etc., this paper has taken the PRV (position and setting) and the working status of variable speed pumps (VSPs) as decision variables and the cost savings contributed by leakage reduction and energy consumption savings as the objective function, which maximized the economic benefits brought by PRV and/or VSP scheduling. A genetic algorithm (GA) was used to optimize the solution under multiple working conditions. The performance of three control strategies (PRV-only scheduling, VSP-only scheduling, and joint scheduling of PRVs and VSPs) are compared to each other based on a small network. Joint scheduling has achieved the best economic benefits in reducing the gross cost (contributed by leakage and energy consumption) of the three control strategies, which results in a leakage reduction of 33.4%, an energy consumption reduction of 25.4%, and a total cost reduction of 33.1%, when compared to the original network, and saving about 1148 ${\mathrm{m}}^3$ water (7% of the original consumption) and 722 $\mathrm{kWh}$ electric energy (25.4% of the original consumption) per day.

Leak detection is nowadays an important task for water utilities as leakages in water distribution systems (WDS) increase economic costs significantly and create water resource shortages. Monitoring data such as pressure and flow rate of WDS fluctuate with time. Diagnosis based on time series monitoring data is thought to be more convincing than one-time point data. In this paper, a threshold selection method for the correlation coefficient based on time series data is proposed based on leak scenario falsification, to explore the advantages of data interpretation based on time series for leak detection. The approach utilizes temporal varying correlation between data from multiple pressure sensors, updates the threshold values over time, and scans multiple times for a scanning time window. The effect of scanning time window length on threshold selection is also tested. The performance of the proposed method is tested on a real, full-scale water distribution network using synthetic data, considering the uncertainty of demand and leak flow rates, sensor noise, and so forth. The case study shows that the scanning time window length of 3–6 achieves better performance; the potential of the method for leak detection performance improvement is confirmed, though affected by many factors such as modeling and measurement uncertainties.