Accurate calibration of hydraulic models of water distribution systems (WDSs) is essential for reliable simulations. After eliminating gross errors, including those in estimated demands, pipe roughness coefficients (PRCs) are the most often used calibration parameters. Although numerous PRC calibration methods exist to ensure model simulations align well with field observations, they typically assume error-free pressure gauge elevations and overlook the compensatory interactions between elevation errors and PRC uncertainties. This often results in biased PRC calibration outcomes. To overcome this limitation, this paper introduces a novel framework that decouples elevation errors by minimizing the standard deviation of pressure residual time series, rather than relying on traditional residual minimization techniques. Additionally, a clustering-based data preprocessing approach is employed to reduce the impact of uncertain nodal demands and measurement noise. Tests on three benchmark networks demonstrate that the proposed method accurately calibrates PRCs, even when accounting for elevation inaccuracies, nodal demand uncertainties and measurement noise simultaneously. This establishes a new paradigm that leverages the statistical characteristics of residual time series to enable error-decoupled model calibration. Crucially, the method also quantifies pressure gauge elevation errors through post-calibration residual analysis, eliminating the need for costly field surveys. This advancement is particularly valuable for regions with missing or erroneous elevation data, significantly improving WDS calibration practices.

The inflow and infiltration (I&I) is an issue for many urban sewer networks (USNs), which can significantly affect system functioning. Placing sensors within the USNs is a typical approach to detect large I&I event, but deploying a limited number of sensors while achieving maximum detection reliability is challenging. While some methods are available for sensor placement, they are generally heuristic search-based methods (HSBMs) and hence the resultant sensor placement strategies (SPSs) are variable over different algorithm runs or parameterizations. This paper develops a new deterministic two-stage clustering method for SPS optimization based on information entropy. Within the first stage, the Spectral Clustering method is applied to assign USN nodes to different clusters according to their joint entropy. In the second stage, the topology structure property is considered to enable further clustering for improving detection reliability. Average I&I detection reliability is used to select clusters and the optimal SPS is identified by maximizing joint entropy of all possible solutions where a single sensor is assigned to each selected cluster. The proposed method and two existing HSBMs are applied to a real USN and their performance is compared. The results obtained show that: (i) a strong correlation coefficient R (R > 0.95) is observed between joint entropy and SPS's detection reliability, which has not been revealed before, (ii) the proposed method consistently outperforms the other two approaches in efficiently offering SPSs with about 7–15 % higher detection reliability, and (iii) the proposed method provides the optimal SPS in a deterministic manner, which makes it attractive for engineering applications.

Bursts in water distribution systems (WDSs) lead to water wastage and negative environmental impacts. Optimizing pressure sensor placement (PSP) for effective burst detection is crucial for prompt response and adverse event mitigation. While many optimization methods are available for this purpose, their resulting PSP strategies often exhibit large variability caused by background noise or metering accuracy, representing low robustness. This consequently hinders the wide application of existing PSP methods for burst detection in WDSs. This study proposes a novel multi-objective PSP method aimed at maximizing burst detection while minimizing the number of sensors. Within this method, a new threshold metric is introduced to quantify the minimum detectable burst outflow across all pipes. The metric has the key advantage of taking into account the fact that the relative magnitude of the detectable threshold for different pipes is independent of noise, thereby addressing the low robustness issue. The proposed method is applied to three WDSs, and results show that it can consistently achieve robust optimal PSP strategies across diverse noise conditions. Comprehensive comparisons between the proposed method and the other benchmark approaches are conducted using five metrics, including detection performance and layout characteristics. These comparisons reveal the properties of each optimization method and show how detection performance is affected by various placement features. It is anticipated that the proposed method can be useful in engineering practice due to its great robustness in determining the optimal PSP strategies for burst detection in WDSs compared to other alternatives.

The effects of pore throat structure on gas permeability in tight sand reservoir were investigated using helium-measured porosity, pulse decay permeability, casting thin sections, scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), and constant-rate mercury injection for five sandstone samples from the Upper Triassic Yanchang Formation in the Western Ordos Basin, China. Results show that permeability values are in the range of 0.474-2.290 mD. The strong variation may be due to the effect of mineral composition and pore-throat structure. Generally, sandstone samples of higher content of quartz, chlorite film, and slit-shaped throat tend to have higher permeability. The movable fluid porosity numerically equals to the product of porosity and the movable fluid saturation based on the film bound water, which can comprehensively reflect the influence of fluid flow channels on porosity while neglecting the influence of bound fluids in the fine pores of clay minerals in the tight reservoirs. Larger movable fluid porosity values mean larger pore spaces for the movable fluids, which contribute to larger permeability. It is more reasonable to analyze the movable fluid pore-throat radius as a range. The homogeneous pore throat structure lead to a narrow lower limit range of the movable fluid pore-throat radius, which is favorable for the fluid flow. Meanwhile, if the lower bound of the lower limit range of the movable fluid pore-throat radius is small, pores and throats are less plugged, and thus make the conditions favorable for fluid flow. The contribution of throats to the pore volume is critical in influencing reservoir permeability as it can directly control the lower limit of the movable fluid pore-throat size. The pore-throat radius where throats account for the smallest amount in the pore space increases with the permeability, which indicates that larger proportions of large throats result in larger throat radius and higher pore connectivity, leading to higher permeability.