Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Civil and Environmental Engineering | en_US |
| dc.contributor.advisor | Duan, Huan-feng (CEE) | en_US |
| dc.creator | Wang, Manli | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14435 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Development and implementation of an efficient time-domain factorized wave analysis method for detecting pipe leaks in water supply systems | en_US |
| dcterms.abstract | As global urbanization accelerates and urban populations continue to grow, concerns over urban water safety, particularly water shortages, are becoming increasingly prominent. Urban water supply systems play a crucial role in water distribution, and their integrity and conveyance efficiency are essential for ensuring a safe and reliable water supply. However, leaks in water distribution networks not only result in significant freshwater and economic losses but also undermine the resilience and long-term sustainability of urban water infrastructure. Nevertheless, achieving fast, accurate, non-intrusive, and wide-ranging leak detection remains a significant challenge. In recent decades, the transient-based method, which utilizes the system information collected by the transient wave, has received immense attention. Among these, the time-domain full-waveform inversion method stands out for its accuracy and robustness by minimizing the discrepancies between the measured and modeled transient signals to identify leaks. However, the inverse analysis requires simultaneous estimation of the coupled leak location and size, resulting in high computational cost and a complex, multidimensional parameter space, especially in large pipe networks. The challenge grows more complex with multiple leaks, where the problem becomes a 2N-parameter optimization problem (with N being the number of leaks), which is high-dimensional, non-convex, and riddled with local optima. Therefore, a more efficient approach is necessary to enhance the time-domain transient wave analysis and improve the leak detection technique in the urban water supply systems. | en_US |
| dcterms.abstract | In this thesis, a time-domain factorized wave analysis method is developed. First, a factorized wave model with decoupled leak parameters is analytically derived. Based on this model, an efficient two-stage leak detection algorithm is proposed for single-leak scenarios, which reduces the original two-dimensional optimization to two sequential one-dimensional estimations. For multiple-leak detection, an iterative beamforming algorithm is developed, building on the single leak estimation to decompose the complex 2N-dimensional optimization problem into 2N sequential one-dimensional sub-problems for multiple leak detection. This approach largely reduces the complexity of both forward modeling and inverse estimation, enabling real-time leak localization. Both numerical and experimental applications are applied to validate the proposed time domain factorized wave analysis method for leak detection across different pipe systems. The results confirm its effectiveness and reliability in detecting leaks within both simple and complex pipe systems. Additionally, a sensitivity analysis is performed to assess the method's robustness under various uncertainties, further deepening the understanding of its practical applicability. | en_US |
| dcterms.abstract | The factorized wave model and detection approach developed in this thesis may contribute to advancing the application of current time-domain transient-based methods for leak detection. Based on the findings and achievements presented, recommendations for future research are also provided at the end of the thesis. | en_US |
| dcterms.extent | xxvii, 176 pages : color illustrations, maps | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2026 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | open access | en_US |
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