管道故障下配水网络弹性增强:水力启发的复杂网络方法

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2023-11-01 DOI:10.2166/aqua.2023.180
Mohsen Hajibabaei, Azadeh Yousefi, Sina Hesarkazzazi, Amin Minaei, Oswald Jenewein, Mohsen Shahandashti, Robert Sitzenfrei
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引用次数: 0

摘要

应主动评估配水网络的恢复能力,以减少破坏性事件的潜在影响。本研究提出了一种新的水力激励复杂网络方法(HCNA)来评估和增强单管故障情况下WDN的弹性。与传统的基于水力的模型不同,HCNA不需要水力模拟来进行弹性分析。相反,它通过将拓扑属性与故障触发的流量重新分配结合起来,量化了WDN图上边缘(管道)的故障后果。这个HCNA程序可以识别临界边缘(管道),以及受影响的边缘,这些边缘更容易受到其他边缘失效的影响。然后,通过将HCNA与基于图形的设计方法集成,系统地调整受影响边缘的大小,从而获得广泛的弹性增强解决方案。对三种wdn的HCNA和基于水力的模型进行了比较研究,证实了HCNA在识别各种网络规模中最关键的管道方面的有效性。此外,HCNA提供了类似的弹性增强解决方案,采用基于水力的进化优化,但计算量显著降低(快1400倍)。因此,它可以有效地用于大规模wdn的弹性增强,而传统优化的应用由于大量的计算工作量而受到限制。
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Resilience enhancement of water distribution networks under pipe failures: a hydraulically inspired complex network approach
Abstract The resilience of water distribution networks (WDNs) should be proactively evaluated to reduce the potential impacts of disruptive events. This study proposes a novel hydraulically inspired complex network approach (HCNA) to assess and enhance WDN resilience in the case of single-pipe failure. Unlike conventional hydraulic-based models, HCNA requires no hydraulic simulations for resilience analysis. Instead, it quantifies the failure consequences of edges (pipes) on the WDN graph by incorporating topological attributes with flow redistribution triggered by failures. This HCNA procedure leads to the identification of critical edges (pipes), as well as impacted ones, representing edges more susceptible to the failure of others. The impacted edges are then systematically resized by integrating HCNA with a graph-based design approach, obtaining a wide range of resilience enhancement solutions. A comparative study between HCNA and a hydraulic-based model for three WDNs confirms HCNA's effectiveness in identifying the most critical pipes in various network sizes. Furthermore, HCNA provides comparable resilience enhancement solutions with a hydraulic-based evolutionary optimization but with significantly lower computational effort (1,400 times faster). Thus, it can efficiently be used for resilience enhancement of large-scale WDNs, where the application of conventional optimizations is limited due to the intensive computational workload.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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