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基于压力相关水力模拟的熵理论在给水管网压力计优化布置中的应用

Optimal Placement of Pressure Gauges for Water Distribution Networks Using Entropy Theory Based on Pressure Dependent Hydraulic Simulation.

作者信息

Yoo Do Guen, Chang Dong Eil, Song Yang Ho, Lee Jung Ho

机构信息

School of Civil and Environmental Engineering, The University of Suwon, Gyeonggi-do 18323, Korea.

School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Korea.

出版信息

Entropy (Basel). 2018 Aug 4;20(8):576. doi: 10.3390/e20080576.

Abstract

This study proposed a pressure driven entropy method (PDEM) that determines a priority order of pressure gauge locations, which enables the impact of abnormal condition (e.g., pipe failures) to be quantitatively identified in water distribution networks (WDNs). The method developed utilizes the entropy method from information theory and pressure driven analysis (PDA), which is the latest hydraulic analysis method. The conventional hydraulic approach has problems in determining the locations of pressure gauges, attributable to unrealistic results under abnormal conditions (e.g., negative pressure). The proposed method was applied to two benchmark pipe networks and one real pipe network. The priority order for optimal locations was produced, and the result was compared to existing approach. The results of the conventional method show that the pressure reduction difference of each node became so excessive, which resulted in a distorted distribution. However, with the method developed, which considers the connectivity of a system and the influence among nodes based on PDA and entropy method results, pressure gauges can be more realistically and reasonably located.

摘要

本研究提出了一种压力驱动熵方法(PDEM),该方法可确定压力表位置的优先级顺序,从而能够在供水管网(WDN)中定量识别异常情况(如管道故障)的影响。所开发的方法利用了信息论中的熵方法和压力驱动分析(PDA),这是最新的水力分析方法。传统的水力方法在确定压力表位置时存在问题,这是由于在异常情况(如负压)下会产生不现实的结果。所提出的方法应用于两个基准管网和一个实际管网。得出了最佳位置的优先级顺序,并将结果与现有方法进行了比较。传统方法的结果表明,每个节点的压力降差异变得过大,导致分布失真。然而,所开发的方法基于PDA和熵方法的结果考虑了系统的连通性和节点之间的影响,能够更现实、合理地定位压力表。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f40/7513101/63ebb4dcf489/entropy-20-00576-g001.jpg

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