Research on Remote Visualized Management System of Water Supply Pipe Network Based on Palmprint Recognition

2021 ◽  
Author(s):  
Mengyue Liu ◽  
Yanxue Xue ◽  
Meng Xue ◽  
Zhiqiang Wang ◽  
Yue Wang
Keyword(s):  
Water Supply ◽  
Management System ◽  
Palmprint Recognition ◽  
Pipe Network ◽  
Supply Pipe

Analysis of Causes for Pipe Explosion of Urban Water Supply Pipe Network

2013 ◽  
Vol 864-867 ◽  
pp. 2039-2042
Author(s):  
Gao Jie Hang ◽  
Lei Zhang ◽  
He Zhang
Keyword(s):  
Human Factors ◽  
Water Supply ◽  
Urban Water ◽  
Urban Water Supply ◽  
Pipe Network ◽  
Pipe Networks ◽  
Natural Factors ◽  
Supply Pipe

There is a great many reasons for pipe explosion of urban water supply pipe networks. Three major aspects of water supply pipe explosion causes were analyzed comprehensively in this paper: natural factors, pipeline factors and human factors, to raise awareness about the causes of pipe explosion.

scholarly journals Research on Intelligent Location Method of Water Supply Pipe Network Burst Based on BP Neural Network Deep Learning

2018 ◽  
Vol 246 ◽  
pp. 02029
Author(s):  
Luohua Wang ◽  
Mou Lv ◽  
Xiaobo Miao ◽  
Li Li ◽  
Fengchao Liang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Water Supply ◽  
Bp Neural Network ◽  
Neural Network Modeling ◽  
Water Pipe ◽  
Pipe Network ◽  
Pipe Burst ◽  
Supply Pipe ◽  
Water Pipe Network

Based on the in-depth analysis of the causes of the large-scale water supply pipe network explosion at home and abroad, the paper discusses the neural network modeling technology for quickly and accurately locating the water pipe network. Furthermore, the remedial measures of the pipe network squib in the field were adopted, and the BP neural network deep learning method was proposed to carry out the intelligent positioning of the water pipe network bursting. Based on the construction of a miniature hydraulic model based on BP neural network analysis, through the correlation analysis of the flow change of 5 positions and the pressure monitoring point change of 17 positions when the pipe network bursts, the artificial neural network deep learning is further used to diagnose the position of the pipe network where the pipe burst is located. In this paper, the small-scale water supply pipe network built by the laboratory is taken as an example to verify the research method of the pipe burst positioning.

Division method for water distribution networks in hilly areas

2015 ◽  
Vol 16 (3) ◽  
pp. 727-736 ◽  
Author(s):  
Tao Tao ◽  
Jiada Li ◽  
Kunlun Xin ◽  
Peng Liu ◽  
Xiaolan Xiong
Keyword(s):  
Water Supply ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Distribution Networks ◽  
Pipe Network ◽  
Assessment Index ◽  
Supply Pipe ◽  
Low Degree ◽  
Pressure Limitation

Water distribution systems in hilly areas are always divided into several zones due to the undulating terrain. The present approach of dividing water distribution systems lacks an assessment index and is characterized by a low degree of automation. With the building of a mathematical model, this paper introduces two indicators – pressure limitation and pressure variation – to enable the automatic division of the water supply pipe network. It prioritizes economic index as the objective function in the evaluation of the division of water distribution systems in hilly areas, and then selects the optimal division scheme by generic algorithm in a large number of candidates. The SY terrain in YW City China is used for verification. Compared to traditional water supply partition methods, this procedure is easier to operate time-savingly by staff and is more automatic.

scholarly journals Braess's paradox and power-law nonlinearities in networks

1993 ◽  
Vol 35 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Bruce Calvert ◽  
Grant Keady
Keyword(s):  
Water Supply ◽  
Potential Function ◽  
Power Law ◽  
Equilibrium Flow ◽  
Pipe Network ◽  
Pipe Networks ◽  
Braess’S Paradox ◽  
Supply Pipe ◽  
The Difference ◽  
Increasing Function

AbstractWe study flows in physical networks with a potential function defined over the nodes and a flow defined over the arcs. The networks have the further property that the flow on an arc a is a given increasing function of the difference in potential between its initial and terminal node. An example is the equilibrium flow in water-supply pipe networks where the potential is the head and the Hazen-Williams rule gives the flow as a numerical factor ka times the head difference to a power s > 0 (and s ≅ 0.54). In the pipe-network problem with Hazen-Williams nonlinearities, having the same s > 0 on each arc, given the consumptions and supplies, the power usage is a decreasing function of the conductivity factors ka. There is also a converse to this. Approximately stated, it is: if every relationship between flow and head difference is not a power law, with the same s on each arc, given at least 6 pipes, one can arrange (lengths of) them so that Braess's paradox occurs, i.e. one can increase the conductivity of an individual pipe yet require more power to maintain the same consumptions.

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