Article(id=1223202683553698030, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223202678788965355, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20230295, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1675267200000, receivedDateStr=2023-02-02, revisedDate=1679414400000, revisedDateStr=2023-03-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1769563845765, onlineDateStr=2026-01-28, pubDate=1703433600000, pubDateStr=2023-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769563845765, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769563845765, creator=13701087609, updateTime=1769563845765, updator=13701087609, issue=Issue{id=1223202678788965355, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='12', pageStart='1', pageEnd='228', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769563844630, creator=13701087609, updateTime=1769563913308, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223202966899901286, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223202678788965355, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223202966899901287, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223202678788965355, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=109, endPage=112, ext={EN=ArticleExt(id=1223202683830522111, articleId=1223202683553698030, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Inversion of Hydraulic Parameters of Rural Water Supply Network Based on Intelligent Optimization Algorithm, columnId=1222925283779400191, journalTitle=Water Resources and Power, columnName=WATER CONSERVANCY AND HYDROPOWER ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

The friction factor of pipeline is a key parameter in the design calculation, operation scheduling optimization and fault diagnosis of water supply system. In order to determine this parameter accurately, an intelligent back-analysis method of pipe section friction factor based on dynamic search fireworks algorithm (dynFWA) coupled with hydraulic calculation model of pipe network was proposed. The partial derivative relationship between node water pressure and friction factor was taken as the node sensitivity. In the improved genetic algorithm, the maximum sum of node maximum sensitivity was taken as the goal to optimize the layout of monitoring points. Based on the optimized water pressure monitoring value at the monitoring point, the dynFWA algorithm was used to inverse the friction factor of each pipe section with the objective of minimizing the average double error between the water pressure monitoring value and the calculated value. In order to verify the inversion performance of dynFWA algorithm, the inversion of friction factor by dynFWA algorithm and particle swarm optimization (PSO) algorithm were compared. The results show that the maximum relative errors of the inverse value of the friction factor are 17.7% and 0.7% before and after the optimization of the monitoring points, which proves the necessity of the monitoring point selection and the superiority of the improved genetic algorithm for the monitoring point selection. Under the condition that the water pressure at the monitoring node is added to noise, the relative errors of the friction factor inversion results based on the dynFWA algorithm and the PSO algorithm are 9.67% and 14.33% respectively, and the maximum relative errors between the actual water pressure value and the simulated water pressure value at the monitoring point are 0.358% and 0.655%, which proves that the dynFWA algorithm has higher accuracy in the parameter inversion problem compared with the PSO algorithm.

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管道的摩阻因数是供水系统设计计算、运行调度优化及故障诊断中的关键参数。为准确确定该参数,提出了一种动态搜索烟花算法(dynFWA)耦合管网水力计算模型的管段摩阻因数智能反分析方法。将节点水压与摩阻因数的偏导关系作为节点灵敏度,在改进遗传算法中以节点最大灵敏度之和最大为目标,优化布置监测点;基于优化后监测点处的水压监测值,以水压监测值与计算值的最小二乘误差值为目标,采用dynFWA算法反演各管段的摩阻因数。同时,为验证dynFWA算法优化反演的性能,对比分析了dynFWA算法及粒子群算法(PSO)反演摩阻因数的情况。结果表明,在监测点优化前后摩阻因数反演值最大相对误差分别为17.7%、0.7%,证明了监测点选取的必要性和改进遗传算法进行监测点选取的优越性;在监测点水压加入噪声的情况下,基于dynFWA算法与PSO算法的摩阻因数反演结果最大相对误差分别为9.67%、14.33%,监测点处实际水压值与模拟水压值之间最大相对误差为0.358%、0.655%,证明了相较于PSO算法,dynFWA算法在参数反演问题中具有更高的准确性。

, correspAuthors=null, authorNote=null, correspAuthorsNote=
郄志红(1969-),男,博士、教授,研究方向为社会基础设施的健康监测等,E-mail:
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刘成荣(1997-),男,硕士研究生,研究方向为供水系统优化调度,E-mail:

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刘成荣(1997-),男,硕士研究生,研究方向为供水系统优化调度,E-mail:

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刘成荣(1997-),男,硕士研究生,研究方向为供水系统优化调度,E-mail:

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基于智能优化算法的农村供水管网水力参数反演
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刘成荣 1 , 郄志红 1 , 吴鑫淼 1 , 张红梅 2 , 王伟哲 3
水电能源科学 | 水利水电工程 2023,41(12): 109-112
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水电能源科学 | 水利水电工程 2023, 41(12): 109-112
基于智能优化算法的农村供水管网水力参数反演
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刘成荣1 , 郄志红1 , 吴鑫淼1, 张红梅2, 王伟哲3
作者信息
  • 1.河北农业大学城乡建设学院,河北 保定 071001
  • 2.河北省农村供水总站,河北 石家庄 050011
  • 3.保定市水利水电勘测设计院,河北 保定 071001
  • 刘成荣(1997-),男,硕士研究生,研究方向为供水系统优化调度,E-mail:

通讯作者:

郄志红(1969-),男,博士、教授,研究方向为社会基础设施的健康监测等,E-mail:
Inversion of Hydraulic Parameters of Rural Water Supply Network Based on Intelligent Optimization Algorithm
Cheng-rong LIU1 , Zhi-hong QIE1 , Xin-miao WU1, Hong-mei ZHANG2, Wei-zhe WANG3
Affiliations
  • 1.College of Urban and Rural Construction, Agriculture University of Hebei, Baoding 071001, China
  • 2.Head Office of Rural Water Supply, Shijiazhuang 050011, China
  • 3.Baoding Survey and Design Institute of Water Conservancy and Hydropower, Baoding 071001, China
出版时间: 2023-12-25 doi: 10.20040/j.cnki.1000-7709.2023.20230295
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管道的摩阻因数是供水系统设计计算、运行调度优化及故障诊断中的关键参数。为准确确定该参数,提出了一种动态搜索烟花算法(dynFWA)耦合管网水力计算模型的管段摩阻因数智能反分析方法。将节点水压与摩阻因数的偏导关系作为节点灵敏度,在改进遗传算法中以节点最大灵敏度之和最大为目标,优化布置监测点;基于优化后监测点处的水压监测值,以水压监测值与计算值的最小二乘误差值为目标,采用dynFWA算法反演各管段的摩阻因数。同时,为验证dynFWA算法优化反演的性能,对比分析了dynFWA算法及粒子群算法(PSO)反演摩阻因数的情况。结果表明,在监测点优化前后摩阻因数反演值最大相对误差分别为17.7%、0.7%,证明了监测点选取的必要性和改进遗传算法进行监测点选取的优越性;在监测点水压加入噪声的情况下,基于dynFWA算法与PSO算法的摩阻因数反演结果最大相对误差分别为9.67%、14.33%,监测点处实际水压值与模拟水压值之间最大相对误差为0.358%、0.655%,证明了相较于PSO算法,dynFWA算法在参数反演问题中具有更高的准确性。

农村供水管网  /  改进遗传算法  /  动态搜索烟花算法  /  监测点优化  /  水力参数反演

The friction factor of pipeline is a key parameter in the design calculation, operation scheduling optimization and fault diagnosis of water supply system. In order to determine this parameter accurately, an intelligent back-analysis method of pipe section friction factor based on dynamic search fireworks algorithm (dynFWA) coupled with hydraulic calculation model of pipe network was proposed. The partial derivative relationship between node water pressure and friction factor was taken as the node sensitivity. In the improved genetic algorithm, the maximum sum of node maximum sensitivity was taken as the goal to optimize the layout of monitoring points. Based on the optimized water pressure monitoring value at the monitoring point, the dynFWA algorithm was used to inverse the friction factor of each pipe section with the objective of minimizing the average double error between the water pressure monitoring value and the calculated value. In order to verify the inversion performance of dynFWA algorithm, the inversion of friction factor by dynFWA algorithm and particle swarm optimization (PSO) algorithm were compared. The results show that the maximum relative errors of the inverse value of the friction factor are 17.7% and 0.7% before and after the optimization of the monitoring points, which proves the necessity of the monitoring point selection and the superiority of the improved genetic algorithm for the monitoring point selection. Under the condition that the water pressure at the monitoring node is added to noise, the relative errors of the friction factor inversion results based on the dynFWA algorithm and the PSO algorithm are 9.67% and 14.33% respectively, and the maximum relative errors between the actual water pressure value and the simulated water pressure value at the monitoring point are 0.358% and 0.655%, which proves that the dynFWA algorithm has higher accuracy in the parameter inversion problem compared with the PSO algorithm.

rural water supply network  /  improved genetic algorithm  /  dynamic search fireworks algorithm  /  monitoring point optimization  /  inversion of hydraulic parameters
刘成荣, 郄志红, 吴鑫淼, 张红梅, 王伟哲. 基于智能优化算法的农村供水管网水力参数反演. 水电能源科学, 2023 , 41 (12) : 109 -112 . DOI: 10.20040/j.cnki.1000-7709.2023.20230295
Cheng-rong LIU, Zhi-hong QIE, Xin-miao WU, Hong-mei ZHANG, Wei-zhe WANG. Inversion of Hydraulic Parameters of Rural Water Supply Network Based on Intelligent Optimization Algorithm[J]. Water Resources and Power, 2023 , 41 (12) : 109 -112 . DOI: 10.20040/j.cnki.1000-7709.2023.20230295
  • 河北省水利科技计划项目(2020-54)
2023年第41卷第12期
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doi: 10.20040/j.cnki.1000-7709.2023.20230295
  • 接收时间:2023-02-02
  • 首发时间:2026-01-28
  • 出版时间:2023-12-25
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  • 收稿日期:2023-02-02
  • 修回日期:2023-03-22
基金
河北省水利科技计划项目(2020-54)
作者信息
    1.河北农业大学城乡建设学院,河北 保定 071001
    2.河北省农村供水总站,河北 石家庄 050011
    3.保定市水利水电勘测设计院,河北 保定 071001

通讯作者:

郄志红(1969-),男,博士、教授,研究方向为社会基础设施的健康监测等,E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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