孙昭晨

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:水利工程系

学科:港口、海岸及近海工程

电子邮箱:sunzc@dlut.edu.cn

扫描关注

论文成果

当前位置: 中文主页 >> 科学研究 >> 论文成果

基于BP神经网络原理的长输管道泄漏点定位及其实验研究

点击次数:

发表时间:2022-10-10

发表刊物:工程力学

卷号:27

期号:8

页面范围:169-173

ISSN号:1000-4750

摘要:Research work based on pressure gradient method shows that the location
   of leakage depends on the friction factor. And the traditional methods
   using the friction coefficient are not suitable for determining the
   leakage location of running pipelines. The method to locate leak point
   is put forward based on BP neural network forecasting friction
   coefficient. The BP neural network forecasting friction factor is
   determined from the average flow rate before and after leakage
   occurrence. Experimental results confirm that this method can
   effectively forecast the friction coefficient and reasonably locate the
   leak point of long-distance pipeline

备注:新增回溯数据