基于节点递归算法的污水排放管道网规划仿真
Simulation of Sewage Discharge Pipeline Network Planning Based on Node Recursive Algorithm
郑瑶 1于景晓2
作者信息
- 1. 吉林建筑科技学院市政与环境工程学院,吉林 长春 130114
- 2. 浙江海洋大学石油化工与环境学院,浙江 舟山 316022
- 折叠
摘要
污水管网是城市公共设施和排水系统中最为关键的部分.管道基础设施的合理性是确保污水排放需求的重要指标.设计人员一般采用传统计算构图模式设计管道构图,不仅效率偏低,且无法全面考虑各管段水力坡度、流速以及输水能力等参数.为此,提出一种基于节点递归算法的污水排放管道网规划方法.将管道网污水流动过程定义为有向图,利用关联矩阵描述节点和管段的耦合关联,将管道网拟作若干有向树森林,采用链式表标记管道网,利用节点递归算法计算污水每小时最高排水量与排放最小服务压力,得到各管段水力坡度、流速和输水能力等参数.构建污水排放管道网规划模型,实现污水排放管道网的规划.实验结果显示,所提方法能准确评估污水流动状态,增强污水排放能力,具有理想的应用可靠性.
Abstract
Sewage pipe network is the key part of urban public facilities and drainage system.The rationality of pipeline infrastructure is an important indicator to ensure sewage discharge.Generally,designers adopt the traditional algorithm to design the composition of pipeline,but the method is not only inefficient,but also unable to fully consider the hydraulic gradient,velocity and water-carrying capacity of each pipe section.Therefore,this article presented a method of planning sewage discharge pipeline network based on node recursive algorithm.Firstly,the flow process of sewage in pipeline networks was defined as a directed graph.Secondly,an association matrix was used to describe the coupling relation between nodes and sections.Thirdly,pipeline networks were used as a number of directed forests the tube draft several directed forest,and then the chained list was adopted to mark pipeline networks.Moreover,node re-cursive algorithm was used to calculate the maximum hourly discharge and minimum service pressure,thus obtaining each section parameters such as hydraulic gradient,flow velocity and water-carrying capacity.Finally,a model was constructed to plan the sewage discharge pipeline networks.The experimental results show that the proposed method can accurately evaluate the flow condition of sewage,enhance the discharge capacity of sewage,and has ideal reliability.
关键词
节点递归/污水排放/污水管道网/规划仿真/有向图Key words
Node recursion/Sewage discharge/Sewage pipeline network/Planning simulation/Directed graph引用本文复制引用
出版年
2024
NETL
NSTL
万方数据