Object-oriented integrated algorithms for efficient water pipe network by modified Hardy Cross technique

In this work, object-oriented integrated algorithms for an efficient flow analysis of the water pipe network are developed. This is achieved by treating the pipe network as a graph data structure with its nodes as the graph’s nodes and the pipes as the edges. The algorithm for cycle (real cycle or pseudo-cycle) extraction has been developed using nested breadth-first search that gives ordered cycles. Pseudo-loops are found using the shortest path algorithm between the nodes. Pipes are initialized loop by loop using conservation of mass at nodes. A modified Hardy Cross method is used in the proposed work with third-order convergence. The friction factor is updated for every change in discharges. The pressure calculation has been done by the graph traversal algorithm between the reference nodes and node where the pressure is to be calculated using the energy equation. The pressure at all intermediate nodes is obtained in the course of the traversal. Balanced discharges and nodal pressure in the pipe network are compared with the simultaneous loop flow adjustment method and EPANET software. The proposed work gives more efficient flow analysis than the traditional Newton–Raphson-based techniques for complex networks.

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

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.