Year: 2024
Type: article
Abstract: When water pipelines undergo scenarios such as valve closure or leakage, they often operate in a gas-liquid two-phase flow state, which can easily cause abnormal pressure fluctuations, exacerbating the destructiveness of water hammer and affecting the safe operation of the pipeline. To study the problem of abnormal fluctuations in complex water pipelines, this paper establishes a transient flow model for gas-containing pipelines, considering unsteady friction, and solves it using the discrete gas cavity model (DGCM). It also studies the influence of factors such as valve closing time, initial flow rate, gas content rate, leakage location, and leakage amount on the end-of-valve pressure. Furthermore, it locates the leakage position using a genetic algorithm-backpropagation neural network (GA-BP neural network). The results show that increasing the valve closing time, increasing the gas content rate, decreasing the initial flow rate, and increasing the leakage amount all reduce the pressure peak inside the pipeline. The model constructed using the GA-BP neural network effectively predicts the leakage location with a mean absolute percentage error (MAPE) of 9.26%. The research results provide a reference for studies related to the safety protection of water conveyance projects.
Source: Water
DOI: 10.3390/w16172459
Institution: Xi'an University of Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
Awards: G3205205063, G7756739768
OpenAlex ID: W4402052994