Reconstructing transient pressures in pipe networks from local observations by using physics-informed neural networks

Abstract

Reconstructing transient states presents significant challenges, particularly within complex pipe networks. These challenges arise due to nonlinear behaviours, inherent uncertainties in the system, and limitations in data availability. This work proposed a novel approach employing Physics-Informed Neural Networks (PINN) to reconstruct transient states in pipe networks, even with limited sensor data. To integrate the complex topology of pipe network systems into neural networks, the method integrates the PINN framework with an efficient elastic water column (EWC) model which can be simply formulated across diverse pipe network configurations. The results showed the proposed PINN method can accurately reconstruct the pressure and flow variation at unmonitored locations, even provided with noisy data at a limited number of locations. One of its advantages lies in its ability to effectively capture extreme values that hold potential significance for pipe infrastructure, providing a promising avenue for pipe failure analysis and enhanced safety management. Laboratory experiments have also been conducted to validate the efficacy and reliability of this method, thus further underlining its potential for real-world applications.