Hydraulic Transient Data Assimilation in Pipe Networks Using the Kalman Filter

Hydraulic transient data assimilation in pipe networks plays a critical role in monitoring the network’s behaviours, thereby ensuring the efficiency and reliability of water supply systems. However, the existing Kalman filter-based methods integrated with traditional numerical models face a severe computational burden with a significant number of state variables due to the need to discretise pipes into multiple pipe segments. This paper presents a novel Kalman filter approach that implements an efficient hydraulic transient model that requires fewer pipe segments and is particularly suited when the frequency of the transient fluctuation is low. As the number of state variables is reduced, a faster estimation of the system hydraulic states is enabled, as is an enhanced accuracy of transient predictions. The proposed method was tested in two pipe network simulations with user demands: a 7-pipe network and a 51-pipe network. The results indicate that the method provides accurate transient predictions and robust estimation of transient states in real time, and has high performance and efficiency for large pipe networks.