Impact of uncertainty in the physics-informed neural network on pressure prediction for water hammer in pressurized pipelines

Abstract

Abstract In pressurized pipeline systems, accurate prediction of water hammer pressure is crucial for ensuring safe system operation. When the boundary conditions are unknown and measured data is sparse, both traditional methods fully based on physical equations and data-driven neural network methods have difficulty in accurately predicting water hammer pressure. The physics-informed neural network (PINN) overcomes these challenges by simultaneously incorporating measured data and physical equations during the network training process. However, PINN has uncertainties and their impact on the accuracy of pressure prediction is not yet clear. In this study, the valve closing water hammer in a reservoir-pipeline-valve system is taken as the research object, we investigate the influence of the uncertainty of physics and data in the PINN on prediction accuracy by using water hammer equations with various friction models and training data with various noise levels. The results show that using the water hammer equation with the Brunone model, the PINN model has higher prediction accuracy. Furthermore, data noise levels less than 10% have a relatively small impact on pressure prediction accuracy, indicating that the PINN model has good robustness in terms of data noise levels.