Physics-informed neural networks for hydraulic transient analysis in pipeline systems. (1st August 2022)

• Record Type: Journal Article
• Authors: Ye, Jiawei; Do, Nhu Cuong; Zeng, Wei; Lambert, Martin
• Is Part Of: Water research. Volume 221(2022)
• Publication Date: 2022-08-01
• Subjects: Hydraulic transient -- Physics-informed neural network -- Artificial intelligence -- Pipeline system -- Partial differential equations; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2022.118828
• Languages: English
• ISSNs: 0043-1354

Abstract

Highlights: A data-driven transient method is proposed using physics-informed neural network. It incorporates both measured information and physical laws of the transient flow. The transient analysis can be conducted without a complete physical model. Transient pressure traces at unmonitored locations can be accurately predicted. Abstract: In water pipeline systems, monitoring and predicting hydraulic transient events are important to ensure the proper operation of pressure control devices (e.g., pressure reducing valves) and prevent potential damages to the network infrastructure. Simulating transient pressures using traditional numerical methods, however, require a complete model with known boundary and initial conditions, which is rarely able to obtain in a real system. This paper proposes a new physics-based and data-driven method for targeted transient pressure reconstruction without the need of having a complete pipe system model. The new method formulates a physics-informed neural network (PINN) by incorporating both measured data and physical laws of the transient flow in the training process. This enables the PINN to learn and explore hidden information of the hydraulic transient (e.g., boundary conditions and wave damping characteristics) that is embedded in the measured data. The trained PINN can then be used to predict transient pressures at any location of the pipeline. Results from two numerical and one experimental case studies showed a high accuracy of the pressure reconstruction using the proposed approach. In addition, a series of sensitivity analyses have been conducted to determine the optimal hyperparameters in the PINN and to understand the effects of the sensor configuration on the model performance. Graphical abstract: Image, graphical abstract