Modeling and Simulation of Water Hammer Phenomena Using Artificial Neural Networks (ANN)

Abstract

The water hammer phenomenon, characterized by transient pressure surges due to rapid fluid deceleration in pipelines, poses significant risks to hydraulic systems. Valve closure time is a critical parameter influencing pressure magnitude, necessitating precise calibration to ensure system safety. While numerical methods like the MacCormack scheme provide accurate solutions, their computational intensity limits practical applications. This study addresses this limitation by proposing a machine learning (ML) framework employing a multilayer perceptron (MLP) artificial neural network (ANN) to predict optimal pressure values—defined as the lowest maximum pressure obtained for several closure laws at a given closure time—corresponding to specific valve closure times. The ANN was trained on 637 simulations generated via the MacCormack method, which solves the hyperbolic partial differential equations governing transient flow in a reservoir-pipeline-valve (RPV) system. Performance evaluation metrics demonstrate the ANN’s exceptional robustness and accuracy, achieving a root mean square error (RMSE) of 0.068, Nash-Sutcliffe efficiency (NSE) of 0.99, and a correlation coefficient (R) of 0.99, with a maximum relative error below 1%. The results highlight the ANN’s superior predictive accuracy and flexibility in capturing complex transient flow dynamics, outperforming conventional numerical methods. Notably, the ANN reduced computational time from days for iterative simulations to mere seconds, enabling rapid prediction of pressure-time curves critical for real-time decision-making. This framework offers a computationally efficient and reliable alternative for optimizing valve closure strategies, mitigating water hammer risks, and enhancing pipeline safety. By bridging numerical rigor with machine learning, this work enhances hydraulic infrastructure resilience across industrial and urban networks.