Analysis and Data Regression of Water Hammer with Column Separation Based on Machine Learning

Current research on water hammers mainly focuses on improving prediction accuracy, optimizing control strategies, and enhancing numerical models. To quickly obtain water hammer waveforms, in this paper, a numerical model was constructed for predicting the pressure waveform of water hammer events with column separation. This model is based on a two-fluid framework, enhanced by an improved pressure relaxation model and the Godunov-Harten-Lax-van Leer (HLL) numerical calculation method. The deep forest machine learning approach was utilized to predict and regress the water hammer waveforms with column separation at different flow rates. The results indicate that the deep forest method achieves a prediction effect that is both faster and in line with the experimental waveform trends compared to actual water hammer experiments, thereby reducing dependence on physical experiments and highlighting the advantages of the deep forest method in terms of shorter time requirements and high accuracy. Furthermore, when employing machine learning to study merging water hammer waveforms, it is necessary to account for the overlapping coupling characteristics of the waveforms that affect regression accuracy and to appropriately shrink the feature distribution zones to ensure distinct differences in waveform partition characteristics. Accurate capture of water hammer waveforms can prevent pipeline rupture and equipment damage, and by predicting the peaks of the waves, it enables water hammer suppression, thereby effectively controlling the potential impact of water hammer on the system. This approach validates the applicability of machine learning for predicting merging water hammer waveforms and provides a reference for subsequent pressure transient perception across entire catchments.