A Deep Learning Framework for Predicting Fluid-induced Vibration and Fatigue Life in Pipelines
Wang Xiao, Ruiqi Li, Minxin Xie, Wei Xu, W. Ostachowicz
The safe and reliable operation of natural gas compressor units is crucial for ensuring a secure and stable gas supply. However, the interaction between natural gas and pipelines inevitably induces flow-induced vibrations, leading to long-term cyclic stress variations in the pipelines. This results in the accumulation of fatigue damage and the initiation of cracks in stress concentration areas, thereby threatening the health of the compressor unit system. In this context, this study proposes a deep learning framework for predicting flow-induced vibration and fatigue life in pipelines, which is based on numerical simulations and experimental measurements, enabling accurate prediction of pipeline vibration responses and fatigue states under different gas transmission conditions. This study first conducts modal analysis on numerical and experimental models, updating the finite element model using the first three natural frequencies to accurately reflect the dynamic characteristics of the experimental model. Subsequently, a deep learning approach based on a multi-layer perceptron is proposed for predicting pipeline flow-induced vibration responses. The time-frequency acceleration responses of the pipeline under different gas flow conditions are predicted via numerical simulation, and the corresponding strain fields at stress concentration locations are evaluated using a quantitative relationship with the predicted accelerations. The results demonstrate that the proposed deep learning framework, leveraging simulated and measured pipeline data, enables accurate prediction of flow-induced vibrations and fatigue life.