Application and Experimental Study of RBF Neural Network Algorithm in Flow-Induced Vibration of Pipelines

To address the issue of time-consuming traditional fluid-structure interaction methods, which make it difficult for nuclear power plant pipeline designers to perform targeted vibration reduction calculations during the design phase, this study adopts a data-driven radial basis function (RBF) neural network algorithm for pipeline flow-induced vibration analysis. This method can quantitatively calculate the pipeline flow-induced vibration in a short period of time by training on a large amount of load data of throttling fittings in the database. Compared to traditional fluid-structure interaction methods, it greatly improves the computational efficiency of pipeline flow-induced vibration. To validate the calculation results, experimental studies were conducted on on ball valves at different opening degrees and elbow pipes. Due to the presence of external structural vibrations such as pump excitation, experiments have found that when the flow-induced vibration dominates the total vibration, the simulation results are relatively close to the experimental results. When external structural vibration dominates the total vibration, the simulation and experimental results are of the same magnitude and have consistent variation patterns. The results demonstrate that the data-driven based RBF neural network method is reliable and effective for analyzing flow-induced vibrations in pipelines.