Abstract
In this article, a classification model is established for the flow-induced vibration response based on the numerical and experimental data, using a deep neural network-based machine learning approach. The model effectively distinguishes between hard galloping and soft galloping in flow-induced vibrations by identifying the corresponding range of system parameters. Moreover, a regression model is established to determine the relationship between the critical reduced velocity of hard galloping and system parameters, and then, an exploratory function strategy is utilized to establish the functional relationship between the critical reduced velocity of the hard galloping and the system parameters. The results reveal that the system parameter range with the occurrence of hard galloping is fn < 0.85∪ζ > −0.1fn + 0.19. Additionally, the functional relationship between the critical reduced velocity and system parameters facilitates the adjustment of vibration states in flow-induced vibrations and enables deeper investigation into the phenomenon of hard galloping.
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