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This study presents a data-driven model for predicting flow-induced vibrations (FIV) in bluff bodies, specifically addressing vortex-induced vibration (VIV) and galloping. Utilizing the Ensemble Kalman Filter (ENKF) for data assimilation, the model significantly enhances prediction accuracy, achieving up to ten times greater precision compared to traditional semi-empirical models. The research highlights the importance of integrating experimental data with semi-empirical approaches to improve the modeling o

Author

shinchan6717

Language

EN