Dynamic design and performance prediction of tuned particle dampers based on co-simulation

Tuned particle dampers (TPDs) are recognized for their innovative approach to nonlinear energy dissipation, maintaining the dynamic characteristics of the systems they protect. However, the inherent complexity of particle system nonlinearities poses substantial challenges in developing accurate dynamical models. Traditional analysis methods are inadequate in addressing the dynamic characteristics of such complex coupled systems. This study introduces a novel integration of multibody dynamics with discrete element methods through a bidirectional coupling approach to a comprehensive model of the TPD system. Then, extensive investigations into the system's responses and its vibration control efficacy under diverse external forces and frequencies are conducted, and the energy dissipation attributes and dynamic adjustments are also delved into system mass. Given the extensive simulation times and substantial resource demands of such systems, a neural network-based predictive model has been devised to forecast the vibration absorption capabilities of TPDs, offering essential insights that enhance engineering applications.