Advanced Modeling and Applications of Smart Materials and Structures on Passive Vibration Suppression

In the past two decades, smart materials and structures have been increasingly used in active and passive structural vibration suppression, since such materials or structures can convert kinetic energy of vibrations into other forms. A useful property in smart materials and structures is their hysteretic behavior, which has an energy dissipating effect during vibration suppression. However, the nonlinearity of the hysteresis poses a challenge for structural modeling. This Ph.D. dissertation focuses on advanced modeling of smart materials and structures and their potential applications on passive structural vibration suppression. This dissertation develops several advanced modeling approaches including a nonlinear autoregressive exogenous (NARX) model based on a recurrent neural network (RNN) for smart materials with hysteretic behaviors, a phenomenological model for superelastic shape memory alloy (SMA) helical springs, and a mathematical model using quasi-static electromagnetic theories for a prototype passive electromagnetic (EM) damper. In addition to forward modeling approaches to estimate responses, an inverse NARX RNN model is developed for reference control purposes. The implementation of smart materials and structures with their advanced modeling are applied to two types of applications, a base isolation system and a subsea jumper system. Both numerical simulation and experimental results have proven the advanced modeling methods perform accordingly, and the implementations can dramatically reduce structural vibration and improve structure safety.