A genetic algorithm-based optimization design on self-sensing active constrained layer damped rotating plates

This article investigates the vibration of a rotating constrained layer damped plate system. Although, currently, most existing research utilizes rotating structures as modeled beams, this work, however, models rotating structures as plates with constrained layer damping. Through the models investigated, this article develops a single-layer plate finite element model for a rotating structure to improve in both accuracy and versatility. Concurrently, existing research shows that the damping of the active constrained layer can provide more damping than the damping of the passive constrained layer. Therefore, in this study, the constraining layer is made of piezoelectric material and, thus, will work as both the self-sensing sensor and the actuator. In addition, a proportional control strategy is implemented to effectively control the damping in the rotating plate. Furthermore, due to a large number of design variables in the complex model incorporating viscoelastic damping, this study examines the application of genetic algorithm (GA) in optimizing the first two resonance amplitudes of the driving point mobility at the center of the rotating plate. A GA is applied to simultaneously determine several design parameters that maximize an objective function. Compared with a typical gradient search approach, Quasi-Newton method, GA can be more efficient and effective in finding the optimum configuration with the highest objective function value in the numerical example.