Optimization and Dynamic Analysis of a Vibro-Impact Nonlinear Energy Sink with Electromagnetic Coil for Vibration Suppression and Energy Harvesting

TL;DR

This paper presents an optimized vibro-impact nonlinear energy sink with electromagnetic coil for vibration suppression and energy harvesting, using genetic algorithms to enhance performance and analyze dynamic behavior including chaos.

Contribution

It introduces a novel optimization approach for cavity length and restitution coefficient in a VI-NES with electromagnetic coil, improving vibration suppression and energy harvesting efficiency.

Findings

Optimized parameters reduce LO amplitude response more effectively than approximate methods.

System exhibits transition from predictable to chaotic responses under certain conditions.

Validation confirms the effectiveness of the multi-objective genetic algorithm approach.

Abstract

This study investigates a system comprising a linear oscillator (LO) equipped with a Vibro-Impact Nonlinear Energy Sink (VI-NES) and a coil. The LO's damping and stiffness coefficients are represented by ( C ) and ( k ), respectively, while a ball inside the LO moves within a cavity, colliding with the walls at both ends. The primary focus is on optimizing the cavity length (( L_c )) and the coefficient of restitution (( \kappa )) and coil parameter () using a Genetic Algorithm (GA). The motion equations for the LO incorporating the VI-NES are derived, considering the electromagnetic force (( F_e )) and contact force (( F_c )). The study also explores the energy harvesting circuit, which generates electric power by connecting a load resistance to the coil, transforming mechanical energy into electrical energy. The dynamic behavior of the system is analyzed, highlighting the transition from predictable patterns to chaotic responses. Efficiency metrics are defined to measure the VI-NES's performance in absorbing and dissipating excitation energy. The optimization formulation addresses the influence of initial conditions, proposing a multi-objective approach to handle uncertainties. Validation against existing studies confirms the reliability of the proposed optimization method. The results demonstrate that optimizing both ( L_c ) and ( \kappa ) using GA effectively minimizes the amplitude response of the LO, outperforming approximate methods.