Improving vibration suppression by applying stochastic optimization to a coil-contained vibro-impact nonlinear energy sink within a linear oscillator framework

This study investigates a coupled system consisting of a linear oscillator (LO) with a damper and spring, interacting with a Vibro Impact Nonlinear Energy Sink (VI-NES) enclosed by a coil. Inside the LO, a ball moves within a cavity, colliding with its boundaries. The research optimizes key parameters- cavity length, restitution coefficient, and coil characteristics -using a Genetic Algorithm (GA). It derives motion equations incorporating electromagnetic and collision forces while assessing an energy-harvesting circuit that converts mechanical vibrations into electrical energy via a load resistance connected to the coil. The analysis highlights transitions in system behavior, ranging from regular to chaotic under certain conditions. Efficiency metrics evaluate the VI-NES effectiveness in energy absorption and dissipation. The optimization method accounts for initial condition variations and introduces an approach to manage uncertainties. Moreover, a multi-objective optimization framework is introduced using the Pareto front approach, defining two objectives: minimizing the vibrational energy of the system and maximizing the electrical energy generated by the coil. Comparing results with prior studies confirms the reliability of this technique, demonstrating that GA optimization significantly reduces LO amplitude response, surpassing approximate methods.