AI-driven optimization of dynamic vibration absorbers with hydraulic amplifier and mechanical inerter integration - Shamseldin, Ahmed; Abido, Mohammad A.; Alofi, Abdulrahman (Frontiers in Mechanical Engineering, 2024)
Dynamic vibration absorbers (DVAs) have been widely employed in vibration suppression applications for decades. While DVAs offer an effective solution, they are limited by the need for a high mass ratio between the DVA and the primary system to achieve significant vibration attenuation. To overcome this, researchers have introduced lever mechanisms, allowing for enhanced vibration suppression without increasing the mass ratio. However, levers, commonly used as amplification mechanisms, suffer from high inertia and limited amplification, particularly in larger applications. Another limitation is when DVAs are employed for energy harvesting as a secondary objective, they exhibit high sensitivity to system parameter variations, requiring extensive optimization. Various optimization techniques have been applied to DVAs for multi-objective optimization, including fixed-point theory, which is complex and requires intensive mathematical derivation, and simple metaheuristic techniques such as genetic algorithms (GA). This study proposes four novel DVAs using a hydraulic amplifier (HA) to address the limitations of traditional lever mechanisms and a mechanical inerter to improve the vibration damping. Also, multi-objective optimization was performed using particle swarm optimization (PSO) which is considered innovative in this application and compared with commonly used genetic algorithms (GA). The governing equations were derived using Newton’s second law and solved numerically with the Runge-Kutta method. An AI-based approach was utilized for HA design. The results show that integrating HA and mechanical inerters significantly enhances vibration attenuation and broadens the frequency response. Additionally, the location of the mechanical inerter is critical in reducing vibration amplitude. Also, the multi-objective PSO outperforms GA in solution diversity and quality. The proposed integration of HA in DVAs offers potential applications across various engineering fields.