Performance enhancement of a tuned mass damper for vibration suppression of an offshore jacket platform utilizing a novel metaheuristic optimization technique

Abstract

Offshore platforms are key structures in energy provision that are subjected to continuous and harsh environmental loadings. A tuned mass damper (TMD) has been proposed as a robust and effective vibration control method for marine structures. However, selecting optimum TMD parameters under certain constraints may become a multimodal optimization problem. Several studies focused on finding the TMD optimum parameters using different metaheuristic optimization algorithms. Nonetheless, it becomes a necessity to find an answer to the question: which algorithm can find the most effective TMD parameters to minimize the structure response? This paper presents the use of a novel metaheuristic optimizer, the Improved Grey Wolf Optimizer (IGWO), for optimum tuning of a TMD aimed at reducing the wave-induced vibration level of a jacket type platform. Moreover, a comprehensive performance analysis of four different metaheuristic algorithms for optimizing a TMD has been investigated. They are IGWO, popular algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA). In order to achieve this purpose, a finite element (FE) model representing a prototype of a jacket-type platform under the influence of irregular waves is numerically solved in COMSOL Multiphysics. Irregular waves are presented using the JONSWAP spectrum and hydrodynamic forces are modeled using Morison's equation. The comparative algorithms are evaluated for the independent minimization of three different objective functions. The target parameters of TMD to be tuned are the mass ratio (μ), the frequency ratio (fopt), and the damping ratio (ζopt). Finally, the optimized TMD will be applied to the FE model and the resultant response will be investigated. The results demonstrate the superior ability of the IGWO optimizer to optimally tune a TMD attached to marine structures compared with the other algorithms. In addition, IGWO proved its unique ability to steadily converge toward the global minimum value.