Developing a predictive method based on the vibration behavior of a naval ship hull model using hybrid fuzzy meta-heuristic algorithms.

Ships are complex structures composed of various components with exclusive dynamic behaviors and natural frequencies, so assessing their vibration behavior is essential. Some situations in practical applications could alter the ship's dynamic characteristics and cause significant changes in its vibration behavior. Since the ship's mass is one of the most important dynamic parameters in determining vibration behavior, local mass change can lead to changes in its dynamic characteristics and must be considered. This study aims to develop a method to predict the effect of the location and magnitude of mass change on the ship hull's vibration behavior. It would be feasible to enhance the dynamic behavior and reduce undesirable noises by locating the mass change on the ship hull. In this regard, experimental and numerical modal analysis is performed on a scaled model of a naval ship hull. The baseline FE model is used to calculate the variation in frequencies of the model caused by different local mass change scenarios. Using these measurements a fuzzy system is generated and optimized by Genetic and Particle Swarm Optimization algorithms. Finally, the efficiency of the fuzzy-PSO is validated by different mass change scenarios foreseen on the physical model of the ship hull. • Experimental and numerical study on the vibration behavior of naval ship hull in the case of local mass change. • Combine fuzzy logic and meta-heuristic algorithms to predict the location and magnitude of local mass change. • Validate the efficiency of the fuzzy meta-heuristic algorithms through experimental tests. • The proposed method is successful in identifying the location and magnitude of local mass change. [ABSTRACT FROM AUTHOR]