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...
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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.
Regardless of their nature of stochasticity and uncertain nature, wind and solar resources are the most abundant energy resources used in the development of microgrid systems. In microgrid systems and distribution net...
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Regardless of their nature of stochasticity and uncertain nature, wind and solar resources are the most abundant energy resources used in the development of microgrid systems. In microgrid systems and distribution networks, the uncertain nature of both solar and wind resources results in power quality and system stability issues. The randomization behavior of solar and wind energy resources is controlled through the precise development of a power prediction model. fuzzybased solar PV and wind prediction models may more efficiently manage this randomness and uncertain character. However, this method has several drawbacks, it has limited performance when the volumes of wind and solar resources historical data are huge in size and it has also many membership functions of the fuzzy input and output variables as well as multiple fuzzy rules available. The hybrid fuzzy-pso intelligent prediction approach improves the fuzzy system's limitations and hence increases the prediction model's performance. The fuzzy-pso hybrid forecast model is developed using MATLAB programming of the particle swarm optimization (pso) algorithm with the help of the global optimization toolbox. In this paper, an error correction factor (ECF) is considered a new fuzzy input variable. It depends on the validation and forecasted data values of both wind and solar prediction models to improve the accuracy of the prediction model. The impact of ECF is observed in fuzzy, fuzzy-pso, and fuzzy-GA wind and solar PV power forecasting models. The hybrid fuzzy-pso prediction model of wind and solar power generation has a high degree of accuracy compared to the fuzzy and fuzzy-GA forecasting models. The rest of this paper is organized as: Section II is about the analysis of solar and wind resources row data. The fuzzy-pso prediction model problem formulation is covered in Section III. Section IV, is about the results and discussion of the study. Section V contains the conclusion. The ref-erences and abbreviat
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