Abstract
The majority of the world's wind resources are located in water depths that exceed 60 meters, making the use of bottom-mounted structures both difficult and expensive. Consequently, the complexity of the dynamics between the floating structures and turbines has led to an increase in the number of studies on floating offshore wind turbine (FOWT) solutions. Designing optimal mooring arrangements for large-scale FOWT is currently a significant challenge. A meta-heuristic solution is advised for the resolution of a complex optimization problem, such as the mooring configuration of a floating wind turbine. The Evolutionary Algorithms (EA) are among these, and they employ mechanisms that are inspired by biological evolution. The Genetic Algorithm (GA) is a multi-objective search and optimization algorithm that is derived from Darwin's theory of evolution. It is classified as an Evolutionary Algorithm. It integrates the concept of survival of the fittest with the principle of randomized information exchange. According to the literature, hydrostatic and frequency-domain models are frequently applied in conjunction with multi-objective optimization algorithms, including the GA, for the purpose of optimizing FOWT. However, these models are less precise than time-domain models. The objective of this study is to optimize the mooring system of a FOWT through the use of time-domain simulations in OpenFAST. An in-house code is created to perform Open-FAST calculations and employ GA to determine the most optimal mooring arrangements for specific design variables that satisfy predefined multi-objectives, while adhering to all specific constraints for the FOWT. Lastly, the optimized configurations are compared to the Accidental Limit State (ALS) and Ultimate Limit State (ULS) for the mooring system in a damaged state. The UMaine VolturnUS-S semisubmersible platform is optimized using the newly devised process to optimize power quality and reduce line costs.
