Evolving offshore wind: A genetic algorithm-based support structure optimization framework for floating wind turbines

Abstract

This paper presents a genetic algorithm-based optimization framework for floating offshore wind turbine support structures. Using a nine-variable support structure parameterization, this framework spans a greater extent of the design space than preexisting optimization approaches in the literature. With a frequency-domain dynamics model that includes linearized hydrodynamic forces, linearized mooring forces, and linearized wind turbine effects, the framework provides a good treatment of the important physical considerations while still being computationally efficient. The genetic algorithm optimization approach provides a unique ability to visualize the design space. Application of the framework to a hypothetical scenario demonstrates the framework's effectiveness and identifies multiple local optima in the design space - some of conventional configurations and others more unusual. By optimizing to minimize both support structure cost and root-mean-square nacelle acceleration and plotting the design exploration in terms of these quantities, a Pareto front can be seen. Clear trends are visible in the designs as one moves along the front: designs with three outer cylinders are best below a cost of $6M, designs with six outer cylinders are best above a cost of $6M, and heave plate size increases with support structure cost. The complexity and unconventional configuration of the Pareto optimal designs may indicate a need for improvement in the framework's cost model.