An integrated multi-objective optimization framework for large-scale floating offshore wind turbine

This paper proposes a multi-objective optimization framework for large-scale floating offshore wind turbines, addressing the limitations of current optimization methods in handling motion performance interaction effects during the co-optimization of floating platforms and mooring systems. A multi-objective genetic algorithm was employed to minimize the cost of the floating platform and mooring system while ensuring motion performance and safety, accelerating the design cycle through an automated optimization workflow. The constraint functions are mainly established according to industry standards and requirements, then given adequate safety margins for motion through reference points. The optimization process conducts systematic feasibility verification through integrated calculations under multidirectional wave headings and coupled wind-wave load conditions, encompassing equilibrium analysis, intact stability evaluation, mooring line tension failure assessment, natural period computation, and frequency-domain RAO analysis. Time-domain simulation is used to verify the effectiveness of the optimal pareto front solutions concerning motion performance and mooring safety, demonstrating the reliability of the proposed multi-objective optimization framework. The proposed approach offers new insights into the efficient design optimization of large-scale FOWTs, providing an efficient and reliable tool for their commercial engineering design optimization.