Reliability-based design optimization of a mooring system for a floating wind turbine

Despite the rapid development of the floating wind industry, mooring systems optimization remains challenging and critical for reducing cost. The process involves a large design space, nonlinear constraints, and high computational demands. Moreover, uncertainties in marine environments and construction processes significantly affect system responses, yet their impact on mooring optimization remains unclear. To address these gaps, this paper proposes an integrated framework for accurate and efficient reliability-based design optimization (RBDO) of mooring systems. First, a stepwise constraint screening method was employed to obtain the coupled dynamic response of floating offshore wind turbines (FOWT), including platform motion and mooring tensions. The Kriging surrogate model was utilized to establish correlations between mooring design variables and system output responses. Subsequently, the most sensitive design variables were selected as random variables, and the Kriging-based subset simulation (KSS) method was proposed to estimate the mooring system's reliability index. Compared to conventional Monte Carlo simulation (MCS), the proposed method significantly improves computational efficiency. With the well-trained Kriging model, the gradient-based algorithm was employed to explore the design space and quickly identify optimal mooring solutions. Validation demonstrates excellent consistency between Kriging predictions and the simulation results. Furthermore, a comparison was made between the proposed RBDO framework and deterministic optimization, which considers mooring strength safety using partial safety factors. The cost and mooring strength safety performance of the optimal solutions were analyzed. Results demonstrate that the proposed integrated RBDO framework efficiently yields reliable, cost-effective mooring solutions for FOWTs under extreme conditions.