Surrogate-Based Design Optimization of Floating Wind Turbines in Time Domain

Abstract. Floating wind turbine (FWT) design involves higher costs and greater uncertainty than onshore or fixed-bottom offshore turbines due to low technology maturity, limited operational experience, and harsh marine environments; these factors have led to conservative design practices. To address these challenges, we introduce a novel two-step deterministic surrogate-based optimization framework that enables efficient time-domain design optimization for FWTs. In the first step, analytical design constraints are applied to refine the design space and establish a feasible region. In the second step, a surrogate model is trained on high-fidelity aero-hydro-elastic simulations, covering the reduced design space defined from step 1. During an optimization run, the surrogate model replaces computationally expensive direct time-domain analyses, capturing the dynamic response of the system with significantly reduced computational effort. This approach effectively balances model fidelity and computational cost, bridging the gap between conceptual and detailed design phases for floating wind structures. We demonstrate the framework on a semisubmersible platform (UMaine VolturnUS) coupled with the IEA 15 MW reference wind turbine, a representative large-scale FWT. Two primary design variables – the buoyancy column diameter and the overall floater radius – are optimized to minimize the levelized cost of energy (LCOE) of the system. The optimization incorporates global structural limit state constraints covering ultimate (ULS), fatigue (FLS), and serviceability (SLS) requirements to ensure the design’s structural feasibility. The surrogate-assisted optimization yields a design that achieves a LCOE of 176.9 /MWh, which is a 3.7 % reduction in LCOE relative to the baseline, with feasibility validated against all ULS, FLS, and SLS criteria. These results highlight the framework’s potential to reduce FWT costs and improve design reliability by enabling time-domain optimization without excessive computational expense.