| 15 Jul 2025
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.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.
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The paper investigates the potential for optimizing floating wind turbines (FWTs) through a novel two-step hybrid optimization framework. This framework introduces innovation by combining design space reduction with surrogate-based modelling to enhance computational efficiency.
In the first step, the design space is reduced by applying design constraints aimed at excluding infeasible solutions. During this phase, the design variable vector Xᵈ (representing buoyancy column dimensions and floater radius) and the environmental condition vector Xᵉ are defined. Additionally, analytical design constraints are introduced based on the floater’s dynamic behaviour and overall geometric limitations.
In the second step, a surrogate model based on feedforward neural networks is trained using aero-hydro-servo-elastic simulations. This surrogate model enables efficient evaluation of the system’s dynamic performance, significantly reducing computational cost. The design space and environmental conditions are further refined using Latin Hypercube Sampling.
The framework is demonstrated using the UMaine VolturnUS semisubmersible platform coupled with the IEA 15 MW Reference Wind Turbine (RWT). The optimization design variables include the external column diameter and floater radius. The objective is to minimize the Levelized Cost of Energy (LCOE), subject to constraints related to ultimate loads, serviceability, and fatigue performance.
Comments and suggestions:
The method is overall interesting. However, claiming that it is general for FOWT is too pretentious as this stage since there are design variables in the rotor (especially large and very flexible ones) and in the coupling between the rotor and the floater/moorings that still need to be proven as feasible with surrogate models. It is recommended that the paper title and abstract more clearly convey the fact that the procedure is so far applied only to floater design (and also assuming only some of the design variables).