| 25 Jun 2026
Fatigue-constrained gradient-based design optimization of main bearing–shaft systems for floating wind turbine drivetrains
Abstract. Floating offshore wind turbines have significant potential for economical and environmentally-efficient solutions for energy production. However, current state-of-the-art witnesses a lack of integrated analysis and optimization of these large, dynamically-coupled complex engineering machines and their critical sub-systems, such as the drivetrain, with research and experience still in their infancy. The aim of the paper is to facilitate efficient and holistic gradient-based multi-disciplinary constraint optimization of drivetrains by exploiting numerical models with varied fidelity-levels. This is illustrated through a constraint optimization of the main-shaft assembly, which includes critical fatigue-limit constraints of main-bearings, bridging design load case-based load spectra reduction with analytically-differentiable damage equivalent loads in contrast to conventional non-smooth bin-counting. The importance of fatigue as a design driver is shown by comparison to only static structural design. The proposed methodology is implemented as a modular extension within the open-source WISDEM-WEIS framework based on OpenMDAO, enabling efficient integration into existing multidisciplinary system-level wind turbine design workflows.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.
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This paper achieves an multidisciplinary constrained mass optimization method for floating wind turbine drivetrains by introducing Damage Equivalent Loads (DEL) into a gradient-based optimization framework and proposing a main bearing analytical model that accounts for moment-reacting characteristics. Few comments are listed below:
1. The introduction repeatedly emphasizes the limitations of sequential optimization methods in industry and calls for fully coupled, system-level optimization. However, the experimental setup uses a traditional "two-step global-local decoupled approach," assuming fixed hub loads throughout the drivetrain optimization process. When system mass undergoes drastic changes of up to 48%, the structural frequencies will inevitably shift, subsequently altering the aeroelastic loads. Assuming constant loads weakens the credibility of the "coupled MDAO" core contribution.
2. The optimization process relies entirely on the analytical DEL surrogate model. After obtaining the optimal design (e.g., the converged design in Tab. 9), the paper fails to feed this design back into OpenFAST for full-time-series IEC DLC simulations. This step is crucial to verify whether the bearing fatigue life optimized using smooth DEL still strictly meets the standards in true transient time-domain conditions.
3. The authors criticize current research for "utilizing expensive genetic algorithms for drivetrain component design" in the background section. However, in the results section, they only compare the convergence of different gradient calculation methods. There is no direct quantitative comparison of computational time or global optimal solution quality against the criticized gradient-free/heuristic algorithms (like GAs, PSO), leaving the argument for its computational superiority insufficiently supported.