Uncertainty-Aware Surrogate for Fatigue Assessment of Moorings in Offshore Wind Turbines
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Abstract
Floating Offshore Wind Turbines (FOWTs) deployed in deep waters extract wind energy via floating platforms that are station kept by mooring lines subjected to complex, highly dynamic loading conditions. The cyclic nature of these dynamic loads induces fatigue damage in the mooring lines, which can culminate in catastrophic failures with substantial operational, economic, and safety implications. The remote offshore location of FOWTs renders conventional, sensor-intensive structural health monitoring in deep water both costly and logistically challenging. Indirect sensing approaches offer a promising alternative; however, existing methods typically neglect inherent aleatoric material uncertainties arising from manufacturing variability, installation effects, and long-term corrosion, thereby limiting their reliability for informed decision making.
To overcome this limitation, the present study introduces an uncertainty-aware surrogate-based indirect sensing framework that quantifies mooring line fatigue damage in a fully probabilistic manner by constructing confidence regions of damage conditional on the prevailing environmental loading. This probabilistic characterization supports more reliable, risk informed inspection, maintenance planning, and life-extension strategies. The surrogate model is trained over a wave scatter table representative of the Gulf of Khambhat region, encompassing a wide range of sea states. Training and validation datsets are generated using high fidelity numerical simulations generated with OpenFAST, based on the NREL 5 MW OC4 semisubmersible wind turbine reference model.
How to Cite
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FOWT, Mooring lines, Fatigue, Renewable energy, Stochastic kriging
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