AI summaryⓘ
The authors focus on floating offshore wind turbines (FOWTs), which are needed for deep water where fixed foundations don't work well. They highlight the challenge of predicting how much fatigue damage the turbine towers will experience due to wind and waves, especially for large 22 MW designs. To help compare different prediction methods fairly, the authors created FLOATBench, a large, publicly available dataset with detailed fatigue damage information from many simulations across three turbine designs. FLOATBench includes different testing approaches to evaluate model accuracy in various conditions and helps identify where models perform well or poorly. This work is the first to provide a standardized benchmark for fatigue prediction in FOWTs using tabular surrogate models.
floating offshore wind turbinesfatigue damagesurrogate modelingOpenFAST simulationaero-hydro-servo-elastic loadswind/wave operating envelopemachine learning benchmarkregime-aware evaluationtransfer learningtabular data
Authors
João Alves Ribeiro, Bruno Alves Ribeiro, Francisco Pimenta, Sérgio M. O. Tavares, Faez Ahmed
Abstract
Most of the world's offshore wind resource lies in waters too deep for fixed-bottom foundations, making floating offshore wind turbines (FOWTs) essential for deep-water deployment. As the industry scales toward $22$ MW class designs, tower fatigue becomes increasingly critical because larger structures amplify the coupled aero-hydro-servo-elastic loads induced by continuous wind and wave excitation. Accurate fatigue-damage prediction is therefore central to certification, design optimization, and cost reduction. Yet the field lacks a shared surrogate benchmark: studies report different simulations, splits, and metrics, making methods difficult to compare. We present FLOATBench, a public tabular benchmark with $582{,}120$ per-section fatigue-damage labels across three $22$ MW FOWT tower geometries, derived from $19{,}404$ high-fidelity OpenFAST simulations across the three towers ($6{,}468$ per tower: $1{,}078$ aligned wind/wave operating points $\times$ six turbulence seeds), labeled at $30$ cross-sections per tower. FLOATBench includes a regime-aware alpha-shape partition of the joint wind/wave operating envelope, stratifying test points into in-train, interpolation, and extrapolation regimes. It is paired with a reproducible evaluation harness covering three protocol levels: random validation (E1), within-tower regime-aware evaluation (E2), and cross-tower transfer (E3). The regime-aware protocol reveals rank shifts between global and extrapolation performance that random-split leaderboards cannot detect. To the authors' knowledge, FLOATBench is the first FOWT fatigue benchmark for tabular surrogate modeling, and offers an evaluation protocol that generalizes to engineering surrogates defined over physical operating envelopes. Dataset and code available at: https://github.com/Joao97ribeiro/FLOATBench.