FLOATBench: A Dataset and Benchmark for Floating Offshore Wind Turbine Tower Fatigue

2026-05-25

AI Summary

This study addresses the lack of a standardized benchmark for fatigue damage prediction in floating offshore wind turbine towers, which has hindered fair comparison of existing methods. To bridge this gap, the authors propose FLOATBench—the first tabular surrogate modeling benchmark for this task—built upon 19,404 high-fidelity OpenFAST simulations encompassing three distinct 22 MW floating turbine configurations. The resulting open dataset comprises 582,120 fatigue damage labels and introduces a region-aware partitioning scheme under combined wind-wave conditions, along with a three-tier evaluation protocol (random split, intra-tower region-aware, and cross-tower transfer). FLOATBench reveals substantial discrepancies among current approaches in terms of extrapolation capability and ranking consistency, thereby establishing a standardized platform to advance reliable fatigue prediction models and support optimized design and certification of floating wind turbines.

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.

Problem

• floating offshore wind turbine
• tower fatigue
• surrogate benchmark
• fatigue-damage prediction
• aero-hydro-servo-elastic loads

Innovation

• floating offshore wind turbine
• fatigue damage prediction
• surrogate benchmark
• regime-aware evaluation
• aero-hydro-servo-elastic loads