FLOATBench: Wind Turbine Tower Damage

Tabular fatigue dataset for 22 MW floating offshore wind turbine (FOWT) towers. Contains 582,120 labelled tower section fatigue damage records across three tower geometries: the IEA-22 reference turbine baseline (ref) and two FLOAT-derived re-designs (opt1, opt2).

Layout

FLOATBench/
├── ref/                      IEA-22 reference turbine baseline
│   ├── data.csv              194,040 rows × 16 cols (full table, is_train flag)
│   ├── train_damage.csv       51,840 rows × 15 cols
│   ├── test_damage.csv       142,200 rows × 15 cols (with regime labels)
│   └── metadata.json         counts, split summary
├── opt1/                     FLOAT-derived re-design
│   └── ...                   same files
└── opt2/                     FLOAT-derived re-design
    └── ...                   same files

Schema

Identifiers

Column	Type	Meaning
sim_id	int	Unique simulation identifier (ties together the 30 sections of one run)
section_id	int	Tower section index ∈ {1,...,30}, 1 (base) to 30 (top)

Environmental features

Column	Type	Meaning
wind_speed	float	Nominal hub-height wind speed (m/s)
mean_wind_speed	float	Realised 10-min mean hub-height wind speed (m/s)
std_wind_speed	float	Realised 10-min std of hub-height wind speed (m/s)
wave_hs	float	Significant wave height (m)
wave_tp	float	Wave peak period (s)
wind_seed_id	int	Turbulence seed index ∈ {1,...,6}

Tower section geometry

Column	Type	Meaning
section_height_m	float	Tower section midpoint height along tower axis (m)
section_radius_m	float	Tower section outer radius (m)
section_thickness_m	float	Tower section wall thickness (m)

Regime labels

Column	Type	Meaning
wind_group	str	In-train / Interpolate / Extrapolate (all train rows are In-train)
wave_group	str	In-train / Interpolate / Extrapolate (all train rows are In-train)

Split flag (only in data.csv)

Column	Type	Meaning
is_train	bool	True for train rows, False for test rows

Damage targets

Column	Type	Meaning
damage	float	Miner-summed fatigue damage at the section (dimensionless)
damage_weight	float	Probability of occurrence over the 25-year service life

Lifetime damage at a section is recovered as sum(damage_i * damage_weight_i) over all conditions.

Split

The recommended train/test partition is regime-aware, an alpha-shape over the joint wind/wave operating envelope, populating all nine cells of the In-train / Interpolate / Extrapolate × wind/wave grid. Per tower:

Subset	Rows	Conditions	Description
Train	51,840	288	All In-train/In-train cell
Test	142,200	790	Spans the remaining 8 wind×wave regime cells

All rows carry per-row wind_group and wave_group labels (train rows are In-train/In-train by construction) so the regime-aware evaluation can be reproduced directly.

Quickstart

import pandas as pd

train = pd.read_csv("ref/train_damage.csv")
test  = pd.read_csv("ref/test_damage.csv")

# evaluate on the worst-case wind+wave extrapolation cell
ex_ex = test[(test.wind_group == "Extrapolate") &
             (test.wave_group == "Extrapolate")]

License

Released under CC-BY-4.0.

Authors

• João Alves Ribeiro (corresponding), Massachusetts Institute of Technology, jpar@mit.edu
• Bruno Alves Ribeiro, Brown University
• Francisco Pimenta, University of Porto
• Sérgio M. O. Tavares, University of Aveiro
• Faez Ahmed, Massachusetts Institute of Technology, faez@mit.edu

Contact

For questions or issues with the dataset, contact the corresponding author João Alves Ribeiro at jpar@mit.edu.