Training and validation datasets for training probabilistic machine learning models on NREL's 10-MW reference wind turbine

doi: 10.4121/21939995.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21939995
Datacite citation style:
Singh, Deepali (2023): Training and validation datasets for training probabilistic machine learning models on NREL's 10-MW reference wind turbine. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/21939995.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

This repository consists of two databases- CASE-ONSHORE and CASE-OFFSHORE, generated using OpenFAST v2.4 on NREL's 10-MW reference wind turbine for training data-driven probabilistic load surrogate models. The data is to be used for mapping 10-minute average environmental conditions to the corresponding 10-minute load statistics such as load average, fatigue and range at various locations on the tower and blades.


history
  • 2023-01-25 first online, published, posted
publisher
4TU.ResearchData
format
dat files
funding
  • Novel deSign, producTion and opEration aPproaches for floating WIND turbine farms (grant code 860737) [more info...] European Commission
organizations
TU Delft, Faculty of Aerospace Engineering, Department of Flow Physics and Technology

DATA

files (7)