ML-RANS closure and LES wake clustering of horizontal-axis wind turbines: data and code This record contains the data and code of two studies on the wake of the NREL 5-MW reference turbine in a turbulent atmospheric boundary layer. All simulations were run with OpenFOAM v2212 and an actuator-line model of the rotor. ML-RANS_dataset.zip contains the data and code of the article "A Machine-Learning-Defined Eddy-Viscosity Closure for RANS Simulations of Horizontal-Axis Wind Turbine Wakes" (International Journal of Thermofluids, submitted). A K-means clustering of the flow and a neural network are trained on a single LES at 11.4 m/s to predict the LES-equivalent eddy viscosity of a k-omega SST URANS model. The trained models run inside a modified OpenFOAM solver (ML-RANS) on three grids and at 8 and 15 m/s. The archive holds the LES and RANS reference slices, the training set, the trained models, the source code and results of the solver, the rotor-disk metrics, and the scripts that produce the figures and tables of the article. LES_wake_clustering.zip contains the data and code of the paper "Data-Driven Decomposition of Horizontal-Axis Wind-Turbine Wakes" (Journal of Engineering for Gas Turbines and Power, 2026, doi:10.1002/jegt.70173; presented at ASME Turbo Expo 2026 as GT2026-178848). It holds LES of the same turbine at 8, 11.4 and 15 m/s with yaw angles from 0° to 40° (13 cases), plus one case without turbine. For each case it provides two planes of the flow field, the OpenFOAM case set-up, and the rotor, blade and probe time series. For the 11.4 m/s cases at 0°, 10° and 20° yaw it also holds the K-means models and cluster fields of the paper, together with the clustering code. Each archive includes a README that describes its content and how to reproduce the results, and a list of SHA-256 checksums. Licences: BSD 3-Clause for all data and code, except the ML-RANS solver (ML-RANS_dataset/code/03_solver). The solver is derived from OpenFOAM and is distributed under GPL-3.0-or-later. If you use this material, please cite the article that goes with each archive: ML-RANS_dataset: A. Carloni, G. Delibra, F. De Girolamo, L. Tieghi, A. Corsini, V. F. Barnabei, A Machine-Learning-Defined Eddy-Viscosity Closure for RANS Simulations of Horizontal-Axis Wind Turbine Wakes, International Journal of Thermofluids (submitted). LES_wake_clustering: A. Carloni, G. Delibra, F. De Girolamo, V. F. Barnabei, L. Tieghi, Data-Driven Decomposition of Horizontal-Axis Wind-Turbine Wakes, Journal of Engineering for Gas Turbines and Power (2026), doi:10.1002/jegt.70173. OPENFOAM® is a registered trade mark of OpenCFD Limited. This offering is not approved or endorsed by OpenCFD Limited.
2026 Carloni, Andrea; Delibra, Giovanni; De Girolamo, Filippo; Tieghi, Lorenzo; Barnabei, Valerio Francesco; Castorrini, Alessio; Corsini, Alessandro
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Scopri
Tipologia
- 15 Dataset113
Data di pubblicazione
- 2020 - 202691
- 2010 - 201918
- 2006 - 20094
Editore
- Mendeley Data3
- Zenodo3
- Figshare2
- Istituto centrale per il catalogo e la documentazione2
- Mendeley2
- UC San Diego Library Digital Collections2
- ZENODO2
- Ahmed Abouaiana1
- ArXiv1
- Association for Computational Linguistics1
Settore disciplinare
- Settore BIO/10 - Biochimica4
- Settore BIO/11 - Biologia Molecolare4
- Settore ICAR/17 - Disegno3
- Settore ING-INF/03 - Telecomunicazioni3
- Settore BIO/03 - Botanica Ambientale e Applicata2
- Settore BIO/02 - Botanica Sistematica1
- Settore BIO/05 - Zoologia1
- Settore BIO/08 - Antropologia1
- Settore BIO/18 - Genetica1
- Settore FIS/05 - Astronomia e Astrofisica1
Prodotti per parola chiave
- remote sensing3
- 3D laser scan2
- dome2
- gis2
- intrados2
- Italian2
- Lexical gemination2
- linked open data2
- mangrove cover assessment2
- metadata2
Lingua
- eng56
- ita18
- heb1
- lat1
- por1
Accesso al fulltext
- no fulltext112
- open1