Articles | Volume 8, issue 6
https://doi.org/10.5194/wes-8-947-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-947-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jun 2023
Research article |
| 07 Jun 2023
| 07 Jun 2023
Grand challenges in the digitalisation of wind energy
Andrew Clifton
CORRESPONDING AUTHOR
Stuttgart Wind Energy at the Institute of Aircraft Design, University of Stuttgart, Stuttgart, Germany
now at: enviConnect, TTI GmbH, 70569 Stuttgart, Germany
Sarah Barber
Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland
Andrew Bray
MXV Ventures, Oakland, California, USA
now at: Aurora Energy Research, Oakland, California, USA
Peter Enevoldsen
Centre for Energy Technologies, Aarhus University, Aarhus, Denmark
Jason Fields
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO, USA
Anna Maria Sempreviva
Department of Wind Energy, DTU, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
Lindy Williams
Computational Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
Julian Quick
The Paul M. Rady Department of Mechanical Engineering, University of Colorado, Boulder, CO, USA
now at: Department of Wind Energy and Energy Systems, DTU, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
Mike Purdue
NRG Sytems, Hinesburg, VT, USA
Philip Totaro
IntelStor LLC, Houston, TX, USA
Yu Ding
Department of Industrial and Systems Engineering, Texas A & M University, College Station, TX, USA
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Gianluca De Fezza and Sarah Barber
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Vasilis Pettas, Matthias Kretschmer, Andrew Clifton, and Po Wen Cheng
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Joseph C. Y. Lee and M. Jason Fields
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Alain Schubiger, Sarah Barber, and Henrik Nordborg
Wind Energ. Sci., 5, 1507–1519, https://doi.org/10.5194/wes-5-1507-2020, https://doi.org/10.5194/wes-5-1507-2020, 2020
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A large-eddy simulation using the lattice Boltzmann method (LBM) Palabos framework was implemented to calculate the wind field over the complex terrain of Bolund Hill. The results were compared to Reynolds-averaged Navier–Stokes and detached-eddy simulation (DES) using Ansys Fluent and field measurements. A comparison of the three methods' computational costs has shown that the LBM, even though not yet fully optimised, can perform 5 times faster than DES and lead to reasonably accurate results.
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Short summary
Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
Wind energy creates huge amounts of data, which can be used to improve plant design, raise...
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