Articles | Volume 4, issue 4
Wind Energ. Sci., 4, 645–651, 2019
https://doi.org/10.5194/wes-4-645-2019
Wind Energ. Sci., 4, 645–651, 2019
https://doi.org/10.5194/wes-4-645-2019
Brief communication
02 Dec 2019
Brief communication | 02 Dec 2019

Brief communication: Wind-speed-independent actuator disk control for faster annual energy production calculations of wind farms using computational fluid dynamics

Maarten Paul van der Laan et al.

Related authors

Brief communication: A clarification of wake recovery mechanisms
Maarten Paul van der Laan, Mads Baungaard, and Mark Kelly
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-56,https://doi.org/10.5194/wes-2022-56, 2022
Preprint under review for WES
Short summary
Wind turbine wake simulation with explicit algebraic Reynolds stress modeling
Mads Baungaard, Stefan Wallin, Maarten Paul van der Laan, and Mark Kelly
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-50,https://doi.org/10.5194/wes-2022-50, 2022
Preprint under review for WES
Short summary
Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022,https://doi.org/10.5194/wes-7-1069-2022, 2022
Short summary
RANS modeling of a single wind turbine wake in the unstable surface layer
Mads Baungaard, Maarten Paul van der Laan, and Mark Kelly
Wind Energ. Sci., 7, 783–800, https://doi.org/10.5194/wes-7-783-2022,https://doi.org/10.5194/wes-7-783-2022, 2022
Short summary
FarmConners Wind Farm Flow Control Benchmark: Blind Test Results
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Ju Feng, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit Dighe, Marcus Becker, Maarten van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Frédéric Blondel, Ishaan Sood, and Johan Meyers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-5,https://doi.org/10.5194/wes-2022-5, 2022
Revised manuscript accepted for WES
Short summary

Related subject area

Aerodynamics and hydrodynamics
FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models
Michael J. LoCascio, Christopher J. Bay, Majid Bastankhah, Garrett E. Barter, Paul A. Fleming, and Luis A. Martínez-Tossas
Wind Energ. Sci., 7, 1137–1151, https://doi.org/10.5194/wes-7-1137-2022,https://doi.org/10.5194/wes-7-1137-2022, 2022
Short summary
High-Reynolds-number investigations on the ability of the full-scale e-TellTale sensor to detect flow separation on a wind turbine blade section
Antoine Soulier, Caroline Braud, Dimitri Voisin, and Frédéric Danbon
Wind Energ. Sci., 7, 1043–1052, https://doi.org/10.5194/wes-7-1043-2022,https://doi.org/10.5194/wes-7-1043-2022, 2022
Short summary
Experimental investigation of mini Gurney flaps in combination with vortex generators for improved wind turbine blade performance
Jörg Alber, Marinos Manolesos, Guido Weinzierl-Dlugosch, Johannes Fischer, Alexander Schönmeier, Christian Navid Nayeri, Christian Oliver Paschereit, Joachim Twele, Jens Fortmann, Pier Francesco Melani, and Alessandro Bianchini
Wind Energ. Sci., 7, 943–965, https://doi.org/10.5194/wes-7-943-2022,https://doi.org/10.5194/wes-7-943-2022, 2022
Short summary
Parked and operating load analysis in the aerodynamic design of multi-megawatt-scale floating vertical-axis wind turbines
Mohammad Sadman Sakib and D. Todd Griffith
Wind Energ. Sci., 7, 677–696, https://doi.org/10.5194/wes-7-677-2022,https://doi.org/10.5194/wes-7-677-2022, 2022
Short summary
High-Reynolds-number wind turbine blade equipped with root spoilers – Part 1: Unsteady aerodynamic analysis using URANS simulations
Thomas Potentier, Emmanuel Guilmineau, Arthur Finez, Colin Le Bourdat, and Caroline Braud
Wind Energ. Sci., 7, 647–657, https://doi.org/10.5194/wes-7-647-2022,https://doi.org/10.5194/wes-7-647-2022, 2022
Short summary

Cited articles

Andersen, S.: Simulation and Prediction of Wakes and Wake Interaction in Wind Farms, PhD thesis, Wind Energy Department, Technical University of Denmark, 2014. a
Barthelmie, R. J., Frandsen, S. T., Nielsen, M. N., Pryor, S. C., Rethore, P. E., and Jørgensen, H. E.: Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at middelgrunden offshore wind farm, Wind Energy, 10, 517–528, https://doi.org/10.1002/we.238, 2007. a
Göçmen, T., van der Laan, M. P., Réthoré, P. E., Peña Diaz, A., Larsen, G. C., and Ott, S.: Wind turbine wake models developed at the technical university of Denmark: A review, Renewable and Sustainable Energy Reviews, 60, 752–769, 2016. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, Tech. rep., National Renewable Energy Laboratory, 2009. a
Michelsen, J. A.: Basis3D – a platform for development of multiblock PDE solvers., Tech. Rep. AFM 92-05, Technical University of Denmark, Lyngby, Denmark, 1992. a
Download
Short summary
Wind farm layouts are designed by simple engineering wake models, which are fast to compute but also include a high uncertainty. Higher-fidelity models, such as Reynolds-averaged Navier–Stokes, can be used to verify optimized wind farm layouts, although the computational costs are high due to the large number of cases that are needed to calculate the annual energy production. This article presents a new wind turbine control method to speed up the high-fidelity simulations by a factor of 2–3.