Articles | Volume 7, issue 2
https://doi.org/10.5194/wes-7-539-2022
https://doi.org/10.5194/wes-7-539-2022
Research article
 | 
11 Mar 2022
Research article |  | 11 Mar 2022

Four-dimensional wind field generation for the aeroelastic simulation of wind turbines with lidars

Yiyin Chen, Feng Guo, David Schlipf, and Po Wen Cheng

Related authors

Design of a scaled wind turbine model for matching multiple structural load transients under blade-detachment conditions
Hongwen Liu, Qingfu Song, Feng Guo, Lin Yang, Wenhao Xu, Lijun Zhang, and Zhen Gao
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-167,https://doi.org/10.5194/wes-2026-167, 2026
Preprint under review for WES
Short summary
Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements
Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng
Wind Energ. Sci., 9, 2175–2193, https://doi.org/10.5194/wes-9-2175-2024,https://doi.org/10.5194/wes-9-2175-2024, 2024
Short summary
Dynamic performance of a passively self-adjusting floating wind farm layout to increase the annual energy production
Mohammad Youssef Mahfouz, Ericka Lozon, Matthew Hall, and Po Wen Cheng
Wind Energ. Sci., 9, 1595–1615, https://doi.org/10.5194/wes-9-1595-2024,https://doi.org/10.5194/wes-9-1595-2024, 2024
Short summary
Identification of electro-mechanical interactions in wind turbines
Fiona Dominique Lüdecke, Martin Schmid, and Po Wen Cheng
Wind Energ. Sci., 9, 1527–1545, https://doi.org/10.5194/wes-9-1527-2024,https://doi.org/10.5194/wes-9-1527-2024, 2024
Short summary
A Comprehensive Design Methodology of Shared Mooring Line Configurations for Assessing Mooring Costs and Performances of Floating Offshore Wind Turbines
Qi Pan, Dexing Liu, Feng Guo, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-44,https://doi.org/10.5194/wes-2024-44, 2024
Preprint withdrawn
Short summary

Cited articles

Banakh, V. and Smalikho, I.: Estimation of the turbulence energy dissipation rate from the pulsed Doppler lidar data, J. Atmos. Ocean. Tech., 10, 957–965, 1994. a
Bartlett, M. S.: Smoothing periodograms from time-series with continuous spectra, Nature, 161, 686–687, https://doi.org/10.1038/161686a0, 1948. a
Bos, R.: Extreme gusts and their role in wind turbine design, Dissertation, Delft University of Technology, https://doi.org/10.4233/uuid:d6097e3a-1cdd-4845-a71c-90f469d28b7a, 2017. a, b
Bossanyi, E.: Un-freezing the turbulence: Application to LiDAR-assisted wind turbine control, IET Renew. Power Gen., 7, 321–329, https://doi.org/10.1049/iet-rpg.2012.0260, 2013. a, b
Bossanyi, E. A., Kumar, A., and Hugues-Salas, O.: Wind turbine control applications of turbine-mounted LIDAR, J. Phys.-Conf. Ser., 555, 012011, https://doi.org/10.1088/1742-6596/555/1/012011, 2014. a, b
Download
Short summary
Lidar-assisted control of wind turbines requires a wind field generator capable of simulating wind evolution. Out of this need, we extend the Veers method for 3D wind field generation to 4D and propose a two-step Cholesky decomposition approach. Based on this, we develop a 4D wind field generator – evoTurb – coupled with TurbSim and Mann turbulence generator. We further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide practical suggestions.
Share
Altmetrics
Final-revised paper
Preprint