Fast prediction of rain erosion in wind turbine blades using a data-based computational tool
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42241-024-0044-4.pdf
Reference36 articles.
1. Mishnaevsky Jr L., Hasager C. B., Bak C. et al. Leading edge erosion of wind turbine blades: Understanding, prevention and protection [J]. Renewable Energy, 2021, 169: 953–969.
2. Xu D., Wen C., Liu J. Wind turbine blade surface inspection based on deep learning and UAV-taken images [J]. Journal of Renewable and Sustainable Energy, 2019, 11(5): 055305.
3. Aird J. A., Barthelmie R. J., Pryor S. C. Automated quantification of wind turbine blade leading edge erosion from field images [J]. Energies, 2023, 16(6): 2820.
4. Castorrini A., Corsini A., Rispoli F. et al. Computational analysis of wind-turbine blade rain erosion[J]. Computers and Fluids, 2016, 141: 175–183.
5. Walayat K., Haeri S., Iqbal I. et al. Hybrid PD-DEM approach for modeling surface erosion by particles impact [J]. Computational Particle Mechanics, 2023, 10(6): 1895–1911.
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