Rain erosion of wind turbine blades represents an interesting topic of study due to its non-negligible impact on annual energy production of the wind farms installed in rainy sites. A considerable amount of recent research works has been oriented to this subject, proposing rain erosion modelling, performance losses prediction, structural issues studies, etc. This work aims to present a new method to predict the damage on a wind turbine blade. The method is applied here to study the effect of different rain conditions and blade coating materials, on the damage produced by the rain over a representative section of a reference 5MW turbine blade operating in normal turbulence wind conditions.
Machine learning aided prediction of rain erosion damage on wind turbine blade sections / Castorrini, A.; Venturini, P.; Gerboni, F.; Corsini, A.; Rispoli, F.. - 1:(2021). (Intervento presentato al convegno ASME Turbo Expo 2021: turbomachinery technical conference and exposition, GT 2021 tenutosi a Conferenza virtuale) [10.1115/GT2021-59156].
Machine learning aided prediction of rain erosion damage on wind turbine blade sections
Castorrini A.;Venturini P.
;Corsini A.;Rispoli F.
2021
Abstract
Rain erosion of wind turbine blades represents an interesting topic of study due to its non-negligible impact on annual energy production of the wind farms installed in rainy sites. A considerable amount of recent research works has been oriented to this subject, proposing rain erosion modelling, performance losses prediction, structural issues studies, etc. This work aims to present a new method to predict the damage on a wind turbine blade. The method is applied here to study the effect of different rain conditions and blade coating materials, on the damage produced by the rain over a representative section of a reference 5MW turbine blade operating in normal turbulence wind conditions.File | Dimensione | Formato | |
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