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- W4283076615 abstract "With the increase of wind power capacity in cold climate, an improved understanding of the effects of ice accretion on wind turbine blades would prove to be useful to better estimate the power losses due to icing. Currently, computational fluid dynamics (CFD) is used in conjunction with blade element momentum (BEM) theory to provide power losses estimations. However, most studies so far do not line up with the experimental observations by generally underestimating icing losses. An accurate ice accretion model is a crucial part of the icing estimation process. This paper proposes a method to improve the modelling of ice accretion on a wind turbine nacelle by combining variable high-resolution atmospheric data with the LEWICE software. To validate the methodology, 8 icing events were selected over the winter of 2018–2019, in a Canadian wind farm. Atmospheric data were obtained by a Meteorological Conditions Monitoring Station (MCMS) and images were provided by a camera, both installed on a wind turbine nacelle. The models were compared with experimental data provided by camera images of a reference cylinder. This paper shows that the usage of the LEWICE model improves the concordance with the camera observations compared to the Makkonen model. It was found that the wind orientation on the nacelle could vary significantly during an event and caused conditions outside of the validation range of LEWICE. It was observed that the choice of the meteorological conditions averaging time has a significant impact on the final ice shape. This paper provides valuable insights on the use of high-resolution data and on the LEWICE ice accretion model for ice modelling on the nacelle structures. It becomes a first step before transposing ice accretion to the blades to provide better performance predictions." @default.
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- W4283076615 date "2022-09-01" @default.
- W4283076615 modified "2023-10-13" @default.
- W4283076615 title "Validation of a numerical ice accretion model on a wind turbine with high-resolution field data" @default.
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- W4283076615 doi "https://doi.org/10.1016/j.coldregions.2022.103620" @default.
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