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EPRC is a leading institute in Europe for comparative research on public policy, with a particular focus on regional development policies. Spanning 30 European countries, EPRC research programmes have a strong emphasis on applied research and knowledge exchange, including the provision of policy advice to EU institutions and national and sub-national government authorities throughout Europe.

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Predicting unsteady blade loads of a wind turbine using RANS and vorticity transport methodologies

Kim, Da Hye. and Yu, Dong Oh and Kwon, Oh Joon and Fletcher, Timothy M. and Scheurich, Frank and Brown, Richard (2010) Predicting unsteady blade loads of a wind turbine using RANS and vorticity transport methodologies. In: European Wind Energy Conference, 2010-04-20 - 2010-04-23.

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Abstract

Numerical simulations of the NREL phase VI wind turbine operating in yawed conditions have been performed using two computational methods; one based on the solution of the Reynolds-averaged Navier-Stokes equations (RANS) using unstructured overset meshes and one known as the Vorticity Transport Model (VTM) that is based on the solution of the vorticity transport equation. Simulations with and without the instrumentation enclosure that was present during the NREL experiments were performed. It was found that the enclosure influenced the loading significantly at the inboard part of the blade when the blade passed through the wake that was developed by the enclosure. Both the RANS and VTM codes are able to predict well the unsteady and time-averaged aerodynamic loadings on the wind turbine blades at low wind speeds. At high wind speeds, leading-edge flow separation and strong radial flow are observed on the suction surface of the blades, when the blades are at the retreating side of the rotor. Both the RANS and VTM codes provide less accurate predictions of the blade loads. However, at the advancing side of the rotor, the flow is mostly attached to the surface of the blade, and both the RANS and VTM predictions of the blade loads are in good agreement with the measured data.