화학공학소재연구정보센터
Renewable Energy, Vol.52, 31-39, 2013
Wind turbine CFD modeling using a correlation-based transitional model
This paper describes the development of a Horizontal Axis Wind Turbine 3D CFD model using the Ansys Fluent solver. The model was developed to predict wind turbine performance and evaluate the capabilities of the 11) model (based on BEM Theory) developed by the authors. The two models were compared in terms of accuracy, predictability and calculation time. The strategy of generating a high quality mesh and optimizing the turbulence models (two equations SST k-omega fully turbulent and four equations Transitional SST models) is presented. In particular, a high quality unstructured 3D grid was generated to optimize spatial discretization and meet turbulence model requirements. The mesh was subsequently converted from a tetrahedral into a polyhedral geometry to considerably reduce the number of cells and better align the cell faces and flow. Polyhedral cells also reduce interpolation errors and false numerical diffusion. The empirical correlations of the Transitional SST turbulence model were modified to improve it for wind turbine applications. A significant number of numerical 2D airfoil tests were implemented to calibrate the turbulence model. The results of these tests were applied to the turbulence model by modifying the local correlation parameters. The same parameters were used in the 3D wind turbine model. A Moving Reference Frame model was used to simulate rotation and evaluate 3D flow along the rotor blades. The numerical results were compared to the fully turbulent SST k-omega simulation data to demonstrate the superior capabilities of the modified Transitional model. The 3D CFD model was validated using NREL PHASE VI experimental data available from scientific literature. An application of the 3D model to a new micro wind turbine is presented at the end of this paper. The micro rotor was designed and optimized using the 1D code and actually built. (C) 2012 Elsevier Ltd. All rights reserved.