CFD ANALYSIS OF WIND TURBINE BLADE AT VARIOUS ANGLE OF ATTACK AND DIFFERENT REYNOLD NUMBER

Abstract

The rapid expansion of the wind energy market necessitates the need for advanced computational modeling and understanding of wind turbine aerodynamics and wake interactions. The following thesis work looks to study turbulence closure methods widely used in computational fluid dynamics (CFD) and their applicability for modeling wind turbine aerodynamics. The first investigation is a parametric study of turbulence models and their performance on geometries of stationary in-line turbines and disks spaced at different intervals. A variety of Reynolds-averaged Navier-Stokes (RANS) closure schemes (Spalart-Allmaras, Standard k-ε, k-ε Realizable, k-ε RNG, Standard k-ω, k-ω SST) were studied as well as a large eddy simulation (LES) with a dynamic Smagorinsky-Lilly sub-grid scale (SGS) model. The simulations showed the grid refinement to be inadequate for LES studies. The investigation uses only the k-ω SST RANS closure scheme to model wake development and resolution for both a single fully resolved rotating turbine as well as two in-line fully resolved rotating turbines. These simulations were successful in predicting wake development and resolution, as well as predicting velocity deficits experienced by the downstream turbine. Vorticity results also showed an accurate wake structure and helical tendencies. The results of thesis clearly shows the variation of Cl and Cd with angle of attack. Thus for a particular Reynold number the optimum angle of attack came between 350 and 400. With the increase in the Reynold number Cl also increases, and wind turbine efficiency increases by 20%. In contrast to the vast super-computer simulations found in literature, all simulations in this thesis work were calculated using two parallel processors. The accuracy was achieved through assumptions, which were designed to maintain the desired physics while simplifying the complexity of the problem to the capabilities of desktop computing. This research demonstrates the significance of model design and capabilities and accuracy achievable using desktop computing power. This has vast implications of accessibility into academia and the further development of the wind power industry.