Characterization of wake effects and loading status of wind turbine arrays under different inflow conditions

Xiangyu Gao, Purdue University

Abstract

The objective of the present work is to improve the accuracy of Actuator Line Modeling (ALM) in predicting the unsteady aerodynamic loadings on turbine blades and turbine wake by assessing different methods used to determine the relative velocity between the rotating blades and wind. ALM is incorporated into a Large Eddy Simulation (LES) solver in OpenFOAM (Open Field Operations and Manipulations). The aerodynamic loadings are validated by experiment results from National Renewable Energy Laboratory (NREL). Turbine wakes are validated by predictions of large eddy simulation using exact 3D blade geometries from a two-blade NREL Phase VI turbine. Three different relative velocity calculation methods are presented: iterative process in Blade Element Momentum (BEM) theory, local velocity sampling, and Lagrange-Euler Interpolation (LEI). Loadings and wakes obtained from these three methods are compared. It is discovered that LEI functions better than the conventional BEM with iterative process in both loading and wake prediction. Then LES-ALM with LEI is performed on a small wind farm deploying five NREL Phase VI turbines in full wake setting. The power outputs and force coefficients of downstream turbines are evaluated. The LES-ALM with LEI is also performed on a small wind farm deploying 25 NREL Phase VI turbines with different inflow angles (from full wake setting to partial wake setting). The power outputs and force coefficients of each turbine are evaluated under different inflow angles (the angle the rotor has to turn to make the rotor plane face the incoming wind) (0, 5, 15, 30 and 45 degree). The power coefficient distributions and thrust coefficient distributions of the wind farm under each inflow angle are compared. The range of inflow angle which is best for power generation is also discussed. The results demonstrate that the LES-ALM with LEI has the potential to optimize wind farm arrangement and pitch angle of individual turbines.

Degree

M.S.E.

Advisors

Chen, Purdue University.

Subject Area

Engineering|Energy

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