Date of Award

May 2013

Degree Type

Thesis

Degree Name

Master of Science

Department

Engineering

First Advisor

Ryoichi S. Amano

Committee Members

David C. Yu, Woo -J Chang

Abstract

As the prevalence of wind turbines in the energy market increases, so too does the demand for high-wind real-estate. As a result, wind turbines are placed closer together, which leads to structural challenges due to the cyclical fatigue loading from the wake of upwind turbines. Characterizing the wake behind wind turbines with respect to those downwind is especially important given the 20-year wind turbine lifetime that commercial wind turbine consumers expect. This project aimed to characterize the near wake behind a model wind turbine.

In order to accomplish this, a 12.8 meter-long and 1.22 meter-square test section low-turbulence wind tunnel and a 30 cm-diameter three-blade NACA 4412 wind turbine were designed and constructed. Velocity was measured using a 2-axis X-type miniature hotwire anemometer attached to a three axis traverse, which was controlled with LabVIEW 2012. Data acquisition was programed in LabVIEW 2012, and data reduction was performed in MATLAB.

The near wake characterization showed steep velocity gradients, which are indicative of high turbulence, directly behind the wind turbine hub and at the blade tips. At 3 blade diameters downstream from the wind turbine, the beginning of the transition to the far wake could clearly be seen. The previously steep velocity gradients at the blade tips became more diffuse and the large velocity gradients were centered behind the hub. This followed the bell-shaped turbulence intensity curve theory predicts. In the far wake-region turbulence will collapse toward the center of the wind turbine wake and turbulence at the blade tips will expand out and return to ambient.

The data collected matched both theoretical computational fluid dynamics as well as previous experimental results. This work validates and opens the door to the use of the wind tunnel for future work to refine the wake characterization and the prediction of cyclical loading on downwind wind turbines.

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