The study of wind turbine blade aerodynamics during full operation has only recently received attention, due to the high cost of conducting such experiments. Data used in this dissertation was received from the National Renewable Energy Laboratory. Such unsteady aerodynamic processes as dynamic stall, boundary layer separation, and turbulent inflow will contribute to increased blade loadings and reduced machine life. The research presented in this dissertation utilizes time series, spectral, and wavelet techniques to analyse wind turbine aerodynamics. In particular, the tower shadow event at 30, 47, and 63% span is linked to rapid reattachment, followed by detachment of the boundary layer. It is also shown that the intensity of these events may be reduced by operating at a slight yaw error, between $-$4 and $-$10 degrees. Furthermore, a unique wavelet analysis technique has been developed to study a 2p effect seen in pressure and lift coefficient data. This wavelet technique is used to show that 2p results from the splitting of the signal time series into two halves by intense tower shadow spikes. Through careful analysis of C$\rm\sb{p}$ plots, the blade section boundary at 30, 47, and 63% span was seen to be detached in 90% of the revolutions studied. Boundary layer transition to separated conditions occurs when the attack angle surpasses 18 degrees, indicating a stall delay of about 8 degrees. Dynamic stall vortex shedding rarely occurs for the conditions seen in the data set analysed in this dissertation. Slight yaw error, as well as the three dimensional effect of rotation, appears to be beneficial for maintaining a slight favorable pressure gradient at inboard blade section during deep stall operation, thereby reducing the potential for vortex growth.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:dissertations-4211 |
| Date | 01 January 1996 |
| Creators | Slepski, Jonathon Edward |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Language | English |
| Detected Language | English |
| Type | text |
| Source | Doctoral Dissertations Available from Proquest |
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