Horizontal-axis wind turbines operate in a complex, inherently unsteady aerodynamic environment. The flow over the blades is dominated by 3-D effects, particularly during stall, which is accompanied by massive flow separation and vortex shedding. There is always bluff-body shedding from the turbine nacelle and support structure which interacts with the rotor wake. In addition, the high aspect ratios of wind turbine blades make them very flexible, leading to substantial aeroelastic deformation of the blades, altering the aerodynamics. Finally, when situated in a wind farm, turbines must operate in the unsteady wake of upstream neighbors. Though computational fluid dynamics (CFD) has made significant inroads as a research tool, simple, inexpensive methods, such as blade element momentum theory, are still the workhorses in wind turbine design and aeroelasticity applications. These methods are unable to accurately predict rotor loads near the edges of the operating envelope.
In this work, a range of unstructured grid CFD techniques for predicting wind turbine loads and aeroelasticity has been developed and applied to the NREL Unsteady Aerodynamics Experiment Phase VI rotor. First, a kd-tree based nearest neighbor search algorithm was used to improve the computational efficiency of an approximate unsteady actuator blade method. This method was then shown to predict root and tip vortex locations and strengths similar to an overset method, but without the computational expense of modeling the blade surfaces. A hybrid Reynolds-averaged Navier-Stokes / Large Eddy Simulation (HRLES) turbulence model was extended to an unstructured grid framework and demonstrated to improve predictions of unsteady loading and shedding frequency in massively separated cases. For aeroelastic predictions, a methodology for tight coupling between an unstructured CFD solver and a computational structural dynamics tool was developed. Finally, time-accurate overset rotor simulations of a complete turbine---blades, nacelle, and tower---were conducted using both RANS and HRLES turbulence models. The HRLES model was able to accurately predict rotor loads when stalled. In yawed flow, excellent correlations of mean blade loads with experimental data were obtained across the span, and wake asymmetry and unsteadiness were also well-predicted.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/39491 |
| Date | 19 January 2011 |
| Creators | Lynch, Charles Eric |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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