Wind turbines often operate under cold weather conditions where icing may occur. Icing causes the blade sections to stall prematurely reducing the power production at a given wind speed. The unsteady aerodynamic loads associated with icing can accelerate blade structural fatigue and creates safety concerns.
In this work, the combined blade element-momentum theory is used to compute the air loads on the baseline rotor blades, prior to icing. At each blade section, a Lagrangian particle trajectory model is used to model the water droplet trajectories and their impact on the blade surface. An extended Messinger model is next used to solve the conservation of mass, momentum, and energy equations in the boundary layer over the surface, and to determine ice accretion rate. Finally, the aerodynamic characteristics of the iced blade sections are estimated using XFOIL, which initiate the next iteration step for the computation of air loads via combined blade element theory. The procedure repeats until a desired exposure time is achieved. The performance degradation is then predicted, based on the aerodynamic characteristics of the final iced blades.
The 2-D ice shapes obtained are compared against experimental data at several representative atmospheric conditions with acceptable agreement. The performance of a generic experimental wind turbine rotor exposed to icing climate is simulated to obtain the power loss and identify the critical locations on the blade. The results suggest the outboard of the blade is more prone to ice accumulation causing considerable loss of lift at these sections. Also, the blades operating at a higher pitch are expected to accumulate more ice. The loss in power ranges from 10% to 50% of the rated power for different pitch settings under the same operating conditions.
Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/53870 |
Date | 21 September 2015 |
Creators | Ali, Muhammad Anttho |
Contributors | Sankar, Lakshmi N. |
Publisher | Georgia Institute of Technology |
Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
Language | en_US |
Detected Language | English |
Type | Thesis |
Format | application/pdf |
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