Abstract
The Blade Element Momentum (BEM) equations are widely used for analyzing the wind turbine aerodynamics and for turbine blade design. Compared to the classical approach to the solution of BEM equations which is relatively complex and can sometime lead to non-convergent solution, recently Andrew Ning has described a simpler solution method that reduces the solution to the BEM equations to a one-dimensional root-finding algorithm. In the Ning’s approach, the Blade Element Momentum equations are determined for a blade inflow angle instead of the axial and tangential induction factors used in the classical BEM method. This approach simplifies the solution method and also leads to guaranteed convergence. In this thesis, the solution from new approach is compared to the numerical solution from the classical method by comparing the results with the experimental data for the NREL Phase II and Phase III, and Risoe horizontal axis wind turbines.
In the BEM formulation, tip-loss correction factor influences the accuracy of the solution. The traditional tip-loss correction factor used is due to Prandtl and Glauert which is not always consistent with the physical behavior of the flow field near the tip. This research formulates a new more physically correct analytical expression for the tip losses and the results are compared with those obtained using the Prandtl-Glauert tip loss correction.
Committee Chair
Ramesh Agarwal
Committee Members
Swami Karunamoorthy Qiulin Qu
Degree
Master of Science (MS)
Author's Department
Mechanical Engineering & Materials Science
Document Type
Thesis
Date of Award
Summer 8-19-2016
Language
English (en)
DOI
https://doi.org/10.7936/K78G8J1N
Recommended Citation
Mou, Min, "A Comparative Study of Two Solution Methods for Blade Momentum Equations Used in the Aerodynamic Analysis and Design of Wind Turbines" (2016). McKelvey School of Engineering Theses & Dissertations. 179.
The definitive version is available at https://doi.org/10.7936/K78G8J1N
Comments
Permanent URL: https://doi.org/10.7936/K78G8J1N