Date of Award
5-2011
Document Type
Thesis
Degree Name
Master of Science (MS)
Legacy Department
Civil Engineering
Committee Chair/Advisor
Atamturktur, Sez
Committee Member
Ravichandran , Nadarajah
Committee Member
Wagner , John
Abstract
Wind turbine blades are being produced at a larger scale in order to meet demands from the burgeoning U.S. wind energy industry, and are forecasted to only grow larger for off-shore applications. Modeling and Simulation (M&S) provides a cost and time efficient alternative when studying the structural behavior of blades such as for loading conditions that are too difficult to replicate in laboratory conditions, and for various severity of damage in wind turbine blades. For this reason, M&S will continue to play an indispensible role in understanding the behavior of wind turbine blades and is gradually replacing the traditional test, build and design procedure.
There are two distinct sources that degrade the predictive capabilities of numerical models: (i) imprecision in parameters and (ii) incompleteness and inaccuracy in the way underlying physics is represented. The first source, also widely known as known unknowns, can be remedied by parameter calibration. Parameter calibration aims to reduce the uncertainty of the model parameters to the nominal but unknown value that should be used, effectively improving the predictions of the numerical simulation. The second source, also widely known as unknown unknowns, can be remedied by bias correction. Bias correction accounts for the inherent error that exists in numerical modeling due to the inability of a model to replicate all of the physics of a system.
Herein, the levels of accuracy of the finite element models of two wind turbine blades are rigorously and quantitative assessed. By investigating the sources of uncertainty, this study aims to promote the use of M&S as a reliable tool in future studies of wind turbine blades.
Recommended Citation
Van buren, Kendra, "Predictive Modeling of Wind Turbine Blades" (2011). All Theses. 1098.
https://open.clemson.edu/all_theses/1098