Browsing by Author "Joyner, Michele Lynn"

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ADAPTION OF AKAIKE INFORMATION CRITERION UNDER LEAST SQUARES FRAMEWORKS FOR COMPARISON OF STOCHASTIC MODELS
(North Carolina State University. Center for Research in Scientific Computation, 2019) Banks, Harvey Thomas; Joyner, Michele Lynn
An Application of a Reduced Order Computational Methodology for Eddy Current Based Nondestructive Evaluation Techniques
(2001-06-11) Joyner, Michele Lynn; H.T. Banks, Chair; H.T. Tran, Member; Pierre A. Gremaud, Member; Kazufumi Ito, Member
In the field of nondestructive evaluation, new and improved techniques are constantly being sought to facilitate the detection of hidden corrosion and flaws in structures such as airplanes and pipelines. In this dissertation, we explore the feasibility of detecting such damages by application of an eddy current based technique and reduced order modeling. We begin by developing a model for a specific eddy current method in which we make some simplifying assumptions reducing the three-dimensional problem to a two-dimensional problem. (We do this for proof-of-concept.) Theoretical results are then presented which establish the existence and uniqueness of solutions as well as continuous dependence of the solution on the parameters which represent the damage. We further discuss theoretical issues concerning the least squares parameter estimation problem used in identifying the geometry of the damage. To solve the identification problem, an optimization algorithm is employed which requires solving the forward problem numerous times. To implement these methods in a practical setting, the forward algorithm must be solved with extremely fast and accurate solution methods. Therefore in constructing these computational methods, we employ reduced order Proper Orthogonal Decomposition (POD) techniques which allows one to create a set of basis elements spanning a data set consisting of either numerical simulations or experimental data. We investigate two different approaches in forming the POD approximation, a POD/Galerkin technique and a POD/Interpolation technique. We examine the error in the approximation using one approach versus the other as well as present results of the parameter estimation problem for both techniques. Finally, results of the parameter estimation problem are given using both simulated data with relative noise added as well as experimental data obtained using a giant magnetoresistive (GMR) sensor. The experimental results are based on successfully using actual experimental data to form the POD basis elements (instead of numerical simulations) thus illustrating the effectiveness of this method on a wide range of applications. In both instances the methods are found to be efficient and robust. Furthermore, the methods were fast; our findings suggest a significant reduction in computational time.
Information Content in Data Sets: A Review of Methods for Interrogation and Model Comparison
(North Carolina State University. Center for Research in Scientific Computation, 2017) Banks, Harvey Thomas; Joyner, Michele Lynn