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    Weapons bay flow classification by sequential function approximation

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    Author
    Kugler, Justin Wade
    Date
    2007
    Advisor
    Meade, Andrew J., Jr.
    Degree
    Master of Science
    Abstract
    Acoustic tones can impede safe store separation and amplify structural fatigue in airborne military aircraft with open internal bays. Experimentally simulating such cavity flows is essential to understanding the physical phenomena. This study will demonstrate that a machine learning algorithm can accurately classify flow regimes using a limited number of data points and help identify regions of interest for future experiments. McKay's Latin hypercube method was used to select the best data points from a database comprised of the experimental results of tests conducted in the NASA Langley eight-foot transonic pressure tunnel on a variable geometry rectangular cavity. A Galerkin-derived, adaptive, matrix-free scattered data approximation scheme based on artificial neural networks, called Sequential Function Approximation, was trained using the best data subsets and then used to predict the results of the wind tunnel experiments. We first determined whether Sequential Function Approximation could predict the three classes of observed cavity flow. Then, we used our algorithm to predict both flow class and the occurrence of acoustic resonance. These results favorably compared against solutions from publicly-available support vector machines and pre-built classifier programs.
    Keyword
    Aerospace engineering; Mechanical engineering
    Citation
    Kugler, Justin Wade. "Weapons bay flow classification by sequential function approximation." (2007) Master’s Thesis, Rice University. https://hdl.handle.net/1911/20516.
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    • Rice University Electronic Theses and Dissertations [13409]

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    Home | FAQ | Contact Us | Privacy Notice | Accessibility Statement
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
    Site Map