Solution of a nuclear reactor parameter identification problem

Solution of a nuclear reactor parameter identification problem

A continuous identification of parameters is performed on a simulated fast breeder nuclear reactor system using hybrid computation and applying techniques of statistical regression analysis and exponentially-mapped-past functions. Output states which are not directly measurable are estimated by use...

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Bibliographic Details
Main Author: Brain Cánepa, Oscar Eduardo.
Other Authors: Gerba, A. Jr.
Language:en_US
Published: Monterey, California ; Naval Postgraduate School 2012
Online Access:http://hdl.handle.net/10945/15899
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spelling ndltd-nps.edu-oai-calhoun.nps.edu-10945-158992014-11-27T16:11:22Z Solution of a nuclear reactor parameter identification problem Brain Cánepa, Oscar Eduardo. Gerba, A. Jr. Naval Postgraduate School, Monterey, CA. A continuous identification of parameters is performed on a simulated fast breeder nuclear reactor system using hybrid computation and applying techniques of statistical regression analysis and exponentially-mapped-past functions. Output states which are not directly measurable are estimated by use of a Kalman filter. The method developed in this study is applied to a numerical example which demonstrates that unknown parameters can be identified within 3% of their actual value, with signal noise ratios as low as 10:1 in the measured states. The example also demonstrates that convergence occurs in a reasonably short time. (Author) 2012-11-13T23:25:22Z 2012-11-13T23:25:22Z 1971-06 Thesis http://hdl.handle.net/10945/15899 o640090483 en_US Approved for public release; distribution is unlimited. Monterey, California ; Naval Postgraduate School
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language en_US
sources NDLTD
description A continuous identification of parameters is performed on a simulated fast breeder nuclear reactor system using hybrid computation and applying techniques of statistical regression analysis and exponentially-mapped-past functions. Output states which are not directly measurable are estimated by use of a Kalman filter. The method developed in this study is applied to a numerical example which demonstrates that unknown parameters can be identified within 3% of their actual value, with signal noise ratios as low as 10:1 in the measured states. The example also demonstrates that convergence occurs in a reasonably short time. (Author)
author2 Gerba, A. Jr.
author_facet Gerba, A. Jr.
Brain Cánepa, Oscar Eduardo.
author Brain Cánepa, Oscar Eduardo.
spellingShingle Brain Cánepa, Oscar Eduardo.
Solution of a nuclear reactor parameter identification problem
author_sort Brain Cánepa, Oscar Eduardo.
title Solution of a nuclear reactor parameter identification problem
title_short Solution of a nuclear reactor parameter identification problem
title_full Solution of a nuclear reactor parameter identification problem
title_fullStr Solution of a nuclear reactor parameter identification problem
title_full_unstemmed Solution of a nuclear reactor parameter identification problem
title_sort solution of a nuclear reactor parameter identification problem
publisher Monterey, California ; Naval Postgraduate School
publishDate 2012
url http://hdl.handle.net/10945/15899
work_keys_str_mv AT braincanepaoscareduardo solutionofanuclearreactorparameteridentificationproblem
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