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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| Language: | en_US |
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Monterey, California ; Naval Postgraduate School
2012
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| Online Access: | http://hdl.handle.net/10945/15899 |
| Summary: | 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) |
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