Machine Learning of the Reactor Core Loading Pattern Critical Parameters
The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is hig...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2008-01-01
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| Series: | Science and Technology of Nuclear Installations |
| Online Access: | http://dx.doi.org/10.1155/2008/695153 |
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| Summary: | The usual approach to loading pattern optimization involves high degree of
engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code
used for evaluating proposed loading patterns. The speed of the optimization process is highly
dependent on the computer code used for the evaluation. In this paper, we investigate the
applicability of a machine learning model which could be used for fast loading pattern evaluation. We
employ a recently introduced machine learning technique, support vector regression (SVR), which
is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are
automatically determined by solving a quadratic optimization problem. The main objective of the
work reported in this paper was to evaluate the possibility of applying SVR method for reactor core
loading pattern modeling. We illustrate the performance of the solution and discuss its applicability,
that is, complexity, speed, and accuracy. |
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| ISSN: | 1687-6075 1687-6083 |