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Machine learning of the reactor core loading pattern critical parameters

Trontl, Krešimir; Pevec, Dubravko; Šmuc, Tomislav (2008) Machine learning of the reactor core loading pattern critical parameters. Science and Technology of Nuclear Installations, 2008 . ISSN 1687-6075

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Abstract

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 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.

Item Type: Article
Uncontrolled Keywords: machine learning ; reactor core ; SVR method ; optimization
Subjects: TECHNICAL SCIENCES > Electrical Engineering
Divisions: Division of Electronics
Projects:
Project titleProject leaderProject codeProject type
Postupci računalne inteligencije u mjernim sustavima-Ivan Marić098-0982560-2565MZOS
Gospodarenje gorivom standardnih i naprednih nuklearnih reaktora-Dubravko Pevec036-0361590-1579MZOS
Depositing User: Phd Tomislav Šmuc
Date Deposited: 27 Sep 2018 13:27
Last Modified: 27 Sep 2018 13:28
URI: http://fulir.irb.hr/id/eprint/4178
DOI: 1155/2008/695153

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