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Pregled bibliografske jedinice broj: 410131

Machine learning of the reactor core loading pattern critical parameters


Trontl, Krešimir; Pevec, Dubravko; Šmuc, Tomislav
Machine learning of the reactor core loading pattern critical parameters // Science and Technology of Nuclear Installations, 2008 (2008), 695153, 7 doi:1155/2008/695153 (međunarodna recenzija, članak, znanstveni)


Naslov
Machine learning of the reactor core loading pattern critical parameters

Autori
Trontl, Krešimir ; Pevec, Dubravko ; Šmuc, Tomislav

Izvornik
Science and Technology of Nuclear Installations (1687-6075) 2008 (2008); 695153, 7

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Machine learning ; reactor core ; SVR method ; optimization

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA


Projekt / tema
036-0361590-1579 - Gospodarenje gorivom standardnih i naprednih nuklearnih reaktora (Dubravko Pevec, )
098-0982560-2565 - Postupci računalne inteligencije u mjernim sustavima (Ivan Marić, )

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb,
Institut "Ruđer Bošković", Zagreb

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Uključenost u ostale bibliografske baze podataka:


  • INSPEC


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