Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

Machine Learning of the Reactor Core Loading Pattern Critical Parameters (CROSBI ID 533314)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Trontl, Krešimir ; Pevec, Dubravko ; Šmuc, Tomislav Machine Learning of the Reactor Core Loading Pattern Critical Parameters // Proceedings of the International Conference Nuclear Energy for New Europe 2007 / Jenčić, Igor ; Lenošek, Melita (ur.). Ljubljana: Nuclear Society of Slovenia, 2007. str. 113.1-113.10-x

Podaci o odgovornosti

Trontl, Krešimir ; Pevec, Dubravko ; Šmuc, Tomislav

engleski

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 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 employed a recently introduced machine learning technique, Support Vector Regression (SVR), which has a strong theoretical background in statistical learning theory. Superior empirical performance of the method has been reported on difficult regression problems in different fields of science and technology. SVR is a data driven, kernel based, nonlinear modelling 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 modelling. The starting set of experimental data for training and testing of the machine learning algorithm was obtained using a two-dimensional diffusion theory reactor physics computer code. We illustrate the performance of the solution and discuss its applicability, i.e., complexity, speed and accuracy, with a projection to a more realistic scenario involving machine learning from the results of more accurate and time consuming three-dimensional core modelling code.

Support Vector Regression; SVR; reactor physics; machine learning

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o prilogu

113.1-113.10-x.

2007.

objavljeno

Podaci o matičnoj publikaciji

Proceedings of the International Conference Nuclear Energy for New Europe 2007

Jenčić, Igor ; Lenošek, Melita

Ljubljana: Nuclear Society of Slovenia

978-961-6207-28-7

Podaci o skupu

International Conference Nuclear Energy for New Europe 2007

poster

10.09.2007-13.09.2007

Portorož, Slovenija

Povezanost rada

Elektrotehnika