Machine Learning of the Reactor Core Loading Pattern Critical Parameters

المؤلفون المشاركون

Šmuc, Tomislav
Trontl, Krešimir
Pevec, Dubravko

المصدر

Science and Technology of Nuclear Installations

العدد

المجلد 2008، العدد 2008 (31 ديسمبر/كانون الأول 2008)، ص ص. 1-6، 6ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2008-08-27

دولة النشر

مصر

عدد الصفحات

6

التخصصات الرئيسية

العلوم الهندسية و تكنولوجيا المعلومات

الملخص EN

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.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Trontl, Krešimir& Pevec, Dubravko& Šmuc, Tomislav. 2008. Machine Learning of the Reactor Core Loading Pattern Critical Parameters. Science and Technology of Nuclear Installations،Vol. 2008, no. 2008, pp.1-6.
https://search.emarefa.net/detail/BIM-491248

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Trontl, Krešimir…[et al.]. Machine Learning of the Reactor Core Loading Pattern Critical Parameters. Science and Technology of Nuclear Installations No. 2008 (2008), pp.1-6.
https://search.emarefa.net/detail/BIM-491248

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Trontl, Krešimir& Pevec, Dubravko& Šmuc, Tomislav. Machine Learning of the Reactor Core Loading Pattern Critical Parameters. Science and Technology of Nuclear Installations. 2008. Vol. 2008, no. 2008, pp.1-6.
https://search.emarefa.net/detail/BIM-491248

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-491248