Estimation of the three-dimensional power distribution in boiling water reactors using neural networks and fuzzy theory

Y. Kobayashi, E. Aiyoshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In order to operate a nuclear reactor safely, it is necessary to monitor the soundness of the fuel assemblies loaded in the reactor core. The ability to calculate the power distribution in a reactor core is indispensable for monitoring that soundness. This paper proposes a new model for approximating the 3D power distribution in a reactor core using a neuro-fuzzy approach. In the proposed method we use learning algorithms based on a class of Quasi-Newton optimization techniques called the Self-Scaling Variable Metric (SSVM) method. Further, we propose a new learning algorithm for the fuzzy connected neural network (FCNN). The FCNN consists of many neural networks (local neural networks, LNNs). One LNN is connected to other LNNs with a fuzzy membership function, and the LNNs respond to input data as a whole neural network (FCNN). New prediction models were applied to the core of an actual Boiling Water Reactor (BWR) plant. The results demonstrate that the new model can predict the 3D power distribution of a BWR reasonably well.

Original languageEnglish
Title of host publication2002 IEEE International Symposium on Computer Aided Control System Design, CACSD 2002 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages121-126
Number of pages6
ISBN (Print)078037388X, 9780780373884
DOIs
Publication statusPublished - 2002
EventIEEE International Symposium on Computer Aided Control System Design, CACSD 2002 - Glasgow, Scotland, United Kingdom
Duration: 2002 Sep 182002 Sep 20

Other

OtherIEEE International Symposium on Computer Aided Control System Design, CACSD 2002
CountryUnited Kingdom
CityGlasgow, Scotland
Period02/9/1802/9/20

Keywords

  • BWR
  • fuzzy theory
  • neural network
  • Quasi-Newton method
  • SSVM

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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