Improvement of the accuracy of a 3-dimensional simulator of a BWR using a neural network

Y. Kobayashi, E. Aiyoshi

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

Abstract

Boiling water reactors (BWRs) represent a large fraction of the world's installed nuclear power capacity. The core model of a BWR is very complex because it has many fuel assemblies and strong axial heterogeneities such as coolant voiding, partial control rod insertion, and non-uniform fuel assembly design. In order to operate a BWR safely, it is necessary to supervise the performance of the reactor core during the operation cycle. Therefore, the operation of the reactor core in a BWR is supervised using a 3-dimensional on-line simulator. In order to raise the calculation accuracy of this 3-dimensional simulator, the calculation power distribution is corrected using the neutron flux distribution obtained by the in-core instrumentation. In this paper, a model that uses a neural network was developed in order to raise the calculation accuracy of this 3-dimensional simulator further. In order to apply a neural network to the complicated reactor core of a BWR, the learning algorithm needs to be improved. Therefore, a technique using the Quasi-Newton method for renewal of the connection weights of the Back-Propagation (BP) method was developed. Using this algorithm, the convergent ability of neural network learning was improved in the strongly nonlinear problems. When this algorithm was applied to a BWR, the calculation accuracy of a 3-dimensional simulator was improved considerably.

Original languageEnglish
Title of host publicationIEEE International Symposium on Industrial Electronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages294-298
Number of pages5
Volume1
ISBN (Print)0780373693, 9780780373693
Publication statusPublished - 2002
Event2002 IEEE International Symposium on Industrial Electronics, ISIE 2002 - L'Aquila, Italy
Duration: 2002 Jul 82002 Jul 11

Other

Other2002 IEEE International Symposium on Industrial Electronics, ISIE 2002
CountryItaly
CityL'Aquila
Period02/7/802/7/11

Fingerprint

Boiling water reactors
Simulators
Neural networks
Reactor cores
Control rods
Neutron flux
Newton-Raphson method
Backpropagation
Nuclear energy
Coolants
Learning algorithms

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

Cite this

Kobayashi, Y., & Aiyoshi, E. (2002). Improvement of the accuracy of a 3-dimensional simulator of a BWR using a neural network. In IEEE International Symposium on Industrial Electronics (Vol. 1, pp. 294-298). [1026081] Institute of Electrical and Electronics Engineers Inc..

Improvement of the accuracy of a 3-dimensional simulator of a BWR using a neural network. / Kobayashi, Y.; Aiyoshi, E.

IEEE International Symposium on Industrial Electronics. Vol. 1 Institute of Electrical and Electronics Engineers Inc., 2002. p. 294-298 1026081.

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

Kobayashi, Y & Aiyoshi, E 2002, Improvement of the accuracy of a 3-dimensional simulator of a BWR using a neural network. in IEEE International Symposium on Industrial Electronics. vol. 1, 1026081, Institute of Electrical and Electronics Engineers Inc., pp. 294-298, 2002 IEEE International Symposium on Industrial Electronics, ISIE 2002, L'Aquila, Italy, 02/7/8.
Kobayashi Y, Aiyoshi E. Improvement of the accuracy of a 3-dimensional simulator of a BWR using a neural network. In IEEE International Symposium on Industrial Electronics. Vol. 1. Institute of Electrical and Electronics Engineers Inc. 2002. p. 294-298. 1026081
Kobayashi, Y. ; Aiyoshi, E. / Improvement of the accuracy of a 3-dimensional simulator of a BWR using a neural network. IEEE International Symposium on Industrial Electronics. Vol. 1 Institute of Electrical and Electronics Engineers Inc., 2002. pp. 294-298
@inproceedings{a49dcf9f684149c4bfc5ebe34aa8c15a,
title = "Improvement of the accuracy of a 3-dimensional simulator of a BWR using a neural network",
abstract = "Boiling water reactors (BWRs) represent a large fraction of the world's installed nuclear power capacity. The core model of a BWR is very complex because it has many fuel assemblies and strong axial heterogeneities such as coolant voiding, partial control rod insertion, and non-uniform fuel assembly design. In order to operate a BWR safely, it is necessary to supervise the performance of the reactor core during the operation cycle. Therefore, the operation of the reactor core in a BWR is supervised using a 3-dimensional on-line simulator. In order to raise the calculation accuracy of this 3-dimensional simulator, the calculation power distribution is corrected using the neutron flux distribution obtained by the in-core instrumentation. In this paper, a model that uses a neural network was developed in order to raise the calculation accuracy of this 3-dimensional simulator further. In order to apply a neural network to the complicated reactor core of a BWR, the learning algorithm needs to be improved. Therefore, a technique using the Quasi-Newton method for renewal of the connection weights of the Back-Propagation (BP) method was developed. Using this algorithm, the convergent ability of neural network learning was improved in the strongly nonlinear problems. When this algorithm was applied to a BWR, the calculation accuracy of a 3-dimensional simulator was improved considerably.",
author = "Y. Kobayashi and E. Aiyoshi",
year = "2002",
language = "English",
isbn = "0780373693",
volume = "1",
pages = "294--298",
booktitle = "IEEE International Symposium on Industrial Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Improvement of the accuracy of a 3-dimensional simulator of a BWR using a neural network

AU - Kobayashi, Y.

AU - Aiyoshi, E.

PY - 2002

Y1 - 2002

N2 - Boiling water reactors (BWRs) represent a large fraction of the world's installed nuclear power capacity. The core model of a BWR is very complex because it has many fuel assemblies and strong axial heterogeneities such as coolant voiding, partial control rod insertion, and non-uniform fuel assembly design. In order to operate a BWR safely, it is necessary to supervise the performance of the reactor core during the operation cycle. Therefore, the operation of the reactor core in a BWR is supervised using a 3-dimensional on-line simulator. In order to raise the calculation accuracy of this 3-dimensional simulator, the calculation power distribution is corrected using the neutron flux distribution obtained by the in-core instrumentation. In this paper, a model that uses a neural network was developed in order to raise the calculation accuracy of this 3-dimensional simulator further. In order to apply a neural network to the complicated reactor core of a BWR, the learning algorithm needs to be improved. Therefore, a technique using the Quasi-Newton method for renewal of the connection weights of the Back-Propagation (BP) method was developed. Using this algorithm, the convergent ability of neural network learning was improved in the strongly nonlinear problems. When this algorithm was applied to a BWR, the calculation accuracy of a 3-dimensional simulator was improved considerably.

AB - Boiling water reactors (BWRs) represent a large fraction of the world's installed nuclear power capacity. The core model of a BWR is very complex because it has many fuel assemblies and strong axial heterogeneities such as coolant voiding, partial control rod insertion, and non-uniform fuel assembly design. In order to operate a BWR safely, it is necessary to supervise the performance of the reactor core during the operation cycle. Therefore, the operation of the reactor core in a BWR is supervised using a 3-dimensional on-line simulator. In order to raise the calculation accuracy of this 3-dimensional simulator, the calculation power distribution is corrected using the neutron flux distribution obtained by the in-core instrumentation. In this paper, a model that uses a neural network was developed in order to raise the calculation accuracy of this 3-dimensional simulator further. In order to apply a neural network to the complicated reactor core of a BWR, the learning algorithm needs to be improved. Therefore, a technique using the Quasi-Newton method for renewal of the connection weights of the Back-Propagation (BP) method was developed. Using this algorithm, the convergent ability of neural network learning was improved in the strongly nonlinear problems. When this algorithm was applied to a BWR, the calculation accuracy of a 3-dimensional simulator was improved considerably.

UR - http://www.scopus.com/inward/record.url?scp=84902345294&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84902345294&partnerID=8YFLogxK

M3 - Conference contribution

SN - 0780373693

SN - 9780780373693

VL - 1

SP - 294

EP - 298

BT - IEEE International Symposium on Industrial Electronics

PB - Institute of Electrical and Electronics Engineers Inc.

ER -