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 language | English |
|---|---|
| Title of host publication | IEEE International Symposium on Industrial Electronics |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 294-298 |
| Number of pages | 5 |
| Volume | 1 |
| ISBN (Print) | 0780373693, 9780780373693 |
| Publication status | Published - 2002 |
| Event | 2002 IEEE International Symposium on Industrial Electronics, ISIE 2002 - L'Aquila, Italy Duration: 2002 Jul 8 → 2002 Jul 11 |
Other
| Other | 2002 IEEE International Symposium on Industrial Electronics, ISIE 2002 |
|---|---|
| Country | Italy |
| City | L'Aquila |
| Period | 02/7/8 → 02/7/11 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
Cite this
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 proceeding › Conference contribution
}
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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84902345294&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84902345294
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 -