### 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 |
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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 |
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Country | Italy |

City | L'Aquila |

Period | 02/7/8 → 02/7/11 |

### Fingerprint

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Control and Systems Engineering

### Cite this

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*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.

}

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

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 -