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 - 2002/1/1

Y1 - 2002/1/1

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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M3 - Conference contribution

AN - SCOPUS:84902345294

SN - 0780373693

SN - 9780780373693

T3 - IEEE International Symposium on Industrial Electronics

SP - 294

EP - 298

BT - ISIE 2002 - Proceedings of the 2002 IEEE International Symposium on Industrial Electronics

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2002 IEEE International Symposium on Industrial Electronics, ISIE 2002

Y2 - 8 July 2002 through 11 July 2002

ER -