A PWR reload optimisation code (XCore) using artificial neural networks and genetic algorithms

被引：54

作者：

Erdogan, A

Geçkinli, M

机构：

[1] TAEK, Cekmece Nucl Res & Training Ctr, TR-34831 Istanbul, Turkey

[2] Istanbul Tech Univ, Inst Nucl Energy, TR-80626 Istanbul, Turkey

来源：

ANNALS OF NUCLEAR ENERGY | 2003年 / 30卷 / 01期

关键词：

FUEL; DESIGN;

D O I：

10.1016/S0306-4549(02)00041-5

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

A Computer program package has been developed, which supports the in-core fuel management activities for pressurized water reactors. The package generates and recommends an optimum-loading pattern to ensure safe and efficient reactor operation. The search for an optimum fuel-loading pattern has been conducted by predicting several core parameters such as the power distribution by means of an artificial neural network. This reduces the calculation time and makes it possible to analyse more loading patterns in the same time interval by increasing the probability of finding a desired optimum. A genetic algorithm method has been implemented and used to automate the loading pattern generation. The code has been tested using the data from the PWR Almaraz Nuclear Power Station in Spain. (C) 2002 Elsevier Science Ltd. All rights reserved.

引用

页码：35 / 53

页数：19

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