NUCLEAR-FUEL MANAGEMENT OPTIMIZATION USING GENETIC ALGORITHMS

被引：80
作者：
DECHAINE, MD
FELTUS, MA
机构：
[1] Pennsylvania State Univ, University Park, PA
来源：
NUCLEAR TECHNOLOGY | 1995年 / 111卷 / 01期
关键词：
FUEL MANAGEMENT; GENETIC ALGORITHMS; OPTIMIZATION;
D O I：
10.13182/NT95-A35149
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：
The code independent genetic algorithm reactor optimization (CIGARO) system has been developed to optimize nuclear reactor loading patterns. It uses genetic algorithms (GAs) and a code-independent interface, so any reactor physics code (e.g., CASMO-3/SIMULATE-3) can be used to evaluate the loading patterns. The system is compared to other GA-based loading pattern optimizers. Tests were carried out to maximize the beginning of cycle k(eff) for a pressurized water reactor core loading with a penalty function to limit power peaking. The CIC;ARO system performed well, increasing the k(eff) after lowering the peak power. Tests of a prototype parallel evaluation method showed the potential for a significant speedup.
引用
收藏
页码：109 / 114
页数：6
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