摘要
堆芯流量分区是实现堆芯出口温度展平的重要手段,合理地分区可以提高反应堆的安全性和经济性。本文将人工智能优化算法与单通道模型进行耦合,构建了反应堆堆芯流量分区计算模型,分别开展遗传算法、差分进化算法、量子遗传算法在反应堆流量分区问题上的收敛性分析。根据所得最优算法,分别以寿期初功率分布、各燃料组件在整个寿期内最大功率为样本数据,基于小型长寿命自然循环铅铋快堆SPALLER-100开展两种不同流量分区方案对比分析。研究结果表明,在3种智能优化算法中,量子遗传算法在反应堆流量分区问题上收敛性最佳,能较快地搜索到最优分区结果;基于寿期初功率分布样本数据所得燃料组件最大出口温度超出反应堆热工安全限值,而基于各燃料组件在整个寿期内最大功率所得燃料组件最大出口温度降低了140 K,且始终保持在热工安全限值之下;SPALLER-100反应堆最佳分区数为5,再增加分区数对提高反应堆热工安全性能影响较小。
Core flow zoning is an important means to achieve core outlet temperature flattening.Reasonable zoning can improve the reactor safety and economy.In this paper,the artificial intelligence optimization algorithm is coupled with the single channel model,and the calculation model of reactor core flow zoning is constructed.The convergence analysis of genetic algorithm,differential evolution algorithm and quantum genetic algorithm in reactor flow zoning is carried out respectively.According to the obtained optimal algorithm,taking the power distribution at the beginning of life cycle as the sample data and the maximum power of each fuel assembly throughout the life cycle as the sample data,the comparative analysis of two different flow zoning schemes is carried out based on the small long-life natural circulation lead-bismuth fast reactor SPALLER-100.The results show that among the three intelligent optimization algorithms,the quantum genetic algorithm has the best convergence on the reactor flow zoning problem,and can quickly search the optimal zoning results;The maximum outlet temperature of the fuel assembly based on the power distribution at the beginning of the life cycle exceeds the thermal safety limit of the reactor,while the maximum outlet temperature of the fuel assembly based on the maximum power of each fuel assembly during the entire life by 140 K and remains below the thermal safety limit;The optimal number of zones for SPALLER-100 reactor is 5,and increasing the number of zones has little effect on improving the thermal safety performance of the reactor.
作者
凌煜凡
代圣齐
赵鹏程
朱恩平
王继锋
唐欢
Ling Yufan;Dai Shengqi;Zhao Pengcheng;Zhu Enping;Wang Jifeng;Tang Huan(School of Nuclear Science and Technology,University of South China,Hengyang,Hunan,421001,China;Science and Technology on Reactor System Design Technology Laboratory,Nuclear Power Institute of China,Chengdu,610213,China)
出处
《核动力工程》
EI
CAS
CSCD
北大核心
2022年第3期53-57,共5页
Nuclear Power Engineering
关键词
铅铋反应堆
流量分区
智能优化算法
量子遗传算法
Lead-bismuth reactor
Flow zoning
Intelligent optimization algorithm
Quantum genetic algorithm