摘要
基于大规模数据的训练,神经网络模型能迅速准确预测堆芯的有效增殖因数(k eff)、组件功率峰因子(Rad)和棒功率峰因子(FΔH),并以这3个参数作为衡量换料方式优劣的标准,构造改进的遗传算法从大量堆芯燃料方案中迅速搜索出最优排布方案,解决了在大量堆芯换料方案中选择最优方案费时的问题。堆芯装载方式建模时,设计二进制向量作为输入参数,有效减少了网络复杂度、提高了预测精度;最优方案搜索时,具有独特交叉算子、选择算子的遗传算法保证了搜索结果在可行域内,并提高了搜索效率。理论分析和数值实验结果表明,包含1个隐藏层的单隐层自适应BP网络可很好预测keff数据,包含3个隐藏层的自适应BP神经网络可较好地预测Rad和FΔH数据,再运用遗传算法快速搜索出了较理想的换料方案,为人工智能算法在核工业中的进一步深入应用提供参考。
The neural network model was trained by large-scale data,to accurately predict effective multiplication factor(k eff),component power crest factor(Rad)and rod power crest factor(FΔH)of the nuclear reactor core,which were used for core refueling optimization.The improved genetic algorithm can obtain the best solution quickly,and solve time-consuming and cost-effectiveness problem.In modeling of core loading mode,the binary vector was designed as the input parameter,which effectively reduced the neural network complexity and improved the prediction accuracy.In the search of optimal scheme,the genetic algorithm with unique crossover operator and selection operator ensured that the search results were in the feasible region,and improved the search efficiency.The theoretical analysis and numerical experiment results show that,one-hidden-layer adaptive BP network predicts k eff data well,while three-hidden-layer adaptive BP network is more suitable for Rad and FΔH data.Then the ideal core refueling schemes are obtained by the genetic algorithm.These practices are expected to promote a further application of artificial intelligence algorithms in the nuclear industry.
作者
韦子豪
王端
王东东
潘翠杰
WEI Zihao;WANG Duan;WANG Dongdong;PAN Cuijie(China Institute of Atomic Energy,Beijing 102413,China;Graduate School of CNNC,Beijing 102413,China)
出处
《原子能科学技术》
EI
CAS
CSCD
北大核心
2020年第5期825-834,共10页
Atomic Energy Science and Technology
关键词
堆芯换料优化
自适应BP神经网络
遗传算法
core refueling optimization
adaptive BP neural network
genetic algorithm
