正则化径向基函数神经网络在堆芯轴向功率分布重构中的应用

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名

邮箱

手机号码

标题

留言内容

验证码

Application of Regularized Radial Basis Function Neural Network in Core Axial Power Distribution Reconstruction

1. Department of Engineering Physics, Tsinghua University, Beijing, 100084, China;
2. Science Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610041, China

摘要: 将正则化径向基函数(RBF)神经网络应用于堆芯轴向功率分布重构,通过6节堆外中子探测器的读数值重构堆芯轴向功率分布。使用ACP-100模块式小堆的7740套功率分布以及对应的模拟堆外探测器读数,对RBF神经网络重构方法进行了验证,结果表明:正则化RBF神经网络重构方法可以精确地重构出堆芯轴向功率分布,并且具有良好的鲁棒性,可以克服功率分布重构问题所固有的不适定性。
- 正则化RBF神经网络 /
- 轴向功率分布重构 /
- 堆外探测器
Abstract: The core axial power shape reconstruction method based on the regularized radial basis function neural network（r RBFNN） is proposed, and the axial power distribution can be reconstructed from 6-segment ex-core detector signals. The 7740 axial power distributions of ACP-100 modular reactor and the corresponding simulated ex-core detector readings are used to verify the r RBFNN reconstruction method. The results show that r RBFNN reconstruction method can reconstruct the core axial power distribution accurately, and this method is robust enough to deal with the inherent ill-posedness of the power distribution reconstruction problem.
- Regularized radial basis function neural network /
- Axial power distribution reconstruction /
- Ex-core detector