首页> 外文期刊>Nuclear Technology >NEUTRON SPECTROMETRY FOR THE ASSAY OF HIGH FISSILE CONTENT SPENT FUEL
【24h】

NEUTRON SPECTROMETRY FOR THE ASSAY OF HIGH FISSILE CONTENT SPENT FUEL

机译:中子含量高燃料油含量测定的中子光谱法

获取原文
获取原文并翻译 | 示例
       

摘要

A Monte Carlo study of the neutron slowing-down spectrometry technique for measuring fissile isotopic content in irradiated fuel has been completed. The neutron spectrometer system is characterized in terms of design, slowing-down time relation, isotopic response functions, and assay signals. The nonlinear effect of interrogating neutron self-shielding for a high fissile content fuel is compared to the same parameter for a low fissile content fuel. Simulated assays of 23 different fuel assemblies with a broad range of total fissile mass content (1.3 to 83 wt%) and fissile isotopic ratios are performed and analyzed using two different methods: a linear system model using a least-squares regression analysis and a radial basis neural network. Mean errors using the linear system model for the 23 different fuel types were approximately 20% for ~(235)U and 43% for total plutonium. The radial basis neural network assay signal solutions showed promising results, considerably better than the linear model: 4.9% for ~(235)U, 5.4% for total plutonium, and 0.5% for total fissile content.
机译:对用于测量辐照燃料中裂变同位素含量的中子减速光谱技术的蒙特卡罗研究已经完成。中子光谱仪系统的特点是设计,减速时间关系,同位素响应函数和测定信号。将高裂变含量燃料的询问中子自屏蔽的非线性效应与低裂变含量燃料的相同参数进行比较。使用两种不同的方法对23种不同的燃料组件进行了模拟分析,这些燃料组件具有广泛的总易裂变质量含量(1.3至83 wt%)和易裂变同位素比值:使用最小二乘回归分析的线性系统模型和径向基础神经网络。使用线性系统模型对23种不同燃料类型的平均误差对于((235)U)约为20%,对于总total约为43%。径向基神经网络分析信号解决方案显示出令人满意的结果,远优于线性模型:〜(235)U为4.9%,总total为5.4%,总易裂变含量为0.5%。

著录项

  • 来源
    《Nuclear Technology》 |2002年第3期|p.328-349|共22页
  • 作者单位

    Pacific Northwest National Laboratory Radiological and Chemical Sciences, National Security Division P.O. Box 999/MSIN P8-50, Richland, Washington 99352;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 原子能技术;
  • 关键词

  • 入库时间 2022-08-18 00:45:34

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号