Volume 29 Issue 03
Feb.  2017
Turn off MathJax
Article Contents
Huang Kai, Wu Hongchun, Li Yunzhao, et al. Depletion chain compression method via quantitative significance analysis[J]. High Power Laser and Particle Beams, 2017, 29: 036002. doi: 10.11884/HPLPB201729.160302
Citation: Huang Kai, Wu Hongchun, Li Yunzhao, et al. Depletion chain compression method via quantitative significance analysis[J]. High Power Laser and Particle Beams, 2017, 29: 036002. doi: 10.11884/HPLPB201729.160302

Depletion chain compression method via quantitative significance analysis

doi: 10.11884/HPLPB201729.160302
  • Received Date: 2016-06-16
  • Rev Recd Date: 2016-10-09
  • Publish Date: 2017-03-15
  • The depletion calculation in reactor physics requires depletion chain data. However, the depletion chain that originates from evaluated nuclear data library is unnecessarily detailed for assembly and micro-depletion calculation, while conventional depletion chain compression methods are semi-empirical with limited application scope and accuracy. In this paper, a method that compresses depletion chain based on quantitative significance analysis of each nuclide and reaction channel is proposed. The significance analysis uses fine depletion chain computation results of representative problems as data source, and it is carried out by evaluating the influence on neutron absorption, production and number densities of target nuclides, induced by each basic unit compression. The method is applied to depletion chain compression in the context of PWR assembly calculation. Fine and compressed depletion chain computations of selected test cases are performed. The comparison among obtained numerical results show that, while preserving accuracy requirements, the proposed depletion chain compression method is capable of significantly reducing the complexity of the depletion chain, and the demanded storage and time savings are achieved consequently.
  • loading
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Article views (1045) PDF downloads(238) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return