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Research on the relationship between the scattering contribution and physical factors of the reference radiation regulated by ISO 4037–1

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  • Yi-kun Qian
  • Yi-xin Liu
  • Ben-jiang Mao
  • Song Zhang
  • Yanan Liu
  • Peng Feng

Abstract

In the latest version of ISO 4037–1:2019 standard, the minimum dimension of a gamma radiation reference field was not clearly specified, which makes the construction of a minitype gamma reference radiation field lack of scientific basis. This paper carried out the research on the relationship between the scattering contribution and physical factors of the reference radiation regulated by ISO 4037–1. LS-SVM was applied to construct the relational model between physical factors and scattering contribution based on the data simulated by Monte Carlo method. Then the minimum dimension of collimated reference radiation field is obtained by PSO algorithm. For Co-60 source, the minimum size of the radiation field obtained is 93 cm(L)×40 cm(W)×40 cm(H). For Cs-137 source, the minimum size of the radiation field obtained is 153 cm(L)×47 cm (W)×47 cm(H). The results meet the requirements of the standard based on the model and provides a technical reference for the design of a minitype reference radiation field.

Suggested Citation

  • Yi-kun Qian & Yi-xin Liu & Ben-jiang Mao & Song Zhang & Yanan Liu & Peng Feng, 2022. "Research on the relationship between the scattering contribution and physical factors of the reference radiation regulated by ISO 4037–1," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-10, December.
    • Handle: RePEc:plo:pone00:0279188
    DOI: 10.1371/journal.pone.0279188
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    1. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
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