He jishu (Mar 2023)

Super field of view neutron imaging by fission neutrons elicited from research reactor

  • WANG Sheng,
  • LI Hang,
  • LUO Xin,
  • WU Yang,
  • HUO Heyong,
  • LIU Bin,
  • YIN Wei,
  • CAO Chao,
  • SUN Yong,
  • YANG Xin,
  • LI Rundong,
  • TANG Bin

DOI
https://doi.org/10.11889/j.0253-3219.2023.hjs.46.030201
Journal volume & issue
Vol. 46, no. 3
pp. 030201 – 030201

Abstract

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BackgroundNeutron radiography (NR) is an important nondestructive testing method. NR is particularly useful for detection of light materials in medium and large heavy samples. Especially, the fast neutrons can penetrate the heavy materials and reveal the structure of the light materials. Compared to accelerator neutron sources, the fission neutrons elicited from a reactor are stable and of high quality. The fission neutron imaging is a useful complementary testing technology, especially for industrial applications that require high throughput and large-scale testing.PurposeThis study aims to investigate the super field of view neutron imaging by fission neutrons elicited from research reactor.MethodsBased on theoretical analysis and Monte-Carlo simulation, one filter combination was employed to improve the proportion of fission neutrons in the thermal neutron beamline at China Mianyang Research Reactor (CMRR). A fission neutron imaging system was constructed by employing a large field fast neutron fluorescent screen, short focus distance lens, and scientific charge coupled device (CCD) camera. Finally, some samples were tested using fission neutron tomography.ResultsThe fission neutron flux reaches up to 3×105 cm-2·s-1 when the L/D ratio is about 260. The field of view of NR is up to 400 mm×400 mm with resolution was better than 0.5 mm. Using super field of view method, samples less than 600 mm can be tested with this new system.ConclusionsCombination of theoretical calculation and experimental methods, fission neutron imaging can be improved to overcome some of the limitations of traditional neutron radiography techniques, and meet the needs of large sample detection in the future.

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