4.5 Article

Inverse model investigation of radionuclide dispersion in a ventilated room based on the adjoint probability method

Journal

ANNALS OF NUCLEAR ENERGY
Volume 169, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.anucene.2021.108929

Keywords

Ventilated room; Radionuclide dispersion; Inverse model; Computational fluid dynamics

Funding

  1. Open Project of Nuclear Power Safety Monitoring Technology and Equipment [K-A2019.413]
  2. Natural Science Foundation of the Anhui Higher Education Institutions of China [KJ2020A0110]
  3. Natural Science Foundation of Anhui Province [2008085MA23]

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The accurate identification of the radioactive pollutant leakage points in ventilated rooms is crucial for nuclear accident consequence assessment. A reverse model considering radioactive decay and deposition was established to study the dispersion of radionuclides under ventilation conditions. The proposed model can assist nuclear emergency decision-makers in tracking the location of leakage sources and provide scientific information for early emergency response and consequence assessment.
The improved identification of radioactive pollutant leakage points in ventilated rooms is one of the key technologies for nuclear accident consequence assessment. Radionuclides disperse turbulently in ventilated rooms, and leakage locations lead to different concentration distribution patterns. Considering the effects of radioactive decay and deposition, an inverse model of radionuclide dispersion under ventilation conditions was established based on the adjoint probability method coupled with computational fluid dynamics (CFD). The airflow field was calculated by CFD, and the probability of leakage locations for the radioactive source was estimated by the adjoint probability method coupled with CFD. The results showed that the proposed model could help nuclear emergency decision-makers accurately trace the location of leakage sources and provide more scientific information for early emergency response and consequence assessment.

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