Issue |
EPJ Nuclear Sci. Technol.
Volume 8, 2022
|
|
---|---|---|
Article Number | 6 | |
Number of page(s) | 14 | |
DOI | https://doi.org/10.1051/epjn/2022001 | |
Published online | 20 May 2022 |
https://doi.org/10.1051/epjn/2022001
Regular Article
Modelling of radioactive dust for dose calculations with stochastic geometries
Université Paris-Saclay, CEA, Service d'Études des Réacteurs et de Mathématiques Appliquées, 91191 Gif-sur-Yvette, France
* e-mail: alice.bonin@cea.fr
Received:
30
September
2021
Received in final form:
2
December
2021
Accepted:
1
February
2022
Published online: 20 May 2022
Stochastic geometries in Monte-Carlo simulations enable to simulate complex configurations such as the repartition of possible radioactive dust in a glove box. This paper compares several dust models that represent more or less explicitly the heterogeneous repartition of dust speckles in space. Indeed, assessing the contribution of dust to the dose received by the hands of an operator is a key problem for glove boxes. Results show that homogeneous models generally overestimate the dose, which is correct for radioprotection studies, but that dust aggregates produce doses that are much smaller than those obtained by homogenising dust. These heterogeneous models can also help estimating deposited dust quantities from dose measurements inside the glove box, whereas an homogenous model would grossly underestimate dust quantity.
© A. Bonin et al., Published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.