New advances in the computational simulation of Aqueous Homogeneous Reactor for medical isotopes production

Daniel Milian Pérez; Liván Hernández Pardo; Daylen Milian Pérez; Abel Gámez Rodríguez; Daniel Evelio Milian Lorenzo; Carlos Alberto Brayner de Oliveira Lira

doi:10.15392/bjrs.v8i3A.1409

Authors

Daniel Milian Pérez https://orcid.org/0000-0002-3172-0508
Liván Hernández Pardo
Daylen Milian Pérez
Abel Gámez Rodríguez
Daniel Evelio Milian Lorenzo
Carlos Alberto Brayner de Oliveira Lira

DOI:

https://doi.org/10.15392/bjrs.v8i3A.1409

Keywords:

Aqueous Homogeneous Reactor, Radioisotope production, 99Mo, MCNP, ANSYS-CFX

Abstract

Aqueous Homogeneous Reactors (AHRs) or simply solution reactors present nowadays a promising alternative to produce medical isotopes, especially ⁹⁹Mo. The AHR medical production concept has been proposed to produce medical isotopes directly in the fuel solution, resulting in a potentially competitive alternative in comparison with the solid target irradiation method in heterogeneous reactors. Furthermore, the utilization of AHRs for medical isotopes production has been strengthened because of the successful operation of the ARGUS reactor since 1981 and its conversion to low-enriched uranium (LEU) fuel during 2012-2014. Those successes positively influenced in the decisions to construct a Proof-Of-Concept production site based on the ARGUS operational experience in Sarov (500 km from Moscow) and to restore the Argus-FTI at the Umarov Physical and Technical Institute in Dushanbe, Tajikistan. However, demonstrating the viability of the AHRs for medical isotopes requires solving several significant challenges related with the safe operation of these reactors. Consequently, not only for the design, licensing and safe operation of the AHRs, but also for the prediction of accident scenarios it is very important to be able to simulate and predict the behavior of the fuel solutions through a group of relevant physical parameters. Accordingly, this paper aims to show the advances made to improve the predictive capabilities during the multi-physics computational modeling of AHRs.

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