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Neutronic analysis of thorium-uranium mixed fuel for multi-application small light water reactor (MASLWR)

  • Afaf A. E. Ateya , Hend M. Saad ORCID logo EMAIL logo and Esmat H. Amin
Published/Copyright: November 21, 2025
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Kerntechnik
From the journal Kerntechnik

Abstract

In this work, a neutronic analysis estimation of a Multi-Application Small Light Water Reactor (MASLWR) core with the minimum possible modifications in the geometry will be presented. In this study, many cases are investigated, in the first case the reactor core is fuelled by uranium with enrichment 4.95 %. For the second case the reactor core is fuelled by uranium with enrichment 4.95 % and a mixture of 50 % uranium and 50 % thorium. For this case, five different core distributions are modeled. For the third case, a seed and blanket design is investigated where the middle positions of each assembly are fuelled with 4.95 % enriched UO2 while the boundary positions are fuelled once with 50 % ThO2 + 4.95 % enriched UO2 and second with 30 % ThO2 + 4.95 % enriched UO2. MCNP6 code has been used to calculate neutronic parameters such as effective multiplication coefficient (Keff), cycle length for reactor core, the nuclear fuel evolution during the burnup, total neutron flux, kinetic parameters such as delayed neutrons fraction and prompt neutron lifetime and finally, the power peaking factor of the MASLWR different cores. The results show that the utilization of thorium fuel improves the overall performance characteristics of the MASLWR without burnable poisons. The results are compared with the uranium core enrichment 4.95 %. All thorium cases have better conversion and longer reactor cycle and higher fuel burn-up, and the most attractive feature is its resistance to nuclear proliferation.


Corresponding author: Hend M. Saad, Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt, E-mail:

  1. Research ethics: This work is original and has not been published elsewhere.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have the all contribution in this research paper.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: There are no conflicts of interest associated with this work.

  6. Research funding: No funding.

  7. Data availability: Data available on request.

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Received: 2025-06-13
Accepted: 2025-11-01
Published Online: 2025-11-21

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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