Current capabilities and future developments of Monte Carlo code MCS

The Monte Carlo code, MCS was developed at the Ulsan National Institute of Science and Technology (UNIST) in 2011. In the initial development phase, the primary focus was on developing a Monte Carlo code for the high-fidelity multicycle analysis of large-scale power reactors, especially pressurized water reactors. For the power reactor analysis, capabilities including refueling and shuffling of fuel assemblies, on-the-fly Doppler broadening of neutron cross-sections, and multiphysics coupling were implemented in the MCS. Beyond reactor analysis and capabilities, MCS has been developed to extend its applications. The MCS has been used for radiation shielding, group constants generation, sensitivity, uncertainty, and transient analysis. This study provides a general overview of MCS capabilities.

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-03-04
DOI
https://doi.org/10.1051/epjn/2025001/pdf
Primary Topic
Plant pathogens and resistance mechanisms
Type
article
Field-Weighted Citation Impact
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article

Current capabilities and future developments of Monte Carlo code MCS

Springer Link (Chiba Institute of Technology)
Plant pathogens and resistance mechanisms
article

Current capabilities and future developments of Monte Carlo code MCS

article en

Abstract

The Monte Carlo code, MCS was developed at the Ulsan National Institute of Science and Technology (UNIST) in 2011. In the initial development phase, the primary focus was on developing a Monte Carlo code for the high-fidelity multicycle analysis of large-scale power reactors, especially pressurized water reactors. For the power reactor analysis, capabilities including refueling and shuffling of fuel assemblies, on-the-fly Doppler broadening of neutron cross-sections, and multiphysics coupling were implemented in the MCS. Beyond reactor analysis and capabilities, MCS has been developed to extend its applications. The MCS has been used for radiation shielding, group constants generation, sensitivity, uncertainty, and transient analysis. This study provides a general overview of MCS capabilities.

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 41%
Plant pathogens and resistance mechanisms
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