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Diversification and the common ground
Craig Piercycpiercy@ans.org
Who would have thought, just a few years ago, that we would see so many long-standing barriers to new nuclear development falling like dominoes? Public opinion, policy, regulatory reform, finance and investment, design maturity, nuclear fuel enrichment, and fuel fabrication capacity have all advanced with remarkable speed in the United States.
Conventional wisdom holds that the most effective way to scale up the nuclear supply chain is to do so strategically, matching investments to the needs of reactor developers.
Ruihan Li, Junyi Chen, Aixin Zhu, Jingang Liang, Ding She, Hongjian Zhang
Nuclear Science and Engineering | Volume 199 | Number 11 | November 2025 | Pages 1954-1970
Research Article | doi.org/10.1080/00295639.2025.2471712
Articles are hosted by Taylor and Francis Online.
Simulating pebble bed reactors with high fidelity presents significant challenges because of the intricate geometry of the randomly packed pebbles and the requirement for multiphysics coupling. This study introduces an innovative modeling framework that couples neutronics, thermal hydraulics, and pebble flow dynamics of the reactor core. The Monte Carlo (MC) code, computational fluid dynamics (CFD) method, and discrete element method (DEM) are used, utilizing the open-source codes OpenMC, OpenFOAM, and LAMMPS, respectively. The core geometry is explicitly constructed for both the MC and the DEM models, while a porous media approach is adopted for the CFD model to manage computational expenses. Enhancements have been made to OpenMC to facilitate data exchange: The core geometry is allowed to change between depletion steps to simulate pebble motion, and a temperature mesh scheme has been developed for efficient temperature distribution transfer. Validations are provided for the models and modifications implemented in this study. As a practical demonstration, a depletion simulation on a full-core model of a High-Temperature Gas-Cooled Reactor–Pebble-Bed Module (HTR-PM) is conducted, explicitly modeling 420 000 randomly packed fuel pebbles. The results reveal detailed distributions of power, temperature, and burnup, all consistent with expected physical behavior, underscoring the effectiveness of our approach.