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LLNL, Ampera partner to develop thorium-based TRISO fuel
Lawrence Livermore National Laboratory has formed a strategic partnership with Ampera to develop the company’s nuclear fuel concept through a project named THUNDER, for Thorium Unimodal Droplet Ejection for Reactors.
The focus of THUNDER is fabricating TRISO made with kernels of thorium rather than the usual uranium. LLNL and Ampera will evaluate and optimize liquid metal–jetting technology to produce highly uniform, spherical kernels of thorium-232 for later processing into TRISO fuel.
M. Malicki, T. Lind
Nuclear Technology | Volume 211 | Number 12 | December 2025 | Pages 2911-2928
Research Article | doi.org/10.1080/00295450.2025.2465216
Articles are hosted by Taylor and Francis Online.
This paper presents an uncertainty analysis of the source term (ST) in a pressurized water reactor (PWR) station blackout (SBO) accident using the MELCOR2.2 code coupled with DAKOTA via SNAP. Severe accident (SA) simulations in nuclear reactors are fraught with uncertainties due to complex phenomenology and numerical model simplifications. While the best-estimate plus uncertainty (BEPU) methodology is well established in thermohydraulic studies, its application to SA scenarios is relatively new.
This study is part of the European Management and Uncertainties of Severe Accidents (MUSA) project, which to some extent, aims to integrate BEPU methodologies into SA simulations. Two figures of merit focused on ST retention in the steam generator (SG) and releases of selected radionuclides to the environment were analyzed. The uncertain parameters used in this study, chosen based on a literature study and project discussions, are related to core degradation and aerosol phenomena. The results highlight significant uncertainties in the ST calculations influenced by variations in the aerosol behavior and core degradation parameters.
In conclusion, this study illustrates the application of BEPU methodology to SA uncertainty analysis in a PWR SBO plus SG tube rapture scenario. The findings indicate a potential for improving SA models and reducing uncertainties, contributing to the MUSA project’s goals of enhancing nuclear plant safety analyses and reliability.