DOE OSTI · 3016161
Metallic fuel transient fuel-cladding interface liquefaction model assessment platform enabled by integrating BISON with databases
Abstract
A novel platform has been developed within the BISON fuel performance code to assess models of fuel-cladding interface liquefaction for sodium-cooled fast reactor (SFR) metallic fuels. Here, this platform is crucial because liquefaction at the fuel-cladding interface significantly impacts fuel performance and may compromise fuel pin integrity during transient events. To ensure accurate predictions, the platform integrates data collected during the Integral Fast Reactor (IFR) program, now archived in metallic fuel databases. This integration supports verification and validation (V&V) of the models in BISON. Leveraging the extensive US experience with metallic fuel liquefaction and the collections of preserved legacy data, the platform serves as a powerful tool for evaluating existing models and advancing the development of new ones.
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Miao, Yinbin [Argonne National Laboratory (ANL), Argonne, IL (United States)] (ORCID:0000000231284275), Shu, Shipeng [Argonne National Laboratory (ANL), Argonne, IL (United States)] (ORCID:0000000338595014), Oaks, Aaron [Argonne National Laboratory (ANL), Argonne, IL (United States)] (ORCID:000000018552242X), Tomchik, Carolyn [Argonne National Laboratory (ANL), Argonne, IL (United States)], Mo, Kun [Argonne National Laboratory (ANL), Argonne, IL (United States)] (ORCID:0000000304128987), Matthews, Christopher [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)], Zabriskie, Adam X. [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000225375402), Novascone, Stephen R. [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000239384326), Yacout, Abdellatif M. [Argonne National Laboratory (ANL), Argonne, IL (United States)] (ORCID:0000000232583971). 2025-09-22. Metallic fuel transient fuel-cladding interface liquefaction model assessment platform enabled by integrating BISON with databases. https://doi.org/10.1016/j.jnucmat.2025.156168
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