Machine learning models for volumetric swelling in uranium nitride

Chen, Renai; Miller, Zachary; Gibson, Tammie; Mehta, Vedant K.; Fratoni, Massimiliano; Levinsky, Alex; Craven, Galen T.

doi:10.1016/j.jnucmat.2025.155980

Machine learning models for volumetric swelling in uranium nitride

Journal Article · Wed Jun 18 00:00:00 EDT 2025 · Journal of Nuclear Materials

DOI:https://doi.org/10.1016/j.jnucmat.2025.155980· OSTI ID:2570461

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Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Univ. of California, Berkeley, CA (United States)
Univ. of California, Berkeley, CA (United States)

Machine learning methods are applied to predict the volumetric swelling rate of the nuclear fuel uranium nitride (UN) over various temperatures, irradiation conditions, and power densities. Both kernel-based methods and symbolic regression models for UN swelling are developed and compared with multiple experimental datasets. We find that the UN pellet geometry and dimensions must be taken into account to accurately model swelling behavior. Strong agreement is observed between the developed machine learning models and the data. The predictive error generated by the machine learning models improves on empirical models taken from the literature. Sensitivity analysis is performed to determine which properties such as temperature, burnup, and power density, are most important in the swelling process. We find that machine learning can be used to quickly develop accurate swelling models for nuclear materials. In conclusion, the presented results illustrate the potential of machine learning to determine volumetric swelling in UN.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2570461

Report Number(s):: LA-UR--24-28699; 10.1016/j.jnucmat.2025.155980

Journal Information:: Journal of Nuclear Materials, Journal Name: Journal of Nuclear Materials Vol. 615; ISSN 0022-3115

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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