Advanced characterization-informed machine learning framework and quantitative insight to irradiated annular U-10Zr metallic fuels.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
30 Jun 2023
30 Jun 2023
Historique:
received:
11
10
2022
accepted:
21
05
2023
medline:
3
7
2023
pubmed:
1
7
2023
entrez:
30
6
2023
Statut:
epublish
Résumé
U-10Zr Metal fuel is a promising nuclear fuel candidate for next-generation sodium-cooled fast spectrum reactors. Since the Experimental Breeder Reactor-II in the late 1960s, researchers accumulated a considerable amount of experience and knowledge on fuel performance at the engineering scale. However, a mechanistic understanding of fuel microstructure evolution and property degradation during in-reactor irradiation is still missing due to a lack of appropriate tools for rapid fuel microstructure assessment and property prediction based on post irradiation examination. This paper proposed a machine learning enabled workflow, coupled with domain knowledge and large dataset collected from advanced post-irradiation examination microscopies, to provide rapid and quantified assessments of the microstructure in two reactor irradiated prototypical annular metal fuels. Specifically, this paper revealed the distribution of Zr-bearing secondary phases and constitutional redistribution across different radial locations. Additionally, the ratios of seven different microstructures at various locations along the temperature gradient were quantified. Moreover, the distributions of fission gas pores on two types of U-10Zr annular fuels were quantitatively compared.
Identifiants
pubmed: 37391449
doi: 10.1038/s41598-023-35619-1
pii: 10.1038/s41598-023-35619-1
pmc: PMC10313895
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
10616Subventions
Organisme : Laboratory Directed Research and Development
ID : 22A1059-094FP
Informations de copyright
© 2023. The Author(s).
Références
Schmidt, J., Marques, M. R. G., Botti, S. & Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 5, 83. https://doi.org/10.1038/s41524-019-0221-0 (2019).
doi: 10.1038/s41524-019-0221-0
Wang, A.Y.-T. et al. Machine learning for materials scientists: An introductory guide toward best practices. Chem. Mater. 32, 4954–4965. https://doi.org/10.1021/acs.chemmater.0c01907 (2020).
doi: 10.1021/acs.chemmater.0c01907
Morgan, D. et al. Machine learning in nuclear materials research. Curr. Opin. Solid State Mater. Sci. 26, 100975. https://doi.org/10.1016/j.cossms.2021.100975 (2022).
doi: 10.1016/j.cossms.2021.100975
Allen, T., Busby, J., Meyer, M. & Petti, D. Materials challenges for nuclear systems. Mater. Today 13, 14–23. https://doi.org/10.1016/S1369-7021(10)70220-0 (2010).
doi: 10.1016/S1369-7021(10)70220-0
Janney, D. E. & Hayes, S. L. Experimentally known properties of U-10Zr Alloys: A critical review. Nucl. Technol. 203, 109–128. https://doi.org/10.1080/00295450.2018.1435137 (2018).
doi: 10.1080/00295450.2018.1435137
Carmack, W. J. et al. Metallic fuels for advanced reactors. J. Nucl. Mater. 392, 139–150. https://doi.org/10.1016/j.jnucmat.2009.03.007 (2009).
doi: 10.1016/j.jnucmat.2009.03.007
Ogata, T. In Comprehensive Nuclear Materials, 2nd ed. (eds Konings, R. J. M. & Stoller, R. E.) 1–42 (Elsevier, 2020).
Yao, T. K. et al. alpha-U and omega-UZr2 in neutron irradiated U-10Zr annular metallic fuel. J. Nucl. Mater. https://doi.org/10.1016/j.jnucmat.2020.152536 (2020).
doi: 10.1016/j.jnucmat.2020.152536
Benson, M. T. et al. Out-of-pile and postirradiated examination of lanthanide and lanthanide-palladium interactions for metallic fuel. J. Nucl. Mater. https://doi.org/10.1016/j.jnucmat.2020.152727 (2021).
doi: 10.1016/j.jnucmat.2020.152727
Salvato, D. et al. Transmission electron microscopy study of a high burnup U-10Zr metallic fuel. J. Nucl. Mater. 570, 153963. https://doi.org/10.1016/j.jnucmat.2022.153963 (2022).
doi: 10.1016/j.jnucmat.2022.153963
Matthews, C., Unal, C., Galloway, J., Keiser, D. D. & Hayes, S. L. Fuel-cladding chemical interaction in U-Pu-Zr metallic fuels: A critical review. Nucl. Technol. 198, 231–259. https://doi.org/10.1080/00295450.2017.1323535 (2017).
doi: 10.1080/00295450.2017.1323535
Aitkaliyeva, A. Recent trends in metallic fast reactor fuels research. J. Nucl. Mater. 558, 153377. https://doi.org/10.1016/j.jnucmat.2021.153377 (2022).
doi: 10.1016/j.jnucmat.2021.153377
Keiser, D. D. Fuel cladding chemical interaction in metallic sodium fast reactor fuels: A historical perspective. J. Nucl. Mater. 514, 393–398. https://doi.org/10.1016/j.jnucmat.2018.09.045 (2019).
doi: 10.1016/j.jnucmat.2018.09.045
Zhang, J. & Taylor, C. Studies of Lanthanide Transport in Metallic Fuel. Report No. 14-6482, (The Ohio State University, 2018).
Bauer, T. H. & Holland, J. W. In-pile measurement of the thermal-conductivity of irradiated metallic fuel. Nucl. Technol. 110, 407–421. https://doi.org/10.13182/Nse110-407 (1995).
doi: 10.13182/Nse110-407
Yun, D., Yacout, A. M., Stan, M., Bauer, T. H. & Wright, A. E. Simulation of the impact of 3-D porosity distribution in metallic U-10Zr fuels. J. Nucl. Mater. 448, 129–138. https://doi.org/10.1016/j.jnucmat.2014.02.002 (2014).
doi: 10.1016/j.jnucmat.2014.02.002
Cai, L. et al. Understanding fission gas bubble distribution, lanthanide transportation, and thermal conductivity degradation in neutron-irradiated α-U using machine learning. Mater. Charact. 184, 111657. https://doi.org/10.1016/j.matchar.2021.111657 (2022).
doi: 10.1016/j.matchar.2021.111657
Harp, J. M., Capriotti, L. & Cappia, F. Baseline Postirradiation Examination of the AFC-3C, AFC-3D, and AFC-4A Experiments (2018).
Harp, J. M., Chichester, H. J. M. & Capriotti, L. Postirradiation examination results of several metallic fuel alloys and forms from low burnup AFC irradiations. J. Nucl. Mater. 509, 377–391. https://doi.org/10.1016/j.jnucmat.2018.07.003 (2018).
doi: 10.1016/j.jnucmat.2018.07.003
Medvedev, P. G. BISON Investigation of the Effect of the Fuel- Cladding Contact Irregularities on the Peak Cladding Temperature and FCCI Observed in AFC-3A Rodlet 4. Medium: ED; Size: 20 p (2016).
Hofman, G. L., Hayes, S. L. & Petri, M. C. Temperature gradient driven constituent redistribution in U-Zr alloys. J. Nucl. Mater. 227, 277–286. https://doi.org/10.1016/0022-3115(95)00129-8 (1996).
doi: 10.1016/0022-3115(95)00129-8
Liu, X. et al. Fuel-cladding chemical interaction of a prototype annular U-10Zr fuel with Fe-12Cr ferritic/martensitic HT-9 cladding. J. Nucl. Mater. 544, 152588. https://doi.org/10.1016/j.jnucmat.2020.152588 (2021).
doi: 10.1016/j.jnucmat.2020.152588
Xu, F. et al. Understanding fission gas bubble distribution and zirconium redistribution in neutron-irradiated U-Zr metallic fuel using machine learning. Microsc. Microanal. 28, 82–83. https://doi.org/10.1017/S1431927622001234 (2022).
doi: 10.1017/S1431927622001234
Hofman, G. L., Pahl, R. G., Lahm, C. E. & Porter, D. L. Swelling behavior of U-Pu-Zr fuel. Metall. Trans. A 21, 517–528. https://doi.org/10.1007/BF02671924 (1990).
doi: 10.1007/BF02671924