AI-driven uncertainty quantification & multi-physics approach to evaluate cladding materials in a microreactor

The pursuit of enhanced nuclear safety has spurred the development of accident-tolerant cladding (ATC) materials for light water reactors (LWRs). This study investigates the potential of repurposing these ATCs in advanced reactor designs, aiming to expedite material development and reduce costs. The research employs a multi-physics approach, encompassing neutronics, heat transfer, thermodynamics, and structural mechanics, to evaluate four candidate materials (Haynes 230, Zircaloy-4, FeCrAl, and SiC–SiC) within the context of a high-temperature, sodium-cooled microreactor, exemplified by the Kilopower design. While neutronic simulations revealed negligible power profile variations among the materials, finite element analyses highlighted the superior thermal stability of SiC–SiC and the favorable stress resistance of Haynes 230. The high-temperature environment significantly impacted material performance, particularly for Zircaloy-4 and FeCrAl, while SiC–SiC’s inherent properties limited its ability to withstand stress loads. Additionally, AI-driven uncertainty quantification and sensitivity analysis were conducted to assess the influence of material property variations on maximum hoop stress. The findings underscore the need for further research into high-temperature material properties to facilitate broader applicability of existing materials to advanced reactors. Haynes 230 is identified as the most promising candidate based on the evaluated criteria.

Authors

Institutions

Publication Details

Journal
Progress in Nuclear Energy
Published
2025-04-24
DOI
https://doi.org/10.1016/j.pnucene.2025.105793
Citations
3
Primary Topic
Nuclear Materials and Properties
Type
article
Field-Weighted Citation Impact
1.55
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-driven uncertainty quantification & multi-physics approach to evaluate cladding materials in a microreactor

Alexander Foutch, Ayodeji Alajo, Kazuma Kobayashi, Syed Bahauddin Alam et al.
3 citations
Progress in Nuclear Energy
Nuclear Materials and Properties
1.55
article

AI-driven uncertainty quantification & multi-physics approach to evaluate cladding materials in a microreactor

Alexander Foutch, Ayodeji Alajo, Kazuma Kobayashi, Syed Bahauddin Alam, Dinesh Kumar
article en
3 citations

Abstract

The pursuit of enhanced nuclear safety has spurred the development of accident-tolerant cladding (ATC) materials for light water reactors (LWRs). This study investigates the potential of repurposing these ATCs in advanced reactor designs, aiming to expedite material development and reduce costs. The research employs a multi-physics approach, encompassing neutronics, heat transfer, thermodynamics, and structural mechanics, to evaluate four candidate materials (Haynes 230, Zircaloy-4, FeCrAl, and SiC–SiC) within the context of a high-temperature, sodium-cooled microreactor, exemplified by the Kilopower design. While neutronic simulations revealed negligible power profile variations among the materials, finite element analyses highlighted the superior thermal stability of SiC–SiC and the favorable stress resistance of Haynes 230. The high-temperature environment significantly impacted material performance, particularly for Zircaloy-4 and FeCrAl, while SiC–SiC’s inherent properties limited its ability to withstand stress loads. Additionally, AI-driven uncertainty quantification and sensitivity analysis were conducted to assess the influence of material property variations on maximum hoop stress. The findings underscore the need for further research into high-temperature material properties to facilitate broader applicability of existing materials to advanced reactors. Haynes 230 is identified as the most promising candidate based on the evaluated criteria.

Progress in Nuclear EnergyVol. 186
University of Illinois Urbana-Champaign (US), Missouri University of Science and Technology (US), University of Bristol (GB), National Center for Supercomputing Applications (US)
Openalex Percentile: Top 18%
Nuclear Materials and Properties
1.55
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.