Data-driven predictive modeling of FeCrAl oxidation

Abstract

FeCrAl alloys are among the most promising candidates for accident-tolerant fuel cladding material in light water nuclear reactors. Despite their high-temperature oxidation resistance in corrosive environments coupled with their hydrothermal corrosion resistance, a key challenge remains in optimizing the composition of the alloy that can be achieved through statistical analysis. However, the current literature on FeCrAl alloy design lack studies for designing alloys based on oxidation resistance. This study addresses that gap by developing a predictive model for the oxidation of FeCrAl alloys based on an experimental dataset, which lays the groundwork for model-based optimization for alloy composition.

Keywords

• FeCrAl
• Machine Learning
• Oxidation