Simulation of the nanostructure evolution under irradiation in Fe-C alloys

TL;DR

This paper presents a kinetic Monte Carlo model simulating nanostructure evolution in Fe-C alloys under neutron irradiation, effectively reproducing experimental defect behaviors relevant to nuclear materials.

Contribution

The study introduces a physically-based, trap-inclusive kinetic Monte Carlo model for Fe-C systems, integrating recent atomistic data to accurately simulate irradiation effects.

Findings

Model reproduces defect cluster densities and sizes up to 700 K

Carbon's role as vacancy traps influences defect evolution

Simulation aligns with experimental irradiation and annealing data

Abstract

Neutron irradiation induces in steels nanostructural changes, which are at the origin of the mechanical degradation that these materials experience during operation in nuclear power plants. Some of these effects can be studied by using as model alloy the iron-carbon system. The Object Kinetic Monte Carlo technique has proven capable of simulating in a realistic and quantitatively reliable way a whole irradiation process. We have developed a model for simulating Fe-C systems using a physical description of the properties of vacancy and self-interstitial atom (SIA) clusters, based on a selection of the latest data from atomistic studies and other available experimental and theoretical work from the literature. Based on these data, the effect of carbon on radiation defect evolution has been largely understood in terms of formation of immobile complexes with vacancies that in turn act as traps for SIA clusters. It is found that this effect can be introduced using generic traps for SIA and vacancy clusters, with a binding energy that depends on the size of the clusters, also chosen on the basis on previously performed atomistic studies. The model proved suitable to reproduce the results of low (<350 K) temperature neutron irradiation experiments, as well as the corresponding post-irradiation annealing up to 700 K, in terms of defect cluster densities and size distribution, when compared to available experimental data from the literature. The use of traps proved instrumental for our model.