Simulation of radiation damage in Fe alloys: an object kinetic Monte Carlo approach
Source abstract
Abstract
Abstract The reactor pressure vessel (RPV) steels used in current nuclear power plants embrittle as a consequence of the continuous irradiation with neutrons. Among other radiation effects, the experimentally observed formation of copper-rich defects is accepted to be one of the main causes of embrittlement. Therefore, an accurate description of the nucleation and growth under irradiation of these and other defects is fundamental for the prediction of the mechanical degradation that these materials undergo during operation, with a view to guarantee a safer plant life management. In this work we describe in detail the object kinetic Monte Carlo (OKMC) method that we developed, showing that it is well suited to investigate the evolution of radiation damage in simple Fe alloys (Fe, Fe–Cu) under irradiation conditions (temperature, dose and dose-rate) typical of experiments with different impinging particles and also operating conditions. The still open issue concerning the method is the determination of the mechanisms and parameters that should be introduced in the model in order to correctly reproduce the experimentally observed trends. The state-of-the-art, based on the input from atomistic simulation techniques, such as ab initio calculations, molecular dynamics (MD) and atomic kinetic Monte Carlo, is critically revised in detail and a sensitivity study on the effects of the choice of the reaction radii and the description of defect mobility is conducted. A few preliminary, but promising, results of favorable comparison with experimental observations are shown and possible further refinements of the model are briefly discussed.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nuclear Materials and Properties · Fusion materials and technologies · Nuclear reactor physics and engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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