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Irradiation of reactor pressure vessel (RPV) steels causes the formation of nanoscale microstructural features (termed radiation damage), which affect the mechanical properties of the vessel. A key tool for characterizing these nanoscale features is atom probe tomography (APT), due to its high spatial resolution and the ability to identify different chemical species in three dimensions. Microstructural observations using APT can underpin development of a mechanistic understanding of defect formation. However, with atom probe analyses there are currently multiple methods for analyzing the data. This can result in inconsistencies between results obtained from different researchers and unnecessary scatter when combining data from multiple sources. This makes interpretation of results more complex and calibration of radiation damage models challenging. In this work simulations of a range of different microstructures are used to directly compare different cluster analysis algorithms and identify their strengths and weaknesses.
The Metropolis Monte Carlo (MMC) algorithm is a computational method to study equilibrium thermodynamic properties of a system at the atomic level. The algorithm accounts for all terms that contribute to defining the free energy difference between states: not only chemical, configurational and interfacial, but also due to strain fields and thermal vibrations. In this work, the MMC method with a two bands empirical many-body potential is used to predict the ordering properties of Fe1-xCrx alloys at various compositions and temperatures in the absence of defects. The particular goal of the work was to reveal the effect of atomic relaxations and vibrations on the phase diagram. It is found that vibrations and local relaxation effects contribute to lowering the order-disorder transition temperature by about 25 percent as compared to MMC predictions with a rigid lattice.
This is a copy of the slides presented at the meeting but not formally written up for the volume.
Understanding the basic mechanisms that determine microstructure changesin neutron irradiated steels is vital for a safe lifetime management of existing nuclear reactors and a safe design of future nuclear options. Low-alloyed ferritic steels containing Cu, Ni, Mn and Si as principal solute atoms are used as structural materials for current reactor vessels, while high-Cr ferritic-martensitic steels will be used in future nuclear options. The microstructural evolution under irradiation in alloys is decided by the interplay between defect formation and thermodynamic driving forces, together determining the appearance of phase transformations (precipitation, segregation, ...) and favouring or delaying the nucleation and growth of point-defect clusters, their diffusion and their mutual recombination or removal at sinks. A reliable description of the production, evolution and accumulation of radiation damage must therefore start from the atomic level and requires being able to describe multicomponent systems for timescales ranging from few picoseconds to years. This goal demands firstly the fabrication of interatomic potentials for alloys that must be both consistent with the thermodynamic properties of the system and capable of reproducing correctly the characteristic solute-point defect interactions, versus ab initio or experimental data. Secondly the performance of extensive molecular dynamics (MD) simulations, to grasp the main mechanisms of defect production, diffusion, mutual interaction, and interaction with solute atoms and impurities. Thirdly, the development of simulation tools capable of describing the microstructure evolution beyond the timeframe and lengthscale of MD, while reproducing as much as possible the atomic-level origin of the mechanisms governing the evolution of the system, including phase changes.In this presentation the results of recent efforts made in this direction in the case of Fe-Cu and Fe-Cr alloys, as basic model alloys for the description of steels of technological relevance, are highlighted. In particular, advanced techniques to fit interatomic potentials consistent with thermodynamics are proposed and the results of their application to the mentioned alloys are presented. The results of the use of advanced potentials to study the effect of high concentrations of solute atoms on self-interstitial cluster mobility and their correlation to changes in macroscopic properties, such as swelling, are summarised. And the development of advanced methods, based on the use of artificial intelligence, to improve both the physical reliability and the computational efficiency of kinetic Monte Carlo codes for the study of point-defect clustering and phase changes beyond the scale of MD, is reported. These recent progresses bear the promise of being able, in the near future, of producing reliable tools for the description of the microstructure evolution of realistic model alloys under irradiation.
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