Void lattice formation in electron irradiated CaF 2 : Statistical analysis of experimental data and cellular automata simulations

Zvejnieks, G.; Merzlyakov, P.; Kuzovkov, V. N.; Kotomin, E. A.

doi:10.1016/j.nimb.2015.11.037

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…To overcome this last limitation, Kinetic Monte-Carlo (KMC) methods were developed enabling the spatial resolution of individual class of defects (point defect, dislocations, cavities..). In radiation damage processes, elementary processes (thermal diffusion, ballistic exchanges) occur over time scales smaller (at least over three orders of magnitude) than the longtime evolution of point defects (Frenkel pairs), atomic species (ordering/disordering) and the micro-structure [4] (dislocation loops). Decoupling between these time scales insures that a new micro-structure can be treated as resulting from a Markov process.…”

Section: Introductionmentioning

confidence: 99%

Phase field modelling of irradiated materials

Siméone

Ribis

Lunéville

2018

Nuclear Instruments and Methods in Physics Research Section B:

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Almost forty years after Turings seminal paper on patterning, progress on modeling instabilities leading to pattern formation has been achieved. The initial concept of dissipative structure is now clearly understood within the Phase-Field framework. So far, such an approach obtained promising results in various aspects of materials research from pattern formation during solidification to defect dynamics. In this work, we will try discussing experimental results observed during aging of solids under irradiation within this framework. The approach followed in this presentation is comprehensive and not specialized in specific aspects of the Phase-Field modelling (mechanics, mathematics, or numerical methods) at the expense of a holistic picture.

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Section: Introductionmentioning

confidence: 99%

Phase field modelling of irradiated materials

Siméone

Ribis

Lunéville

2018

Nuclear Instruments and Methods in Physics Research Section B:

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“…We use simplified Y 2 O 3 formation model that allows us to make a direct quantitative comparison of an average cluster radii, cluster growth rate and cluster density with both available experimental and theoretical data. In the KMC simulations, we use the standard model and the pair algorithm approach [17] that was successfully applied earlier for studying complex kinetics both in 2D catalytic systems [18][19][20] and void self-organization in 3D [21]. In order to forecast cluster growth kinetic results beyond the scope of KMC calculation limits, we complement the KMC simulations with the autoregressive integrated moving average (ARIMA) method [22].…”

Section: Introductionmentioning

confidence: 99%

Kinetic Monte Carlo modeling of Y2O3 nano-cluster formation in radiation resistant matrices

Zvejnieks

Anspoks

Kotomin

et al. 2018

Nuclear Instruments and Methods in Physics Research Section B:

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As known, Y 2 O 3 nano-clusters considerably increase radiation resistance of reactor construction materials. To model the nano-cluster formation kinetics, we propose the simplest possible mathematical model and perform kinetic Monte Carlo (KMC) simulations. We extended the KMC simulated results to the experimentally relevant times using autoregressive integrated moving average forecasting. Within the model, we have studied prototypical attractive interaction energies and particle concentrations, and compared the simulations with experiments. We have observed the standard Lifshitz-Slyozov-Wagner (LSW) theory, predicting the average cluster radius growth with time, R ∼ t 1/p , with p=3 in the long-time limit, for weak (0.1 eV) mutual particle attraction. However, the respective cluster growth rates in these KMC simulations are overestimated compared to the experiments. The best agreement with experiment is obtained for a medium (0.3 eV) and strong (0.5 eV) attractions, when nano-cluster formation occurs during intermediate asymptotic time scale, where power order p ranges from 5 to 7.6 depending on interaction, without reaching actually the LSW long-time limit. Such a stronger interaction leads also to a more compact {110}-faceted nano-clusters.

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