Modeling of helium bubble nucleation and growth in austenitic stainless steels using an Object Kinetic Monte Carlo method

Backer, Andrée De; Adjanor, Gilles; Domain, Christophe; Lescoat, M.-L.; Jublot-Leclerc, S.; Fortuna, F.; Gentils, A.; Ortiz, C.J.; Souidi, Abdelkader; Becquart, Charlotte

doi:10.1016/j.nimb.2014.11.110

“…He bubbles can then form in W and their morphology depends on the exposure conditions 4 – 7 . Such bubbles have been observed using Molecular Dynamics (MD) 3 , 8 – 12 and Object Kinetic Monte Carlo 13 , 14 . He bubbles can alter the mechanical properties of W 6 , 15 , 16 and reduce the thermal properties of components 17 , 18 .…”

Section: Introductionmentioning

confidence: 99%

Influence of exposure conditions on helium transport and bubble growth in tungsten

Ialovega

¹

,

Hodille

²

,

Mougenot

³

et al. 2021

Sci Rep

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Helium diffusion, clustering and bubble nucleation and growth is modelled using the finite element method. The existing model from Faney et al. (Model Simul Mater Sci Eng 22:065010, 2018; Nucl Fusion 55:013014, 2015) is implemented with FEniCS and simplified in order to greatly reduce the number of equations. A parametric study is performed to investigate the influence of exposure conditions on helium inventory, bubbles density and size. Temperature is varied from 120 K to 1200 K and the implanted flux of 100 eV He is varied from $$10^{17}\,{\text{m}^{-2}\, \text{s}^{-1}}$$ 10 17 m - 2 s - 1 to $$5 \times 10^{21}\, {\text{m}^{-2}\, \text{s}^{-1}}$$ 5 × 10 21 m - 2 s - 1 . Bubble mean size increases as a power law of time whereas the bubble density reaches a maximum. The maximum He content in bubbles was approximately $$4 \times 10^{8}$$ 4 × 10 8 He at $$5 \times 10^{21}\,{\text{m}^{-2}\, \text{s}^{-1}}$$ 5 × 10 21 m - 2 s - 1 . After 1 h of exposure, the helium inventory varies from $$5 \times 10^{16} \,{\text{m}^{-2}}$$ 5 × 10 16 m - 2 at low flux and high temperature to $$10^{25} \,{\text{m}^{-2}}$$ 10 25 m - 2 at high flux and low temperature. The bubbles inventory varies from $$5 \times 10^{12}$$ 5 × 10 12 bubbles m$$^{-2}$$ - 2 to $$2 \times 10^{19}$$ 2 × 10 19 bubbles m$$^{-2}$$ - 2 . Comparison with experimental measurements is performed. The bubble density simulated by the model is in quantitative agreement with experiments.

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“…Since a change in the chemical potentials affects only the cluster concentrations in Eq. 3, KineCluE is a crucial step in developing a general and efficient framework to provide accurate atomic-based kinetic properties for higher-scale models such as object kinetic Monte Carlo [58][59][60][61][62][63][64], cluster dynamics [65][66][67][68] or phasefield models [69][70][71].…”

Section: Cluster Transport Coefficientsmentioning

confidence: 99%

KineCluE: A kinetic cluster expansion code to compute transport coefficients beyond the dilute limit

Schuler

¹

,

Messina

²

,

Nastar

³

2020

Computational Materials Science

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This paper introduces the KineCluE code that implements the self-consistent mean-field theory for clusters of finite size. Transport coefficients are obtained as a sum over cluster contributions (in a cluster expansion formalism), each being individually computed with KineCluE. This method allows for the calculation of these coefficients beyond the infinitely dilute limit, and is an important step in bridging the gap between dilute and concentrated approaches. Inside a finite volume of space containing the components of a given cluster, all kinetic trajectories are accounted for in an exact manner. The code, written in Python, adapts to a wide variety of systems, with various crystallographic structures (possibly under strain), defects and solute amount and types, and various jump mechanisms, including collective ones. The code also features a set of useful tools, such as the sensitivity study routine that allows for the identification of the most important jump frequencies to get accurate transport coefficients with minimum computational cost. PROGRAM SUMMARYProgram Title: KineCluE (KINEtic CLUster Expansion) Licensing provisions: LGPL Programming language: Python 3.6 Nature of problem: Providing a general method for computing transport coefficients from atomic jump frequencies, taking into account kinetic correlations.Solution method: The program relies on the selfconsistent mean field (SCMF) theory. The system is described in terms of lattice sites, defects and jump mechanisms. The first part of the code translates the diffusion problem for such system into an analytical linear eigenvalue problem. The second part of the code assigns numerical values to each analytical variable and then solves the linear problem.

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“…Other simulations underline the role of point defects, and in particular the vacancies, and impurities (C for instance) that can trap the He atoms and thus impact on the size distribution of He clusters or on the rate of He desorption (Morishita et al 2003) (Caturla et al 2003) (Bringa et al 2003) (Wirth and Bringa 2004) (Deo et al 2007) (Caturla et al 2008) (Becquart and Domain 2009) (Guo et al 2013) (De Backer et al 2015) (Perez et al 2017). Based on the same ideas that He interacts quite strongly with point defects, other studies focus more on the impact of He atoms on the long term evolution of the primary damage and the formation of voids (Bringa et al 2003) (Galloway andAckland 2013) (De et al 2013).…”

Section: Iv-4 Solute or Impurity / Gas Atoms Diffusion And The Formatmentioning

confidence: 99%

“…All these studies underline the complex balance between point defect and helium clustering, cluster growth, trapping and detrapping reactions that lead to different microstructure evolutions. Most of these investigations have been done in bcc Fe and W, but a few studies in fcc metals (Figure 10) and even austenitic steels are available (Caturla et al 2003) (Gámez et al 2009) (De Backer et al 2015. A preliminary study comparing the formation of mixed He vacancy clusters in bcc and fcc Fe seems even to find no major difference in clustering behavior (Suzudo et al 2010).…”

Section: Iv-4 Solute or Impurity / Gas Atoms Diffusion And The Formatmentioning

confidence: 99%

Kinetic Monte Carlo Simulations of Irradiation Effects

Becquart

¹

,

Mousseau

²

,

Domain

³

2020

Comprehensive Nuclear Materials

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Modeling of helium bubble nucleation and growth in austenitic stainless steels using an Object Kinetic Monte Carlo method

Cited by 19 publications

References 28 publications

Influence of exposure conditions on helium transport and bubble growth in tungsten

Influence of exposure conditions on helium transport and bubble growth in tungsten

KineCluE: A kinetic cluster expansion code to compute transport coefficients beyond the dilute limit

Kinetic Monte Carlo Simulations of Irradiation Effects

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