The effect of beryllium on deuterium implantation in tungsten by atomistic simulations

Lasa, A.; Heinola, K.; Nordlund, K.

doi:10.1088/0029-5515/54/12/123021

Cited by 12 publications

(12 citation statements)

References 36 publications

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“…It is noteworthy to mention that the effect of deuterium (D) from the plasma is an important and competing effect in Be deposition and in other interactions with W divertor. The effects of the ternary system W-Be-D are discussed elsewhere [21]. Further, typical electron energies at the divertor can range up to 40 eV [5,22].…”

Section: Introductionmentioning

confidence: 99%

Atomistic simulations of Be irradiation on W: mixed layer formation and erosion

Lasa¹,

Heinola²,

Nordlund³

2014

Nucl. Fusion

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In the recent ITER-Like Wall experiment at JET, tungsten (W) and beryllium (Be) are used as the first wall plasma-facing materials. Due to the plasma–wall interactions, these materials will erode, be transported, re-deposit and mix. We present the first computational, atomistic, systematic study on the W–Be material mixing under fusion-relevant conditions. To this end, W surfaces were irradiated by Be, varying the impacting energy and angle, followed by annealing the mixed W–Be layers. At low energies, a Be layer is deposited on W, suppressing the W erosion. The materials mix as the W atoms migrate towards the Be layer due to the heat of mixing. Be2 and BeW molecules eroded, both physically (dimer sputtering) and chemically (sputter etching). All the mixed layers show an underlying hcp-like Be structure and the Be : W ratios are close to those in the intermetallic phases (Be2W—Be12W). However, no crystalline alloy structure formed, even after annealing. Further, we present a geometrical model for the angular dependence of the Be reflection, which strongly affects the W sputtering.

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Section: Introductionmentioning

confidence: 99%

Atomistic simulations of Be irradiation on W: mixed layer formation and erosion

Lasa¹,

Heinola²,

Nordlund³

2014

Nucl. Fusion

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“…Such case assumes that Be − an impurity present in the edge plasma − also contributes to production of BeD molecules. In fact, this process has been observed in Molecular Dynamics (MD) simulations [39], although its contribution to BeD production is not well quantified yet. The second approach assumes that the molecular yield is a fraction of the yield obtained for a pure D background plasma.…”

Section: Detailed Magnetic Shadowing Of the Densitymentioning

confidence: 99%

ERO modeling and sensitivity analysis of locally enhanced beryllium erosion by magnetically connected antennas

et al. 2017

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See the author list of "Overview of the JET results in support to ITER" by X. Litaudon et al. to be published in Nucl. Fusion Special issue: overview and summary reports from the 26th FEC (Kyoto, Japan, 17-22 Oct 2016) ERO Modeling and Sensitivity Analysis of Erosion Enhanced by Magnetically Connected Antennas2 sensitivity analysis highlights a qualitative effect (i.e., change in emission patterns) of magnetic shadowing, anomalous diffusion, and inclusion of neutral fluxes and molecular release of Be. The LCFS location, and energy and angular distribution of background plasma fluxes impact erosion quantitatively. ERO simulations including all features described above match experimentally measured Be I and Be II signals. However, increases in erosion with variation of ICRH antenna's RF power are not fully captured by ERO's emission measurements, as most contributions from plasma wetted surfaces fall outside the volume observed by sightlines.

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“…While many potentials exist for tungsten 32 , only one exists for modeling W and Be 33 , which is a Tersoff style bond order potential (BOP) 10 . This potential has been used to study both beryllium implantation in tungsten 34 and mixed beryllium-deuterium implantation in tungsten 35 . However, this potential form is not robust enough to capture the complex interactions between tungsten, beryllium, and their intermetallic structures.…”

Section: Introductionmentioning

confidence: 99%

Data-driven material models for atomistic simulation

et al. 2019

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The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and find accurate parameterizations for potentials using traditional approaches. Machinelearning has emerged as an effective alternative approach to develop accurate and robust interatomic potentials. Starting with a very general model form, the potential is learned directly from a database of electronic structure calculations and therefore can be viewed as a multiscale link between quantum and classical atomistic simulations. Risk of inaccurate extrapolation exists outside the narrow range of time-and length-scales where the two methods can be directly compared. In this work, we use the Spectral Neighbor Analysis Potential (SNAP) and show how a fit can be produced with minimal interpolation errors which is also robust in extrapolating beyond training. To demonstrate the method, we have developed a new tungsten-beryllium potential suitable for the full range of binary compositions. Subsequently, large-scale molecular dynamics simulations were performed of high energy Be atom implantation onto the (001) surface of solid tungsten. The new machine learned W-Be potential generates a population of implantation structures consistent with quantum calculations of defect formation energies. A very shallow (< 2nm) average Be implantation depth is predicted which may explain ITER diverter degradation in the presence of beryllium.

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The effect of beryllium on deuterium implantation in tungsten by atomistic simulations

Cited by 12 publications

References 36 publications

Atomistic simulations of Be irradiation on W: mixed layer formation and erosion

Atomistic simulations of Be irradiation on W: mixed layer formation and erosion

ERO modeling and sensitivity analysis of locally enhanced beryllium erosion by magnetically connected antennas

Data-driven material models for atomistic simulation

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