An efficient implicit time integration method for discrete dislocation dynamics

Péterffy, Gábor; Ispánovity, Péter Dusán

doi:10.1088/1361-651x/ab76b2

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…14 b) 51 . The equations of motion (( 12 ), ( 13 )) are solved using a fully implicit scheme that makes usage of annihilation unnecessary, so, it is not implemented 52 .…”

Section: Methodsmentioning

confidence: 99%

Dislocation avalanches are like earthquakes on the micron scale

et al. 2022

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Compression experiments on micron-scale specimens and acoustic emission (AE) measurements on bulk samples revealed that the dislocation motion resembles a stick-slip process – a series of unpredictable local strain bursts with a scale-free size distribution. Here we present a unique experimental set-up, which detects weak AE waves of dislocation slip during the compression of Zn micropillars. Profound correlation is observed between the energies of deformation events and the emitted AE signals that, as we conclude, are induced by the collective dissipative motion of dislocations. The AE data also reveal a two-level structure of plastic events, which otherwise appear as a single stress drop. Hence, our experiments and simulations unravel the missing relationship between the properties of acoustic signals and the corresponding local deformation events. We further show by statistical analyses that despite fundamental differences in deformation mechanism and involved length- and time-scales, dislocation avalanches and earthquakes are essentially alike.

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“…14 b) 51 . The equations of motion (( 12 ), ( 13 )) are solved using a fully implicit scheme that makes usage of annihilation unnecessary, so, it is not implemented 52 .…”

Section: Methodsmentioning

confidence: 99%

Dislocation avalanches are like earthquakes on the micron scale

et al. 2022

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“…Here, instead of developing more sophisticated mathematical time-integration procedures, e.g. such as implicit integrators [53,54] or the subcyling approach [28], we take a different route and propose instead to rely on a learnable GNN model trained to predict nodal displacements given the current state of the dislocation network. For instance, in this work our model was trained using a multi-stepping time-integrator.…”

Section: Discussionmentioning

confidence: 99%

Accelerating discrete dislocation dynamics simulations with graph neural networks

Bertin¹,

Zhou²

2022

Preprint

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Discrete dislocation dynamics (DDD) is a widely employed computational method to study plasticity at the mesoscale that connects the motion of dislocation lines to the macroscopic response of crystalline materials. However, the computational cost of DDD simulations remains a bottleneck that limits its range of applicability. Here, we introduce a new DDD-GNN framework in which the expensive time-integration of dislocation motion is entirely substituted by a graph neural network (GNN) model trained on DDD trajectories. As a first application, we demonstrate the feasibility and potential of our method on a simple yet relevant model of a dislocation line gliding through a forest of obstacles. We show that the DDD-GNN model is stable and reproduces very well unseen ground-truth DDD simulation responses for a range of straining rates and obstacle densities, without the need to explicitly compute nodal forces or dislocation mobilities during time-integration. Our approach opens new promising avenues to accelerate DDD simulations and to incorporate more complex dislocation motion behaviors.

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“…12b) 42 . The equations of motion (5,6) are solved using a fully implicit scheme that makes usage of annihilation unnecessary, so, it is not implemented 43 .…”

Section: Discrete Dislocation Dynamicsmentioning

confidence: 99%

Dislocation Avalanches: Earthquakes on the Micron Scale

Ispánovity¹,

Ugi²,

Péterffy³

et al. 2021

Preprint

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Metals usually deform irreversibly as a result of the motion of dislocations that are line-like defects in the crystal lattice. Compression experiments of micron-scale specimens 1, 2 as well as acoustic emission (AE) measurements performed on bulk samples 3, 4 revealed that the motion of dislocations resembles a stick-slip process. As a result, deformation proceeds in a series of unpredictable local strain bursts with a scale-free size distribution 5, 6 . Here we use a unique, highly sensitive experimental set-up, which allows us to detect the weak AE waves of dislocation slip during the compression of micron-sized Zn pillars. This opens up new vistas for studying the stop-and-go dislocation motion in detail and understanding the physical origin of AE events. Profound correlation is observed between the size of the deformation events and the total energy of the emitted signals that, as we conclude, are induced by the collective dissipative motion of dislocations. We also show by statistical

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An efficient implicit time integration method for discrete dislocation dynamics

Cited by 6 publications

References 52 publications

Dislocation avalanches are like earthquakes on the micron scale

Dislocation avalanches are like earthquakes on the micron scale

Accelerating discrete dislocation dynamics simulations with graph neural networks

Dislocation Avalanches: Earthquakes on the Micron Scale

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