Identification of dislocation reaction kinetics in complex dislocation networks for continuum modelling using data-driven methods

Katzer, Balduin; Zoller, Kolja; Weygand, D.; Schulz, Katrin

doi:10.1016/j.jmps.2022.105042

Cited by 5 publications

(3 citation statements)

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“…In the CDD model, we incorporate unzipping of Lomer junctions and therefore the dissolution of dislocation network density in a simplified way [45]. However, the incorporation of a more accurate dissolution process of dislocation network density in CDD can be discussed, since we observe a limitation of the content of Lomer dislocation density in DDD simulations [47,51]. But, as an overall conclusion to minimize the observed deviations in the regime of higher strains, we assume that the incorporation of simulations with higher strains into the database would yield a more precise input parameter prediction and avoid the well-known issues of extrapolation in data-driven models.…”

Section: Discussionmentioning

confidence: 99%

“…The parameter C fr,multi is a constant between 0 and 1, which captures the shape of the bow-out of the cross-slipped dislocation [44]. Reaction model We consider dislocation reactions of dislocation on different slip systems on a continuum level [43,51]. Here, we consider the input parameters for collinear and Lomer reactions.…”

Section: Dislocation Cross-slipmentioning

confidence: 99%

“…where C r corresponds to a collinear coefficient C col or a Lomer coefficient C lom , which leads to reactions of mobile ρ k mob,r and stabilized ρ k sta,r dislocation density of reaction r. The two interacting slip systems i and j yield a reaction on slip system k. It has to be remarked that k can be identical to i or j or represent a third slip system [51]. Furthermore, Lomer reactions lead to the generation of Lomer dislocation density ρ k lom , which is given as…”

Section: Dislocation Cross-slipmentioning

confidence: 99%

See 2 more Smart Citations

Combining simulation and experimental data via surrogate modelling of continuum dislocation dynamics simulations

Katzer,

Betsche,

von Hoegen

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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Several computational models have been introduced in recent years toyield comprehensive insights into microstructural evolution analyses. However, theidentification of the correct input parameters to a simulation that corresponds to acertain experimental result is a major challenge on this length scale. To complementsimulation results with experimental data (and vice versa) is not trivial since, e.g.,simulation model parameters might lack a physical understanding or uncertaintiesin the experimental data are neglected. Computational costs are another challengemesoscale models always have to face, so comprehensive parameter studies can becostly. In this paper, we introduce a surrogate model to circumvent continuumdislocation dynamics simulation by a data-driven linkage between well-defined inputparameters and output data and vice versa. We present meaningful results for aforward surrogate formulation that predicts simulation output based on the inputparameter space, as well as for the inverse approach that derives the input parameterspace based on simulation as well as experimental output quantities. This enables, e.g.,a direct derivation of the input parameter space of a continuum dislocation dynamicssimulation based on experimentally provided stress-strain data.

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Section: Discussionmentioning

confidence: 99%