EPJ Nuclear Sci. Technol.
Volume 7, 2021
|Number of page(s)||21|
|Published online||19 February 2021|
Prediction of crack propagation kinetics through multipoint stochastic simulations of microscopic fields
EDF R&D, Département MMC, Avenue des Renardières,
2 EDF R&D, Département PRISME, 6 Quai Watier, 78401 Chatou, France
* Present address: Ecole Centrale de Nantes, 1 rue de la Nöe 44300 Nantes, France.
** e-mail: email@example.com
Received in final form: 18 December 2020
Accepted: 4 January 2021
Published online: 19 February 2021
Prediction of crack propagation kinetics in the components of nuclear plant primary circuits undergoing Stress Corrosion Cracking (SCC) can be improved by a refinement of the SCC models. One of the steps in the estimation of the time to rupture is the crack propagation criterion. Current models make use of macroscopic measures (e.g. stress, strain) obtained for instance using the Finite Element Method. To go down to the microscopic scale and use local measures, a two-step approach is proposed. First, synthetic microstructures representing the material under specific loadings are simulated, and their quality is validated using statistical measures. Second, the shortest path to rupture in terms of propagation time is computed, and the distribution of those synthetic times to rupture is compared with the time to rupture estimated only from macroscopic values. The first step is realized with the cross-correlation-based simulation (CCSIM), a multipoint simulation algorithm that produces synthetic stochastic fields from a training field. The Earth Mover’s Distance is the metric which allows to assess the quality of the realizations. The computation of shortest paths is realized using Dijkstra’s algorithm. This approach allows to obtain a refinement in the prediction of the kinetics of crack propagation compared to the macroscopic approach. An influence of the loading conditions on the distribution of the computed synthetic times to rupture was observed, which could be reduced through a more robust use of the CCSIM.
© E. Le Mire et al., published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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