Numerical Simulation of Pit Evolution in a Deep Geological Repository
Bibliographic record
Abstract
The concept of Adaptive Phased Management, approved by Canadian federal authorities and implemented by the Nuclear Waste Management Organization, forms a comprehensive strategy for the long-term safe management of used nuclear fuel in Canada. 1 The used nuclear fuel will ultimately be placed within a container in a deep geological repository (DGR). Current container designs employ steel vessels with a protective copper outer layer, strategically selected to reduce corrosion damage. The copper-coated container may be subjected to various corrosion mechanisms over time as the DGR environment evolves. Specific to this work and in the early aerated environment of the DGR, efflorescing salt impurities present on the container surface may lead to the formation of an Evan’s droplet. To understand the behavior of the copper-coated container with respect to localized corrosion and ensure the effectiveness of this protection strategy, a preliminary two-dimensional axisymmetric time-dependent model for the corrosion of copper under an Evans droplet was developed. 2, 3 The mathematical model employs the finite-element method (COMSOL Multiphysics). Some unique features of the model are the inclusion of six heterogeneous and fifteen homogeneous reactions, implicit calculation of nm-scale films, and treatment of the influence of films on surface concentrations and potentials. The model shows the time-dependent localized corrosion rates and depths, calculated for an elapsed time of one hundred years. It also shows time-dependent radial distributions for current density and surface coverage of films. The influence of oxygen conditions and temperature was included, and the model accounted for the influence of films on reaction rate constants. Temperature and oxygen concentration were shown to have a strong contribution to copper corrosion. Preliminary results showed that the corrosion of copper and growth of films in the droplet was almost uniform on the electrode surface. Pitting is normally initiated by localized damage to the oxide film caused by physical cracks or chemical attacks. The evolution process of pits entails formation, growth, and re-passivation. Two methods of simulating pit evolution were developed. First, a unique approach to model pitting was developed in which boundary conditions were modified to define a simulated pit without changing finite-element meshing. Simulations were performed under the assumption that the atmospheric oxygen concentration decreased exponentially with time. The simulated pit was assumed to be created when the oxide film first formed and after it reached its maximum value, representing two extreme conditions for atmospheric oxygen concentration. For both pit formation times, simulations were performed for both small rate constants yielding kinetic control and larger rate constants trending toward oxygen-transport control. 4 The pit repassivated for each simulation performed, and the resulting pit depth ranged from 30 nm to 3,560 nm. The largest pit depth was calculated for large rate constants and for pits formed shortly after the oxide film was grown. A second approach for simulating pit evolution accounts for the dynamic growth process by combining the boundary condition method for predefined deep pits with mesh deformation physics. The mesh-deformation approach showed changes within the pit environment. The pH and oxygen concentration within the pit changed before and after pit re-passivation. Boundary-condition-modification and mesh-deformation approaches yield the same answers for repassivation time and resulting pit depth. References NWMO, “Choosing a Way Forward. The Future Management of Canada’s Used Nuclear Fuel. Final Study,” Nuclear Waste Management Organization, Toronto, Ontario, 2005. C. You, S. Briggs, and M. E. Orazem, “Model development methodology for localized corrosion of copper,” Corrosion Science, 222 (2023) 111388. C. You, Y. Chuai, S. Briggs, and M. E. Orazem, “Model for corrosion of copper in a nuclear waste repository,” Corrosion Science, 226 (2024) 111658. Y. Chuai, S. Briggs, and M. E. Orazem, “A Parametric Study of Mathematical Model for Long-Term Localized Corrosion of Copper in Canadian Deep Geological Repository,” ECS Meeting Abstracts, 2024, MA2024-02, 1701. Acknowledgement This work was supported by the Nuclear Waste Management Organization, Canada, under project 2000904.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".