Simulation of the Anaerobic Corrosion of Carbon Steel Used Fuel Containers and the Impact of Corrosion Products on Other Barriers in the Repository
Bibliographic record
Abstract
Abstract A model has been developed to predict the anaerobic corrosion behaviour of carbon steel used fuel containers in a sealed deep geological repository. The Steel Corrosion Model Version 1.0 (SCM V1.0) is based on a series of one-dimensional reactive-transport equations that describe the various mass-transport, redox, adsorption/desorption, precipitation/dissolution, and chemical speciation processes of each of the species considered in the model. Solution of these equations involves the use of a mixed-potential model based on the electrochemical reactions involved in the corrosion of the container, from which the time dependent corrosion rate and corrosion potential can be predicted, leading to an estimate of the container lifetime. The effects of film formation, interaction of Fe(II) ions with the bentonite sealing materials, gas generation and transport, and of the slow saturation of the repository by ground water are also simulated by the model. A series of simulations has been performed to predict the long-term corrosion behaviour of the container. Predicted mean corrosion rates are of the order of 1 μm·a-1 and are consistent with values measured experimentally and those derived from the study of archaeological artifacts. The model results suggest that gaseous H2 will be formed in the repository and periodically released through the sealing materials. Although some alteration of the clay is predicted to occur due to reactions with Fe(II) species, the long-term sealing function of the bentonite buffer material is retained.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".