Exploring the governing transport mechanisms of corrosive agents in a Canadian deep geological repository
Bibliographic record
Abstract
All nuclear energy producing nations face a common challenge associated with the long-term solution for their used nuclear fuel. After decades of research, many nuclear safety agencies worldwide agree that deep geological repositories (DGRs) are appropriate long-term solutions to protect the biosphere. The Canadian DGR is planned in either stable crystalline or sedimentary host rock (depending on the final site location) to house the used nuclear fuel in copper-coated used fuel containers (UFCs) surrounded by highly compacted bentonite . The copper-coating and bentonite provide robust protection against many corrosion processes anticipated in the DGR. However, it is possible that bisulfide (HS − ) produced near the host rock-bentonite interface may transport through the bentonite and corrode the UFCs during the DGR design life (i.e., one million years); although container performance assessments typically account for this process, while maintaining container integrity. Because the DGR design life far exceeds those of practical experimentation, there is a need for robust numerical models to forecast HS − transport. In this paper we present the development of a coupled 3D thermal-hydraulic-chemical model to explore the impact of key coupled physics on HS − transport in the proposed Canadian DGR. These simulations reveal that, although saturation delayed and heating accelerated HS − transport over the first 100s and 10,000s of years, respectively, these times of influence were small compared to the long DGR design life. Consequently, the influence from heating only increased total projected HS − corrosion by <20% and the influence from saturation had a negligible impact (<1%). By comparing the corrosion rate results with a simplified model, it was shown that nearly-steady DGR design parameters governed most of the projected HS − corrosion. Therefore, those parameters need to be carefully resolved to reliably forecast the extent of HS − corrosion.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".