Pre-Designing Method for Engineered Barrier System in Deep Geological Repository Based on Corrosion Resistance of Copper Canister
Bibliographic record
Abstract
Designing deep geological repositories (DGRs) for nuclear waste requires balancing long-term safety with engineering feasibility, where canister corrosion is a primary constraint. While complex models exist to assess canister integrity, they are often impractical for the initial design and screening phases of a DGR program, as they require detailed engineering designs and site-specific environmental data that are typically unavailable at this stage. To address this gap, this study introduces a simplified, conservative predesign method based on an analytical equation for sulfide-induced corrosion. The method evaluates corrosion depth by considering canister thickness, bentonite buffer thickness, the effective diffusion coefficient of sulfide, and sulfide concentration. It also introduces a logarithmic severity index S for the clear visualization of corrosion depth. The analysis suggests that a bentonite buffer thickness of 0.3 to 0.4 m provides a robust starting point for design, a range consistent with several international DGR programs. The model establishes sulfide concentration thresholds, indicating that sites with concentrations exceeding 10 and 100 mol·m−3 are unsuitable for copper canisters of 10- and 50-mm thickness, respectively. Application of the method to existing national programs demonstrates its utility. The designs for Sweden (SKB) and Finland (Posiva Oy) are shown to be highly conservative (S < −1, corresponding to less than 10% corrosion of the canister wall), while Canada’s 3-mm canister design is also robust (S = –1.45, corresponding to 3.5% corrosion). This predesign tool offers a rapid and effective means for optimizing engineered barrier designs and screening potential repository sites in the early stages of DGR development, facilitating more focused and efficient subsequent detailed analyses.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".