Modelling long term conditions in Canadian deep geological repository
Bibliographic record
Abstract
Canada’s plan for long term (1 million years) management of high-level nuclear waste includes a deep geological repository (DGR). The DGR design involves an engineered barrier system (EBS) within a low permeability host rock (crystalline or sedimentary) that serves as a natural barrier. The EBS includes copper coated used fuel containers (UFCs) within highly compacted bentonite. Over the DGR lifetime, different hydrogeological and geochemical conditions can evolve in the repository. These transient conditions include bentonite saturation, UFC heating, evaporation and condensation, geochemical reaction, adsorption, and microbial activity. Depending on site-specific conditions, bisulfide (HS-) produced by sulfate reducing bacteria in the host rock could slowly transport (diffuse) through the bentonite to the UFC surface and corrode the copper coating and produce hydrogen (H2). Therefore, HS- corrosion assessment is complex and requires a robust numerical model. This thesis describes the development of a HS- transport and reaction model and explores how DGR transient hydrogeological and geochemical conditions affect HS- transport and UFC corrosion. The model predicted slower saturation evolution in the sedimentary DGR due to the rock’s low permeability compared to the crystalline DGR. The slower saturation evolution in the sedimentary DGR delayed HS- transport and therefore HS- corrosion. The model also assessed the relative importance of different processes (e.g. heating, saturation, reaction, adsorption), and system behaviour over time due to the inclusion of these processes, was understood. For example, heating accelerated bisulfide transport while partially saturated bentonite and bisulfide reaction, or adsorption, limited it. In addition, the combined effects of heating, saturation, and bisulfide reaction or adsorption with bentonite were not pronounced over the long DGR life span. Bisulfide transport was simulated for the entire DGR lifespan and was found to be delayed (~50-800 years) due to HS- and iron (Fe2+) reaction or HS- adsorption. However, the HS- diffusion delays are relatively short in a DGR lifespan (1 million years) and does not impact long term HS- corrosion, which stays below Canada’s HS- corrosion depth tolerance. Lastly, amongst various modelling scenarios, the H2 solubility limit was never surpassed, indicating the unlikelihood of H2 gas pressure build-up in a DGR under explored modelling conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".