Thermally Constrained Conceptual Deep Geological Repository Design under Spacing and Placing Uncertainties
Bibliographic record
Abstract
The temperature evolution within a deep geological repository (DGR) is a key design consideration for the safe and permanent storage of the high-level radioactive waste contained inside used nuclear fuel containers (UFCs). Due to the material limitations of engineered components with respect to high temperature tolerance, the Nuclear Waste Management Organization of Canada requires the maximum temperature within a future Canadian DGR to be less than 100 °C. Densely placing UFCs within a DGR is economically ideal, but greater UFC placement density will increase the maximum temperature reached in the repository. This paper was aimed to optimize (i) the separation between UFCs, (ii) the distance between container placement rooms, and (iii) the locations of the age-dependent UFCs in the placement rooms for a conceptual DGR constructed in crystalline rock. Surrogate-based optimization reduced the amount of computationally expensive evaluations of a COMSOL Multiphysics model used to study the temperature evolution within the conceptual DGR and determined optimal repository design points. Via yield optimization, nominal design points that considered uncertainties in the design process were observed. As more information becomes available during the design process for the Canadian DGR, the methods employed in this paper can be revisited to aid in selecting a UFC placement plan and to mitigate risks that may cause repository failure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".