A Complementary Analysis for the Structural Safety Evaluation of the Spent Nuclear Fuel Disposal Canister for the Canadian Deuterium and Uranium Reactor 권 영 주†
Bibliographic record
Abstract
In this paper, a complementary analysis for the structural safety evaluation of the spent nuclear fuel disposal canister developed for the Canadian Deuterium and Uranium(CANDU) reactor for about 10,000 years long term deposition at a 500m deep granitic bedrock repository has been performed. However this developed structural model of the spent nuclear fuel disposal canister which has 33 spent nuclear fuel baskets and whose diameter is 122cm is too heavy to handle without any structural safety problem. Hence a lighter structural model of the spent nuclear fuel disposal canister which is easy to handle has been required to develop very much. There are two methods to reduce the weight of the CANDU canister model. The one is to alleviate severe design conditions such as external loads and safety factor. The other is to optimize the cross section shape of the canister by reducing the spent nuclear fuel basket number. Hence, in this paper a complementary analysis to alleviate such severe design conditions is carried out and simultaneously structural analyses to optimize the cross section shape of the canister by reducing the spent nuclear
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".