Reactivity Control Method for Direct Use of Spent Pressurized Water Reactor Fuel in CANDU Reactors (DUPIC)
Bibliographic record
Abstract
A method to reduce the fuel composition heterogeneity effect on the core performance parameters has been studied for DUPIC fuel, which is made from spent pressurized water reactor (PWR) fuel by a dry refabrication process. This study focuses on the reactivity control method, which uses slightly enriched, depleted, or natural uranium to minimize the additional cost on the manufacturing of DUPIC fuel, when adjusting the excess reactivity of the spent PWR fuel. To reduce the variation of isotopic composition of the DUPIC fuel, interassembly mixing operations were assumed to be carried out three times. Three options have been considered: reactivity control by slightly enriched and depleted uranium, reactivity control by natural uranium for high-reactivity spent PWR fuels, and reactivity control by natural uranium for intermediate-reactivity spent PWR fuels. The results of this study have shown that the reactivity of DUPIC fuel can be tightly controlled with the minimum amount of fresh uranium feed. For reactivity control by slightly enriched and depleted uranium, all spent PWR fuel can be utilized as DUPIC fuel, and the fraction of fresh uranium feed is 3.4% on the average. For reactivity control by natural uranium, ~88% of spent PWR fuel can be utilized as DUPIC fuel when intermediate-reactivity spent PWR fuels are used, and the amount of natural uranium feed needed to control the DUPIC fuel reactivity is negligible.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".