Reactivity Control Method for Direct Use of Spent Pressurized Water Reactor Fuel in CANDU Reactors (DUPIC)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it