Assessment of Neutronic Characteristics of Accident-Tolerant Fuel and Claddings for CANDU Reactors
Bibliographic record
Abstract
The objective of this study was to evaluate accident-tolerant fuel (ATF) concepts being considered for CANDU reactors. Several concepts, including uranium dioxide/silicon carbide (UO2-SiC) composite fuel, dense fuels, microencapsulated fuels, and ATF cladding, were modelled in Serpent 2 to obtain reactor physics parameters, including important feedback parameters such as coolant void reactivity and fuel temperature coefficient. In addition, fuel heat transfer was modelled, and a simple accident model was tested on several ATF cases to compare with UO2. Overall, several concepts would require enrichment of uranium to avoid significant burnup penalties, particularly uranium-molybdenum (U-Mo) and fully ceramic microencapsulated (FCM) fuels. In addition, none of the fuel types have a significant advantage over UO2 in terms of overall accident response or coping time, though U-9Mo fuel melts significantly sooner due to its low melting point. Instead, the different ATF concepts appear to have more modest advantages, such as reduced fission product release upon cladding failure, or reduced hydrogen generation, though a proper risk assessment would be required to determine the magnitude of these advantages to weigh against economic disadvantages. The use of uranium nitride (UN) enriched in N15 would increase exit burnup for natural uranium, providing a possible economic advantage depending on fuel manufacturing costs.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".