Progress of the DUPIC Fuel Compatibility Analysis - IV: Fuel Performance
Bibliographic record
Abstract
This study describes the mechanical compatibility of the direct use of spent pressurized water reactor fuel in Canada deuterium uranium (CANDU) reactors (DUPIC) fuel when it is loaded into a CANDU reactor. The mechanical compatibility can be assessed for the fuel management, primary heat transport system, fuel channel, and fuel handling system in the reactor core by both experimental and analytic methods. Because the physical dimensions of the DUPIC fuel bundle adopt the CANDU flexible (CANFLEX) fuel bundle design, which has already been demonstrated for a commercial use in CANDU reactors, the experimental compatibility analyses focused on the generation of material property data and the irradiation tests of the DUPIC fuel, which are used for the computational analysis. The intermediate results of the mechanical compatibility analysis have shown that the integrity of the DUPIC fuel is mostly maintained under the high-power and high-burnup conditions even though some material properties, such as the thermal conductivity, are a little lower compared to the uranium fuel. However, it is required that the current DUPIC fuel design be changed slightly to accommodate the high internal pressure of the fuel element. It is also strongly recommended that more irradiation tests of the DUPIC fuel be performed to accumulate a database for the demonstration of the DUPIC fuel performance in the CANDU reactor.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".