A Parametric Study of the DUPIC Fuel Cycle to Reflect Pressurized Water Reactor Fuel Management Strategy
Bibliographic record
Abstract
For both pressurized water reactor (PWR) and Canada deuterium uranium (CANDU) tandem analysis, the Direct Use of spent PWR fuel In CANDU reactor (DUPIC) fuel cycle in a CANDU 6 reactor is studied using the DRAGON/DONJON chain of codes with the ENDF/B-V and ENDF/B-VI libraries. The reference feed material is a 17 × 17 French standard 900-MW(electric) PWR fuel. The PWR spent-fuel composition is obtained from two-dimensional DRAGON assembly transport and depletion calculations. After a number of years of cooling, this defines the initial fuel nuclide field in the CANDU unit cell calculations in DRAGON, where it is further depleted with the same neutron group structure. The resulting macroscopic cross sections are condensed and tabulated to be used in a full-core model of a CANDU 6 reactor to find an optimized channel fueling rate distribution on a time-average basis. Assuming equilibrium refueling conditions and a particular refueling sequence, instantaneous full-core diffusion calculations are finally performed with the DONJON code, from which both the channel power peaking factors and local parameter effects are estimated. A generic study of the DUPIC fuel cycle is carried out using the linear reactivity model for initial enrichments ranging from 3.2 to 4.5 wt% in a PWR. Because of the uneven power histories of the spent PWR assemblies, the spent PWR fuel composition is expected to differ from one assembly to the next. Uneven mixing of the powder during DUPIC fuel fabrication may lead to uncertainties in the composition of the fuel bundle and larger peaking factors in CANDU. A mixing method for reducing composition uncertainties is discussed.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".