VERIFICATION OF THE DRAGON5 DETERMINISTIC CODE FOR NEUTRONIC AND BURNUP ANALYSIS OF OECD-NEA MOX FUEL BENCHMARK
Bibliographic record
Abstract
This work presents comprehensive and elaborate analysis of the OECD-NEA MOX fuel benchmark based on different nuclear data libraries to investigate the reliability and accuracy of the Dragon5 lattice code developed by École Polytechnique de Montréal for neutronic analysis of mixed uranium-plutonium oxide fuel (MOX) fuel. The neutronics and burn-up calculations for rectangular pin and assembly geometry filling with different compositions of MOX fuel are computed and investigated. Performance of different nuclear data library are evaluated and compared. Parameters such as infinite multiplication factor, reactivity change agree very well with the averaged reference values provided by the other institutions if the JEF2.2 library is used. Inventories of important actinides and fission product nuclides at different burn-up depth are also compared with the published values at the MOX pin cell and assembly level, results of Dragon5 lattice code are consistent with averaged values provided by other codes. Further, the deviation between newer libraries and solutions of benchmark reference should be attributed to the differences of neutron data in these libraries. Therefore, the Dragon5 lattice code is reliable for neutronics and burn-up analysis of MOX fuel and can be applied to the neutronic analysis of mixed uranium-plutonium oxide fuel.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".