Methodology to Design Simulated Irradiated Fuel by Maximizing Integral Indices (ck, E, G)
Bibliographic record
Abstract
Critical experiments are used for validation of reactor physics codes, in particular, to determine the biases and uncertainties in code predictions. To reflect all conditions present in operating reactors, plans for such experiments often require tests involving irradiated fuel. However, it is impractical to use actual irradiated fuel in critical experiments due to hazards associated with handling and transporting the fuel. To overcome this limitation, a simulated irradiated fuel, whose composition mimics the neutronic behavior of the truly irradiated fuel (TRUFUEL), can be used in a critical experiment. Here, we present an optimization method in which the composition of simulated irradiated fuel for the Canadian supercritical water-cooled reactor (SCWR) concept at midburnup (21.3 MWd kg−1 (IHM)) is varied until the integral indices ck, E, and G are maximized between the true and simulated irradiated fuel. In the optimization, the simulated irradiated fuel composition is simplified so that only the major actinides (U233, Pu238-242, and Th232) remain, while the absorbing fission products are replaced by dysprosia and zirconia. In this method, the integral indices ck, E, and G are maximized while the buckling, k∞ and the relative ring-averaged pin fission powers are constrained, within a certain tolerance, to their reference lattice values. Using this method, maximized integral similarity indices of ck=0.967, E=0.992, and G=0.891 have been obtained.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".