An Examination of CANDU Fuel Performance Margins Derived from a Statistical Assessment of Industrial Manufacturing Data
Bibliographic record
Abstract
This study employs a novel approach to the prediction of CANDU [Canada deuterium uranium (reactor)] fuel reliability. Probability distributions are fitted to actual fuel manufacturing data sets provided by Cameco Fuel Manufacturing. They are used to form input for two industry-standard fuel performance codes: ELESTRES for the steady-state case and ELOCA for the transient case—a hypothesized 80% reactor outlet header break loss-of-coolant accident. Using a Monte Carlo technique for input generation, 105 independent trials are conducted, and probability distributions are fitted to key model output quantities. Comparing model output against recognized industrial acceptance criteria, no fuel failures are predicted for either case. Output distributions are well removed from failure limit values, implying that margin exists in current fuel manufacturing and design. To validate the results and attempt to reduce the simulation burden of the methodology, two dimensional reduction methods are assessed. Using just 36 trials, both methods are able to produce output distributions that agree strongly with those obtained via the brute-force Monte Carlo method, often to a relative discrepancy of <0.3% when predicting the first statistical moment and to a relative discrepancy of <5% when predicting the second statistical moment. In terms of global sensitivity, pellet density proves to have the greatest impact on fuel performance, with an average sensitivity index of 48.93% on key output quantities. Pellet grain size and dish depth are also significant contributors, at 31.53% and 13.46%, respectively. A traditional “limit of operating envelope” case is also evaluated. This case produces output values that exceed the maximum values observed during the 105 Monte Carlo trials for all output quantities of interest. In many cases the difference between the predictions of the statistical methods and the limit method is very prominent, and the highly conservative nature of the deterministic approach is demonstrated.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".