Dynamic Sensitivity Analysis of Thermal-Mechanical Deformation of a CANDU Fuel Channel
Bibliographic record
Abstract
In a limiting critical break loss of coolant accident in a CANDU reactor significant degradation of heat transfer from the fuel can occur. As a result of the subsequent increase in fuel temperature it is possible that the pressure tube undergoes heat up at intermediate pressure during blowdown. This can result in ballooning deformation of the pressure tube into contact with its calandria tube. It is required that fuel channels not fail as a consequence of the thermal mechanical deformation of the pressure tube and calandria tube in such events. Dynamic sensitivity functions are derived as analytical partial differential equations derived from the equations used to model the time-dependent behavior of physical systems. The dynamic sensitivity functions can be used to propagate uncertainties using a time-dependent perturbation approach in which the variations in a set of output variables, with respect to perturbations of the input parameters, are evaluated about reference response trajectories of the input parameters and associated output variables. The dynamic sensitivity method is described in this paper and results are presented for the pressure tube heatup phase of a LOCA. These results show the importance of all key parameters with respect to specified safety evaluation criteria. The dynamic sensitivity method is applied in a probabilistic uncertainty analysis to evaluate the probability of a pressure tube experiencing creep strain deformation to contact its calandria tube during the early stages of a LOCA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".