Study of Influences of the Level of Zr Oxidation and Volumetric Heat Generation on Heat Transfer and Crust Formation Within CANDU Corium
Bibliographic record
Abstract
A full-station blackout at a nuclear power plant can lead to fuel failures and radiological release to the environment if there is a breach of the reactor vessel. For Canada Deuterium Uranium (CANDU) reactors, the expected failure mechanism is through thermal stress concentration at the calandria vessel wall, and is thus influenced by local heat flux values. The current study uses computational fluid dynamics to simulate heat transfer, fluid flow, and crust formation within a CANDU geometry. Sensitivity to critical parameters, including the volumetric decay heat generation rate and the percentage of Zr oxidation is explored.The results show that as the volumetric heat generation rate decreases, the crust is thicker, and the wall heat flux is lower. This suggests that the activation of mitigating measures that delay the accident progression result in more favorable outcomes. The percentage of Zr oxidation primarily influences the thermal conductivity, which impacts the crust formation and wall heat flux rates. Specifically, corium with a lower percentage of Zr oxidation has higher thermal conductivity, and thus lower heat transfer resistance. This results in lower corium temperatures, which reduces the radiation heat transfer from the top surface and also increases crust thickness. Higher rates of heat removal from the vessel wall thus occur. In contrast, a higher percentage of Zr oxidation results in lower thermal conductivity, which leads to lower wall heat flux and a thinner crust at the vessel wall. Overall, these findings highlight the importance of considering the effects of sensitivity parameters on the heat flux distribution in the event of severe accidents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".