Denuded zones in zirconium pressure vessels: oxygen’s role examined via multi-scale diffusion model
Bibliographic record
Abstract
Abstract Zirconium alloys are extensively used as cladding material in nuclear reactors. They are vulnerable to hydrogen degradation under the harsh service conditions of the reactors. Optical micrographs taken in some pressure tubes shows the presence of hydride denuded zones closer to the surface, where the hydrides formed in this region are smaller in size compared to the bulk. We investigated the effect of oxygen on diffusivity of hydrogen in α Zr, to check the hypothesis that oxygen slows the diffusion of hydrogen and thereby encourages the occurrence of hydride denuded zones. We used a multi-scale model to simulate H diffusion in Zr with different O concentrations to identify the effect that O has on H diffusivity. From the study we found that oxygen indeed decreases the diffusivity of hydrogen in α Zr for moderate oxygen concentrations. We investigated the diffusion processes of individual H atoms, which showed that the reduction in diffusivity is caused by a decrease in the hopping rates and the formation of hydrogen traps by the combination of several interstitial sites. Though the diffusivity of H seems to be reduced by O, looking at the O concentration profiles found in Zircaloy pressure tubes, we see that the slowing down is insufficient to cause a significant enough change to the size of hydride precipitates. This causes us to reject the hypothesis as the main reason for the formation of denuded zones in Zircaloy pressure tubes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".