Overview of Computational Methods for Hydrogen Diffusion Coupled With Stress and Temperature Gradients in Zirconium Reactor Components
Bibliographic record
Abstract
In order to better understand and predict hydride blister formation and hydride cracking in zirconium alloy CANDU(1) fuel channels, specialized computational methods are required. Hydride blister formation involves the coupled action of gradients in temperature and hydrogen concentration, while hydride cracking involves coupling of stress and concentration gradients. Hydride accumulation and crack growth in a leaking crack involves a complete coupling of concentration, stress and temperature gradients. In all cases, the action of dissolution or precipitation of hydride adds complexity to the numerical analysis procedure. Dedicated finite difference and finite element programs have been developed and applied to blister formation and uniform temperature cracking problems. On the basis of experience gained in the use of such specialized codes, a fully coupled capability has been integrated into a general-purpose finite element program. This program can more realistically address complex load and temperature histories that may be encountered during fuel channel operation. An overview of important computational features is given along with applications relevant to current experimental research and fuel channel assessments. (1)CANDU is a registered trademark of Atomic Energy of Canada Limited.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".