EFFECT OF IODINE AND MOISTURE ON THE MICROSTRUCTURE OF ZIRCALOY-4 UNDER SERVICE CONDITION IN PHWR
Bibliographic record
Abstract
Fuel failures are always a cause of concern in any nuclear reactors as it increases the manrem consumption of radiation workers. Although performance of the fuels in pressurized heavy water reactors is good, but still fuel failures occur occasionally. Post irradiation examination (PIE) of the failed fuel elements indicates internal hydriding, not deuteriding, as a major cause for the failures, although secondary deuteriding occurs and, in a few cases, failures are associated with defects in the end plug weld. The sources of hydrogen are either fuel pellets or the clad or the graphite coating. Restriction has been imposed on maximum content of total hydrogen in the fuel element to 1 mg to prevent hydriding of the Zircaloy clad tube. Accidental pick up of hydrogen occurs, which could lead to failure of the fuel bundles. Experimental investigations have been conducted to understand the individual effect of iodine and accidental pick up of moisture on the microstructure of Zircaloy-4 end cap welded samples with graphite coating. Results indicate that severe hydriding in Zircaloy-4 samples due to the existence of internal moisture in presence of graphite under service condition may result in fuel failure and justifies the findings of PIE.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".