Development of Crystallographic Texture in CANDU Calandria Tubes
Bibliographic record
Abstract
The Zircaloy-2 calandria tubes in a CANDU nuclear reactor separate the hot Zr-2.5Nb pressure tubes from the cool moderator. These tubes are about 6 m long, have an outside diameter of 132 mm, and a wall thickness of 1.4 mm. To date, their performance has been exemplary. A possible feature for future reactors is to increase the strength of these calandria tubes to reduce the economic consequences of a hypothetical accident. The current method of fabrication is to form a sheet of Zircaloy-2 into a cylinder, then weld along the length. In fixed-end burst tests such tubes always fracture in the weld area because of the differences in crystallographic texture between the parent metal and the weld; eliminating the weld would increase the strength and ductility of the tube. We have evaluated four manufacturing routes for seamless tubes. To realize high biaxial strength, we require a large fraction of basal plane normals in the radial direction, FR. This paper describes these manufacturing routes, the calandria tube properties generated by the individual manufacturing routes, and their applicability for the CANDU system. The results show that the biaxial strength of a seamless calandria tube becomes greater with an increase in FR, which is related to the amount of cold work used to make the tubes, with saturation in FR after about 95% cold work. The results are interpreted in terms of anisotropic factors determined from uniaxial tension tests.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".