Residual Stress Measurements in Welded and Plastically Deformed Target Structural Materials
Bibliographic record
Abstract
Abstract Transmutation of spent nuclear fuels (SNF) is currently being considered to transform long-lived isotopes to species with relatively short half-lives and reduced radioactivity through capture and decay of minor actinides and fission products. This process is intended for geologic disposal of SNF for shorter durations in the proposed repository at the Yucca Mountain site. The structural material (Type 304L stainless steel/Alloy EP-823) surrounding the transmutation target will be subjected to welding operation and plastic deformation during fabrication, which could induce residual stresses in it. Destructive ring-core, and nondestructive x-ray diffraction, neutron diffraction, and positron annihilation spectroscopic techniques were used to evaluate residual stresses in welded and cold-worked specimens of both materials. The results indicate that, in general, for a welded specimen consisting of Alloy EP-823 and Type 304L stainless steel on opposite sides, compressive and tensile residual stresses were observed in the former and latter materials, respectively. However, a welded specimen consisting of only Alloy EP-823 on both sides showed tensile residual stresses. The extent of residual stresses in cold-worked specimens was enhanced with increased level of cold-reduction. In case of a bent specimen, compressive and tensile residual stresses were noticed in the convex and concave sides, respectively.
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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 | 0.001 | 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".