The Effect of Irradiation on Ni-Containing Components in CANDU<sup>®</sup>Reactor Cores: A Review
Bibliographic record
Abstract
For nickel-containing alloys irradiated in thermalized neutron fluxes, the formation and reaction of59Ni with thermal neutrons can lead to time-varying changes in displacement damage rate, expressed as displacements per atom per second (dpa.s-1), gas formation and nuclear heating. Ni-rich alloys are used in PWR and BWR reactors as spacers within fuel assemblies but also as tensioning springs for these same assemblies. Flux thimbles have also been made from Ni-rich alloys in the past but are gradually being replaced by thimbles made from other alloys containing substantially less Ni. In a CANDU®reactor, Ni-alloys are used as tensioning springs, fuel channel spacers (in the form of garter springs) and as cable sheathing and core wires in flux-detector assemblies. Prediction of the irradiation processes that affect the functionality of these CANDU internals, such as irradiation embrittlement and irradiation creep, especially under conditions of extended operation, necessitates a consideration of the effect of the transmutation of Ni. Over the past 10 years, there has been a considerable increase in the understanding of the effects of irradiation on the properties of components made from Ni-rich alloys. The nuclear processes and the effects of irradiation damage on the performance of components made from these alloys will be described.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".