Understanding Pb Transport and Stress Corrosion Cracking using Advanced Characterization Techniques
Bibliographic record
Abstract
Ni- and Fe-base alloys are used for steam generator tubing in nuclear power systems to facilitate heat transfer from the reactor core to the cooler secondary circuit. Typical materials of construction include alloy 800 (Fe-32Ni-21Cr) and alloy 690 (Ni-30Cr-10Fe). While the bulk water chemistry is benign in terms of corrosion, heat transfer crevices between tube and tube support plates can lead to the accumulation of impurities at ppm levels, such as S or Pb, alongside a pH300C that can range from 4 to 10. While the performance of alloy 800 has been exemplary thus far, stress corrosion cracking (SCC) has been demonstrated in laboratory experiments simulating the extreme end of plausible heat transfer crevice chemistries, such as alkaline environments with added Pb. Although this is an off-chemistry condition, understanding the mechanism associated with Pb-alkaline SCC in alloy 800 would be beneficial. Earlier studies have suggested that SCC occurs due to passive film impairment by Pb deposition at the oxide-metal interface. Facilitation of mass and charge transfer across this impaired film can lead to selective dissolution of Fe and Cr, leading to formation of porosity and SCC. This study seeks to provide further mechanistic insight on Pb transport during SCC. In particular, slow strain rate experiments were conducted in a Pb-alkaline solution (500 ppm of Pb) and crack growth rate was measured. Following this initial SCC growth, the test was stopped and the autoclave was cleaned. The experiment was then restart in high temperature water, but with the absence of Pb in the bulk water. Interestingly, SCC propagation continued, suggesting that the Pb already in the crack was sufficient to sustain crack growth for some period of time. Analytical transmission electron microscopy (ATEM) techniques were applied to understand the chemistry along the crack path and to provide insight into the Pb transport mechanism. Also, in-situ testing was performed to evaluate both the mechanics of SCC in Alloy 800 in a Pb-alkaline environment, as well as heating to observe whether Pb transport shared similarities with a liquid metal-type diffusion mechanism. Generally, this research provides a demonstration of how novel microscopy methods, and in-situ techniques can be applied to provide critical mechanistic information for corrosion phenomena.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".