Bibliographic record
Abstract
Steam generator (SG) tubing materials are susceptible to corrosion degradation in certain electrochemical corrosion potential (ECP) regions under aggressive local chemistry conditions developed under deposits or in SG crevices. Because of the hideout of impurities, the areas under sludge and inside SG crevices may be very aggressive and contain high concentrations of chlorides and other impurities. These areas are the locations where SG tubing materials are susceptible to the major forms of degradation such as pitting, crevice corrosion, intergranular attack (IGA) and stress corrosion cracking (SCC). The corrosion susceptibility of each SG alloy is different and is a function of ECP and chemical environment. Electrochemical corrosion behaviors of major SG tube alloys were studied under plausible aggressive crevice chemistry conditions. The potential hazardous conditions leading to SG tube degradation and the conditions, which can minimize SG tube degradation, have been determined and documented in a form of safe ECP/pH zones for SG operation. SCC tests and accelerated corrosion tests were carried out to verify and revise the safe ECP/pH zones. This information has been incorporated and updated into a system health monitor tool, ChemAND®, which was developed by AECL for utilities to monitor online the status of the SG alloys and prevent material degradation surprises through appropriate SG water chemistry management. Recently, further studies were performed to investigate the effect of several aggressive SG impurities on the boundary conditions that will lead to the degradation of SG alloys. These aggressive species include chloride, different sulphur species, lead, copper, magnesium, and calcium. This paper presents the effect of chloride concentration and copper contamination on Alloy 800 SG tubing corrosion degradation at 300°C under SG crevice chemistry conditions. The data provide important information to support SG materials degradation and life management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".