Application of Coupled Electrochemistry and Oxide Layer Growth Models to Water Chemistry Improvement against Flow Accelerated Corrosion in the PWR Secondary System
Bibliographic record
Abstract
Abstract Applicability and accuracy of a computer simulation code describing flow accelerated corrosion (FAC) were confirmed based on verification and validation processes. The code will be applied to evaluate the effects of water chemistry improvement on wall thinning of pressurized water reactor (PWR) secondary piping. In this paper, the validation process of the FAC simulation code, which was based on wall thinning rates measured at a PWR plant was discussed. Corrosive conditions were calculated with a N2H4-O2 reaction analysis code. Precise flow turbulence at major parts of the system was analyzed with 3D CFD codes to obtain mass transfer coefficients at structure surfaces. Then, wall thinning rates were calculated with the coupled model of electrochemical analysis and oxide layer growth analysis by applying the corrosive conditions and the mass transfer coefficients. Comparison of the calculated wall thinning rates with hundreds of measured results at the secondary piping of the actual PWR plant confirmed that the calculated wall thinning rates agreed with the measured ones within a factor of 2 and the accuracy of the evaluation model for residual pipe wall thickness was with an error of less than 20 %. Finally, the FAC simulation code was applied to evaluation of the effects of oxygen injection into the feed water line. It was confirmed that a suitable amount of oxygen was injected at a proper location along the feed water line to provide sufficient mitigation of FAC without any serious adverse effects on steam generator tubing.
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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 | 0.000 | 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".