Determination Procedures of High Risk Zones for Local Wall Thinning Due to Flow-Accelerated Corrosion
Bibliographic record
Abstract
A modified six-step evaluation procedure has been proposed to evaluate local wall thinning due to flow-accelerated corrosion (FAC). In step 1, the one-dimensional (1-D) distribution of flow turbulence and the temperature along pipes in cooling systems were analyzed with a 1-D system simulation code to obtain approximate mass transfer coefficients at structure surfaces, prior to using a three-dimensional (3-D) computational fluid dynamics (CFD) code for precise flow turbulence analysis of the major parts. In step 2, corrosive conditions were calculated with a N2H4-O2 reaction analysis code. In step 3, high FAC risk zones were determined for further evaluation for wall thinning rates, based on five parameters: temperature, pH, oxygen concentration, mass transfer coefficient, and chromium content. Then, in step 4, the 3-D CFD code was used to calculate precise mass transfer coefficients at the high FAC risk zones. In step 5, the wall thinning rates were calculated using a coupled model of electrochemical analysis and oxide layer growth analysis by applying the corrosive conditions and the mass transfer coefficients. Finally, in step 6, the residual lifetime of the pipes and the applicability of countermeasures against FAC were evaluated.This paper introduces procedures for determining major FAC parameters and evaluation procedures for high FAC risk zones by synthesizing the parameters in step 3. The procedures for determination of high FAC risk zones in a pressurized water reactor secondary cooling system are also demonstrated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".