Risk Evaluation of Flow-Accelerated Corrosion Based on One-Dimensional FAC Code
Bibliographic record
Abstract
The possibility of thousands of flow-accelerated-corrosion (FAC) zones causes long and costly inspection procedures for nuclear, as well as fossil-fuel power plants, even if the number of zones is minimized on the basis of temperature and flow velocity. In order to decrease the number of inspection zones, suitable prediction or estimation procedures for FAC occurrence should be applied, and the resulting computer programs should be tuned with as many inspection data as possible. Such coupling of the estimation and inspection procedures should allow effective and reliable preparation to be made against FAC occurrence and propagation.This paper defines the FAC risk as the mathematical product of the possibility of the occurrence of wall thinning and its hazard scale. The possibility of the occurrence of wall thinning was designated as the time margin for pipe rupture determined by applying a one-dimensional FAC code, which could predict the wall-thinning rate with an accuracy within a factor of 2, while the hazard scale was defined as the volume of effluent steam and water from the ruptured mouth, which was enthalpy of water originally flowing in the pipe multiplied by the square of the pipe inner diameter. High FAC risk zones along entire cooling systems could be evaluated in only one-tenth or one-hundredth of the computer time as for a three-dimensional FAC code to determine the priority for inspection-order importance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".