Stress Classification and Assessment of Locally Thinned Class 1 Piping Components
Bibliographic record
Abstract
Carbon steel piping components, such as elbows, and bends are extensively used in the primary heat transport systems of nuclear power plants. During the design stage, a corrosion allowance of 1/32” to 1/8” is normally assumed for wall thickness calculations. The corrosion is assumed to occur uniformly around the circumference of the piping component. The Class 1 piping components are designed to ASME Section III rules. Flow Accelerated Corrosion (FAC) affects ‘local’ areas creating ‘patches’ or ‘pits’ of thinned wall in critical piping components. If a single thickness value corresponding to the thinnest section is chosen to analyze these components, then the analysis would be too conservative. In addition, the analysis would not reflect the true state of the stress and displacement field in the localized thin wall area. To address these issues, a comprehensive scheme utilizing Finite Element Analysis is developed in this paper. This scheme employs the NB-3200 approach in calculating and classifying the stresses in irregularly or non-uniformly thinned nuclear piping components. The allowable are derived based on the ASME Section III rules. The stresses are classified as primary, secondary, membrane, bending, and peak. The combined primary and secondary stresses, primary and secondary and peak stresses are evaluated and compared to established Code allowable to assure that the requirements are met.. It is shown that the stress classification approach developed in this paper is robust and ensures adequate safety of the structure to withstand design and operational conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".