Probabilistic Assessment of Fretting Wear in Steam Generator Tubes Under Flow Induced Vibrations
Bibliographic record
Abstract
Successful life management of steam generators requires an ongoing operational assessment plan to monitor and address all potential degradation mechanisms. A degradation mechanism of concern is tube fretting as a result of flow-induced vibration. Flow induced vibration predictive methods routinely used for design purposes are based on deterministic nonlinear structural analysis techniques. In previous work, the authors have proposed the application of probabilistic techniques to better understand and assess the risk associated with operating power generating stations that have aging re-circulating steam generators. Probabilistic methods are better suited to address the variability of the parameters in operating steam generators, e.g., flow regime, support clearances, manufacturing tolerances, tube to support interactions, and material properties. In this work, an application of a Monte Carlo simulation to predict the propensity for fretting wear in an operating re-circulation steam generator is described. Tube wear damage is evaluated under steady-state conditions using two wear damage correlation models based on the tube-to-support impact force time histories and work rates obtained from nonlinear flow induced vibration analyses. Review of the tube motion in the supports and the impact/sliding criterion shows that significant tube damage at the U-bend supports is a result of impact wear. The results of this work provide the upper bound predictions of wear damage in the steam generators. The EPRI wear correlations for sliding wear and impact wear indicate good agreement with the observed damage and, given the preponderance of wear sites subject to impact, should form the basis of future predictions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".