On variations in turbine runner dynamic behaviours observed within a given facility
Bibliographic record
Abstract
Abstract When confronted with cracks or high stresses in turbine runners, we often wonder if the behaviour observed on one specific runner will be present on all other similar runners. In this case, we have a facility with 19 runners having the same blade geometry. In order to answer the question, we selected three runners for measurement campaigns. First, the runners were divided in groups using band length, materials and wicket gate geometries. We then examined two runners with different wicket gate geometries and were thus able to explain why one runner exhibited recurrent fatigue damage problems and not the other. However, even within a given group, significant reliability differences were found when comparing with a third runner. The observed data shows that an important turbine characteristic was overlooked. Our conclusions point toward eccentricities and imperfections in the discharge ring attributable to only the upper part of the labyrinth seal being refurbished in this facility. This may generate a significant imbalance in the force produced by the flow in the runner side chamber. The paper underscores the impact of such imbalance, which could be present in older refurbished facilities.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".