Bridging the gap between metallurgy and fatigue reliability of hydraulic turbine runners
Bibliographic record
Abstract
The failure of hydraulic turbine runners is a very rare event. Hence, in order to assess the reliability of these components, one cannot rely on statistical models based on the number of failures in a given population. However, as there is a limited number of degradation mechanisms involved, it is possible to use physically-based reliability models. Such models are more complicated but have the advantage of being able to account for physical parameters in the prediction of the evolution of runner degradation. They can therefore propose solutions to help improve reliability. With the use of such models, the effect of materials properties on runner reliability can easily be illustrated. This paper will present a brief review of the Kitagawa-Takahashi diagram that links the damage tolerance approach, based on fracture mechanics, to the stress or strain-life approaches. This diagram is at the centre of the reliability model used in this study. Using simplified response spectra obtained from on-site runner stress measurements, the paper will show how fatigue reliability is impacted by materials fatigue properties, namely fatigue crack propagation behaviour and fatigue limit obtained on S-N curves. It will also present a review of the most important microstructural features of 13%Cr- 4%Ni stainless steels used for runner manufacturing and will review how they influence fatigue properties in an effort to bridge the gap between metallurgy and turbine runners reliability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".