Francis turbine runner fatigue reliability and inspection intervals
Bibliographic record
Abstract
Abstract The ranking of turbine runners, based on their cracking probabilities, can be useful for planning and decision-making. However, the analyst doing so needs to choose the level of complexity used. In this study, we propose to use the conditional probability of an event on an interval, given that it did not occur at the start of the interval. This enables one to estimate the probability of the event on a specified inspection interval and obtain a ranking given the time of the last inspection or based on the current inspection interval. This approach will be evaluated on data from Francis runners to look at the impact of operation, fatigue damage parameters and inspection data on cracking probability. A high-risk runner may be at risk because of inspection interval length, lack of knowledge leading to high parameter uncertainties or simply because it is at the end of its remaining useful life. These results will enable us to have a discussion on how one may be able to lower the probability on a given time interval at the cost of either additional inspections, change of operating conditions or a better quantification of parameter uncertainties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".