Comparison of fatigue loading in Francis turbine runners using extreme values interpolation
Bibliographic record
Abstract
Abstract Extreme values of strain signals are important for hydroelectric turbine runners fatigue assessment. However, due to measurements limitations such as short data length and limited subset of measured operation conditions, the extreme values are rarely fully captured. Our study aims to estimate the extreme values of runner strain at non-measured operating conditions by interpolating the extreme components from the measured ones. The method is based on the peaks over threshold technique and the kriging interpolation. A case study with two similar Francis turbines (same design and power plant) is presented. The comparison is made between the use of independent interpolation models and a combined model for the two turbines. This helps assess the assumption that similar turbines share similar fatigue loading. If that is the case, a common model for the whole fleet of similar turbines design in a given facility could be considered, and thus contribute to reduce the uncertainties related to unmeasured loadings without the need for in-situ measurements on every runner.
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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 | 0.000 | 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) | 0.000 | 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".