Modeling the dynamic behavior of turbine runner blades during transients using indirect measurements
Bibliographic record
Abstract
Turbine start-up transients are induced by the wicket gates opening sequence and generate high amplitude stress cycles. These stress cycles have a detrimental effect leading to faster crack growth in the runner blades. Using a series of direct measurements taken on a prototype runner in order to find the optimal start-up parameters exposes both the runner and the instrumentation to a series of successive damaging transient events during the optimization process. To solve this, finding sensors strongly correlated to strain gauges and whose signals can be easily obtained to identify a model to predict the strain, instead of directly measuring it, would reduce the risk, cost and downtime associated with a measurement campaign. This paper shows that turbine shaft torsion measurements is highly correlated to the strain at a runner blade hotspot, and we demonstrate that the ARMAX model can be used to represent the dynamic system in order to minimize the strain on blades.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".