On the correlation between runner blade dynamic stresses and pressure fluctuations in a prototype Francis turbine
Bibliographic record
Abstract
Abstract Francis turbines operating in off-design conditions are subject to pressure fluctuations resulting from the development of hydrodynamic instabilities in the draft tube. Depending on the nature of the flow-induced pressure fluctuations (synchronous or convective), this may induce dynamic stresses on the runner blades, increasing fatigue and the risk of crack propagation. This paper proposes to identify the impact of draft tube flow instabilities on the dynamic stresses of Francis turbine runners. Measurements are conducted on a prototype Hydro-Québec Francis turbine from low-load to full-load, including pressure and strain measurements on the stationary and rotating components, respectively. It is first noted that the convective component of the part-load vortex is the main source of excitation for the runner blades. The amplitude of the corresponding dynamic stresses is however reduced at locations closer to the leading edge, for which the dominant fluctuations result from the propagation of synchronous pressure fluctuations. Finally, correlations between runner dynamic stresses and pressure fluctuations measured in water passages are tentatively established for flow instabilities observed at both deep part-load and part-load conditions. This aims to evaluate the feasibility of estimating runner blade dynamic stresses based on signals measured in the stationary components for further investigation.
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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 | 0.000 | 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) | 0.001 | 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".