The Measured Flow at the Inlet of a Francis Turbine Runner Operating in Speed No-Load Condition
Bibliographic record
Abstract
Abstract For Francis turbines, speed-no-load (SNL) represents one of the most detrimental operating conditions, marked by significant pressure and strain fluctuations on the runner. Mitigating these fluctuations necessitates a comprehensive understanding and characterization of the flow phenomena responsible for their generation. This paper presents an experimental investigation of the flow at the inlet of a Francis turbine runner model operating in speed-no-load condition using high-speed stereoscopic and endoscopic particle image velocimetry (PIV). The measurements are made in a radial-azimuthal plane that covers the vaneless space and a large region in the interblade channel. This study marks the first-time measurement of critical flow phenomena at this operating point, performed in the runner. Instantaneous and average velocity fields are analyzed, along with other statistical data. The results not only confirm the stochastic nature of the flow at speed-no-load but also highlight the general structure of the flow observed in other studies. The high velocity fluctuations on the suction side are associated with a backflow extending into the vaneless space and a circulation zone occasionally generated by this backflow. Both phenomena are frequently present, but fluctuate stochastically. Additionally, two other circulation zones intermittently form on the pressure side of the blades. The presence of vortices, smaller than the circulation zones, near the blade's leading edge correlates with the backflow intensity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".