Analysis of the Runner Behavior During the Start-Up Sequence in a Bulb Turbine Model
Bibliographic record
Abstract
An experimental analysis of the start-up sequence of a bulb turbine was performed in a closed-loop circuit, and analyses of global performances were conducted under three start-up conditions. In order to conduct a deeper analysis of the runner behavior, a runner blade was equipped with 26 sensors, which were used to evaluate the transient pressure field using an interpolation method. By checking the global performances of all the conditions, the flow rate evolution follows the guide vane opening (GVO) evolution only for the two slowest GVO test cases. Additionally, the use of defined dimensionless numbers allowed for some degree of universal evolution to be revealed, and for the peak of torque to be characterized. The pressure on the runner blades was also investigated. Although the runner operates like an impulse turbine at the beginning of the start-up sequence, its blades act like more airfoils when the torque reaches its peak. Moreover, the fluctuations at the end of the start/up sequence suggest that the stress on the blade could be more important than the stress observed on the driven shaft. Furthermore, local phenomena, such as suction on the pressure side of the runner blade near the shroud, were also observed on the pressure distribution, which is incongruent with the theoretical inlet velocity field estimated from global performances. These observations suggest the presence of a backflow and a cavitation pocket. Moreover, a flow instability probably occurs at low GVO speed and produces some torque fluctuations around the maximum torque value.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".