Experimental study of the pressure fluctuations on propeller turbine runner blades: part 2, transient conditions
Bibliographic record
Abstract
Transient conditions such as load rejection will often lead to high amplitude pressure fluctuations that will affect a turbine residual-life. If Computational Fluid Dynamic offers a promising tool to study the flow dynamic under transient regime, focused validation data on the runner are still lacking to assess the accuracy of different simulation strategies. Hence within the framework of the AxialT project of the International Consortium on Hydraulic Machines, exploratory measurements of the pressure field on the runner blades of a propeller turbine model were performed in transient conditions. The model was setup on the test stand of the LAMH of Laval University. The test stand control procedures were adapted to mimic transient condition such as load rejection or the transition from a normal operating condition to a speed-no-load condition. The pressure on the runner blades were measured using miniature piezo-resistive transducer linked to a high frequency telemetric system. Using specifically adapted data processing routines, it was possible to characterize the variations of the energy content during the transient runs. Specifically, the main fluctuations appear to occur in the sub-synchronous range in both cases.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".