Detection of cavitation vortex in hydraulic turbines using acoustic techniques
Bibliographic record
Abstract
Cavitation phenomena are known for their destructive capacity in hydraulic machineries and are caused by the pressure decrease followed by an implosion when the cavitation bubbles find an adverse pressure gradient. A helical vortex appears in the turbine diffuser cone at partial flow rate operation and can be cavitating in its core. Cavity volumes and vortex frequencies vary with the under-pressure level. If the vortex frequency comes close to one of the eigen frequencies of the turbine, a resonance phenomenon may occur, the unsteady fluctuations can be amplified and lead to important turbine and hydraulic circuit damage. Conventional cavitation vortex detection techniques are based on passive devices (pressure sensors or accelerometers). Limited sensor bandwidths and low frequency response limit the vortex detection and characterization information provided by the passive techniques. In order to go beyond these techniques and develop a new active one that will remove these drawbacks, previous work in the field has shown that techniques based on acoustic signals using adapted signal content to a particular hydraulic situation, can be more robust and accurate. The cavitation vortex effects in the water flow profile downstream hydraulic turbines runner are responsible for signal content modifications. Basic signal techniques use narrow band signals traveling inside the flow from an emitting transducer to a receiving one (active sensors). Emissions of wide band signals in the flow during the apparition and development of the vortex embeds changes in the received signals. Signal processing methods are used to estimate the cavitation apparition and evolution. Tests done in a reduced scale facility showed that due to the increasing flow rate, the signal -- vortex interaction is seen as modifications on the received signal's high order statistics and bandwidth. Wide band acoustic transducers have a higher dynamic range over mechanical elements; the system's reaction time is reduced, resulting in a faster detection of the unwanted effects. The paper will present an example of this new investigation technique on a vortex generator in the test facility that belongs to ICPE- CA.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".