Accelerometer and Pit Counting Detection of Cavitation Erosion on a Laboratory Jet and a Large Francis Turbine
Bibliographic record
Abstract
The two techniques, vibratory monitoring and pit counting, show promising results for the prediction of prototype cavitation erosion from model tests of Francis turbines. These cavitation detection methods are compared in two set-ups at different power levels, a laboratory high velocity cavitation jet and a full scale 270 MW Francis turbine. Excellent quantitative correlations are obtained in the jet tests between erosion rate, volume pitting rate measured on polished metal surfaces of different hardness with a laser profilometer and the mean square value of forces on the eroded specimen inferred from measurements with a high frequency accelerometer on the specimen holder. On the large prototype good coherent results are also obtained but the vibratory information requires much finer analysis. In particular the varying erosive cavitation intensity with power output level is well detected by both methods. The two cavitation detection techniques exhibit great dynamic range and can prove very useful in characterizing the erosive aggressiveness of cavitating flow both in large machines and in reduced scale models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".