Prediction of rotating stall within an impeller of a centrifugal pump based on spectral analysis of pressure and velocity data
Bibliographic record
Abstract
Experimental data, which was acquired in two centrifugal pumps and provided by Grundfos A/S, were analysed to determine if rotating stall could be detected from the velocity and pressure time series. The pressure data, which were uniformly acquired in time at high sample rates(10 kHz), were measured simultaneously in four adjacent di.user channels just downstream of the impeller outlet. The velocity data, which were non-uniformly sampled in time at fairly low rates(100 Hz to 3.5 kHz), were acquired either in or downstream of the impeller. Two di.erent methodologies were employed for detection of stall. The first method, which involved direct analysis of raw data, yielded qualitatively useful flow reversal information from the time series for the radial velocity. The second approach, which was based on power spectrum analysis of velocity and pressure data, could detect the onset and identify the frequency of rotating stall to a satisfactory extent in one of the two pumps. Nearly identical stall frequencies were observed in both velocity and pressure power spectra and this rotating stall phenomenon, which occurred at a very low frequency relative to the impeller speed, did not reveal any noticeable degree of sensitivity to the flow rate. In the other pump, where the available data was limited to velocity time series, the power spectrum analysis was successful in detecting stationary stall for a 6 bladed impeller but did not provide conclusive results for the existence of stall in the case of the 7 bladed impeller. Recommendations on the type of experimental data required for accurate detection of stall are provided based upon the present study.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".