Numerical method to predict vibration characteristics induced by cavitation in centrifugal pumps
Bibliographic record
Abstract
Abstract Vibration is a common side effect of cavitation in centrifugal pumps, which are also affected by other physical processes. Therefore, the study of cavitation based on raw vibration signals may introduce potential discrepancies under practical operating conditions. With the objective of accurately identifying the cavitation stages in a centrifugal pump, this study employs the wavelet packet transform (WPT) to process the original signal. Subsequently, the different vibration acceleration signals obtained by processing the raw signals using the proposed method under different working conditions are compared. The unequal interval weight grey (UIWG) model is proposed based on the measurements performed to reconstruct the functional relationship between cavitation and vibration, including in cases where the physical parameters of the research object are unavailable. The results reveal that the post-signal energy exhibits monotonic characteristics in certain frequency bands, despite the presence of interference under practical operating conditions. In addition, the UIWG model is verified to be robust as it is capable of self-correcting its monotonicity based on limited discrete data. In conclusion, the UIWG model equipped with WPT is an effective means to predict cavitation-induced vibrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".