Nozzle resonance mechanism and cooperative optimization of self-excited oscillating pulse cavitation jet
Bibliographic record
Abstract
The peak value and pulsation amplitude of the self-excited oscillating pulse cavitation jet nozzle are essential indices to evaluate the jet performance. We established a simulation model of the jet process of the nozzle to investigate the evolution mechanism of the inner and outer flow fields. We used the chamber fillet, chamber diameter, chamber length, and outlet-tube diameter as the design variables, and the peak value of the striking force and the amplitude of the pulsation of the striking force as the target variables. The collaborative optimization design method of the nozzle was determined by combining the orthogonal test method, the back propagation neural network, and the nondominated sorting genetic algorithm. As indicated by the results, when the inlet pressure was 3 MPa, the factors ranked as follows in terms of their effects on the jet performance of the nozzle: the chamber fillet, the outlet-tube diameter, chamber diameter, and the chamber length. To verify the feasibility of the collaborative optimization method, the nozzle was fabricated via 3D printing, and the simulation model was verified by testing. This study provides support to the development of design theory for self-oscillating pulsed cavitation jet nozzles.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".