Bubbly Cavitating Flow Through a Converging Nozzle
Bibliographic record
Abstract
In the present work, the bubbly cavitating flow phenomena after passing through the converging nozzle is numerically investigated.The dynamic of the cavitating bubbles is modeled by the use of the mass and momentum phase's equations, which are coupled with the Rayleigh-Plesset equation of the N bubbles dynamics.However, assuming that the same initial conditions of all bubbles are identical and that all bubbles are equi-distant from each other simplifies the governing equations.Equation set is numerically resolved by the use of a fourth order Runge-Kutta scheme.The numerical resolution of the previous equations set let us found that the bubble radius distribution, fluid velocity and fluid pressure change dramatically with upstream void fraction and an instability appeared just after the passing the converging nozzle for both cases one bubble N=1 and two bubbles N=2.Indeed, for the case of one bubble N=1, the flashing flow phenomena occurs for an upstream void fraction α s =11.2x10 -3 , which corresponds to a critical bubble radius R c =1.8.Whereas, for bubble number N=2, the same phenomenon occurs for α s = 8.9x10 -3 , with R c =2.This difference is due to the bubble interaction.Also, we found that, the bubble number N strongly affect the bubble frequency.However, with increase the bubble number, the maximum size of the bubbles increases and bubble frequency oscillation decrease.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".