The Influence of Inflow Swirl on Cavitating and Mixing Processes in a Venturi Tube
Bibliographic record
Abstract
A study of the mixing flows (Schmidt number = 103) in a cavitating Venturi tube that feature linear and swirling flows is presented in this paper. The Large Eddy Simulation (LES) turbulence model, the Schnerr–Sauer cavitation model, and the mixture multiphase model, as implemented in the commercial CFD ANSYS FLUENT 16.2, were employed. The main emphasis is spending on the influence of different inlet swirling ratios on the generation of cavitation and mixing behaviors in a Venturi tube. Four different inflow regimes were investigated for the Reynolds number Re = 19,044, 19,250, 19,622, 21,276: zero swirl, 15% swirl, 25% swirl and 50% swirl velocity relative to the transverse inflow velocity, respectively. The computed velocity and pressure profiles were shown in good agreement with the experiment data from the literature. The predicted results indicate that the imposed swirl flow moves the cavitation bubbles away from throat surfaces toward the throat axis. The rapid mixing between two volumetric components is promoted in the divergent section when the intense swirl is introduced. Additionally, the increase in the swirl ratio from 0.15 to 0.5 leads to a linear increase in the static pressure drop and a nonlinear increase in the vapor production. The reduction in the fluid viscosity ratio from μ2μ1=10 to μ2μ1=1 generates a high cavitation intensity in the throat of the Venturi tube. However, the changes in the pressure drop and vapor volume fraction are significantly small of pure water flow.
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 | 0.001 |
| 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 | 0.000 | 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 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".