Numerical simulation of bubbly jets in crossflow using OpenFOAM
Bibliographic record
Abstract
This paper conducted a computational fluid dynamics study of bubbly jets (not bubble plumes due to pure gas injection) in crossflow to explore the hydrodynamics that are still unknown. A three-dimensional model was developed, calibrated, and validated by coupling the Euler–Euler two-fluid model with unsteady Reynolds-averaged Navier–Stokes approach in OpenFOAM. The results showed that the modeled gas void fraction, bubble velocity, water jet centerline trajectory, and jet expansion agree well with the experimental data. The vertical distribution of turbulent kinetic energy evolves from mono-peak to dual-peaks as the jet penetrates farther for the bubbly jet due to the interactions between bubbles and ambient water flow. Water velocity distribution was examined at cross sections of both the air- and water-phases of bubbly jets in crossflow, and counter-rotating vortex pairs can be clearly observed for both phases. Generally, the center-plane maximum concentration decreases in the crossflow direction. Compared to pure water jets, bubbly jets are stretched wider in the vertical direction due to the lift of bubbles, and thus, dilution is larger. Interestingly, the vorticity at water jet cross sections of bubbly jets evolves from two vertical “kidney-shapes” to two axisymmetric “thumb-up-shapes.” Moreover, effects of ambient crossflow on bubbly jet behaviors were systematically examined. As the crossflow velocity increases, the locations of maximum concentration, maximum velocity magnitude, maximum vorticity magnitude, as well as water jet centerline, all tend to be lower for bubbly jets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".