Multiscale modelling of mass transfer in gas jets and bubble plumes
Bibliographic record
Abstract
Abstract The injection of high‐speed gas streams into liquids is common in many industrial applications, such as sparging in multiphase reactors and contacting in mass transfer devices. Modelling the fluid dynamics and associated heat and mass transfer processes in such a system is complex because it involves many governing scales and drastic changes in physical properties. In this study, one formulation of a multiscale computational fluid dynamics model is proposed to simulate the fluid dynamics and mass transfer in such systems. The model uses volume‐of‐fluid interface capturing in regions where high mesh resolution can be attained and the drift‐flux or mixture model approximation in regions where mesh resolution is too low to directly resolve interface dynamics. The model was developed to provide a tunable, automatic transition between the two modelling approaches for both fluid dynamics and mass transfer predictions. The approach was validated through a comparison with results from two published studies. In the first case, the implementation of the drift‐flux model was validated through the simulation of a dispersed gas bubble plume injected into a cylindrical tank. In the second case, the fluid dynamics and mass transfer predictions were compared to results from an experimental study involving the horizontal injection of air into a rectangular tank filled with water for the application of aeration. The results show that the modelling approach can provide a good prediction of the experimental data using only limited fitting of empirical parameters, making it applicable to a broad range of other applications.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".