CFD Simulations of an Air-Water Bubble Column: Effect of Luo Coalescence Parameter and Breakup Kernels
Bibliographic record
Abstract
In this work, CFD simulations of an air-water bubbling column were performed and validated with experimental data. The superficial gas velocities used for the experiments were 0.019 m/s and 0.038m/s and were considered as an homogeneous regime. The former involves simpler physics when compared to a heterogeneous regime where the superficial velocities are higher. In order to simulate the system, a population balance model (PBM) was solved numerically using a discrete method and a closure kernels involving the Luo coalescence model as well as two different breakup models: Luo’s and Lehr’s. For the multi-phase calculations, an eulerian framework was selected and the interphase momentum transfer included drag, lift, wall lubrication, and turbulent dispersion terms. A sensitivity analysis was performed on a Luo coalescence kernel by changing the coalescence parameter (c0) from 1.1 to 0.1 and results showed that the radial profiles of gas holdup and axial liquid velocity were significantly affected by such parameter. From the simulation results, the main conclusions were: (a) A combination of the Luo coalescence and Luo breakup kernels (Luo-Luo) combined with a decreasing value of c0 improves the gas holdup profiles as compared to empirical values. However, at the lowest value of c0 investigated in this work, the axial liquid velocity deteriorates with regards to experimental data when using a superficial gas velocity of 0.019 m/s. (b) A combination of the Luo coalescence and Lehr breakup models (Luo-Lehr) was shown to improve the gas holdup values with experimental data when compared to the Luo-Luo kernels. However, as c0 decreases, the Luo-Lehr models underestimate the axial liquid velocity profiles with regards to empirical values. (c) A first and second order numerical schemes allowed predicting similar radial profiles of gas holdup and axial liquid velocity. (d) The mesh sensitivity results show that a 3 mm mesh size can be considered as reasonable for simulating experimental data. (e) The inclusion of wall lubrication parameter was found to be significant, although only when using finer meshing. In addition, it allows an improvement of the axial liquid velocity at the core of the bubble column.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".