A computational study of axial dispersion in segmented gas-liquid flow
Bibliographic record
Abstract
Axial dispersion of a tracer in a two-dimensional gas-liquid flow is studied computationally using a finite-volume/front-tracking method. The effects of Peclet number, capillary number, and segment size are examined. At low Peclet numbers, the axial dispersion is mainly controlled by the convection through the liquid films between the bubbles and channel walls. In this regime, the computational results are found to be in a very good agreement with the existing model due to Pedersen and Horvath [Ind. Eng. Chem. Fundam. 20, 181 (1981)]. At high Peclet numbers, the axial dispersion is mainly controlled by the molecular diffusion, with some convective enhancement. In this regime, a new model is proposed and found to agree well with the computational results. These Peclet number regimes are shown to persist for different slug lengths. The axial dispersion is found to depend weakly on the capillary number in the diffusion-controlled regime. Finally, computational simulations are performed for the cases of six bubbles to mimic bubble trains, and results are compared with the theoretical models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".