CFD analysis of flow regimes in airlift reactor using Eulerian‐Lagrangian approach
Bibliographic record
Abstract
Abstract The hydrodynamic aspects of the concentric tube airlift reactor have been studied using two‐phase CFD simulations with a Eulerian‐Lagrangian approach. The three‐dimensional CFD simulations are carried out with the experimental geometry of airlift reactor (H/D = 12) reported in the published literature. The standard k‐ϵ turbulence model is used with additional consideration of production and dissipation of buoyancy‐induced turbulence. Two different bubble size distributions (BSDs) and their Sauter mean diameters have been considered to represent the gas distribution at sparger in the reactor. This study shows that the BSD1 (1–5–10 mm) containing a high fraction of small bubbles (≤ 5 mm) represents the hydrodynamics of flows appropriately as compared to the BSD2 (5–10–15 mm) containing a high fraction of large bubbles (≥ 10 mm) or the single size bubble diameters (5.25 mm and 10 mm). Further, three regimes of operation such as no gas bubbles in the downcomer (regime I), stationary gas bubbles in the downcomer (regime II), and gas bubble recirculation from the downcomer section to the riser section (regime III), are verified using CFD simulations with Lagrangian particle tracking. Satisfactory agreement (within 15 % deviation) with the experimental data was observed for parameters such as the gas holdups in the riser and downcomer and the liquid circulation velocity in the flow regimes I and II for BSD1.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".