Bubble size distribution and gas holdup in bubble columns employing non‐ <scp>Newtonian</scp> liquids: A <scp>CFD</scp> study
Bibliographic record
Abstract
Abstract The hydrodynamics involved with the rise of air bubbles in shear‐thinning non‐Newtonian liquids in bubble columns was investigated using computational fluid dynamics (CFD). In bubble columns, the bubble size distribution (BSD) and gas holdup significantly affect the mass transfer rates and the reactor design. Consequently, the influence of superficial gas velocity, flow index, consistency index, sparger bubble size, and yield stress were analyzed on a local and global scale. The bubble sizes and the coalescence and breakage phenomena were incorporated using the homogeneous discrete method of the population balance model (PBM). The bubbles underwent coalescence during ascent and exhibited bimodal distribution. The radial homogeneity of the gas phase increased axially. Moreover, the zones of low liquid dynamic viscosity produced zones of a high gas holdup. Though the dual effect of viscosity on gas holdup was non‐existent, the gas holdup surged after 35.1 mPa s at the mid‐zone and remained constant thereafter. The noteworthy differences in simulation results put further emphasis upon the importance of sparger bubble size in CFD modelling. A decrease in the overall gas holdup and the axial air velocity with yield stress was observed. CFD simulation proved capable of providing results in reasonable agreement with experiments.
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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".