Gas Dispersion in Non-Newtonian Fluids with Mechanically Agitated Systems: A Review
Bibliographic record
Abstract
<p>Gas dispersion in non-Newtonian fluids is encountered in a broad range of chemical, biochemical, and food industries. Mechanically agitated vessels are commonly employed in these processes because they promote high degree of contact between the phases. However, mixing non-Newtonian fluids is a challenging task that requires comprehensive knowledge of the mixing flow to accurately design stirred vessels. Therefore, this review presents the developments accomplished by researchers in this field. The present work describes mixing and mass transfer variables, namely volumetric mass transfer coefficient, power consumption, gas holdup, bubble diameter, and cavern size. It presents empirical correlations for the mixing variables and discusses the effects of operating and design parameters on the mixing and mass transfer process. Furthermore, this paper demonstrates the advantages of employing computational fluid dynamics tools to shed light on the hydrodynamics of this complex flow. The literature review shows that knowledge gaps remain for gas dispersion in yield stress fluids and non-Newtonian fluids with viscoelastic effects. In addition, comprehensive studies accounting for the scale-up of these mixing processes still need to be accomplished. Hence, further investigation of the flow patterns under different process and design conditions are valuable to have an appropriate insight into this complex system.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".