Effect of agitation and aeration on gas dispersion efficiency in coaxial mixers containing yield‐pseudoplastic fluids: Experimental and numerical analysis
Bibliographic record
Abstract
Abstract Aerated stirred vessels are commonly employed to enhance gas dispersion. However, the associated high energy consumption is a challenging feature, particularly when dealing with complex non‐Newtonian fluids. Coaxial mixers comprising a central impeller and a close‐clearance impeller have emerged as an energy‐efficient alternative that effectively intensifies gas dispersion. Hence, the objective of this study is to investigate the effect of aeration and agitation on the gas dispersion effectiveness of a coaxial mixer containing a yield‐pseudoplastic fluid. An anchor‐pitched blade turbine was employed to disperse air into a 1 wt.% xanthan gum solution, and the analysis primarily focused on characterizing the gas holdup and fluid flow behaviour. Gas holdup data were obtained experimentally using electrical resistance tomography (ERT), while computational fluid dynamics (CFD) simulations provided a detailed analysis of fluid flow patterns within the coaxial mixer. The rotational speed of the impeller exhibited a non‐monotonic effect on the gas holdup, and a significant influence of the interaction between variables was identified. For instance, the experimental data showed that the aeration effect varied with the anchor speed. Nevertheless, the variables' interaction effect was explained by the change in flow pattern observed numerically. Furthermore, the CFD results demonstrated that high gas holdup does not necessarily indicate intensified mixing. Therefore, combining experimental data and numerical simulations enables a more accurate characterization of mixing performance. These findings contribute to the understanding and improvement of mixing performance in such a complex system, which is crucial for designing efficient operations.
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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) | 0.000 | 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) | 0.000 | 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".