Mass transfer and Shear Environment of an Aerated Coaxial Mixer Containing a Shear-thinning Fluid
Bibliographic record
Abstract
Gas dispersion in shear-sensitive fluids contained in mechanically agitated tanks is a complex process. This is due to the fact that the fluid viscosity greatly varies with the extent of shear rate As a result, the mixing system efficiency deteriorates under uneven mixing conditions and creation of oxygen depleted zones. In many industries, such as food and pharmaceutical industries, the proper mixing of shear-sensitive fluids is crucial for production of high-quality products Recently, coaxial mixing systems comprising of a central impeller and an anchor impeller demonstrated promising performance in gas dispersion inside shear-thinning fluids in terms of the mass transfer rate, power consumption, and uniform distribution of the shear rate However, based on an extensive literature review, the relation between the shear environment and the mass transfer rate generated by the coaxial mixer is not well understood. Hence, this study aims to investigate the mass transfer characteristics of a coaxial mixer at different scales containing a non-Newtonian fluid. Through the application of electrical resistance tomography, gassing-in, and computational fluid dynamics (CFD) methods, the gas hold-up, mass transfer coefficient, and shear environment of the coaxial mixers were evaluated, respectively.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".