Hydrodynamic difference between inline and batch operation of a rotor‐stator mixer head ‐ A CFD approach
Bibliographic record
Abstract
Abstract Rotor‐stator mixers (RSMs) can be operated in either batch or inline mode. When operating a rotor‐stator geometry in batch mode, it typically experiences an order of magnitude higher volumetric flow through the stator than in inline mode. This is expected to cause differences in the flow and turbulence in the rotor‐stator region. This study uses computational fluid dynamics (CFD) to study the hydrodynamic differences in and near the stator hole as a function of volumetric flow rates between those experienced in inline and batch modes of operation. It is concluded that both radial flow profiles and turbulent kinetic energy across a range of rotor speeds and flow rates can be described by a velocity ratio: average tangential fluid velocity in the stator hole divided by the rotor tip speed. Moreover, the position where dissipation of turbulent kinetic energy takes place—and hence the effective region of dispersion or mixing—differs between the two modes of operation. The relative importance of the two regions can be described in terms of the velocity ratio and the transition can be predicted based on the relative power input due to rotational and pumping power of the mixer. This study provides a starting point for understanding differences between emulsification efficiency between inline and batch modes of operation with relevance for both equipment design and process scale‐up.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".