Numerical and experimental study of homogenization mechanism of high shear rotor‐stator mixer
Bibliographic record
Abstract
Abstract Utilizing computational fluid dynamics (CFD) for analytical purposes, this study developed a fundamental model employing the multiple reference frame (MRF) method, facilitated by the CFX simulation platform. The investigation conducted numerical simulations of the flow field within the rotor‐stator assembly of a high shear mixer, guided by the Navier–Stokes equations and the standard k‐ε turbulence model. To quantify the homogenization efficacy of the high shear mixer under scenarios with and without energy consumption considerations, the study introduced two distinct parameters: the mixing index (γ) and the energy ratio mixing index (λ). The impact of structural parameters such as the number of rotor‐stator teeth, radial clearance, and tooth apex‐base axial clearance on the local flow characteristics—velocity, pressure, turbulent kinetic energy, shear rate distribution, net power consumption, and the specified indices—was meticulously analyzed. This analysis aimed to identify the optimal structural configurations for the mixer, considering energy efficiency and mixing effectiveness, and to determine the relative influence of these structural variations on the mixing index (γ) and the energy ratio mixing index (λ).
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".