CFD simulation of flow and mixing in‐inline rotor‐stator mixers with complex fluids
Bibliographic record
Abstract
The objective is to apply CFD methodology for the validation of the scale‐up of rotor‐stator units formed by flat blades and used as in‐line mixers under laminar flow conditions. The comparison between simulated and experimental data as a function of rotor geometry, rotation speed and flow rate has shown a good agreement in terms of power input and RTD curves both for Newtonian and power‐law fluids with a flow index between 0.2 and 1. The applicability of the virtual Couette analogy has been validated quantitatively and explained by the analysis of the local flow in the mixer. As a result, a shear coefficient independent of fluid rheology has been deduced from CFD data. This has been shown to depend only on the dimensionless gap when the length‐to‐diameter ratio is higher than 2.5. In this case, fast 2D simulations can provide a good approximation of the shear coefficient obtained from 3D calculations from which a master power curve can be deduced, but these are not able to predict flow transition, contrary to 3D computations. Finally, original correlations able to estimate the power and shear coefficients as a function of the mixer geometry have been established.
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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) | 0.001 | 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 | 0.001 | 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".