Bibliographic record
Abstract
Yield stress fluids are commonly encountered in the pharmaceutical, wastewater and bioprocess industries. On agitation of these fluids with an impeller, a zone of significant motion (cavern) is formed surrounded by stagnant regions. These inhomogeneous conditions are undesirable from a product quality standpoint. Therefore, to evolve a mixing system design that would eliminate these problems, experimental measurements of mixing time were obtained and combined with power consumption to provide a measure of mixing system efficiency. The effect of different parameters such as fluid rheology, impeller rotational speed, impeller type and impeller clearance on the mixing times was also investigated. In addition, using CFD, numerical mixing times were calculated and a comparison of the numerical and experimental mixing times were conducted to investigate the capability of the CFD tool to correctly predict the homogenization process in mixing tanks. In general, it was observed that the power characteristics of the different agitators were well reproduced by the computational package. In addition, CFD was able to correctly predict the effect of impeller rotational speed and fluid yield stress on the mixing times. However, the effect of impeller clearance on the mixing time was not correctly predicted by the CFD package when compared with experimental results obtained in this work as well as those obtained by other researchers. A comparison of the impellers used in this study (Pitched Blade Turbine (PBT), marine propeller and Lightnin A320) using the mixing time correlations available in the literature to fit the experimental data revealed that the PBT was superior to the other impellers in mixing yield stress fluids. In addition, the validated CFD model was used to measure the dimensions of the cavern formed around the impeller and it showed good agreement with the Elson's cavern model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".